Design and Development of Robotic Work Cells for Warehouse Automation by Youssef Aroub

Container Removal and Replacement Automation: Design and Development of Robotic Work Cells for Warehouse Automation by Youssef Aroub

Bachelor of Science in Mechanical Engineering, University of Manitoba, 2013

Submitted to the MIT Sloan School of Management and the Department of Mechanical Engineering in Partial Fulfillment of the Requirements for the Degrees of

MASTER OF BUSINESS ADMINISTRATION AND MASTER OF SCIENCE IN MECHANICAL ENGINEERING

IN CONJUCTION WITH THE LEADERS FOR GLOBAL OPERATIONS PROGRAM AT THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY JUNE 2019

The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created.

Signature of Author: ______MIT Sloan School of Management MIT Department of Mechanical Engineering May 10, 2019

Certified by: ______Dr. Stephen Graves, Thesis Supervisor Abraham J. Siegel Professor of Management Science MIT Sloan School of Management

Certified by: ______Dr. Kamal Youcef-Toumi, Thesis Supervisor Professor of Mechanical Engineering MIT Department of Mechanical Engineering

Accepted by: ______Nicolas Hadjiconstantinou, Chair Mechanical Engineering Graduate Program Committee MIT Department of Mechanical Engineering

Accepted by: ______Maura Herson Assistant Dean, MBA Program MIT Sloan School of Management

This page has been intentionally left blank

Container Removal and Replacement Automation: Design and Development of Robotic Work Cells for Warehouse Automation by Youssef Aroub Submitted to MIT Sloan School of Management on May 10, 2019 in Partial Fulfillment of the requirements for the Degrees of Master of Business Administration and Master of Science in Mechanical Engineering

Abstract Amazon has grown at a rapid pace. In 2018, Amazon retail sales amounted to $141.92B, up 19.7% from the previous year. The number of units shipped through its Prime program alone grew by 20% from 5 billion the previous year while third party sellers grew by 27.2%. This growth has repercussions on hiring needs and capital investments. Between 2017 and 2018, fulfillment expenses increased by 34.7% from 25.2B to 34B. In 2018, the Amazon workforce grew by 14.4% to 647,500 employees and the company added more than 8 million square feet to its fulfillment network, a 7% increase from its 2017 footprint. Significant productivity and efficiency gains are required to sustain this increase in throughput, product catalogue, supply chain complexity, and decrease in shipping lead times.

The design and safe deployment of advanced technologies such as robotics is crucial in enabling future growth and creating better customer outcomes. The Inbound Cross Docks (IXDs) are a critical area for automation. At IXDs, products are received, sorted, and shipped to FCs akin to a hub-and-spoke system. A trade-off inherent to the IXD process is that it increases both the number of touches per unit and the end-to-end process time to get units on the shelves. Automation can reduce touches, lead time, and concessions while increasing throughput and labor efficiencies.

This thesis proposes a robotic work cell to automate the removal of full containers from a sorting machine and the replenishment of empty containers in which products will be sorted in IXDs. Using a DMADV approach, the current process operational envelope was measured. A series of controlled experiments tested the effects of automation on process jams and outcomes, as well as methods to lower their occurrence. Concept layouts were completed from robotics physics and FlexSim operational simulations. A test robotic work cell was designed to test functionalities such as the detection and problem solving of containers, the goal being to de-risk and conclusively proof multiple functionalities in support of the full implementation.

The solution proposed is a robot on rail with a single end-effector and a COGNEX vision system to check for overfull containers. If positive, containers would be routed to shaking conveyance, which has been shown to fix 77% of overfull containers. Finally, problem solving intervention would still be required to troubleshoot edge cases. A financial analysis conducted for the deployment of the proposed solution has shown a potential 3-year NPV of $18.1MM USD with a payback period of 2.12 years in 10 IXDs. From this project, a general framework for deployment of automation projects was created from interviews that could be of benefit to companies seeking to develop capabilities in warehouse automation.

Thesis Supervisor: Dr. Stephen Graves Title: Abraham J. Siegel Professor of Management

Thesis Supervisor: Dr. Kamal Youcef-Toumi Title: Professor, Department of Mechanical Engineering

This page has been intentionally left blank

Acknowledgements I am forever indebted to my advisors, Dr. Stephen Graves and Dr. Kamal Youcef-Toumi. I would like to thank Dr. Graves for his consistent high-quality feedback on both the problem faced and solutions proposed; several of the breakthroughs find their genesis in his questions and remarks. I would like to thank Dr. Kamal Youcef-Toumi on his foresight and expert advice on robotics, often pre-empting possible issues before their occurrence.

I would like to express my sincerest gratitude to my managers at Amazon, Willow Primack, Jacqueline Underberg, and Roland Menassa, who always provided advice, support, resources, and funds for this project to move ahead. I would like to express my deepest appreciation for the team that supported me during my internship including Emily Dunne and Mohammad Mosa, their combination of robotics expertise, statistical knowhow, and keen eye have made a tremendously positive impact on this thesis.

I would like to acknowledge the Leaders for Global Operations program for its support of this work. I would also like to acknowledge my classmates who have made the past two years a once-in-a-lifetime experience.

Finally, I owe much of my accomplishments to my family’s love, support, and patience. Thank you to my mother, Saadia, for always making my education a priority and for instilling in me the values of lifelong learning, hard work, and resilience and to my brother, Fay, for supporting me along the way. Without them, I would not have the privilege to author the words that follow.

Table of Contents

Abstract ...... 3 Acknowledgements ...... 5 Table of Contents ...... 6 List of Tables ...... 8 Note on Amazon.com Proprietary Information ...... 9 1. Introduction ...... 10 1.1. Amazon.com ...... 10 1.2. Project Motivation ...... 12 1.3. Problem Statement ...... 14 1.4. Project Hypothesis ...... 15 1.5. Project Approach ...... 16 1.5. Thesis Overview ...... 17 2. Background ...... 19 2.1. Automation Trends ...... 19 2.2. Warehouse Automation at Amazon.com ...... 21 2.3. Amazon’s Inbound Supply Chain ...... 22 2.3.1. Inbound Cross-Dock Operations ...... 22 2.3.2 Receiving ...... 24 2.3.3 Sortation ...... 24 2.3.4 Palletizing & Shipping ...... 25 2.4 IXD Automation ...... 25 2.4.1 Universal Item Sorter ...... 26 2.4.2 Tote Wrangling Associate ...... 28 2.4.3 Robotic Palletizer...... 29 3. Literature Review ...... 31 3.1. Robotics Work Cell Design ...... 31 3.1.1. Modelling and Simulation ...... 31 3.1.2. Developments in 3D Vision ...... 33 3.2. Robotics Deployment ...... 36 3.2.1. Robotics Deployment and Change Management ...... 37 3.2.2. Human-Robot Performance ...... 39 4. Project Methodology ...... 41 4.1. Define ...... 41 4.1.1. UIS Machine ...... 41

4.1.2. Tote Wrangler ...... 43 4.2. Measure ...... 44 4.2.1. UIS Operational Envelope ...... 44 4.2.2. UIS Failure Mode Occurrence...... 46 4.3. Analyze ...... 48 4.3.1. Robotic Simulation ...... 49 4.3.2. FlexSim Process Simulation...... 52 5. Testing and Experiments ...... 58 5.1. First Experiment : Tote Utilization ...... 61 5.2. Second Experiment: Breakdown and Jams...... 63 5.3. Third Experiment: Shaking Experiment ...... 67 5.4. Fourth Experiment: Template Based Sorting ...... 69 6. Organizational Capability Study ...... 76 6.1. Organizational Study ...... 76 6.2. Resources & Capabilities ...... 77 7. References ...... 84

List of Tables

Table 1: Classifying tote wrangling tasks by automation difficulty ...... 44 Table 2: Summary of tote closure rates ...... 45 Table 3: Tote weight (lbs.) at closure ...... 46 Table 4: Roboguide cycle time and reach analysis ...... 51 Table 5: Baseline tote utilization distribution for experiment...... 62 Table 6: Summary of three-year NPV analysis of quoted RWC ...... 73

Note on Amazon.com Proprietary Information In order to protect information that is proprietary to Amazon.com, Inc., the data presented throughout this thesis has been modified and does not represent actual values. Data labels have been altered, converted or removed in order to protect competitive information, while still conveying the findings of this project.

1. Introduction 1.1. Amazon.com Jeff Bezos founded Amazon.com in 1994 with the initial vision to grow the company to become “Earth’s Biggest Bookstore”[1]. Four years after Tim Berners-Lee had built all the necessary tools for a working World Wide Web, Bezos believed the Internet would be the future of retail and first chose to sell books online as no brick-and-mortar store could possibly stock the number of titles available worldwide. By opening day, in July 1995, Amazon had 1M titles ready to be purchased by customers in the United States. The company would drop ship the books from distributors. From the onset, the company’s mission was to become “Earth’s most customer- centric company” and to live up to this mission, accelerated growth became the constant leitmotif to ensure more selection and better service for customers around the world. By 1996, the company had 2.5M titles on sale and saw its revenues double every quarter. At first, Amazon carried little inventory choosing rather to fill orders from local book wholesalers. In May 1997, Amazon.com completed an Initial Public Offering with a valuation of $438M, which in turn was invested to build its fulfillment network infrastructure. The guiding vision was simple: “Put customer first, invent, be patient.”[1]. Bezos believed that “there are two kinds of companies: those that work to try to charge more and those that work to charge less”. He believed Amazon ought to always be the latter. Amazon’s virtuous circle was encapsulated in its flywheel. Growth was the nucleus by which the company could attain a larger selection and a lower cost structure. The lower cost structure would drive for lower prices which would, along with a larger selection, provide a better customer experience. Delighting the customer would drive traffic and third-party sellers to the website thereby increasing selection and growth. In January 2002, Amazon introduced free shipping for orders above $99, with the required minimum dropping to $49 and then $25 in the span of a few months. The critical insight from internal research was that shipping costs were a barrier to purchase for a large portion of customers and that, in fact, those same customers would be willing to wait a few more days to receive their package in exchange for free shipping. In this manner, the company believed it could segment the customers and price discriminate between those who wanted their package early and those who were willing to wait. This experimentation was bred in the company’s leadership values and Bezos believed that “a small number of winners pay for dozens, hundreds of failures[1]”; taking risks to be innovative was the key to growth.

In 2005, Amazon launched Prime, a membership service with free two-day delivery (with one-day discount shipping in select areas) for an annual fee (See Fig.1). By 2018, it is clear that prime is a bet that has paid off; prime members spend on average $1450 per year on Amazon.com or more than double the amount of non-prime members [1]. Amazon has since expanded beyond books to include nearly every retail line: food & beverage, apparel & accessories, personal care & beauty, toys, furniture, and music and video streaming (See Fig.1). Amazon has also successfully launched many hardware products such as the Echo smart speakers and Kindle e-readers.

Fig. 1: Growth in Amazon retail sales by category (left) and growth in total number of US households with an Amazon Prime subscription (right) In FY 2017, Amazon reported revenues of $177.87B USD and on December 31st, 2017 had grown to 566,000 employees, most of these were associates working within its fulfillment network (see Fig.2). That same year, 5 billion packages were delivered globally, with 1.2 billion packages through the United States Postal Service alone [1]. In 2018, Amazon is the second largest e-commerce website in the world commanding a 15% global market share and a 49% US market share of e-commerce [2]. With the e-commerce channel approaching 10% of total US retail sales[2], Amazon’s infrastructure becomes key in enabling its competitiveness.

Fig. 2: Growth in Amazon’s revenue by segment (left) and number of employees (right) The company has chosen to develop its Warehouse Management Systems (WMS) and IT infrastructure in-house to maintain control over and ease integration between its 75 fulfillment

and 25 sortation centers in North America[3], now spanning more than 190 million square feet (See Fig. 3). The growth in footprint has been staggering. It is estimated that 50% of the US population lives within 20 miles of a fulfilment node, up from 5% in 2015[3]. With total fulfillment costs, including fulfillment centers and transportation figures, approaching 18% of an item’s retail price, Amazon has made cost reduction a central focus of its continuous improvement. In 2012, Amazon acquired Kiva Systems, a company that manufactured warehouse automated drives for picking and placing, for $775M USD. This strategic acquisition gave Amazon the resources and capabilities to deploy the drives within Amazon warehouses. By 2017, more than 45,000 drives were deployed across facilities which were named Amazon Robotics (AR) Sortable Fulfillment Centers (FC)[1]. The robots were responsible for 75% reduction in picking cycle time from 65 to 15 minutes and increased inventory per square foot by 50% [1].

Fig. 3: Global E-Commerce Market Share of Leading E-retailers (left) and the US Amazon Fulfillment Capacity by Total Square Footage (right) The early success of AR was proof that advanced automation was a necessary complement to associates for the company to achieve its growth goals. Automation would help the company build a robust infrastructure that could scale effectively with the number of customers and orders while handling ever-increasing complexity in inventory, logistics, and customer expectation. Amazon’s automation philosophy is best encapsulated in Bezos’s quote: “we automate tasks that computer and machines can do well, when we cannot fully automate a task, we will build a simple and intuitive workflow which allows humans to provide the system the inputs that it needs to execute a task.” This project follows this mantra by combining process redesign and automation.

1.2. Project Motivation Amazon has experienced rapid growth in fulfillment demand that has outpaced productivity gains. Estimates show that, if current trends hold, the fulfillment network will require an additional 1.5M associates by 2022 with triple the building footprint. Thus, the company is

investing heavily in robotics and automation solutions to drive productivity gains, reduce variable cost, improve associate safety, and delight customers. Amazon fulfillment network is composed of several types of buildings, categorized by the type of inventory handled, their goal within the supply chain logistics network, and the type of technologies that support goods movement. For the sake of brevity, three will be discussed: Inbound Cross-Docks (IXD), Fulfillment Centers (FC), and Sortation Centers (SC). Amazon’s supply chain network is designed as a hub-and-spoke system. Inbound Cross- Docks are hub facilities in which products are received either from vendors (spoke) or other IXDs, sorted inside the building, and shipped to FCs (spoke). IXDs allow the company to achieve granular inventory placement at FCs, drive lower outbound shipping costs, as well as simplify vendor and information management. FCs can be divided into two types: AR sortable and traditional. In an AR sortable fulfillment center, AR drives move pods in which small-and-medium sized products are stored. These products are stowed in and picked from mixed-ASIN (Amazon Stock Identification Number) bins by associates from a Robotic Storage Platform (RSP) [4]. (See. Fig.4).

1 – AR Drives used to transport pods in the protected area 2

2 – Small, Medium, and Large Mixed-ASIN bins 3 4 3 – Associate picking/stowing item into bin as prompted

1 4 – Totes are used to move picked or stowed items in facilities

Fig. 4: Associates store and collect a set of products which are determined by the system into and from specific bins (8th Generation FC)

In traditional FCs, associates stow and retrieve products along rows of shelves using carts, forklifts, and bins. The processes at both of these type of facilities can be broken down into inbound and outbound. Inbound processes include receiving and stowing. Once a product has been received, it is stowed by an associate into the RSP. It is at this moment that products are added to the inventory and can be purchased online. Outbound processes include picking, sorting, packing, and shipping. Once a product is ordered, it is picked by an associate, sorted to join other

possible purchases from the same customers, and packaged. The package could be either as a single or a multi, depending on whether it was the only product purchased by a customer or part of a larger order. The package is then shipped to the customer. Much of the complexity in order fulfillment stems from this process step – the combination of multiple items within an order. Sort Centers or delivery stations are mid-sized facilities, which represent the last leg of the delivery network. Customer packages traveling from FCs to regional sort centers are sorted by zip code and dispatched via couriers to the customer. This network system was designed to reduce shipping lead time and shipping cost. The Advanced Technology group is tasked with designing, developing, and deploying automation to support the fulfillment network. Short term deployable and long-term process redesigns are actively in study with repetitive tasks, bottlenecks, and space inefficiencies as the primary targets for automation[5].

1.3. Problem Statement All of these buildings are critical areas for automation as they contain processes such as sorting, stowing, picking, and packaging that are labor intensive and time consuming. For the purpose of this project, the focus will be on the IXDs (see Fig. 5). A trade-off inherent to the IXD process is that it increases both the number of touches per unit and the end-to-end process time to get the units on the shelves.

Vendor

Fig. 5: Simplified Visualization of Inbound Cross-Dock Process Multiple projects are being considered to automate the full IXD process; the goal is to reduce touches and lead time, while increasing throughput and labor efficiencies. One such automation is the Universal Item Sorter (UIS), an automated sort solution developed over the last three years, which is slated for 2018 scale deployment within the IXD network (see Fig. 6). The sorting process, previously manual, was slow and constituted a bottleneck for both IXD and FC buildings while being a critical process in the IXD. Each shipment had to be disaggregated, broken into single items referred to as “eaches”, and then sorted by an associate into the correct tote (yellow bin), along with other items. The totes were palletized and shipped to a destination FC. The UIS machine, is a modified mail-matrix modular machine that operates as a high-speed, high destination sorter. Along with deploying a sort solution, automating the tote removal &

placement process from the UIS becomes necessary to achieve the vision and operational intents of highly automated IXDs.

Fig. 6: Universal Item Sorter machine in operation within an IXD. The machine inducts eaches, sorts them within destination totes (yellow bins), and the totes, once full, are wrangled by an associate who transfers them onto conveyance, to be routed to palletizing and shipping.

1.4. Project Hypothesis The thesis explores two areas of opportunity that might yield improvement to both the inbound cross-dock process specifically and to the Advanced Technology group’s design, development, and deployment process generally. 1. Inbound Cross-Dock Process Automation: I. The intermediate process step between sorting (UIS) and palletizing, that is the removal of full totes from the UIS and the replenishment of empty totes in which products will be sorted for Inbound Cross-Dock (IXD) operations can be automated to reduce total cost and number of touches. II. Using process simulations, robotic physics simulations, and on the floor testing, an automated storage and retrieval system can be developed to automate this process step while returning meaningful capital to the business, with a positive 3-year NPV. III. Sufficient quality can be achieved through proper process controls and mechanical design along with modification to the current sorting process. 2. Advanced Technology Resources and Capabilities: IV. Using this project as a pilot, a set of best practices and tools can be developed and established to guide key Amazon’s decision making regarding strategic resources and capabilities for future robotics and automation projects. From a broader perspective, a literature review and a three lens analysis of the Advanced Technology Group will serve to research and validate the latest thought on warehousing

15 automation as it pertains to hardware integration, project initiation and prioritization, and worker change management. The methods, frameworks, and insights from this thesis are applicable to multiple sorting processes at Amazon using the UIS, as well as to the development of automation in warehouses for companies in North America.

1.5. Project Approach To test hypotheses I-III, the author designed a robotic work cell and tested components of the system at the Amazon R&D center. Using the DMADV Lean Six Sigma framework [6], the project was split into the five constituent phases. In the first step, Define, a value stream map was developed for the current process of manual tote wrangling. The VSM was developed from interviews and analysis of tote wrangling associates on the job to define the project scope, process steps, and manual tasks to be automated. In the second step, Measure, the operational envelope of the UIS machine and the cycle times of tote wrangling activities were measured. This includes the distribution of tote closure rates, tote weights, induction rates, units per tote, defect/jams, process breakdowns, challenges, and the overall resource planning process for IXDs. In this stage, Critical to Quality Characteristics were developed using an issue tree listing requirements for the work cell regarding safety, quality, cost, environment, responsiveness, and operational metrics. In the third step, Analyze, physics simulations of robotic solutions were developed to determine appropriate robot model and application. Furthermore, a FlexSim process simulation along with calculations using queuing theory were completed to determine servicing speeds, robot utilization, and process capability. In the Fourth step, Design, a prototype robotic work cell using 6-axis articulated robots was designed. In the final step, Validate, the author ran tests to quantify impact of automation on key process metrics, such as tote utilization, jams/breakdowns, cycle times, and throughput. These tests or design experiments were key in determining the forecasted process quality as a function of input variability. Learnings and lessons from each test were used to refine the design and drove hardware/software changes. It should be noted that system level design recommendations were derived from simulation and modeling, as well as live testing. The latest model was then used to complete a business analysis (Discounted Cash Flow) to determine the financial feasibility of the overall system.

To test hypothesis IV, interviews of 40 internal employees and 4 external experts were conducted to determine the modern resources and capabilities required for automated hardware integration. The framework used was a three-lens analysis [7] to map the current organizational strengths and weaknesses of Amazon’s Advanced Technology Group along with an analysis of key resources and capabilities using the resource-based theory of competitive advantage [8]. From these studies, a set of recommendations will be proposed to improve the design and development processes, as well as to gather research to answer key questions for companies seeking to develop in-house hardware integration capabilities. Two conclusions were generated from these analyses. First, the proposed design has a payback period of less than 3 years, the time horizon used by Amazon to evaluate projects, and is financially viable; however, the solution is unable to deliver the current process quality without key redesigns. Due to the shape and volumetric variability of the product sorted into these totes, the current proposed hardware solution is unable to deliver the expected process quality without key redesigns. Associates influence performance metrics such as tote utilization and tote closure rates, while ensuring that items do not protrude from totes, which need to be stacked downstream. Associates also problem solve, dealing with edge cases and exceptions. Automating this process step requires a complete understanding of the operational envelope and process breakdowns, along with testing the effect of automation on these metrics. Second, the main insight from the organizational analysis is that to scale as a hardware integrator, a robotics organization must build three core skills. The team must view its suppliers as partners and build procurement standards capable of vetting and assessing these suppliers. The team must develop a repeatable hardware development process that is tailored to specific projects while standardizing knowledge sharing. Lastly, the team must have an in-depth understanding of current operational processes and a systematic method of evaluating opportunities.

1.5. Thesis Overview The thesis begins with an in-depth analysis of automation in the context of warehouses followed by an overview of Amazon’s inbound supply chain. Each stage of the inbound cross- dock process is reviewed as IXDs are the area of focus for this automation project. The process goals, inputs/outputs, and resources will be emphasized. This will be followed by a deep-dive of the UIS machine and the tote wrangling process. A two-part literature review will follow in the third section of the thesis. The first part of the literature review will delve into recent research on the design and development of robotic work

cells. The section will focus on the use of simulation software and new technologies (AR/VR) in developing robotic work cell designs. The section will also focus on the current capabilities of 3D vision systems in picking and placing items within bins or totes. Most literature reviewed will pertain to the Amazon Robotics Challenge which has been a catalyst for research on pick and place technology. The second part of the literature review will delve into historical research on the organizational implications of robotics, the impact of automation on companies, and the human-robotics interface process design. The goal of this research is to inform companies on the resources and capabilities necessary to successfully implement automation projects in warehousing environments. The fourth section will begin with an overview of two simulations: The FlexSim process simulation and the robot physics simulation. The test results are analyzed and reported. Key hypotheses developed prior to analysis will be presented and discussed in light of simulation results. A discussion of the validity and accuracy of the models are also included. This will be followed by a discussion on system development and testing plans. Methodology for live tests will be explained, results shared, and a discussion on the ramification of these results on the overall work cell design will conclude this section. In the fifth section, a design recommendation will be made based on system requirements, testing results, and business cases. In the fifth section, the organizational study will be presented along with the methodology, the approach (audience surveyed, the questions posed), and the findings. The thesis concludes by presenting future areas of improvement for the proposed work cell design along with novel applications in other processes that could benefit from this automation. Recommendations, focusing on automation resources and capabilities, are presented.

2. Background 2.1. Automation Trends Developing a framework for robot integration, as this thesis seeks to do, is critical for companies who leverage warehousing as a capability. The e-commerce retail distribution channel has grown at a 15% CAGR globally. This had the double-effect of making warehousing, once an oft-forgotten capability, into a hotbed of innovation and a key differentiator in the marketplace as companies awaken to its cost saving and customer service potentials. Today, with the rise of direct-to-consumer options, companies are feeling the pressure to satisfy e-commerce customers. Three main issues arise: more variability, faster pace, and more complex combinations of smaller packages. Companies have to double down on this capability in order to improve goods movement from the supply chain to the customer – automation becomes a clear candidate to achieve this goal. As of 2016, 10% of warehouses in the US have some sort of automation and 40,000 industrial robots (defined as automated, programmable, multi-axial robot systems) are operational in warehouses across the United States. However, it is believed that this number will grow to 620,000 robots by 2021 in warehousing alone [9]. The main drivers behind automation trends were wider adoption of e-commerce which resulted in higher end customer expectations (dubbed the Amazon effect), more frequent and complex orders, increased labor and land cost, increased number of exception handling, and the cost reduction of automated solutions compared to labor costs. Fig. 7 shows the growth in employment and number of warehousing establishment in the US from 2008 to 2018. A key insight from an analysis done by the US bureau of Labor Statistics is that, since 2016, strong demand for labor has pushed wages higher by 16% with 41% of warehouse managers reporting “an inability to attract and retain quality/hourly workforce.” Furthermore, the average US warehouse size has grown tremendously and renting rates were up by 28% between 2011-2015 making the cost of land a prohibitive expense for companies as US vacancy rate fell to 5.3% [10].

Fig.7: Growth in warehousing establishment and employment in the US (left) and Growth in average US warehouse size (right)19

To further complicate the situation, the average # of SKUs in warehouses has increased by 18%[9]. The International Federation of Robotics believes that warehousing automation spend will grow from $1.9B in 2016 to $22.4B in 2021[11]. Lastly, by 2015, industrial robot prices were 20% of what they were in 1993 when one accounts for price and hardware advances [12]. Warehouse automation can be split into four types: low automation, system automation, mechanized automation, and sophisticated automation. Low automation describes rudimentary inventory recording systems whereas system automation describes WMS that store and direct goods movement information such as a Pick-to-Light system. Mechanized automation encompasses goods movement solutions such as conveyance and ASRS systems whereas sophisticated automation encompasses automated sorters (UIS), AGV, robot picking systems (AR), and automated palletizers. Automation plays an important role in economic growth. Graetz and Michael calculated that automation increased US GDP and labor productivity by 0.37 and 0.36 points respectively and has contributed to 15% of GDP growth between ‘93 and ’07 [12]. The Centre for Economics and Business Research posited that a one-unit increase in robot density (defined as the number of robots per million hours worked) increase productivity by 0.04%. Furthermore, there is a strong link between automation, productivity, competitiveness, and strong increase in demand which increases employment [13]. Fig. 8 summarizes the current robot density by country along with the growth of global industrial robot sales.

Fig. 8: Global industrial robot density with US highlighted (left) and global growth in industrial robot sales (right) Research has also shown that countries that invested more in robotics lost fewer jobs than those who did not [14] and the US Bureau of Labor Statistics has shown that wide integration of robotics in automotive, electronics, and metals processing led to higher wages, a 20% increase in the number of mechanical and industrial engineers, and 2x the number of installation of MRO workers[11]. Graetz and Michael have demonstrated that robots have increased wages without

significant effect on hours worked in total or the number of jobs [12]. Furthermore, countries such as South Korea, Singapore, and Germany who have the highest robot density also tend to have low unemployment. There remains an open debate on the link between those two variables. It may be that low unemployment pushes companies to automate or as other studies suggest, since these economies are more competitive, they exhibit high automation [13]. Although this debate is outside of the scope of this research, it should be noted that McKinsey predicts that 57% of warehousing activities are ripe for automation[15].

2.2. Warehouse Automation at Amazon.com The Amazon effect is an acknowledgement of Amazon’s early and continuing role in the disruption of retail markets with key innovations within its supply chain network to serve its customers. The company has been a catalyst for innovations in supply chain automation and through its rapid growth, other companies have begun to understand the key role played by robust supply chain networks in their success. It is also an acknowledgement of the increasing bar that customers expect. A Statista survey administered in 2017 showed that 59% of US respondents used Amazon periodically. Furthermore, 65% of respondents indicated that “lower prices” were the main reason for shopping on Amazon.com whereas 56% of respondents indicated that “free shipping” was the main reason. For another 27% of shoppers, “fast shipping” was the number one reason for shopping on Amazon.com.

Fig. 9: US consumer usage of Amazon service & products (left) and key factors influencing US shoppers on Amazon (right). Customer preferences, such as cost and time, drive the operational decisions made by Amazon as it seeks to become the world’s most customer-centric company. Therefore, cost and time, along with safety and quality, are focal metrics in most improvement projects at Amazon. However, the financial metric by which process improvement projects are measured, notwithstanding

quality and safety, is the reduction in the variable cost per-unit (VCPU). It is only one element of a project:

∑𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 The VCPU is calculated by dividing𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 the total labor hours∗ 𝑊𝑊 required𝑊𝑊𝑊𝑊𝑊𝑊 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 to complete a specific process ∑𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 by the number of units processed during a specific time unit multiplied by the wage rate of hourly employees. This metric can be used to determine the improvement or increase in VCPU from a process change or redesign. VCPU served as the primary metric for the business analysis for this thesis. By reducing this metric, Amazon can reduce overall cost to the customer.

2.3. Amazon’s Inbound Supply Chain 2.3.1. Inbound Cross-Dock Operations IXDs are an initiative to minimize end-to-end transportation and labor costs for both inbound and outbound operations by applying a hub-and-spoke distribution architecture to the Amazon fulfillment network. By routing inbound orders through a small handful of IXD nodes, freight is consolidated and economies of scale leveraged for high volume receiving, sorting, and second leg transportation. .

Fig. 10: Amazon's Inbound Shipment Process [4] By using an IXD as an intermediate step, all orders from a vendor can be consolidated into denser shipping methods and sorted to different destination inside the IXD node. Each destination FC volume can be combined into densely packed transfer trucks consisting of units

from several vendors. The scale of the volume passing through allows the company to serve numerous FCs from a single IXD node while striving to maintain high truck density by increasing the number of units shipped inside each truck. IXDs reduce FC cost by removing the receiving operations from FCs so that the latter can use this increase in effective capacity to increase storage capacity (their main goal). Furthermore, IXDs play a central role in defect detection and reduction as the process is built to catch them. Instead of correcting defects at each FC, the IXD process can problem solve quantity, quality, or product defects at the head of the process. The guiding theme throughout Amazon’s supply chain processes is that products must be moved efficiently and movement information recorded accurately. Only by ensuring these two conditions can Amazon deliver on-time and on-cost to the customer with the quality promised. Two types of products are handled by Amazon: Retail and Fulfillment by Amazon (FBA). For retail, the challenge begins at the vendor who ships a predetermined quantity of product that Amazon ordered to either an IXD hub or directly to a fulfillment center. For FBA, businesses or individual sellers pay a fee to sell products directly through Amazon. These sellers use fulfillment capacity and website space to sell through the e-commerce platform. Like the vendors, FBA users send their products through the network, some of which pass by IXDs and some directly to fulfillment centers, this is called a 1st leg. Fig. 10 shows a process diagram adapted from [4]. Two payment method options exist for vendors. The “we pay” (Amazon pays) method enables vendors to piggyback on Amazon’s logistics infrastructure by sending orders to the nearest FC or IXD from the vendor. Amazon consolidates and disaggregates shipments throughout its network using its economies of scale to guarantee a lower shipping rate than would otherwise be possible by the vendor, if they had shipped to each FC. The second method, “they pay”, sees vendors choosing their preferred shipment methods and placement locations while bearing all the logistics cost. Inventory placement is managed by a complex WMS that dictates routing. As of December 2018, there are 10 IXDs in the US. The cross-dock process is made up of three main steps with the overall goal to receive product and transfer them to a final inventory location while combining small parcel and less-than-truckload shipments to create more efficient truckloads bound to FCs [4].

2.3.2 Receiving Once a shipment is received at an IXD dock, the pallets are removed from the truck, disaggregated, and sent through to downstream processes. Items can either be received in yellow totes or cases depending on whether they arrived from another IXD (totes) or a vendor (cases). Products can be received in three main ways: Each Receive Process, Pallet Receive Process, or License Plate (LP). The each receive method is a manual receive process in which associates open the cases to scan each product. After this process, the product may be returned in the case or placed in a yellow tote. The pallet receive process is another manual receive process in which associates receive single-ASIN pallet by verifying the manifest for the pallet. In this process, the items are not yet disaggregated. Lastly, the LP process is an automatic reception process in which the shipment is received through an automated bar-code scan using a parcel identification unit (PID). The information is then triangulated with pallet weight and size measurements. This process is used with trusted vendors to improve efficient reception of merchandise.

Fig. 11: (left) Plastic yellow tote with standard (HxLxW) dimensions (10.63” x 23.31” x 15.71”). The yellow tote has two handles with identification bar codes and two stackable positions (stack full totes or nested empty totes). (right) Cardboard case arriving from vendor with either one type of ASIN or multiple ASINs within one case. 2.3.3 Sortation Once product has been received through one of the three methods (Each Receive, Pallet Receive or License Plate), the Inventory Transfer Service (ITS), a network optimization algorithm, determines its routing and final location. The process of sorting has two distinct modes: each sort or case sort. In each sort, the system determines the next location of the items. The main decision at this junction is whether the eaches will be sorted into several totes to be routed to multiple FCs/IXDs. By sorting products into multiple totes, each with a different destination, Amazon is able to better place product throughout its network to minimize outbound shipping lead time and cost once they are ordered by customers on the website. Being able to

24 meet Prime’s two-day free shipping is contingent on optimizing the placement of these products. For example, multiple copies of books may be routed into totes, with each tote being shipped to a different region’s FC or IXD. In case sort, the decision criterion is whether to break down the product into individual cases. The decision takes into account the level of inventory at destination along with the velocity of the item, that is, how quickly it is purchased and must move through the process. A low velocity item will normally have a lower inventory threshold whereas a high velocity item may require higher inventory numbers to fulfill demand. Although this explanation is an over-simplification and proprietary details have been omitted, the ITS algorithm determines whether sorting is a necessary process for each product received. As of October 2017, the Universal Item Sorters was deployed in select IXDs to fulfill the sort process and replace a traditionally manually-intensive task. Section 2.4 will deep dive into sortation automation as this will be the process around which this automation project will revolve. The conclusion of the sorting process ends the inbound portion of the IXD value stream map.

2.3.4 Palletizing & Shipping The outbound portion begins with cases and totes arriving to the ship sorter, a process in which cases and totes are routed to specific production lines in outbound that correspond with target FC/IXD destinations. At this point, the ship sorter also determines whether the case or tote will be palletized or floor loaded to an FC destination. For palletization, the tote or case is scanned onto a pallet and an associate stacks the container onto the destination pallet. The pallet, once full, is shrink wrapped, moved to a buffer area, and once ready to be loaded, is forklifted into the truck. For floor loading, the case or tote is scanned directly to a truck trailer and stacked within that trailer by an associate. Section 2.4 will delve into automated palletization.

2.4 IXD Automation The advanced technology (AT) team’s mission is to invent, develop, and deploy technologies using advanced mechatronics and robot system directed towards improving associate safety and efficiency, reducing cost, and increasing process quality. There are two overarching visions for the future of fulfillment: a short-term highly automated fulfillment center(HAFC) and a longer- term fully-automated fulfillment center (FAFC). For IXDs, the acronyms become HAIXD and FAIXD.

The HAFC initiative is defined as a fulfillment center in which automation results in a level of labor content equivalent to 40% of an AR sortable fulfillment center. Associates will be deployed in remaining difficult-to-automate processes and to handle exceptions. In a FAFC, 100% of ASINs are processed without human intervention. The AT team includes multiple groups that are focused on delivering work streams: sortation, storage, packing, industrial robotics, etc. For the purpose of this thesis, the two automations that will be explained are the Universal Item Sorter (UIS) and the robotic palletizer as these are adjacent to this automation.

2.4.1 Universal Item Sorter The item sortation process consists of two major programs: the UIS, a modified conventional mail sorter that has been heavily modified to sort e-commerce eaches, and a Linear Item Sorter (LIS), which is a cross-belt sort system designed to receive items and sort to destination totes for transship.

Induction: Totes and cases are routed from the IXD receiving area to the UIS on conveyance. These totes contain items that have yet to be sorted. Conveyance routes the totes or cases to the UIS entry conveyance belt. An associate, often referred to as an inductor, stages the tote on a platform, picks one item at a time, and places the item on the conveyor. The conveyor moves the item through a six-sided barcode scanner. Once the barcode is scanned, the WMS determines the arc destination (final destination) and the corresponding tote in which the item will be routed. The conveyance routes the item onto a shuttle, a moveable bot with a conveyor whose role is to move the item from the induction point until its destination tote.

Shuttle: The shuttle moves on tracks and follows a pre-defined path to the destination tote. The shuttle moves in two dimensions: vertically (up/down) or laterally (left/right). The shuttle picks the item up, and drops the item into its destination tote. The shuttle’s conveyor belt moves the item onto the center of its platform and also delivers the item into its destination tote. Sensors on the shuttle are used to determine whether an item has successfully been picked up or delivered.

1 – Items are singulated and inducted through a 6-sided scan

2 – items are transported from conveyance onto shuttles

3 3 – shuttles route the items and 4 2 drop them into a destination tote 1

4 – Tote wranglers use a PTL system to remove totes once full Fig. 12: Universal Item Sorter machine in operation within an IXD.

Destination Tote: Conveyance moves the item onto shuttles, fast-moving platforms that route the item within the machine to a destination tote. The WMS assigns an arc destination once the item’s volume and weight are read from the ASIN catalog. The UIS attempts to sort the item into a tote with an available arc destination as long as the volume or weight, within that tote, will not exceed set software thresholds that can be adjusted. In that case, the UIS machine will open a new tote destination corresponding to the arc transshipment. Once a tote is closed, having reached either its volumetric or weight threshold, a pick-to-light system warns the associate to replace the tote.

PTL System: The Pick-To-Light system is used to communicate machine status to the tote wrangler or problem solver on hand. There are multiple light settings, however, the three most important setting are: green, blue, and orange. When the PTL is green, it means operation is currently normal and machine is running undisturbed. Once a tote is deemed full by the WMS, a flashing blue light replaces the green light, notifying the tote wrangler that the tote is ready to be removed from its slot and replaced with an empty tote. A steady orange light notifies the tote wrangler of an error or jam that must be troubleshot by the tote wrangler or problem solver. A flashing orange light is used when a tote must be replaced due to inventory issues, for example, an empty tote was scanned into the machine, however, virtually an item is attached to that tote. The system rejects the tote and notifies the problem solver or tote wrangler to replace the “dirty” tote. They replace the tote with another empty tote and hand the “dirty” tote to a problem solver to fix the inventory allocation issue.

UIS Layouts: Two distinct layouts will be used for UIS deployment: Brownfield and Greenfield. Brownfield, refers to existing IXDs, that have been retrofitted with single-sided 5 lbs. UIS machines laid out in a serial formation. The second layout, for newer Greenfield IXDs, consists of single-sided 5 lbs. UIS machines laid out in parallel with multiple machines in a row. A UIS machine has 45 totes plus an additional 4 totes for problem solving. Not only will it be important for the solution devised in this thesis to accommodate for two-sided machines as there are plans to retrofit these machines with totes on both sides, but it will also be critical for the solution to accommodate both machine variants (5lbs and a new 20lbs machine). Section 4 will delve into the operational envelope of the UIS machine as well as map out the machine’s failure modes.

2.4.2 Tote Wrangling Associate A typical IXD UIS machine requires three associates to operate: an inductor, a problem solver, and a tote wrangler. In the field, problem solvers may be shared between multiple machines, however, a machine necessitates one dedicated inductor and tote wrangler. The tasks of the tote wrangler, can be split into primary and secondary tasks. The primary tasks consist of removing full totes from the UIS machine, placing full totes on outbound conveyance, retrieving an empty tote from inbound conveyance, and inserting the empty totes into the UIS machine. A time study completed at an IXD showed a tote removal and replacement process time approximating a normal distribution with mean 13.5 seconds and standard deviation of 1.5 seconds. Shuffling items within totes, difficulty scanning barcodes, and associate speed account for the variation. With the median rate of tote closure at 1.4 totes per minute, primary tasks account for 27.5% of process time as calculated using a queuing theory model (See Section 4). The tote wrangler spends the remaining time adjusting items dropped into totes to improve tote density. An often-overlooked part of their role is to fix sorter jams, shuffle items within totes in order to increase tote density and shuffle protruding items to avoid overfull totes. The tote wrangler also manually delivers kick-out items, items that have no destination tote and are routed to a problem solving tote, into their correct destination totes.

Fig.13: UIS Tote Wrangler Value Stream Map These issues are caused by a mixture of hardware complexity at the sorter, a lack of an algorithmic understanding of volumetric packing (an intractable physics problem), and the large variation of shapes processed by the sorting machine. Therefore, if automation is to be successful, three questions have to be answered: which issues (type, frequency, duration) occur during normal operation of the sorting machine? How can we detect and troubleshoot these issues using automation? At which point in the process would be most ideal (cost, reliability, throughput) to solve these issues? Section 4 will deal with these questions. For the purpose of this thesis, it is this manual task that is being automated. Once a tote has been removed from the UIS wall (colloquial reference to its slot on the UIS machine), the tote wrangler places it onto outbound conveyance which routes it to a palletizer.

2.4.3 Robotic Palletizer The robotic palletizer is a six-axis industrial robot that has been programmed to physically palletize totes. The robotic work cell is installed at the end of a transshipment out conveyor line and automatically builds pallets that are then wrapped and brought into trucks for the second leg transshipment. Once a tote is put onto conveyance by the tote wrangler (see section 2.4.2), it is routed to its respective outbound lane. The tote enters the robotic work cell and reaches a pick infeed location. The tote ID is scanned by a photo eye and the robot sends a message to the WMS requesting a final destination for the tote. Once the WMS confirms the destination the robot picks the tote, moves the tote over its programmed pallet and stacks the tote onto the pallet. During the operation, the robot keeps a virtual count of how many totes have been placed onto pallet locations and uses this count to determine the next tote location. Once a pallet is full and has been removed from the location, sensors on the robot reset the pallet location to zero and record the addition of a new empty pallet.

The most important aspect of this process, for the purpose of this thesis, is that in order for totes to be stacked, no items can protrude from the stack line, the four edges on which the next tote is stacked – located 0.75” from the top of the tote (See Fig. 14). Therefore, once an infeed tote has been deemed “overheight” by an inspection bar placed in its path, the tote is moved to a buffer tote area. An associate rearranges within the tote or splits the items to a new tote so that the tote can be stacked onto a pallet.

Fig. 14: Tote CAD design showcasing (in blue) support on which the next tote rests Throughout the process of sorting and palletizing totes, multiple vision systems, scanners, photo eyes, and other process redesigns have been implemented to ensure quality and process capability. However, edge cases exist and in these instances, associates are the primary line of defense to ensure problems are solved and exceptions are handled. In automating tote wrangling, the variation in input must be controlled while the visual processing ability and pattern recognition of associates will have to be replicated in some form or designed out by the automated system to ensure process flow and capability.

3. Literature Review Automation is a rich and multidisciplinary area of research that has, over the years, been developed and advanced by academia, industry, and government. For the purpose of this paper, two main areas of research will be investigated and summarized: robotic work cell design and robotics deployment. The discussion on robotic work cell (RWC) design will be divided into two topics: 1) a review of literature on RWC modelling and simulation, as well as 2) a summary of recent breakthroughs in robotic pick-and-place and 3D vision systems. This area of research is concerned with the integrity of the design and the technical performance of the RWC in accomplishing a set of programmed tasks in an effective, efficient, safe, and repeatable manner. This will be followed by a discussion of literature on robotics deployment with an emphasis on the organizational impact. The research reviewed seeks to diagnose the consequences of robot deployment in organizations and develop best practices in workforce change management. Furthermore, a fertile ground of research is in the area of human-robotic interaction with a subset field that researches the impact of robotics on metrics, performance, and employee engagement.

3.1. Robotics Work Cell Design 3.1.1. Modelling and Simulation A simulation can be defined as a set of numerical experiments on dynamic models, describing existing or planned systems, for the purpose of investigating the behavior of the studied system over time [16]. Simulations enable off-line improvements and iterations to the design thereby contributing to the increased efficiency and effectiveness of the robotic system [17]. Based on a set of mathematical inputs, the agent-designer can improve performance against a set of chosen criteria by analyzing simulation output results. The process of RWC simulations can be viewed as a mechatronic design process which includes the kinematic simulation, the dynamic simulation, and the mechatronic function [18]. For the purpose of this paper, only the first two will be developed. The first is a physics simulation to determine robot reach, path, and actual cycle time. The second simulation is a process simulation to determine whether the automated tote wrangling system is able to service one or multiple UIS machines at different speed factors. . Whereas the previous simulation was concerned with robot physics, this simulation aims to replicate the UIS machine using operational data to understand process capability, robot utilization, and tote queue.

Five stages are critical in the modeling and simulation process [16]: modeling of robotic components of a workcell, arrangement of components in a workcell, defining the kinematics of machines and devices, defining the paths of a robot, and testing and verifying the created model. By designing a software version of the system, the agent-designer reaches a high level of flexibility, automation, and reconfiguration [18]. Currently, most robot manufacturers such as KUKA, FANUC, ABB, and COMAU offer virtual software to program robots, run full simulations, and develop realistic work cycles to determine dimensions, components, programming, and limitations of the design. The model developed by (Gwiazda, 2013) and further refined for the purpose of simulating robotized production workcells by (Sekala et al., 2017) was used to gather data and construct simulations. Fig. 15 summarizes the approach to simulation that was followed for this research.

Fig. 15: Proposed conception of a design process [16]

The benefits of this approach is that it links the system-level approach of a DMADV methodology with an agile design method. The production system is disaggregated into three subsystems that can be studied and developed concurrently: the main components of a workcell, the system of kinematics and dynamic dependencies of elements, and the spatial arrangement of the workcell [16].

Augmented Reality (AR) and Virtual Reality (VR) are emerging domains of research in robotics modelling and simulation. Although in their infancy and not yet robust enough to provide the level of detail, accuracy, or analysis required to deploy a robotic workcell, these field promise to be novel collaborative methods for designing and refining RWC. Early work done by (Pai et al., 2014) in developing an AR based robotic work cell with a virtual robot arm, conveyor belt, pallet, and CNC machine to simulate a manufacturing plan has been successful [19]. First, a definition of each technology is required to contrast both technologies. AR enhances or modifies reality by superimposing virtual objects whereas VR replaces reality. VR development is more expensive than AR since it requires higher computational power. Another benefit of AR is that current technology exhibits high accuracy in tracking scenery and environment [19]. In this experiment, the robotic arm’s kinematics are simulated using Denavit- Hartenberg’s theorem making it possible to manipulate the end-of-arm-tooling in 3D space. Collision detection is represented in two ways: marker based detection of nearby objects for robot arm movement and pick-and-place integration of virtual objects within the environment. A live heads-up-display overlays live information on the behavior of the system that can be analyzed. The use of AR for immersive modelling and planning through augmented technical drawing has helped reduce overall RWC production time by 30-40%, increased productivity, and showcased a more streamlined approach in designing production cells between multiple manufacturing functions[19]. More importantly, a fully functional RWC can be modelled without extensive programming knowledge and can be easily manipulated by operators for the purpose of training. 3.1.2. Developments in 3D Vision Since 2013, Amazon has hosted the Amazon Robotics Challenge, a competition in which academic teams compete to 1) pick and stow items into containers and 2) pack items into boxes [5]. This competition has been a catalyst for research into 3D vision systems. To be successful, robot pick-and-place systems must recognize/grasp objects in a cluttered environment and place them in a predefined end-location (a tote, a case, a bin, or a package). In humans, visual perception in particular plays a key role in human scene recognition – it is both flexible and robust – enabling humans to grasp a multitude of novel objects successfully [20]. For proof, fifty percent of the cortex, the surface of the brain, is devoted to processing information. The ultimate goal of 3D vision systems is to mimic visual perception with detection and isolation algorithms to trigger

required motion planning and manipulation [21]. Several leading papers were authored by teams who competed in the challenge. These papers will be discussed below. The biggest challenges in picking-and-placing are pose estimation and grasp planning – how to successfully pick-up an object. Historically, robots have been designed and programmed for a relatively static environment with the expectations that they will work within a very narrow range of variation. However, as RWCs have been implemented in more complex and novel processes replacing or interacting with humans, designers have sought to imbue them with human-like capabilities to deal with high variability and edge cases. Multiple algorithms and hardware architectures have been developed to mimic human’s ability to visualize and grasp unfamiliar objects with little prior knowledge – success has been mixed due to heavy clutter, severe occlusion, and object variability. The ability to perform complex tasks in semi-structured or even unstructured environments is strategic in industrial robotic applications [21].

Hardware: At its most basic, the hardware required for 3D vision systems are a 3D camera for visual perception, a laser sensor for depth perception, and a robot with one or more end-effectors. Recently, full integrated vision systems, which combine both the visual perception and laser sensing hardware, have been developed [22]. These systems can vary from the top of the spectrum, such as the FANUC Integrated iRVision System, to a cheaper Motoman ‘MotoSight’ 3D Spatial Vision System, which costs $11,000 and can only be integrated with Motoman robots [22]. Low cost systems have been developed (Sandhu et al., 2015), which use a simple color VGA video camera, a Microsoft Kinect depth sensor, and a multi-array microphone. The results are promising but underwhelming: out of 27 pick-and-place tests attempted, 17 were successful (63%), with a cycle time of 32.61 seconds in automatic mode [22]. Other RWCs, such as the Roboscan developed by (Sansoni et al., 2014), used an IDS video camera and a laser slit projector showing robust performance with simple shapes (cylinders, cones, spheres) [23]. (Pochyly et al., 2017) used a 3D camera Sick Ranger E above the RWC with 2 rotating linear lasers for depth perception. The cycle time for one object pick-and-place was 25 seconds[20]. The MIT- Princeton winning team of the 2017 Amazon Robotics Challenge used four statically mounted Real Sense SR300 RGB-D cameras. Two cameras overlooked the storage bins to predict grasp affordance while two cameras are used to recognize objects in the gripper[24].

Hardware Limitations: Hardware placement is the most obvious challenge. For example, the vision system can either be mounted on the end-effector or elsewhere in the workcell. If it is mounted on the end-effector, no other hardware is needed, however, it adds weight (bigger robot) and must be considered when designing the robot path to avoid collision. The bigger the end- effector (including the vision system), the more likely collisions may occur between the robot and the scene[25]. The advantage, in this case, is that the robot can be used to position the camera and sensor with respect to the scene. If the vision system is mounted elsewhere, additional hardware is required and the vision system must have unobstructed view of the objects to be manipulated [25], which can be difficult due to other equipment in the cell.

Software: Traditionally, vision systems approached the bin-picking challenge by first computing a gripper pose. This is done by acquiring a picture and depth map of the workspace using the camera and a laser. The 2D depth images are converted into 3D point cloud data [25] which are then matched to a library/database of 3D templates [23]. The output parameters are pose information such as the graspable region of the object. The end-effector approaches and grasps the object at the computed gripper pose and executes a depart motion. At this point, a more appropriate pose is estimated inside the gripper and the end-effector places the object in a defined way to increase the probability of successfully picking-and-placing the object [25]. To accomplish this, systems require a database of 3D object models (CAD, Point Cloud Library, IMAQ, etc.) and a large amount of data to segment and associate grasp detection with object identities [26]. The obvious pitfall of this approach is that it is unable to deal with never before seen objects. To circumvent this shortcoming, the MIT-Princeton team developed a new pick- and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments [24]. To accomplish this, the team defined four basic motions using a dual end-effector (suction, grasp) to grab any object and an affordance prediction algorithm that used RGB-D images from the scene to predict the best grasp from local geometry data.

Fig. 16: Out-of-the-box MIT-Princeton picking-and-placing system (Image from Zeng et al., 2017)

Once the object is grasped and isolated, a cross-domain image matching is completed between the observed object and the library of product images. The goal is to determine the best match in geometry similarities instead of direct object matching using a two-stream convolutional neural network. One stream computes the features of the observed image while the other computes the features of product images to converge onto a match [24]. This enables the network to learn features such as object shape, color, and other properties that can be isolated. Once this is done, the end-effector grabs the object and moves it into the desired location. Of all the teams in the competition, the MIT-Princeton team was the only team to have stowed all objects within the allotted time frame. They were able to stow all twenty objects within 24 suction attempts and 8 grasp attempts. Overall, they had a 58.3% pick success with suction, a 75% pick success with grasping, and a 100% recognition accuracy [24].

Limitations: In industry, time is an expensive resource. Every cycle, a typical vision system must complete object pose computation before the robot can begin the motion. Grasp planning run- times vary widely from 13.5 seconds [27], to10-15 seconds [26], to 5-40 seconds [28], to 0.8 seconds [29], to 0.06 seconds [24]. Furthermore, current time saving heuristics come at the cost of data accuracy as picture acquisition and analysis time are shortened and algorithms to filter through noise are applied but can fall prey to shading or shadows [25]. Although the MIT- Princeton “pick-first-ask-questions-later” approach is promising, these 3D vision systems function under ideal conditions in pre-determined settings and have yet to scale to handle Amazon’s product range, which would mean tens of thousands of novel objects every hour. These systems would have to deal with extraordinary variance (size, color, shape), short cycle times, and large throughput requirements with minimal supervision and maintenance. Lastly, these systems have yet to match an associate’s dexterity, speed, or accuracy in dealing with both known and novel objects, as well as an associate’s ability to troubleshoot edge cases.

3.2. Robotics Deployment Beyond the technical difficulty of developing and deploying robotic systems in mission- critical operational environments, companies will also have to contend with a more ambiguous question: the organizational implication of robotics.

3.2.1. Robotics Deployment and Change Management Organizations can be viewed as the sum of three basic components – people, technology, and structure – that, together, achieve agreed-upon goals [30]. It is the compatibility and interplay between these components that determine performance against these goals. For an organization such as Amazon, performance metrics can be productivity goals (units per hour, labor hours), financial goals (VCPU), flexibility goals (associate training), human resources goals (absenteeism, turnover), safety goals (reported incidents), or quality goals (bin quantity mismatch). Companies seek robotic systems to directly or indirectly improve performance against some or all of these goals. To truly benefit from the advantages of automated systems, it follows that companies must be cognizant of the impact of robotics on these three components. That is not to say that the introduction of robots implies consequences for the organization [30], however, the manner in which they are introduced, their effect on the reporting structure, and how they interface with humans within the organization must be determined from the onset.

Design: Research suggests that the level of autonomy is a critical variable to determine at the onset of an automation project [31]. A framework developed by (Beer et al, 2014) could help companies determine the desired level of autonomy by answering guideline questions such as: what tasks is the robot able to perform? What aspect of the task should the robot perform? To what extent can the robot perform these aspects of the task? Can it complete task given some level of autonomy? How critical are these tasks? [31]. It is imperative for an organization to answer these questions since autonomy drives employee interaction and training. Another important aspect is the time it takes to develop a viable solution with acceptable reliability. Higher autonomy may translate in less frequent interaction with and less control by associates whereas low autonomy may require constant human interaction [30]. The former could make associates more dependent on technical staff while the latter could increase associates’ mental load especially in peak or edge case conditions [32]. Research suggests that less control translates in more stress for employees due to an increased reliance on others (support functions), a work pace determined by robot, and more rules of engagement to remember [32]. Often a job redesign is required. Organizations should answer questions such as: are employees able to perform the new activities? Do they like to perform new activities? The most successful automation projects ensure that the RWC is compatible with employee’s skills and preferences. It has been shown that automation may change a manual task to a cognitive task

37 leading to boredom or worse inadequacy [32]. Designing rotations and multiple job profiles becomes important to maintain an engaged workforce. Research has shown that more flexible work rules lead to a more productive and happier workforce [30]. The shrewd organizations will ensure that this does not spiral into worker exploitation. It is also important for the organization to design more opportunities for interaction between production employees and support functions [30] in the work cell’s modus operandi. This could create an asymmetric independence conflict, in which operations needs engineering more than engineering needs operations. Developing multi-functional teams to lead projects is critical in avoiding this type of conflict. Teams can be staffed with key stakeholders from each group to foster cooperation, understand each other’s constraints, and inform design choices [30]. Furthermore, a reward structure that emphasizes performance of all groups is required as interdependence makes it difficult to reward performance on the basis of individual contribution. In parallel, as employees feel less control over their work environment, they may feel less satisfied, motivated, and more stressed [33]. It is especially important if the RWC is complex or exhibits a higher failure rate. In fact, a level of reliability below 70% is believed to be worse than no automation at all [32]. Failure to deal with these concerns may slow down implementation speed, reduce effectiveness of RWC, and lead to employee stress [33].

Implementation: The adage “communicate more” holds true in robotic implementation. Often, shop floor employees learn about a new automation project upon seeing or hearing from informal sources, that a robot has arrived [33]. A communication plan must be developed earlier to engage and inform the organization. A lower level of engagement may necessitate a simple communication to balance the delta between the information wanted and received. It has been shown that demonstrations are the most effective method as they lead to better beliefs and attitudes towards robotics than any other communication source used [30]. These should be planned as part of the communication strategy. Furthermore, communication must balance the positive and negative aspect of the automation project to seem more realistic and believable to the workforce. While this may seem like a daunting task for executives, research has shown that using first line supervisors is quite effective [32]. The organization must give them the necessary details and tools to help them communicate effectively. To be effective, supervisors must be trained, upper management must provide them with support, and employees must know that they can approach their supervisors with relevant questions.

Participation is a key variable in determining the extent to which employees react positively to change [33]. In change management studies, employees wanted a higher level of engagement through participation yet it was not influence in all aspects of the project that they expected but rather involvement in decisions related to their areas of expertise [30]. This could take the form of an automation task force that has a clear set of goals to engage the workforce. These could vary from engaging the workforce in design, testing, or to help introduce new automation. A more creative cultural vision could be to ensure that employees, with the increase in automation, feel less isolated.

3.2.2. Human-Robot Performance In studied applications, automation has been successful in reducing workload, process variability, error frequency, and cost, whilst increasing efficiency [34] [32]. However, the process-specific definition of performance must be revisited. In short, mission effectiveness (output) metrics are poor choice of metrics after automation is implemented since humans often compensate for design deficiencies making it difficult to detect design flaws and design processes that can successfully support the overall system [35]. Furthermore, employees feel as though they are the reason the system is robust and may grow resentful of ineffective automation.

Human Factors: Studies have shown that operators exhibit less situational awareness when dealing with automation [36]. As well, employees can find workloads taxing under abnormal circumstance [37]. An associate may need to assimilate more info or continuously monitor automation [36]. Guidelines have been developed to deal with this such as building constraints in the time required to analyze metrics, developing multiple tasks with a predetermined switching time between them, and having a second person visit the area of interest to help monitor activity. This is further complicated by the fact that historically, RWCs are isolated, in both a physical and sensorial sense, from humans by barriers or demarcated areas with light curtains. This creates an environment in which interaction with work cell is impoverished [38]. Trust and accountability between the employee and automation can be impacted. Employees who lack trust in the automation may double check work eliminating gains from efficiency and may shirk their responsibilities in ensuring the work is carried out properly due to a loss of control. Studies have shown that employees prefer to work alongside a tele-operated robot rather than an autonomous one [38].

The form of the automation is also important for how it is perceived and should be appropriate for its designed task. For example, non-humanoid forms appear more friendly and are judged to have better personalities than their humanoid counterparts. They are viewed as independent actors who are “good” [38]. Moreover, employees are more accountable when working with non- humanoid robots and ensure tasks are complete [38]. For management, this means developing rules of engagement. Will the robot be managed by a team or by an individual? Will the robot system be abstracted away? These questions require great consideration and emphasis as they are critical to the perception of employees towards the automation. For example, unless autonomy is sufficient to deal with all eventualities, humans will have to troubleshoot the system, however, if it is abstracted away, operators will find it difficult to apply their experience or learn from incidents. Even worse, operators will only interact with the robot when things go wrong – impacting their opinion of the usefulness of such automation. Designing the teleoperation and monitoring of the system, with the goal of keeping employees as active participants in the process, is critical.

Performance: Human-robot interaction research conducted by (Kim and Hinds, 2006) sought to answer the question of who gets the blame and the credit when semi-autonomous systems and operators interact to accomplish set tasks [39]. Research into this field is critical for companies as they seek to implement robotics to improve process performance. Two variables were tested, autonomy and transparency, with surprising results. Autonomy was determined by a robot’s ability to accomplish a task without human intervention whereas transparency was determined by a robot explaining how they were accomplishing a task. Their experiments showed that test subjects laid more blame on high autonomy robots than low autonomy robots when tasks were not accomplished successfully. Furthermore, test subjects blamed themselves less for errors that occurred with high autonomy robots than low autonomy robots. In short, they abdicated their responsibility. For transparency, test subjects laid less blame on (and gave less credit to) other participants when the latter worked with high transparency robots, however, robot transparency led to more blame but did not lead to more trust. The reason given is that trust was highly dependent on the match between the robot’s explanation and the background knowledge of the test subject. The language used must be suited for the user’s background as unclear explanations created more confusion [39]. Organizations must seek to develop robust processes to deal with the impact of automation on the workforce.

4. Project Methodology The goal of the thesis is to automate the intermediate process step between sortation and palletizing, that is, the removal of full totes from the UIS and the replenishment of empty totes in which items will be sorted. Currently, the removal and replenishment of totes are being completed by tote wranglers. Automating tote removal would provide several advantages. Wrangling is a labor intensive job that poses an ergonomic risk to associates. An automated solution would eliminate safety risks. The UIS machine is modular, columns can be added to the machine to increase the number of tote locations (final destinations), it is currently constrained by associate reach. To accomplish this, a Lean Six Sigma DMADV (Define, Measure, Analyze, Design, Validate) methodology was used to map the process qualitatively and quantitatively, determine root causes of failure modes, and design experiments to validate a RWC design.

4.1. Define The goal of this first phase is to “identify the purpose of the project, process, or design” [6]. In this section, we will discuss in-depth both the UIS machine and the tote wrangler role by mapping decision points and breakdowns.

4.1.1. UIS Machine In Section 2.4.1, the author introduced the UIS machine, a new generation sorter in use at Amazon. In this section, we will map out and quantify the failure modes of the machine.

Failure Modes: Machine warnings or jams may occur at multiple areas. Spatially, these failure modes can be segmented into three broad areas: at induction, within the machine, at the wall unload. Induction encompasses errors that occur prior to the item entering the machine and can include errors such as a barcode that is not properly scanned. Errors such as a cylindrical or spherical item rolling off a shuttle and falling within the machine or a shuttle jamming can be classified as errors within the machine. The third category, and the one of most concern to this application, is composed of errors that occur at item unload (items delivered to tote). Several UIS warnings and jams were mapped – for this application, two warnings are of concern: cannot unload and bin not present. ‘Cannot unload’ describes a shuttle’s inability to deliver the product, that is, the shuttle is unable to unload the items into the tote. These errors are caused by products accumulating at

the aft of the tote restricting item delivery, bagged or wrapped products caught on the shuttle track, or items with high coefficient of friction that get stuck on the chute. A subset of this error is ‘cannot unload – item not present’, in which the shuttle believes the item has not been delivered or is missing. From observation at IXDs, most often, the shuttle has delivered the item; however, the item did not trip the sensor during unloading. In rare cases, the item may have rolled off the shuttle and fallen into the machine – a trained associate retrieves it.

Fig. 17: Examples of Failures to Unload (left) stuck on shuttle, (right) items jammed at the back) of the tote The ‘bin not present’ describes an error in which the shuttle sensor, which ensures tote presence before delivering the product, erroneously concludes that a tote is missing. Small misalignment of the tote within its holding bracket is largely the cause of this issue. It is important to note that although these warnings do not stop induction, they halt item delivery and in turn may stop other shuttles from delivering items since shuttles share tracks. After sixty seconds without resolution (this rarely happens since the associate is currently there to fix issues), the machine goes into jam mode and stops – these warnings become shuttle stalled or failure to unload jams. Duly and promptly fixing these errors is critical to maintaining rate as the machine will not restart until it is troubleshot.

Process Failure Modes: Three process failures are important to highlight: wall blowouts, overfull totes, dirty totes. For the UIS to maintain rate, inductors must continuously induct items. Consequently, a tote must always be available in which to sort the item. A possible failure

is due to tote unavailability. For example, if all totes are closed/full, a tote is not present, or if the destination arc is not available, the item is diverted to a kick-out bin or the machine can no longer induct – colloquially referred to as a “wall blowout”. Currently, tote wranglers move fast enough that there are virtually no kick-outs (0.0078% of all inducted items in 2018) due to unavailability of totes. The second failure mode occurs downstream from the UIS. Conveyance routes full totes, once removed, to a manual or automated palletizer in order to be stacked for shipping. Totes cannot be stacked if they are overfull. This failure mode is caused because items may fall into the tote in such a way as to occupy more effective space than their actual volume, with edges or full items protruding.

4.1.2. Tote Wrangler In all of these cases, the tote wrangler troubleshoots these errors. The tote wrangler shuffles items to the front of the tote to reduce the occurrence of ‘cannot unload’ or retrieves caught items with a grabber. For missing totes, the tote wrangler move totes until the shuttle notices their presence and in the case of shuttle stalls, the tote wrangler trips the sensor using the same grabber as for failures to unload. Analysis of these warnings and jam is included in section 4.2. A critical role of the tote wrangler is to ensure that totes are not overfull, that is, that no item is protruding from the tote. Lastly, dirty totes are empty totes that have virtual items attached yet have no items physically in the tote. For example, a tote is empty yet the inventory system believes that an item is present in the tote. The PTL system highlights the error, by flashing orange, and the tote wrangler replaces the tote with a new tote and hands the dirty tote to be problem solved. Fig. 18 shows a simplified process map of tote wrangler’s actions during breakdowns.

Fig. 18: UIS Problem Solving Value Stream Map (VSM has been modified to conceal proprietary info)

As discussed in Section 2.4.2, tote wrangler tasks can be broken down into primary and secondary tasks. A different way to segment these tasks is by how easy or difficult it would be to automate them. Doing this enables the designer to measure the occurrence frequency and root causes of these errors. Once that is done, targeted process redesigns and improvements can be incorporated to simplify or work around difficult tasks.

Table 1: Classifying tote wrangling tasks by automation difficulty Easy to Automate Difficult to Automate Communicate with WMS Troubleshoot UIS wall errors Scan tote barcode Ensure no items are protruding from tote Remove tote from UIS Arrange or shuffle items to increase density Place full tote on output conveyor Troubleshoot process errors Grab empty tote from input conveyor Replace tote on UIS

4.2. Measure An understanding of the operational envelope of the machine is critical to design the automated solution. Using data from the Amazon WMS Dashboard, which tracks key metrics for the machines in service, the author completed multiple analysis to understand tote closure rates, tote weight, and defects.

4.2.1. UIS Operational Envelope Data sets from February 18, 2018 to May 2, 2018 were obtained for four separate machines in the form of action time logs. The goal was to compute the tote closure rate, from which can be derived the required cycle time for the robot. The tote closure rate is defined as the number of totes that are closed by the system (deemed full) in a minute. The tote closure rate is critical as it determines the rate at which the robot would have to service the wall, that is, pick up a closed tote, place it on the output conveyance, pick up an empty tote, and place it on the UIS wall. The action log showed minute by minute operation of the machine. The data was treated by removing all non-operational hours, and hours in which the machine was down or not inducting. For the

remaining operational hours, a report showing number of totes closed at each minute was downloaded from four machines and a distribution was built with this data.

Fig. 19: Distribution of Tote Closure Rates for four different machines, with summary data shown in Table 2.

Table 2: Summary of tote closure rates Machine 1 Machine 2 Machine 3 Machine 4 Mean (tote per minute) 0.815 0.805 1.025 0.646 Standard Deviation 0.427 0.523 0.561 0.411 Coefficient of Variation 0.524 0.650 0.548 0.636

The histogram in Fig. 19 shows the distribution of tote closure rates from four machines with summary data shown in Table 2. The data for each machine was cut into six quantiles with thickness showing the range of equal proportions. The insight from these analyses is that tote closure rates vary but that on average, the rate is quite low and certainly manageable. An interview with an area manager shed light on the difference between machine mean values. The higher mean for Machine 3 was due to associates choosing to induct larger items at that machine, resulting in faster tote fills and higher tote closure rates. Using Machine 3 as a worst case scenario and building a 99.7% service level interval (three standard deviations from the mean), we find an upper control limit of 3 totes/minute from OPEX03 or a cycle time of 20 seconds/tote. For perspective, the maximum closure rate from the four data sets was 8 totes/minute (7.5 seconds), and the probability of seeing such a value in the OPEX machine profile is infinitesimally small (near zero) in normal operations. Therefore, a 20 second robot cycle time was chosen as a design limit to enable smooth operation during peak periods.

From the same data sets, tote weight at closure was analyzed to understand the load that the robot end-effector would have to transport from the machine to the conveyor.

Table 3: Tote weight (lbs.) at closure (values do not include weight of tote) Machine 1 Machine 2 Machine 3 Machine 4 Mean weight (lbs.) 12.300 12.780 13.063 8.900 Standard Deviation 4.663 4.781 4.980 3.100

In general, tote weight is quite manageable across all machines. As with volume, the totes have a permissible weight limit to ensure associate’s ergonomic safety. This weight limit is set to 25 lbs. and since an empty tote weighs approximately 5 lbs., it results in a maximum weight of 30 lbs. However, since the weight of items is derived from a catalogue, discrepancies may occur in which the item weight is higher than the catalogue weight. To protect against this, the design weight limit for the end-effector was set to 40 lbs. constituting a safety factor of 1.33.

4.2.2. UIS Failure Mode Occurrence From January 1st, 2018 to April 1st, 2018, the R&D UIS machine inducted 85,009 units with a total of 2552 shuttle interruptions (3.0%). Of those interruptions, we are concerned with two, Load Unload Timeout (183) and Failed to Unload - Tote Missing (14) as these are the only ones occurring at the UIS tote wall. These represent less than 7% of all interruptions and occurred on 0.2% of all items inducted. SHUTTLE Interruption in a Single UIS machine

Fig. 20: shuttle interruption in a single UIS machine – dataset 1.

SHUTTLE Interruption in a Single UIS machine

Fig. 21: shuttle Interruptions in a single machine – data set 2 To validate the data in Fig.20, action logs were extracted from another time period for another machine in the R&D laboratory. From April 1st, 2018 to July 23, 2018, the machine inducted 198,094 units, with a total of 3395 shuttle interruptions (1.7%). Of those interruptions, we are again concerned with two, Load Unload Timeout (418) and Failed to Unload - Tote Missing (67). These represent less than 14% of all interruptions, on 0.25% of all items inducted. Lastly, data from operational machines at IXDs across the US was taken to understand the occurrence of failure to unloads in the field. The number of items inducted was removed to protect Amazon’s operational data. In summary, the occurrence of these warnings or jams is quite low,

Fig. 22: Occurrence of failure to unload from 07/09 to 08/14 in ten UIS machines

47 however, if left unresolved after sixty seconds, they can bring the machine and overall process to a halt. Although Universal Item Sorter failures could be mapped due to an abundance of data from the WMS, the occurrence of process failures (overfull tote, dirty totes, and wall blow outs) would have to be the subject of specific designs of experiment since they are not captured in the WMS since they are not monitored by the WMS. These will be discussed in Section 5.

4.3. Analyze The design of an automated tote wrangling solution had three steps. First, the author determined the UIS operational envelope (tote closure rates, weight), quantified the occurrence and mean time between UIS failures, as well as developed simulations to inform final design and test key hypotheses. Two simulations were developed: a robotic physic simulation and a FlexSim process simulation. The first simulation was to understand which type of automation would integrate into current layouts and maintain required rates. The second simulation was used to calculate robot utilization, determine how many machines one robot could serve, and whether failure would lead to wall blow outs (inability to induct).

4.3.1. Robotic Simulation Rationale: Using Roboguide, a simulation software, physics simulations were built to calculate robot reach (number of UIS columns), loading, and actual cycle time. The cycle time was defined as the time for a robot, from its zero position, to remove a tote from the UIS wall, drop a full tote on output conveyance, pick up an empty tote from input conveyance, replenish the UIS wall, and return to zero position. The CAD drawing of the UIS was imported into the model to mimic exact dimensions. This simulation is critical in designing a system that meets physics and layout constraints from a robotics perspective. To calculate cycle time, random tote locations were simulated and the average cycle time was taken as the output figure. Two types of automation configurations were tested (robot on rail or static robot). A gantry and AGV systems were not tested as these solutions could not meet basic constraints (cycle time, cost, readiness, and reliability). For example, AGV solutions investigated could not lift more than 22 lbs. when reaching higher totes and had cycle times that were around 50 seconds – these were deemed unacceptable for this process. Three hypotheses were being tested in this simulation.

Hypothesis I: a FANUC M710ic/45M (capable of lifting up to 45 kg) with one end-effector would suffice to lift a tote. Hypothesis II: Only two static robots could meet the stringent cycle times while reaching all columns. Hypothesis III: Having two end-effectors, one holding an empty tote, while the other one holding a full tote would decrease cycle time.

Two robot models and configurations were simulated. A smaller FANUC M710ic/45M capable of lifting 45kg and a larger FANUC R2000ic/125L model capable of lifting 125kg. Each model was simulated with single and dual end-effector (or end-of-arm-tooling). Multiple conveyance configurations were also simulated (behind robot, to the side).

Fig. 23: Single and dual end-effector configurations 49

Output & Analysis: For hypothesis I, loading analysis showed that J6 (wrist of the robot arm) payloads were over maximum allowable for the M710ic/45M in both configurations (single and dual end-effectors), which means we must reject the hypothesis. From further analysis (see Fig. 24 and 25) we can conclude that a more appropriate robot would be the R2000iC/125L with either single or dual end-effectors as all J6 payloads, moments, and inertias are significantly below their respective maximum allowable.

Fig. 24: m710ic/45M payload calculation with single and dual end-effectors

Fig. 25: R2000iC/125L payload calculations with single and dual end-effectors For hypotheses II, two static robots can achieve a throughput of 8 totes/minute while one robot on rail can only serve 3 totes/minute, which is above the 99.7% service level defined in Section

4.2.1. Therefore, hypotheses II must be rejected, as a robot on rail is able to service the required 20 seconds cycle time while being able to reach all columns on the UIS wall with a 20’ rail. The simulation shows that with one end-effector, the minimum distance from the UIS to the center of the robot would be 94” inches whereas with two end-effectors, the distance would increase to 107” since it would necessitate a larger robot model and more turning space. For hypothesis III however, having two end-effectors did not reduce the cycle time since a heavier robot would be slower and would still need to pick up an empty tote each time. Hypothesis III was also rejected.

Table 4: Roboguide cycle time and reach analysis Robot Configuration Static (1 EOAT) Static (2 EOAT) Rail Conveyor Configuration Stacked Stacked Stacked Maximum Payload 40 kg 65 kg 40 kg Infeed Conveyor Height 30" 12" 20" Outfeed Conveyor Height 52" 34" 42" Robot Riser 70" 53" 75" Distance from Robot Center to wall 94" 107" 94" Cycle Time 13 seconds 14.5 seconds 19.5 seconds Robot Specified M-710iC/45M R-2000iC/125L R-2000iC/125L

(a) (b) (c)

Fig. 26: Robot simulations with multiple configurations and models. a) Static 1 EOAT, b) Static 2 EOAT, c) Rail 1 EOAT Summary of Results: In conclusion, the preferred setup from a physics standpoint would be to have an R2000iC/125L 94 inches away from the UIS with one end-effector. Having two end- effectors would increase total cost without improving overall cycle time. To determine whether the system will necessitate one robot on rail or two static robots, a financial analysis will be done using quotes from suppliers. Results of that analysis are available in Section 5.5.

4.3.2. FlexSim Process Simulation FlexSim is a simulation software to “model, simulate, predict, and visualize systems in manufacturing and material handling”[40]. It is a powerful process simulator capable of handling multiple layouts, components, and object relationships.

Rationale: The goal of the simulation is to determine whether the automated tote wrangling system on rail (and by extension static) is able to service one or multiple UIS machines at different speed factors. Whereas the previous simulation was concerned with robot physics, this simulation aims to replicate the UIS machine using operational data to understand process capability, robot utilization, and tote queue. A realistic benchmark would be to determine whether a robot could move fast enough to avoid kick-outs, wall blowouts, or a lengthy queue of closed totes. Using actual scan data from operational machines, multiple configurations of robots were tested at different speeds. The arrival times, sizes, and arc destinations of objects were not simulated from a distribution, but pulled from raw data. The two UIS machines used, had one day’s worth of data pulled (June 19th. 2017) as these were two machines with the highest output and June 19th was the highest volume day for these machines in the month of June when data was pulled. Three configurations of the UIS were considered: a robot servicing one, two parallel, or two adjacent machines. The simulation will remove and replace full totes from the UIS machine. The simulation assumes that the automated system will not fulfill any of the problem solving functions currently being done by associates such as rearranging totes or correcting jams. The simulation assumes that the robot would be stopped while an associate remediates the jam.

Fig. 27: FlexSim Simulation Snapshot of single machine

Methodology: An explanation of the model is described below:

1. UIS machine induction feed based on raw data directly taken from June 19th from two operational machines. The log used for this simulation contained scan times, item size, item weight, and arc destination corresponding to actual inducted objects. 2. During induction, if no tote is available in which to sort an item, the simulation kicks it out. To model this, the simulation sends the kick-outs to a failure queue to record the occurrence frequency. 3. The simulation sorts the item to existing UIS software configurations. The simulation assigns an arc destination once its volume and weight are read from the ASIN catalog. The UIS attempts to sort the item into a tote with an available arc destination as long as the volume or weights will not exceed 35,000 cubic cm or 25 lbs. once the item is added. In that case, the UIS machine will open a new tote destination corresponding to the arc transshipment. The only gap from reality is due to tote wranglers manually closing totes due to physical volume or weight discrepancies noticed in operations. 4. The nodes pictured above represent stops within the robotic path; these correspond to a rail in a production environment. The speed factors tested range from 0.5x to 2.0 normal speed. 5. The robot removes the full totes and places them on a queue object called “Outfeed”. Speed factors were used to model sensitivity analysis. The base speed is for the robot to laterally move at the rate of 4.91 ft/sec and accelerate at 6.562 ft/sec2. The load and unload times are taken from kinematics and dynamics simulation in the previous section. Robot parameters were taken from FANUC 710iC/45M joint speeds and kinematic simulations. A speed factor of 2 corresponds to a robot performing the load and unload actions in half the time. 6. The queue circled is the Outfeed point. The queue is positioned by the end of the ‘rail’, as this more closely resembles a future IXD state of having an outfeed conveyor positioned at the end of the machine rather than having operators place totes on any open spot on a conveyor running parallel to the UIS. 7. Downtime data was taken from the same operational machines on the day the volume data was provided. The data lacked granularity; the only available metric was the percentage of time a UIS was operating or stalled each hour. To remediate this, the simulation operated as if each hour, all the downtime was consecutive, albeit starting at a random time. In practice, jams do not always stop the UIS from continuing to sort items to other areas and do not

always require the inductor to stop. At these times, however, a tote wrangler resolves the issue, necessitating that the robot be sidelined even if items are still arriving and being sorted into totes.

Hypothesis I: The automated tote wrangling system will be capable of servicing a single machine without wall blow-outs (inability to induct) at normal speed (speed factor = 1). Hypothesis II: The automated tote wrangling system will be capable of servicing dual machines in parallel without wall blow-outs at normal speed (speed factor = 1). Hypothesis III: The automated tote wrangling system will not be capable of servicing dual machines in series without wall blow-outs at normal speed (speed factor = 1).

Fig. 28: (top) Parallel configuration (bottom) Series configuration

Output & Analysis: For the model to prove useful, it needs to closely replicate the tote closure logic currently witnessed at IXDs. As such, some outputs will change across scenarios, since the same number of objects are sorted in FlexSim as were sorted on that day. Moreover, the same

rules for tote filling are in place as well as the number of totes removed from the UIS. This means that the number of units per tote can be used to compare the simulation to the actual operational data as this number will vary:

1 – Tote wranglers constantly rearrange items in the tote thereby affecting which items are in which tote. 2 – Tote wranglers may sometimes close out totes early if there is a physical-virtual mismatch. If the item is larger in the tote compare to the catalog volume, an associate may close a tote that is physically full while the UIS believe there is virtual space remaining.

The actual average number of units per tote was 44.7 and 42.5 for the two machines. Across all simulations, the average was 43.5 with all scenarios between a +/- 6% band. This is satisfactory for the purpose of this simulation. All graphs show speed factor (o.5x – 2x) as the dependent variable.

Fig. 29: Number of machine blowout for all three configurations

Fig. 29 shows the number of machine blowouts for all three setups as a function of robot speed. For a normal speed factor, no machine blowouts occur in the single machine and two parallel machine layout whereas a high number of blowouts occur for in-series dual machines. This would mean that the process would be stopped because the robot is unable to reach all totes in time and

55 a queue would form due to process breakdown. Therefore, we must accept all three hypotheses presented above.

Fig. 30: Robot percentage utilization for all three configuration Fig.30 shows the robot utilization for all three configurations across the full spectrum of speeds simulated. The main insight from this chart is that on average the workload is quite manageable for all three configurations, however, the utilization does increase significantly from a single to two parallel to two series machines. Furthermore, as speed factors increase the utilization drops down as the robot is able to service totes quickly. This intuitive result is further reinforced by Fig.28, in which longer queues accumulate for the two series configuration. At our desired speed factor of 1, only the single and two parallel machine show acceptable queues below 5.

Fig. 31: Tote Queue Length for all three configuration

Summary of Results: The FlexSim model suggests that both a robot on rail and by extension, two static robots, would be able to service one or two parallel UIS machines without generating kick-outs. However, serving two machines in series would not be feasible due to high kick-outs at lower speeds. From these results, we can conclude that depending on cost and layout, both two static or one rail robot configurations are able to service one or two parallel machine exhaustively at normal speeds. For both the static and rail robot configurations, the Mean Time Between Failures (MTBF) was 160 seconds (averaged from testing data) with a Mean Time To Repair (MTTR) approximated as a triangular distribution with minimum of 5, peak value of 15, and maximum value of 30 seconds for all speed factors. The MTBF would mean that if the robot did not have any tote fixing abilities, it would stop working every 160 seconds due to a machine warning or jam, highlighting the importance of decreasing the number of warnings at the machine and designing a robotic work cell that is able to detect and fix UIS errors. Lastly, the model replicated tote filling and closure logic; however, in real life, tote wranglers may rearrange items in totes or close totes earlier. This being said, the actual number of units per tote was within +/-6% of actual for all scenarios.

5. Testing and Experiments Current technology at Amazon has shown that the primary tasks (tote pick and place) can be automated. Section four further reinforced that the automation can accomplish these tasks in a timely manner. The secondary tasks will account for a significant portion of the testing and validation phases. Tasks such as picking items that fell into the machine will have to be problem solved by a human, whereas the design of this solution will have to incorporate a method to tackle tote density, tote over-fullness, and protruding items. Up until now, simulations have assumed that the automation is unable to deal with jams/breakdowns. Section three has shown that although 3D vision systems could robustly identify protruding items from totes, the ability to fix the totes by shuffling items within the tote is a problem that has yet to be solved algorithmically. Furthermore, machine geometries such as the clearance between two totes or the narrow gap above a tote would make it impossible for an end-effector to shuffle protruding items from the back of the tote or on the shuttle chute. To alleviate the issue of overfull totes, a two-pronged approach is proposed, fix proactively and reactively. Starting with the latter, once a tote is closed, the proposed solution would be for the robot to move to the tote and visually scan for protruding items. In this option, the sensor or vision system would be on the end-effector. A second option would be to have a vision system or sensor over the conveyance rather than on the end-effector. This option would make the “blind” end-effector lighter and easier to move due to less hardware. This option, however, could create situations in which overfull totes are moved by a blind end-effector causing items to fall out. If the tote is overfull, multiple options can be used. The tote could be diverted through shaking conveyance to shuffle items in the tote. The hypothesis would be that shaking items will help settle them into the tote and clear overfull totes, promising results have been noted for shaking conveyance in (Walker, 2018) [5]. A second option would be to route it to a bin-picking robot that would move items within the tote to improve tote density, however, as shown in Section three, this would create a long lead-time and unproven process that could become a bottleneck depending on how many totes are routed to this station. Furthermore, it is a cost prohibitive option since more RWCs would be added necessitating more complex integration and floor space. The goal is to avoid sending overfull totes to the automated tote palletizer. The proactive solution would be to reduce the incidence of overfull totes while the totes are being filled. This could be done either by a more robust sortation algorithm that takes into account size/shape to

58 determine tote destinations or by using the robot during downtime to inspect totes as they are being filled. As shown in Section four, the robot utilization for a static or robot on rail is low which means that during downtime, the robot could inspect totes with the high volumetric utilization or number of items by preemptively placing them on a shaker and returning them on the machine, thereby improving tote density in the same manner that associates fix totes on their downtime. Ideally, the bulk of these issues must be designed out at the UIS level through updated packing algorithms, internal sensors, and ASIN volumetric data. Multiple tests were conducted to answer important questions and shed light on the best course of action for the final RWC design. The critical questions to answer are: 1. What is the relationship between the number of items within a tote and its volumetric utilization? 2. What effect does the tote wrangler have in increasing tote volume utilization? 3. What effect does the tote wrangler have in decreasing the occurrence of jams and breakdowns? 4. Would a simple shaking mechanism help to decrease the number of overfull totes? 5. Would a template or size based sortation algorithm help improve tote density or decrease the number of overfull totes? Question 2-5 were the subject of testing, which will be discussed in this Section. Question 1 can be answered by looking at volumetric and unit data for closed totes. Fig.29 shows a scatter plot of these two variables for 794 closed totes.

Fig. 32: Relationship between number of units in tote and volume utilization

It is evident from the plot that no correlation exists between the number of items within a tote and its volumetric utilization. There are a few reasons for this:

1. The variability in the size of the items can have a massive effect on the number of items within a tote and its utilization. For example, fifty small items could be packed into a tote yet only account for 10% of its volumetric utilization whereas one large item could cause a tote to be 90% full. 2. Tote utilization is a theoretical metric that is calculated from ASIN catalogue information. This database uses supplier data, however, there are examples of discrepancies in which an item’s actual dimension is not captured correctly in the system. Amazon has invested in creating full 3D scans and measurements of its products, however, only a small portion of products has been sampled. This is a massive challenge due to the number of products carried by the company. It is also a costly endeavor. 3. Items may fall within the tote in such a way as to occupy more effective volume than its ASIN catalogue data would suggest. This could cause the system to close the tote even though its volumetric utilization is low. The decision to close a tote is made in two ways. In the first method, the WMS system keeps track of the current volumetric utilization of the tote and the UIS attempts to sort the item into a tote as long as the volume will not exceed 35,000 cubic cm. However, this rule can be overridden by the shuttle which are equipped with sensors that verify the content of the tote before delivering an item. If the sensor cannot locate the back of the tote, the system assumes that the tote is full and no new items can be added. In summary, items may be added in the tote in such a way as to cause a premature closure. 4. The Warehouse Management System (WMS) also keeps track of tote weight to ensure it does not exceed 25 lbs for ergonomic lifting purposes. Thus, a heavy item can be added and close the tote prematurely even though the latter has low volumetric utilization. 5. In the field, tote wranglers may choose to close the tote earlier (before the system signal) to optimize for movement or other variables such as breaks, shift changes, and rates.

The WMS does not capture data such as overfull totes or the reason for an early tote closure. Experiments were designed in the R&D laboratory and in an operational IXD to capture this information and answer questions 2-5.

5.1. First Experiment: Tote Utilization

Rationale: The goal of this test, completed in the field, was to understand the effect of automation on the process, that is, what would happen to tote utilization once automation replaces the tote wrangler? It was a first test to shed light on question 2 (What effect does the tote wrangler have in increasing tote volume utilization?) and question 3 (What effect does the tote wrangler have in decreasing the occurrence of jams and breakdowns?).

Hypothesis I: By continuously fixing totes, tote wranglers help increase tote utilization. Hypothesis II: By continuously fixing totes, tote wranglers help reduce warning occurrence.

Methodology: The test, conducted over a day in an operational IXD, consisted of telling one tote wrangler not to “Tetris” or shuffle totes while telling another tote wrangler to continuously fix totes at another machine. Two other machines, serving as control, operated normally and no particular message was communicated to them. All of the tote wranglers would pick and place totes, however, the first tote wrangler would simulate a robot system that cannot troubleshoot issues, the second tote wrangler would be high performing and the control tote wranglers would simulate normal operating conditions. Prior to running the test, tote utilization data for two months was extracted to verify whether these machines had any disparity in their tote utilization distribution that could potentially affect results. Data, in Fig. 30, show that the four machines in this experiment had mean tote utilization of 59% with low coefficients of variation; therefore, tote utilization is not dependent on the machine, at least not within these four machines.

Fig. 33: Baseline tote utilization distribution for machines involved in experiment (two month data summarized in Table 5) 61

Table 5: Baseline tote utilization distribution for experiment Machine Mean Utilization Standard Deviation Machine 1 0.5919 0.0244 Machine 2 0.5949 0.046

Control 1 0.597 0.0284 Control 2 0.5909 0.0308 The histogram in Fig. 33 shows the distribution of tote closure rates from four machines, two test machines and two control machines, with summary data shown in Table 5. The data for each machine was cut into equivalent quantiles with width showing the range of values within each quantile.

Output & Analysis: The experiment ran for 12 hours and results showed that Machine 1, in which the tote wrangler fixed totes, had tote utilization of 62% during that shift, whereas Machine 2, without a tote wrangler, had tote utilization of 55%. The control machines had tote utilization of 60.4%, well within the benchmarked utilization distribution.

Machine 1 Machine 2 Control

Fig. 34: Tote volumetric utilization for experiment

Summary of Results: A t-test comparing the observed tote utilization of each machine after the test with the calculated mean tote utilizations in Table 5, showed that only the low tote utilization of Machine 2 was statistically significant. Therefore, while we cannot conclude that the effort of tote wranglers helps increase utilization, we can say that without a tote wrangler, tote utilization may decrease. If we automate the full process, one could expect that with an increased weight

62 limit of 28 lbs. limit (remove ergonomic limit), tote utilization may increase, however, fewer than 3.56% of totes close due to weight, therefore, we would not expect a higher weight limit to improve tote utilization. Moreover, tote wranglers decreased warnings by 82% by continuously shuffling items and fixing totes. The only warnings that occurred were from shuttles jamming. Therefore, hypothesis I is not substantiated, conversely, for hypothesis II, the data suggests that tote wranglers help reduce the number of warnings by shuffling items within a tote continuously. A more ambitious experiment in terms of scale was designed to understand the effect of tote wranglers on reduction. The belief is that removing tote wranglers (associates) without proper troubleshooting capabilities on the robot (removing overfull items or solving jams) would create machine downtime due to an increased number of jams.

5.2. Second Experiment: Breakdown and Jams

Rationale: Following this initial test, a two-week test was completed to further investigate the previous result. The first goal of the test was to quantify the incidence of warnings such as ‘cannot unload’ and ‘bin not present’. The second goal of the test was to quantify the incidence of overfull totes.

Hypothesis I: By continuously fixing totes, tote wranglers help reduce warning occurrence. Hypothesis II: By continuously fixing totes, tote wranglers help reduce the number of overfull totes.

Methodology: In a similar method to the first test, the team operated the UIS machine in the R&D laboratory without an associate fixing issues or shuffling totes for the first week. In the second week, an active associate was fixing issues and shuffling items. There were three associates and the author. One associate inducted items, one associate tote wrangled, and one associate captured data. The author examined the operation of the machine and mapped root causes. Three warnings were tracked: ‘cannot unload’, ‘cannot unload – bin not present’, and ‘cannot unload – item not present’. Moreover, the distinction between an overfull tote was made at the time of closure, that is, when the UIS closed the tote and notified the associate with a blue light on the PTL, a measure was taken as to whether there was a protruding item from the tote. The measurement was done with a height bar and confirmed by two associates. If the tote could

63 not pass under the height bar, the tote was routed to a shaking table positioned adjacent to the UIS machine.

Fig. 35: Overfull tote with protruding items – unable to palletize

Output & Analysis: a) Cannot unload: Without a tote wrangler, 178 ‘cannot unload’ occurrences were recorded over a three-day period representing 1.6% of inducted items. With a tote wrangler, these warnings decreased to 83 occurrences, representing 0.5% of inducted items. Beyond the number, the main difference between these two tests is the nature of the jams. With a tote wrangler present, the ‘cannot unload’ in which an item is stuck on the shuttle accounted for 45% of overall warnings, whereas without a tote wrangler to fix totes, warnings were mainly due to items collecting at the aft of the tote (80%). With a tote wrangler, the MTBF was 262 seconds, whereas without a tote wrangler the MTBF decreased to 191 seconds after accounting for machine downtime and breaks. An important characteristic is that these warnings tended to cluster depending on the type of items inducted. If several bagged items are inducted, the rate of warnings increased. As well, ‘cannot unload’ warnings are 2.5x more likely to occur in totes that had a previous ‘cannot unload’ warning, the logic is that accumulation of items at the back is worsened by multiple incoming items. The most important insight is that for an automated solution to be effective, it must be able to fix totes while they are on the UIS machine. From anecdotal evidence during testing, it was noticed that simply vibrating the tote while on the UIS would help settle items within it and avoid ‘cannot unload’ warnings due to item collection

at the back. A possible hardware solution would be for the UIS system, during robotic downtime, to direct the robots to grab and gently shake/vibrate totes that have utilization higher than 40% and place them back on the wall.

Fig. 36: Pareto analysis of failure to unload causes for (top) tote wrangler and (bottom) no tote wrangler b) Cannot unload – item not present: This error is caused by the shuttle believing that the item was not delivered to the tote. As discussed in Section 2, the main causes are either items falling into the machine (out of scope for this automation project) or an item being delivered without tripping the shuttle sensor. During testing, this warning occurred 23 times (0.083%) – items were delivered in all cases. It can easily be corrected through software changes to UIS. Once the shuttle conveyance has moved the full length, thereby

assuring that the item has been dropped into the tote, the shuttle can restart and operate normally. c) Cannot unload - bin not Present: During testing, ‘bin not present’ occurred 1581 times (5.7%) with a MTBF of 20 seconds. This error occurs more frequently in certain shuttles due to sensor misalignment and was exacerbated by the fact that the R&D machine used for the test had not been updated for years. The brackets used were also older which meant the tote (bin) was not always positioned optimally in its bracket leading to sensors on the shuttle not identifying its presence. These warnings are rare on newer shuttles. For scale, in all of 2018, this error only happened 67 times on the newer R&D machine (0.036% of all inducted items). A simple fix would be for the robot to gently tap the tote in place to trigger the sensor on the shuttle. The occurrence of these warnings was not affected by the presence of a tote wrangler since it is due to shuttle malfunction. d) Overfull totes: When a tote closure is present, less than 9% of totes were overfull at closure, compared to 35% without an associate. The reason totes were overfull with an associate is that they may be busy fixing or removing totes and suddenly, multiple items are delivered to the same tote without being able to fix them. As would be expected, once totes were removed from the wall, associates were able to fix issues prior to sending them to palletizing.

Summary of Results: Associates conclusively reduce the number of overfull totes and the number of jams by monitoring product delivery and continuously shuffling items. Without a proper solution, removing tote wranglers would increase the number of overfull totes sent to palletizing (current rate is 1.44% in operational IXDs) and would cause the machine to stop due to jams if they are not fixed promptly. Over the course of two weeks, a high number of items were inducted (11,125 items for week 1 and16,600 items for week 2), of all sizes and shapes, to eliminate the influence of shape, size, as well as the order in which items are inducted on results. There are infinite ways in which Amazon products could interact to create jams/overfull totes. These results are an indication of the importance of tote wrangling. This experiment helps to show that automation has to deal with jams if it is to be effective and deployable. The experiment also shows that simple solutions, such as using the robot’s mobility, can help reduce or avoid some of these jams. For example, inspecting totes and endowing the end-effector with a vibrating mechanism or using the end-effectors to shake totes to prevent accumulation of items at the aft

of the tote could help prevent cannot unload errors and overfull totes. Simply using the robot to reposition totes that the system believes are not present is another elegant solution.

5.3. Third Experiment: Shaking Experiment Rationale: As posited earlier, shaking conveyance could replace the need for a bin-picking robot reducing the cost and complexity of the overall system. To test this, a shaking table was used. Each tote that was deemed overfull would be transferred to the shaking table. By developing a description of typical problems and mapping which shaking mechanisms worked, we could decrease the number of overfull totes by shaking the tote in such a way that items nestle into the tote. Typically, tote wranglers would adjust items within a tote to fix overfull totes and currently available robotic technology cannot compete with an associate’s dexterity, problem identification, and space optimization skills. Neither a shaking table nor a 3D vision system can beat the performance of an associate. Therefore, with this test, the validity of shaking conveyance as a reasonable substitute is being investigated.

Hypothesis I: Shaking conveyance could remediate more than 50% of all overfull totes

Fig. 37: Overfull tote on shaking table once the tote has been removed from the wall Methodology: The apparatus used was a servo-hydraulic rotary-vibration table with RPM range between 0-500. Once a tote was deemed overfull, it would be moved to the shaking table where the rotation would begin at 50 RPM and increased by 50 RPMs every three seconds. If the shaking reached 500 RPM and the tote was not fixed after five seconds, it was deemed not

fixed. Multiple variables were recorded: problem description, number of items protruding, number of items in tote, tote utilization, shaking RPM, shaking time in seconds, and whether it was fixed or not. The problem descriptions developed depended on two variables: the position of the item relative to the tote and the position of the item relative to other items. For the first variable, the item(s) could either be standing up (on its longest side defined as H), resting (on its shorter sides either sideways (W) or depth (L), or diagonally (such as Fig. 34). For spheres, the item was assumed to be resting. For the second variable, the item(s) could either be on top of items (stacked), between items (nested), or standing directly on the bottom of the tote (base free). The combination of all of these variable values yielded problem descriptions.

Fig. 38: (top) Tote status at tote closure and (bottom) proportion fixed with shaking tables

Output & Analysis: Of the 208 totes that were overfull (equivalent to 17% of all totes), 77% were fixed through shaking, showing a promising application of shaking conveyance in the prototype RWC. The 20% of totes that were not fixed by shaking had one of three things in common: Protruding items were lodged between several items (45%), bottles standing free (32%), and items resting on top of other items (23%). These were fixed through back and forth movements by hand, the type that can be replicated by the robot. There was no correlation between tote utilization and the incidence of overfull totes. There was also no correlation between the number of units and the incidence of overfull totes. For the shaking value, 250 RPM was the mode, clearing more than 70 totes (33.6%).

Fig. 39: Distribution of tote shaking RPM leading to successful remediation of overfull totes Summary of Results: More testing of shaking mechanisms would be required. Due to a time constraint, a shaking table was used instead of shaking conveyance. This analysis would have to be repeated on typical warehousing shaking conveyance and for many more totes to quantify a process efficiency. With the data captured, the author is optimistic in the use of a shaking conveyance as both a technically viable and economical solution to fix overfull totes.

5.4. Fourth Experiment: Template Based Sorting Rationale: The UIS sortation logic does not take size or shape of the item into account when determining which tote it should be routed to. An idea proposed would be to sort items by size, that is, items would be segmented into small, medium, large and sent to totes that only hold one size category. The evident downside to this is that each arc destination would necessitate three

69 totes (one for each size category), however, it could possibly improve tote density and the occurrence of jams. The rationale is that by packing items of a similar size, one can improve packing density.

Hypothesis I: Template based sorting (TBS) will increase the overall mean tote utilization Hypothesis II: Template based sorting (TBS) will reduce the number of jams from tote accumulation.

Methodology: The WMS service code was updated to segment items, at induction, in one of three categories: small, medium, large. Threshold values were determined by analyzing volumes for 8MM ASINs and splitting these items into the three groups (using knn clustering). Each arc destination had three totes open. When an item was inducted, the WMS would route the product to the correct tote by comparing the item volume to the threshold ranges for small, medium, and large. Tote volume were extracted from both machine logs and the WMS to ensure validity. Jams were recorded by associates. Number of overfull totes were recorded by the author.

Fig. 40: Distribution of (top) tote volumetric utilization (bottom) units per tote for experiments, TW(the test was performed with a tote wrangler), noTW (no tote wrangler), and TB (template based sorting without tote wrangler) 70

Output & Analysis: Test results for template based sorting (TBS) without a tote wrangler were promising. Not only did template based sorting reduce the number of overfull totes, it also reduced the proportion of ‘cannot unload’ that were due to items collecting at the back of the tote. Nonetheless, it comes at a price: large item totes will have fewer units and decreased tote utilization, which would increase the number of totes required to sort items into if there is a high proportion of large items. With TBS, the average number of units per tote was 8.2 whereas with the operational system, the average was 18.5 items per tote. Furthermore, although the mean tote utilization was 3% higher (69%) for TBS, as compared with earlier experiments, the average tote utilization for large item totes was 59%. Simply put, by sorting products by size, smaller and medium items are able to pack more efficiently thereby increasing overall tote utilization, however, large item tote would be unfilled since no new item could be added without surpassing the maximum tote volumetric threshold (35,000 cubic cm). (See Fig. 37 above) Fig. 38 summarizes the results for all three experiments (with tote wrangler, no tote wrangler, and with template based sorting and no tote wrangler). The top graph shows that tote wranglers had the least proportion of overfull totes (9%) whereas without tote wranglers the proportion increased to 35%. Once template based sorting was tested, the proportion went back down to 13.5%. This proportion of overfull totes is much higher than the current operational value of 1.44% that is recorded at the automated tote palletizer. For jams, the proportion of jams that were due to accumulation of items at the aft of the tote was 55% when a tote wrangler is present, 78% when there is no tote wrangling, and improved to 47% with template based sorting. This result is perhaps the most surprising. Intuitively, one could foresee that tote wranglers reduce the number of jams due to item accumulation at the aft of the tote since they shuffle items and clear the way for subsequent items to be delivered without jams. However, TBS significantly reduced the proportion of jams that were due to item collection at the aft of the tote. With TBS, 53% of failures to unload were due to items being stuck on the shuttles, which are outside of the scope.

Fig. 41 : Results of overfull and jams for experiments. (top) proportion of overfull totes (bottom) root cause of jams

Summary of Results: I recommend that template based sorting be refined so that instead of totes being dedicated to only one size category, the WMS would dynamically change the threshold so as to maximize tote utilization. Once large items cannot be sorted into a tote, it could become a medium or small tote depending on available volumetric capacity. The conclusion that can be drawn from this test is that template based sorting is a viable solution to reverse the decrease in tote utilization and increase in failures that is expected from automation, however, more testing is required for these findings to be conclusive.

5.5. Financial Analysis A preliminary business case was developed to estimate the financial benefits of quoted RWCs. Four options were quoted from suppliers: static robot, robot on rail, AGV, and a gantry system. The UIS implementation plan for 10 IXDs was used. Using the average induction rate recorded across all UIS machines, the manual VCPU reduction from automating tote wrangling is $0.01197 for one-sided machines and ranges from $0.0228 to $0.03994 for two-sided machine depending on layout. Both one-sided and a potential two-sided application were quantified (staffing assumptions are proprietary). One-sided machines would necessitate two static robots, one robot on rail, one AGV, and two gantry robots per side depending on application. For two- sided machines, it was assumed that one AGV per side would be required, two RTU robots could reach two machines, and static robots would be able to serve two machines. For rail robotic, two scenarios were studied: one in which the rail would be over conveyance and one in which this was not possible. Table 6 summarizes the 3-year discounted cash flow and pay-back periods for all scenarios. Full automation assumes no intervention from problem solvers for tote wrangling and no changes to the configuration of the UIS. Using quotes from suppliers, the analysis yields the following results: the robot on rail is the most cost-effective option. Overall, from the cycle time requirement and the necessity to scale to serve two UIS machines at once, the use of rail robot is recommended. This will meet both cycle time, reach, payload requirements, and provide the highest three-year NPV investment: $18.1 MM with a payback period of 2.13 years. Table 7: Summary of three-year NPV analysis of quoted RWC NPV Calculation AGV Rail Rail* Static Gantry One-Sided $ 12,947,751.00 $ 15,941,959.00 $ - $ 4,040,704.00 $ (739,896.00) Two-Sided $ 19,096,911.00 $ 18,178,016.00 $ 28,316,584.00 $ 4,219,579.00 $ 239,957.00 One-Sided GF Only $ 7,957,593.00 $ 7,652,513.00 $ - $ 2,306,561.00 $ (38,639.00) Two-Sided GF Only $ 1,657,725.00 $ (2,418,563.00) $ 8,319,370.00 $ 1,509,087.00 $ 87,793.00 5.6. Proposed Design A concept process could use an R2000iC/125L robot on rail. The end-effector would be equipped with sensors to check for overfull totes. The UIS machine will alert the robot to investigate totes with high utilization and shake those that are overfull or may become overfull – this will prevent warnings and jams. Once a tote is closed, the robots will remove the tote, place it on a takeaway conveyor. A COGNEX vision system will check and reroute overfull totes to shaking conveyance and check again once that is completed. If the tote remains overfull, it will be routed to a central associate to fix. All totes will be routed to the automated tote palletizer.

From testing, we can estimate that between 10% to 35% of totes will be overfull, that is, in any given hour, assuming a worst case scenario of three totes per minute, 27 to 63 totes will be closed and picked by the robot yet necessitate shaking. With the shaking cycle lasting 15 seconds, we would have ample time to fix totes without creating a bottle neck. If we assume that between 60% to 80% of these totes can be fixed through shaking, the remaining 5 to 38 totes per hour would have to go to a central associate. At the automated palletizer, associates are stationed to add items to unfilled totes to increase utilization and number of items per tote. This would be a perfect place to fix protruding items prior to induction in the automated palletizer. It should be noted, however, that the automated palletizer does check for overfull totes which it routes to jackpot to be fixed by associates. The reason we would want to fix totes upstream from the palletizer is that with automation, an increase in overfull totes (currently at 1.4% to 2.75%) would send more overfull totes than the cell was designed for.

Fig. 42: Proposed RWC prototype design with robot on rail

Fig. 43: Proposed automated tote wrangling VSM 74

5.7. Recommendations I recommend that 1) RWC testing should concentrate on developing the hardware required to detect and fix the warnings/process failures both before and after removal from the UIS. Development of this solution should be done with the help of associates to ensure that their expertise is included in the final design. Understanding the MTTR will be critical and a process for human intervention within the robotic cell will need to be developed. 2) a 3D physics simulation should be completed to understand how different shapes interact in the tote using ASIN catalog data and then using neural-net classification techniques to quantify a probability that a tote will become overfull once a new item is added. Using results from this potential research would help prevent totes from becoming overfull: once a probability is beyond a threshold, the robot can be called upon to shake the tote or replace it. 3) Mobile robotic solutions must be monitored for future application as cycle time and payloads improve, which are currently, too low for this application. From discussions with experts at MIT, AGVs seem the most natural designs for this type of solution as they offer unparalleled agility, simplicity of layout integration, and above all, are not as severely constrained by the physical features of an IXD such as columns, which translates into lower integration costs. Lastly, the team should work closely with IXDs to ensure that changes to the layouts include space for the RWC. This would be an iterative process as the components of the automated tote wrangler are fine-tuned and the performance of the system converges towards the desirable quality threshold. Although the RWC was not operational prior to the end of the internship, as of December 31st, 2018, the RWC has been installed. The work compiled here should provide a strong foundation for further research.

6. Organizational Capability Study 6.1. Organizational Study The goal of the study is to research best practices in robotics development and deployment, determine the differentiated capabilities necessary for a hardware integrator, and recommend organizational changes to enable these strategic capabilities. The approach was, first, to diagnose the organization’s strengths and pain points by interviewing 30 Amazonians within the robotics organization, support functions, and internal customers. In parallel, a set of experts from MIT, Ford, P&W, and IDEO were interviewed to evaluate best practices. The questionnaire used to understand the robotics organization at Amazon is a three lenses framework [7]. The questions were divided between three lenses: the strategic lens, the political lens, and the cultural lens. Each of these lenses was further divided into two sections. For the strategic perspective, the attributes were internal and external focus. In sum, is the team inward looking or outwardly looking, that is, how do people work within their team and with other teams. For the political perspective, the attributes were power and network which helps us understand how the organization makes decision: who wields power and how does information circulate. For the cultural perspective, the attributes consisted of values and behaviors: what does the organization believe about itself and what behaviors are accepted and rejected by the teams.

Type Employee Question Attribute Strategic What are the strengths of your current team? What are the weaknesses of your current team? Internal Strategic What was your team’s biggest challenge in your last year and what did you learn from it? Internal Strategic How would you rate the effectiveness of the organization in translating objectives in meaningful assignments and goals for the employees Internal Strategic Explain the product development process you use? Which parts do you find most value-added? Which parts would you skip if you had the chance? Internal Strategic How do projects get chosen and prioritized? Internal Strategic Which groups or teams do you work with most often? Who are your customers? External Strategic How well do the persons in these departments share information for the purpose of coordinating project deliverables? External Strategic What biggest change would you make regarding how your team works with other teams? External Strategic Do you have enough opportunities to meet and interact with these teams? What forums exist for you to update each other on changes? External Strategic Do you believe the teams you interact with the most have the same priorities as your team? How do your priorities differ or agree? External Political What are the attributes/traits of the people, at your level or one above, who have the most power to get things done in your team? Power Political What causes conflict? How is conflict solved? Power Political How are decisions made when there is disagreement and stakes are high, especially when there is a disagreement about how to interpret data & results? Power Political How have you worked to build your own credibility in your team? Power Political Do you believe your knowledge and ideas are respected in your team? Power Political How much do you lean on your informal network in order to get things done and facilitate decision making? Give me an example. Network Political How is knowledge shared in your organization? Which methods do you find most effective? Network Political Who controls resources? How do you access these resources? Network Political How do you communicate with people in other teams? Do you feel you can contact anyone or do you need an intermediate? Network Political On what occasions do you escalate issues to your managers? How often do you have to do this? Network Cultural Which three words best describe the culture in your team? Values Cultural What would you change about the team culture if you could? Which one would you prioritize? Values Cultural What is central to who you are as a team that should never change? Values Cultural What key values, if followed, would help this organization compete and thrive? Values Cultural Do you find that your team’s culture promotes creativity and embraces it? Values Cultural Which behaviors lead to individual success within your team? Behaviors Cultural Are there any aspects of culture that hinders your output? Which aspects helps? Behaviors Cultural Is risk-taking encouraged? What happens when people fail? Behaviors Cultural How would you describe the overall atmosphere existing in your organization for open and free exchange of information and ideas? Behaviors Cultural Which aspect of your team’s culture was the hardest to get used to when you joined? Behaviors Fig. 44: Three Lenses Questionnaire

The main insight, from this study, is that to scale as a hardware integrator, the robotics organization must build three core skills. The team must view its suppliers as partners and build procurement standards capable of vetting and assessing these suppliers. The team must develop a repeatable hardware development process that is tailored to specific projects while standardizing knowledge sharing across projects. Lastly, the team must have an in-depth understanding of the company’s operational processes and a systematic method of evaluating opportunities. The Advanced Technology Group had to scale from a small to a now multi- function team and has been working on integrating these recommendations in their organization.

6.2. Resources & Capabilities

Capability 1: Understanding the technology landscape and building partnerships with suppliers

i. Position and resource procurement, not as a support function, but rather as a strategic capability that enables Amazon to become an effective hardware integrator for automation. Two-third of those surveyed listed long procurement timelines as the number one source of frustration in product development. A deeper investigation into root causes highlighted three factors: an under-resourced procurement team, multiple templates for request for proposals (RFP), work orders (WO), and purchase orders (PO) documents, as well as unclear procurement timelines and decision criteria. In short, due to a shortage of procurement professionals, engineers feel rushed to draft documents without proper templates or expertise only to be stalled for weeks in a lengthy approval chain. Moreover, engineers become the de facto liaisons, negotiating and leading talks with suppliers, yet lack the training, skills, or experience in procurement, negotiations, or contracts. The organization must not view procurement as a support function. In hardware integration, procurement becomes critical in assessing the technological landscape, mapping the supplier base, assessing value opportunities, and allocating contracts with the goal to minimize risks, costs, and timelines. As a company chooses to outsource key capabilities, for example the design and installation of integrated robotic work cells, it must have strategic partners that it can rely on to understand its business requirements,

its timelines and technological needs, and above all, align each other’s processes to reduce communication friction, knowledge silos, and multiple onerous redesigns.

ii. Define procurement roles and responsibilities along two verticals: one as strategic sourcing and a second as operational vendor management. Automotive and aerospace companies with expertise in robotics hardware integration have understood the advantages of splitting procurement teams into strategic sourcing and vendor management: a positive impact of 20 - 38.5% to the bottom line across surveyed companies. The goal of strategic sourcing is to build a diverse supplier base with a holistic view on the robotics organization’s performance metrics and tactical goals. The sourcing specialist would look beyond quoted price to review a supplier’s financial sustainability, operational flexibility, and technological innovation. In assessing potential contract, the strategic procurement specialist’s role is to decrease the project’s supply chain risks while granting contracts that fit within the strategic vision of the company. For example, strategic sourcing may choose to award a project to build ties with a new supplier or gain more leverage over an existing one. In tandem, vendor managers would assess current suppliers against specific safety, quality, cost, and delivery metrics to ensure sourced goods are compliant with stated features. With overstretched resources, the procurement organization will find itself unable to scale and deliver these insights at the depth and frequency required putting a risk on project timeline and cost, if not viability.

iii. Create a quarterly review process to analyze supplier base and project allocation, to evaluate supplier performance and adherence with critical delivery, cost, and quality metrics, and to determine key actions required to minimize supply chain risks. Integrating a procurement organization within the robotics group will permit tailored analysis of the supplier base and a prioritization of robotics sourcing. Sourcing decisions cannot hinge on multiple RFPs in silo. In benchmarked companies, vendor managers continuously assess suppliers’ performance, highlight key areas for improvement, lead workshops with suppliers, and document key metrics for leadership to prioritize continuous improvement activities. Strategic sourcing specialists focus on building new relationships by prioritizing the supplier’s fit within the overall technology strategy such as acquiring access to new technology (Automated Guided Vehicles) or a broader set of services (robotics integration and installation). Sourcing would review the current allocation of projects, highlighting capacity or volume concentration risks.

Finally, with engineering, sourcing would establish a map of currently available technologies and brief the organization on potential valuable opportunities for new projects. Capability 2: Developing a tailored hardware development process with a focus on knowledge sharing

i. Create team dashboard, mandate the use of a single file-sharing program, and standardize file archiving rules using the project management WBS as a template for document management. Interviews highlighted a second source of frustration: the difficulty in accessing information on projects. This is attributable to a proliferation of file-sharing applications, multiple project management standards, and no document or drawing revision standards. As proof, engineers designed an end-effector based on an old sorter machine drawing in October 2017, only to be redesigned nine months later due to updated brackets on the sorter machine that were not communicated as part of a department-wide design review process. Single-threaded leadership has several benefits such as shortening development times and reducing workload on engineers, however, it has the side effect of creating silos of information, in which only a few people are aware of a project’s progress. Instituting a common file-sharing method and template following the Work Breakdown Structure (WBS) will enable standardization across projects and ease information sharing. It will also open up opportunities for engineers to jump on another project with minimal ramp-up time since everyone would use similar product development steps, project management tools, and archiving rules. An online central dashboard could connect all the projects and summarize key progress status/dates for all to see, reducing tribal knowledge.

ii. Determine approximate resource allocation required at the onset of every project with clearly defined roles, review estimates as projects evolve, and include support functions in the exercise. The launch of a project is a key moment to establish roles, set expectations, and balance resource requirements. The project owner must hold an official launch meeting to brief the team on the scope/problem statement and draft an initial list of key deliverables/support required for the project. This meeting must include support functions and customers to agree on timeline and commit staff. The three issues facing support functions are late inclusion in projects, lack of resourcing consideration, and limited accessibility to information. Often, due to their reduced

numbers, support functions are pulled among all the projects due to a lack of prioritization. This has the adverse effect of forcing single threaded leaders to compete for support function resources amongst each other with no regard to overall organization goals and timelines. Launching projects and monitoring agreed upon deliverables/timelines will decrease, if not eliminate, this tug of war on resource. The objective of this meeting is two-fold: 1) raise awareness of a new project and 2) align teams in its completion priority. One side effect is that establishing a baseline of information and assumptions makes it easier to update them as projects evolve.

iii. Create periodic project reviews to vet progress, document technical changes, and refresh assumptions. Use these sessions to reprioritize projects, set milestones timelines, and clearly communicate Objectives and Key Results (OKR) to the organization. An often-recurring remark is that the organization’s project success criteria are inconsistent, business cases rarely updated, and progress hard to quantify. For example, a project’s green status means different things for engineers and project managers. The leadership team must define success criteria with clear go-no go gauges. Periodic meetings would serve to review the entire project portfolio, assess changing conditions and timeline status, and determine whether projects are to be put on hold, accelerated, or continued at pace. We often judge the success of an organization on its ability to initiate and deliver projects, however, rarely on how it kills unsuccessful endeavors. As one interviewee pointed out: “the success of the organization should not be tied to the success of projects, we must have a method of killing projects.” Zombie projects can sap morale, use up critical resource, and give the impression that the organization lacks direction. Holding review sessions sends a clear message that priorities are monitored and updated with the changing Amazon reality to enable the organization to deliver on its promises. After the session, the priorities must be communicated. Lastly, the design review sessions, as is done in automotive and aerospace, must be linked with actual operational deadlines. For example, new robotic work cells would be designed and tested only to find out that the warehouse design in which they were to be implemented had not taken into account a required dimension, hardware, or layout change necessary for the work cell to function or be integrated in the warehouse. Program reviews must be in sync with new layout roll-outs, warehouse revisions, and key launches. This will ensure that both warehouse design and robotic development teams are working in parallel while communicating important details and specifications to each other.

The design of the work cell must influence and be influenced by the layout of the warehouse in an iterative and fluid way. To enable this capability, Gantt charts, project deliverables, and design reviews must be synchronous between robotics and warehouse design teams so as to incorporate any changes early, reducing the financial and operational impact of late defect elimination.

Capability 3: Assessing operational opportunity and simplifying team integration

i. Develop an idea proposal form that enables other functions, such as Operations, to pitch, scope, and quantify opportunities for process automation and new robotics development. The template will create a formal idea-vetting process that includes and places the onus on other functions to ideate/define processes they would want to see investigated and potentially automated. Since these functions own the processes, define the metrics, or are privy to pivotal assumptions, they are best positioned to gather key information, answer critical questions, and prioritize their own demands. Early buy-in and constant communication with these functions will help the robotics project team have contacts on which they can pull throughout the project to vet changing scope, assess technical requirements, and refine solutions proactively as the reality on the floor changes. More importantly, the one-pager can then serve as the document with which the organization compares opportunities and quickly assesses project status and viability.

ii. To scale effectively, the organization must redefine roles of engineer or project managers that develop technologies and those who support deployed projects from a value-engineering standpoint. As the number of projects grows and the proportion of deployed products becomes considerable, it will be impossible for the same resources to effectively manage new products and sustain established solutions. There are two main reasons for this: the specialization and skills required for each role differ and the pressure of new product development will make sustaining old ones an afterthought. One such example is that the implementation team, to support Operations through the robotic work cell deployment, had to constantly ask questions to the robotics engineering team regarding the machine. This created an asymmetric dependence relationship between the two departments as the implementation team did not always have the information

81 required to complete the deployment and had to depend on engineering to provide it. However, the engineering team had new projects to tend to and did not feel the urgency to provide the information. To minimize this back-and-forth, the implementation team could create a list of questions that engineers must answer prior to the hand-off (an informational pull system), however, with scale, having liaison engineers whose task is to assist during/after deployment will become critical. Again, as the organization scales, it becomes important to separate the engineers that are innovating with those that are sustaining and improving live project while building, between them, pools of information that can be shared so as to decrease the disruptive back-and-forth enabling the downstream teams to have control over their timelines and workscope.

iii. Deliberately create low-risk forums and opportunities for teams to get to know each other, to brainstorm, or to swarm and solve problems. Ninety percent of those interviewed said they regularly used their informal networks to complete mission-critical tasks for their projects. In an environment with changing priorities, stretched resources, and time pressure, these informal networks make it possible for projects to move forward. As a result, as teams grow, and new members, who will not have access to these networks join the team, it will become important to hold more lunch&learn sessions, informational review sessions, and simple team-bonding events (barbecues, and evening events). An effort must be made to grow these informal networks, nurture communication, and facilitate knowledge sharing. The goal would be to build trust between teams, share best practices, as well as understand the status of projects and current barriers to progress. Sixty percent of those surveyed said that other team’s goals were asymmetrical to their own goals – building bridges will reap benefit.

These capabilities are applicable to all companies seeking to scale their automation efforts. One can summarize the finding by looking at five organizational traits that are critical to successful automation project teams.

Inventiveness: The team must continue to nurture its obsession of testing and invention, while having the patience to see the results through to the end. Employees must be passionate and

given the agency to see their vision through – however the employees’ project accountability must match their accountability. Firebrand Culture: The team should never lose its attitude of scrappiness and adaptability; the team must give itself the freedom and bandwidth/capacity to improvise both for the sake of learning and delivering world-class products. Team hacking sessions are a great way to promote inventiveness and do-it-yourself attitude.

Design with Empathy: Never lose sight of the human interfacing with the machine or in charge of the machine: what are their incentives? How will the cell serve them? How will they interact with and manage this cell? Does the autonomy of the cell match the process requirements? Is the worker authority over the cell match their accountability of the outputs from the cell?

Engaged Leadership: Senior leaders are supportive and intentional in removing barriers. Leadership must also be deliberate and consistent in communicating the strategic roadmap and the fit of each automation project, both to engineers and operation staff. Standardized tools must be designed robustly, however, more importantly, the company must ensure that employees are comfortable and aware of situations in which to deviate from the standard process to tailor the deliverables and requirements for this project and its specific use.

Knowledge Development: Continue facilitating technical training with an emphasis on designing for safety codes and assessing maintainability. Technical training will reap benefits in shorter timelines, lower cost projects, and fewer concept revisions.

Effective automation is not merely adopting existing tools or replacing human beings, it demands deep understanding of the tasks being investigated, repeatable development and deployment processes, in addition to workflows redesigns. Organizations that are serious about automation as a capability must also be honest about its limitations. Organizations must design the automated solution with intent, integrating overall company strategic goals, process metrics, human-robot interaction levels, and employee performance management.

7. References [1] D. Collis, “Walmart Inc. Takes on Amazon.com,” Harv. Bus. Sch. Publ., p. 30, May 2018. [2] I. Lunden, “Amazon’s share of the US e-commerce market is now 49%, or 5% of all retail spend.” [Online]. Available: https://techcrunch.com/2018/07/13/amazons-share-of-the- us-e-commerce-market-is-now-49-or-5-of-all-retail-spend/. [Accessed: 26-Dec-2018]. [3] P. Sisson, “9 facts about Amazon’s unprecedented warehouse empire,” Curbed, 21-Nov-2017. [Online]. Available: https://www.curbed.com/2017/11/21/16686150/black-friday-2018- amazon-warehouse-fulfillment. [Accessed: 26-Dec-2018]. [4] J. (Jeffrey P. ) Birenbaum, “Inbound supply chain optimization with ship-mode variation in a fixed-capacity fulfillment center,” Thesis, Massachusetts Institute of Technology, 2018. [5] A. (Andrew M. Walker, “Automation solutions for E-commerce multi-item packing,” Thesis, Massachusetts Institute of Technology, 2018. [6] A. Graves, “What is DMADV?,” Six Sigma Daily. . [7] J. S. Carroll, “Introduction to Organizational Analysis: The Three Lenses.” MIT Sloan School of Management, 2006. [8] R. M. Grant, “The Resource-Based Theory of Competitive Advantage: Implications for Strategy Formulation,” p. 23. [9] “Warehousing and Logistics Robots,” Tractica, 31-Mar-2017. . [10] “The Robotics Revolution: The Next Great Leap in Manufacturing,” https://www.bcg.com. [Online]. Available: https://www.bcg.com/en- us/publications/2015/lean-manufacturing-innovation-robotics-revolution-next-great-leap- manufacturing.aspx. [Accessed: 24-Dec-2018]. [11] “IFR_The_Impact_of_Robots_on_Employment.pdf.” . [12] G. Graetz and G. Michaels, “Robots at Work,” Rev. Econ. Stat., vol. 100, no. 5, pp. 753– 768, Jul. 2018. [13] M. Arntz, T. Gregory, and U. Zierahn, “The Risk of Automation for Jobs in OECD Countries,” 2016. [14] M. Muro and S. Andes, “Robots Seem to Be Improving Productivity, Not Costing Jobs,” Harvard Business Review, 16-Jun-2015. [15] J. Bughin, “Skill Shift : Automation and the Future fo the Workforce.” McKinsey & Company, May-2018. [16] A. S kala, A. Gwiazda, G. Kost, and W. Bana , “Modelling and simulation of a robotic work cell,” IOP Conf. Ser. Mater. Sci. Eng., vol. 227, p. 012116, Aug. 2017. [17] M. Gadaleta,ę G. Berselli, and M. Pellicciari, “Energyś -optimal layout design of robotic work cells: Potential assessment on an industrial case study,” Robot. Comput.-Integr. Manuf., vol. 47, pp. 102–111, Oct. 2017. [18] M. Bricogne, N. Troussier, L. Rivest, and B. Eynard, “Agile Design Methods for Mechatronics System Integration,” in Product Lifecycle Management for Society, vol. 409, A. Bernard, L. Rivest, and D. Dutta, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 458–470. [19] Y. S. Pai, H. J. Yap, and R. Singh, “Augmented reality–based programming, planning and simulation of a robotic work cell,” Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., vol. 229, no. 6, pp. 1029–1045, Jun. 2015. [20] A. Pochyly, T. Kubela, V. Singule, and P. ihák, “Robotic bin-picking system based on a revolving vision system,” in 2017 19th International Conference on Electrical Drives and Power Electronics (EDPE), 2017, pp. 347–352. Č

[21] G. Sansoni, P. Bellandi, and F. Docchio, “Combination of 2D and 3D vision systems into robotic cells for improved flexibility and performance,” in 2011 4th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI), 2011, pp. 22–30. [22] G. S. Sandhu, C. McGinn, and K. Kelly, “INVESTIGATION INTO A LOW COST ROBOTIC VISION SYSTEM,” p. 9. [23] G. Sansoni, P. Bellandi, F. Leoni, and F. Docchio, “Optoranger: A 3D pattern matching method for bin picking applications,” Opt. Lasers Eng., vol. 54, pp. 222–231, Mar. 2014. [24] A. Zeng et al., “Robotic Pick-and-Place of Novel Objects in Clutter with Multi- Affordance Grasping and Cross-Domain Image Matching,” ArXiv171001330 Cs, Oct. 2017. [25] D. Buchholz, “3D Point Cloud Based Pose Estimation,” in Bin-Picking, vol. 44, Cham: Springer International Publishing, 2016, pp. 13–37. [26] A. Zeng et al., “Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge,” ArXiv160909475 Cs, Sep. 2016. [27] I. Lenz, H. Lee, and A. Saxena, “Deep Learning for Detecting Robotic Grasps,” ArXiv13013592 Cs, Jan. 2013. [28] C. Hernandez et al., “Team Delft’s Robot Winner of the Amazon Picking Challenge 2016,” ArXiv161005514 Cs, Oct. 2016. [29] J. Mahler et al., “Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics,” ArXiv170309312 Cs, Mar. 2017. [30] L. Argote and P. S. Goodman, “The Organizational Implications of Robotics,” p. 34. [31] J. M. Beer, A. D. Fisk, and W. A. Rogers, “Toward a framework for levels of robot autonomy in human-robot interaction,” J Hum-Robot Interact, vol. 3, no. 2, pp. 74–99, 2014. [32] N. Balfe, S. Sharples, and J. R. Wilson, “Impact of automation: Measurement of performance, workload and behaviour in a complex control environment,” Appl. Ergon., vol. 47, pp. 52–64, Mar. 2015. [33] L. Argote and P. S. Goodman, “Investigating the Implementation of Robotics,” p. 27. [34] H. C. Dekker, “Control of inter-organizational relationships: evidence on appropriation concerns and coordination requirements,” Account. Organ. Soc., vol. 29, no. 1, pp. 27–49, Jan. 2004. [35] B. Donmez, P. Elena Pina, and M. Cummings, Evaluation Criteria for Human-Automation Performance Metrics. 2008. [36] D. B. Kaber and M. R. Endsley, “The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task,” Theor. Issues Ergon. Sci., vol. 5, no. 2, pp. 113–153, Mar. 2004. [37] B. H. Kantowitz, H. L. Roediger III, and D. G. Elmes, Experimental psychology: Understanding psychological research, 5th ed. St Paul, MN, US: West Publishing Co, 1994. [38] C. Heyer, “Human-robot interaction and future industrial robotics applications,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010, pp. 4749–4754. [39] T. Kim and P. Hinds, “Who Should I Blame? Effects of Autonomy and Transparency on Attributions in Human-Robot Interaction,” in ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication, Univ. of Hertfordshire, Hatfield, UK, 2006, pp. 80–85. [40] W. B. Nordgren, “Taylor II manufacturing simulation software,” in 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274), 1998, vol. 1, pp. 263–267 vol.1.