Operational Analytics & Data and Implementation Sciences

IISE Annual Conference Orlando 2019 Industry Practitioner Track

12 Nov 2019

The New Industrial and Systems Engineering: Operational Analytics & Data and Implementation Sciences

Scott Sink, Senior Advisor, The Poirier Group Ben Amaba, Global CTO, Data Science and AI, IBM Industrial Mftg. ISE and IISE for Life—how IISE supports you for your entire Career…..

CISE (seasoned executives, CISEISE IAB thought (Highly leaders) successful midIAB-career ISE’s)

Young Young Professionals Professionals (early career)

IISE Student Chapter Professional Chapters are: Alumni Affinity Groups, Local/State/Regional Affinity CareerGroups, Industry Path and and Practitioner Timeline Focused You can get involved in Societies, Divisions and also ‘Affinity Groups’ like Young Professionals, Industry Advisory Board and the Council on Industrial and Systems Engineering Performance Excellence Track—New Orleans 2020

The Performance Excellence Track is focused on: Technology (e.g. AI), Strategy (e.g. shaping Cultures to Support Lean), Process (e.g. Agile), People (e.g. how to successfully navigate politics).

How to Improve Culture Expand and Successfully Extend my Navigating Network of Politics Accelerate Peers Voice of Member and Customer Career Progress led us to this example of our and Success Altitude on my life and Operational job and Programming for the Annual Analytics career and have some Conference in New Orleans. Fun Agile Webinar Line-up

13 June—Chapter #1 Annual Virtual Meeting 9 July—Operational Analytics: ideas on how to sustain visible measurement systems and the process improvement benefits you’ve worked to achieve (Scott Sink) 13 Aug—Virtual Mentoring: Career Choicepoint learnings, lessons, tips from Senior ISE Leaders (David Poirier, President, The Poirier Group; Ron Romano, Sr. Mgr. Business Process Reengineering, Walmart, Canada; Yves Belanger, VP Supply Chain, Wolseley Canada) 27 Aug—The next 7 Habits of Highly Effective Young (ISE) Professionals (Allen Drown, United Airlines; Michael Beardsley, Law Student, Case Western Reserve; Jagjit Singh, Discover) 10 Sept—Winners Presentations from the IISE Outstanding Capstone Sr. Design Projects from 2018-19 (Georgia Tech/Cisco; Ohio State/Abbott Nutrition; Virginia Tech/Eastman Chemical) 1 Oct—Being Successful as a “Covert” ISE (Sean Gionvese, IE Manager, Lockeed Martin)

12 Nov—ISE and Data and Implementation Sciences (Scott Sink and Ben Amaba, CTO, IBM Manufacturing)

3 Dec—The Art and Science of Selling your Ideas to various stakeholder groups in different situations (e.g. Private Equity supported firms) (Brent Miller, West Monroe Partners & David Poirier, CEO, The Poirier Group and President-Elect IISE) https://www.iise.org/details.aspx?id=49715 AGENDA

12:00 Scott Kick-off—Framing Up The New ISE and Operational Analytics and Data & Implementation Sciences

12:20 Ben Data Sciences and AI

12:40 Q&A with Participants, Dialogue between Scott and Ben

12:55 Close-out Review upcoming Line-up--Scott

6 This was a “Seminal” piece of work back in 1990. Clear vision of what has transpired and evolved the past 30 years, and also still relevant for what is ahead.

7 Story Line Key Points for our Webinar today…..

1. Defining Operational Analytics, Data Sciences, Implementation Sciences

2. Frameworks for thinking about it and doing it;

3. Example of this—Digital Transformation in Healthcare

4. Accelerating Benefits Realization—Reducing the Latencies

5. Dialogue/Q&A Operational Analytics

▪ This is a useful visual that conveys much of what we mean by Operational Analytics. How to build Visible Measurement/Management Systems in a way that Decreases Latencies

▪ “Above the line” analyst role • Extract features based on questions you have to answer by ‘torturing’ the data until it speaks to you and others. Pick right metrics of interest!! • Apply curiosity & business acumen to data & analyses – create new knowledge, insights, ‘aha’s’ • Apply data visualization techniques to aid in telling the right story – as in life, so in business: the best story wins …Develop the Art of Great Story Lines and Powerful Visualizations and stay focused on driving the ‘end game’

Goal!!! Story Line Key Points for our Webinar today…..

1. Good analytics come from good problem statements, access to the right data, and applying the right techniques 2. Some people have every skill – business acumen, data, technique – to perform a good analysis – but it tends to result in a slow ‘craft’ process 3. Investment in the right data foundation has a positive ROI, as analysts move faster when they trust the data – results in faster results 4. Good data visualizations can tell the right story quickly, because people are predisposed to believe what they see in a chart … 5. There is very positive ROI in getting these decisions right – small analytics teams can wield disproportionate influence on the bottom line 6. Good analytics drive positive action – indeed, in Intel’s supply chain environment, simple/influential beats complex/impotent every time

S. Cunningham; Intel Corporation; Adapted from 2013 Management Systems Model— depicting latencies

Leadership & Information perception/ management team Information Data management understanding portrayal / insights and Operational (wisdom application, Analytics data/facts to information conversion process) Data Organization Data Analytics

Decisions Latencies Data Actions entry Data capture Data Capture Latencies Decision-Making and Action- Upstream Taking Latencies Systems The Business Downstream and Inputs: Processes/Value Systems and Suppliers & Outputs: customer Streams Orders orders Fulfilled The ISE role in Service Systems Engineering: Digital Transformation in Healthcare

Michael Caesar, MBA Executive Director, Data & Implementation Science University Health Network November 2018

Data & Implementation Science University Health Network - Not for Distribution (Used with Permission-DSS) Knowing, Understanding, Changing in a Digital World DATA-DRIVEN HEALTH ORGANIZATION

Data Supply Drivers Data Demand Drivers

Rich New Data Sources Personalized healthcare • Electronic Health Records • Health Information Exchange (HIE) • • Shared decision making • Genomic Information Systems • Personal ownership of health BioRepositories • Data lakes • record • Engagement & Natural language processing • A Healthcare persuasion hub • Benchmarking • organization that is

Continuous Data Streams data-driven Non-traditional care • Wearable body sensors • environments Implantable systems • Point of care Leading in evidence- • Reduced hospitalization • testing • smart sensors/bandages • based practice Video/tele health • Virtual nanotechnology • and augmented reality • Enables evidence Patient Generated generating medicine Predictive medicine • Personal health applications AI enabled care (apps) • Patient portals • CRM • Resulting in value- • Shift from restrictive Patient engagement portfolios • based healthcare gatekeeper to coordinator • Rapid diagnosis and treatment • Web and Social media Predictive modelling • Learning • Online communities • health system • Public forums • Population health Smart Machines • Patient stratification • • Internet of things • Disease prevention and health Intelligent processors • promotion • Chronic diseases Machine to machine • PEOPLE, PROCESS, & • Value-based care delivery Robotics • TECHNOLOGY models • Integration of all clinical, research, education data and Performance Operations workflows (quality, standards & Efficiency & Optimization • Workflow • Time stamps contribution) • Patient flow • Delivery • Effort • Investment • • Efficiency • Cost •

Data & Implementation Science Caesar, M (2017) University Health Network - Not for Distribution Knowing, Understanding, Changing in a Digital World It is not about adopting digital technologies, it is about changing the way we work in response to the nature of a digital world

University Health Network - Not for Distribution DATA & IMPLEMENTATION SCIENCE

DATA STRUCTURE GOVERNANCE INSIGHT CHANGE VALUE

DATA SCIENCE ANALYTICS

Our Data & Implementation Science approach will…

• Create a deliberate, bi-directional connection between data and value

• Link statistical analysis, computer science and business process understanding to drive insight and change

• Enable deep learning across the organization

• Develop data-driven capacity and capabilities

• Reflect our corporate purpose by providing insight into care, discovery and learning

Caesar, M (2017)

Data & Implementation Science University Health Network - Not for Distribution Knowing, Understanding, Changing in a Digital World TEAM: CAPABILITIES Identifying and building the skillsets to unlock the value of data benefiting the delivery of care

Data Mining Experimentation DATA SCIENCE Explore and Collect and curate experiment with new value data while Develop insights & builds ways of using improving data models through machine learning & statistical algorithms, machine accessibility within a methods learning and AI to community and support, enhance and ecosystem of creators automate decisions and users of data DATA IMPLEMENTATION ENGINEERING SCIENCE Design, build and Inform decision- manage data making and drive infrastructure process and behavioral change

Change the way we provide care, discover Insight-enabled and learn

Caesar, M (2017)

Data & Implementation Science University Health Network - Not for Distribution Knowing, Understanding, Changing in a Digital World DATA SCIENCE: CAPABILITIES Capability and capacity in Data Science enabling personalized medicine, predictive modelling and operational efficiency

Data Modellers Data Engineers Business Analysts Algorithms Data Architects Data Scientists* Tools Automation/AI Machine Learning Experts Applications Operations Research PARTNERS Models Analytics, Mining & BI

IT Infrastructure

* Programatic alignment - including clinical, research and education

Source: Healthcare Analytic Adoption Model 2016 Health Catalyst Data & Implementation Science University Health Network - Not for Distribution Knowing, Understanding, Changing in a Digital World IMPLEMENTATION SCIENCE: CAPABILITIES Identifying and building the skillsets to unlock the value of data benefiting the delivery of care

Impactful delivery of solutions benefiting the health system

• Portfolio, Program & Project Management • Lean SixSigma & Quality Improvement • Agile & Scrum Approaches • Business Analysis • Process Engineering • Change Management • Benefits Realization & Value Measurement

Caesar, M (2017)

Data & Implementation Science University Health Network - Not for Distribution Knowing, Understanding, Changing in a Digital World BEYOND THE TECHNOLOGICAL ADVANCEMENT Enabling Digital Transformation in Healthcare

How are we going to How do we balance New paradigms of how Our collective ability to introduce impactful real investment in governance, work gets done will be become more data-driven time information and processes & policies to introduced - how do we and advance our trust in insight to support and ensure data is of highest surface, assess and automation will be one of automate decision making fidelity and quality - without challenge our risk tolerance? the largest barriers to and change behaviour? creating barriers? overcome

Decision Science Data Fidelity Risk Tolerance Data Literacy

PATIENT & PROVIDER

Data & Implementation Science University Health Network - Not for Distribution Knowing, Understanding, Changing in a Digital World AGENDA

12:00 Scott Kick-off—Framing Up The New ISE and Operational Analytics and Data & Implementation Sciences

12:20 Ben Data Sciences and AI

12:40 Q&A with Participants, Dialogue between Scott and Ben

12:55 Close-out Review upcoming Line-up--Scott

21 9 Game-Changing Technologies Enabling Service 4.0

Big Data & Cloud Computing Robotic Process Bionic Computing Cognitive Analytics Automation Computing Manage huge data Interact naturally with Simulate human Replace humans in volume in open systems virtual agents, digital thought processes and Deeper insights into and provide services on work processes that devices and services provide intelligent, customer behavior, demand are entirely rule based virtual assistance preferences, pathways

Virtualization Ubiquitous Smart Devices Augmented Reality Free services from Technology and Provide the necessary reliance on specific IOT Develop an ecosystem Create and ongoing of apps and cloud information when software and needed in areas as connection in areas as services that utilize hardware and ensure varied as manuals, flexibility, adaptability varied as on-the-spot high-performance service provision and pricing, alerts and robustness devices remote monitoring

Adapted from BCG 2018 analysis on service 4.0: https://www.bcg.com/en-us/capabilities/operations/service-4-0-transforming-customer-interactions.aspx 22 3—ISE interventions that change the system/process and in doing so evoke more ideal Employee Behaviors

• At the highest level, it’s BPR but there are Solution Elements that are created that reflect the reality of the changes:

• 2-second Lean, Walk and Talks • Visible Measurement Systems • Agile/Scrums/Sprints • Value Stream Mapping and Analytics • Hoshin Kanri • Boot Camps (personal and professional mastery training) • Stakeholder value exchange optimization (CRM expanded) • Standard Work • etc.

23 Data accelerated the foundation to model, predict and prescribe actions to expensive challenges. Terms like software factories, data warehouses, “Lean”, Six Sigma and agile techniques for data and software lead Industry 4.0 and the Digital Transformation.

• Furnaces for heat treatment when the power went out, cost $60,000 for a 20-minute power outage. • Chemical companies can incur $2,000 per hour in wasted material inputs. Up to $15,000 an hour. • Drilling platforms can produce 200,000 barrels of oil each day, which breaks down to roughly 8,300 barrels per hour. With oil prices hovering around $60 per barrel, just one hour offline translates to $500,000. • A mining machine down for 24 hours justifies a brand-new replacement between $1 million and $1.5 million, which outweighs the cost of not producing. Industry 4.0 became the next era of digitization and speed where Industrial Engineers began defining the future.

▪ Industry 4.0 optimizes the computerization of Industry 3.0 (Forbes)

▪ The smart factory, also sometimes called “the factory of the future” is the keystone of the fourth industrial revolution. Indeed, it’s often represented as the aggregate of all the Industry 4.0 technologies: cyber-physical systems—physical assets connected to digital twins—the Industrial Internet of Things (IIoT), data analytics, additive manufacturing and artificial intelligence. (engineering.com)

▪ For business leaders accustomed to traditional linear data and communications, the shift to real-time access to data and intelligence enabled by Industry 4.0 would fundamentally transform the way they conduct business. The integration of digital information from many different sources and locations can drive the physical act of doing business, in an ongoing cycle. (Deloitte)

▪ Smart Manufacturing (SM)—the business, technology, infrastructure, and workforce practice of optimizing manufacturing through the use of engineered systems that integrate operational technologies and information technologies (OT/IT). (CESMII Roadmap) Data as a source of truth, and Data as a strategic asset provide higher margin points than the industry average.

26 Industrial engineers can transition data to new business models and transform the impact.

Describe or Model - Predict - Preconceived Prescribe – Action Taken Assume Insight

Operational BI and Data Self-Service New Business Warehousing Analytics Models 90% plan greater Data as a source of Data as a strategic asset investments in data Truth

Value AI Most are here 85% view AI as a strategic priority COST REDUCTION MODERNIZATION INSIGHT-DRIVEN TRANSFORMATION

Cost Efficiency New Products and Services Customer Intimacy The Discipline of Market Leaders‘ M. Treacy and F. Wiersema 2 7

27 McKinsey Global Institute on Artificial Intelligence

28 The Challenge to address is Data, Talent and Trust:

DATA TALENT TRUST The lifeblood of AI, but AI skills are rare Skepticism of AI systems complexity slows progress and in high demand & processes

60% 62% 62% Are challenge in Are challenge to Need an approach managing data acquire talent [and to quality build skills] AI production readiness Stuck in find operationalizing, sustaining Experimentation 51% and scaling AI challenging

Based on 2019 Forrester “Challenges That Hold Firms Back From Achieving AI Aspirations” 29

29 Critical to AI is the domain, process and human factors including trust and factors of bias.

Industrial Engineers can work as Data Scientists combining skills across areas of Expertise

Industrial, Systems, and Software Engineers Mathematical Background combine their talent. Computational Science Mathematics Statistician

Industrial and Process Expertise Scripting, SQL, Domain Knowledge Python, R, Scala, Industry Computer Industry 4.0 Data Pipelines Domain Science Digital Twin Big Data/ Expert Supply Chain Apache Spark OEE, NPT, RAMS, HSSE

A Digital Professional vary in a combinations of these skills

Key Skills – Systems Analysis and Design, Operations Research, Human Factors, Work Design, Logistics, Quality, HSSE The Industrial Professional Engineering License considers the Minimum Standard of Care to protect the public’s interest.

30 Certain domains can be automated with AutoAI AutoAI automates data wrangling and model creation, lessening required skills IBM can transform two of the three domains, but domain knowledge is still required.

Typical AutoAI 61% of a [ML] build time is spent on ”dreaded” data wrangling * AutoML Transfer learning ✓ Required Skills Less Required Skills with AutoAI Neural network search ✓ Data preparation ✓ ✓ Machine Learning Automated ML and Skills Advanced data refinery Computer Data Wrangling ✓ Science Math & Feature engineering ✓ ✓ App Data Statistics Data Science Dev Science Hyper parameter optimization ✓ ✓ One click deployment ✓ Domain Domain Knowledge Knowledge Explainability and de-biasing ✓ AI lifecycle management ✓ AutoAI

* Data Scientist Report, 2018 * O’Reilly Study, 2018 31 The Domain and Social Sciences are difficult to automate, and context becomes critical. People are foundational.

31 More than just data : Intelligence (AI) for the organization Empowering an organization and professional to make faster and more accurate decisions to mitigate disruptions and optimize supply chain operations

Natural Critical Language Insights Query

Generates systematic, predictive or prescriptive Enables advanced text insights through big data analysis, and allows processing, to enable computers and critical business decisions humans talk to be made in real time. seamlessly. Machine Learning Pattern Tracking Chatbot

Enables a system to learn Collaboration from data rather than Unlocks hidden value through explicit in data to find answers, programming. This monitor trends and Quickly builds and progressively improves surface patterns with deploy chatbots and performance on a the world’s most virtual agents across a specific task and it advanced cloud-native variety of channels, provides opportunity to insight engine. including mobile predict the future. devices, messaging platforms, and even robots. Artificial Intelligence APIs Speech to text | Text to speech | Conversation | Discovery | Knowledge Studio | Natural Language Understanding | Natural Language Query

32 Resources are being spent on key personnel that have an understanding on the application of technology and process.

2 4 1 5 3

Data Monetization Internet of Artificial Blockchain Other emerging tech and Analytics Things Intelligence (e.g., RPA)

Collects, aggregates, Embeds sophisticated Develops the ability of a Establishes an Robotics Process and derives actionable sensors and chips in computer or robot to immutable shared Automation leverages insights from data to physical objects, perform tasks or actions ledger, allowing any algorithms to automate create and capture new enabling real-time commonly associated participant in the routine tasks, value monitoring and with intelligent beings network to see all accelerating time to understanding records of transactions value and reducing Average salary of Average salary of Average salary of Average salary of human error $165,000 a year. $122,000 a year. $167,325 a year. $120,280 a year Average salary of $123,936 a year.

33 Industrial Engineering skills are sought out in China, US , UK, Germany and France where Industry 4.0 and Digital Twin projects could use data to optimize service levels, variability, workflow, inventory, machinery, personnel and economic opportunities.

34 Dialogue We’d Like to Spark: Please use the Go2Webinar “ask question” Function and we’ll get to as many as we can

What are your Any Aha Moments for takeaways? you?

What Anything you’d add? Questions do you have?

What topics would you like to see us zero in on in upcoming Webinars in 2020? See you at the Annual IISE Annual Conference in New Orleans? 12/11/2019 Webinar Line-up

12 Nov—ISE and Data and Implementation Sciences (Scott Sink and Ben Amaba, CTO, IBM Manufacturing)

The 2020 emerging line-up of Topics for Chapter #1’s Webinars & “Cutting Edge” Information Calls:

Still in Concept Design Stage right now but here are the topics that are on the drawing board, let us know what you think. [email protected]

Empowering and Engaging Employees to accelerate Performance Improvement

Lean Simulations on Steroids, Tom Duval, ISE at Auburn University

Deploying ISE and Lean at Chick-fil-A, David Reid (invited)

Navigating and Managing Organizational/Corporate Politics (will be an instant classic from our New Orleans Conference, the Performance Excellence Track) Sue Davis, GM; Chris Kelling, John Deere;

Creating Cultures to Support Operational Excellence (will be an instant classic from New Orleans Annual Conference) Sreekanth Ramakrishnan, IBM; David Poirier, The Poirier Group; Scott Sink, OSU

How to become more Skillful at Operational Analytics (instant classic from New Orleans) Scott Sink, The Poirier Group; Matheus Scuta, Ford;

How to Create Work and Life Balance in a 24/7, Hyper-Connected World (instant classic from New Orleans) Jessica Grela, E&Y; Jared Frederici, The Poirier Group;

Personal and Professional Mastery: Becoming a Change Master