Smart Manufacturing: Hope Or Hype?

Back to Basics Smart Manufacturing: Hope or Hype?

The chemical process industries (CPI) are hurtling toward a smarter future, but confusion and Munawar Saudagar exaggeration around new technologies are Minghua Ye causing some to want to stay behind. Sultan Al-Otaibi Khalid Al-Jarba Sober guidance on selection, implementation, SABIC Process Technology and utilization of digital tools can help differentiate the hope from the hype.

erms such as digitalization, big data, and artificial ics, and automation), and final outcomes (e.g., operational intelligence (AI) have become ubiquitous in the past agility, adaptability, and profitability). Industry 4.0 is an Tfew years. Control system vendors, software solu- alternative term for smart manufacturing that is used mostly tions providers, and industry consultants use these terms in Europe and refers to the fourth industrial revolution. to describe myriad offerings that they claim are driving The digital solutions that enable smart manufacturing are the fourth industrial revolution (i.e., Industry 4.0) and will transform manufacturing into so-called smart manufacturing Smart Manufacturing (SM). Executives hear about the benefits of big data, plant Industry 4.0 Cloud operators are promised predictive maintenance, and IT Digitalization Internet of Things (IoT) and R&D are up in a cloud and tangled in an internet of Chemical 4.0 Industrial IoT (IIoT) Cyber-Physical Systems (CPS) things (IoT), or so it seems. Connected Services Intelligent Manufacturing Confusion abounds as these new terms and ideas are Web Services Many Equivalent Terms applied to familiar concepts (Figure 1). We are all left New IT Terms wondering: Are these claims real? What precisely is smart manufacturing? Why do so many terms seem to mean the same thing? Isn’t the chemical industry already automated enough? How can my company deal with this new wave of tools and technologies? This article attempts to answer these questions by defining some of these new terms and describ- New Underlying Tools and ing their relationships. Affects Every Technologies Functional Area R&D Big Data Smart manufacturing Engineering Predictive Analytics Operations Automation and Robotics Smart manufacturing is an evolving state of manufac- Maintenance Digital Twins turing in which digital solutions enable enhanced system Supply-Chain Virtual and Augmented Reality connectivity, data analytics, and functional automation that Finance Mobile Devices improve operational agility, adaptability, and profitability. Customer Relations Machine Learning This definition encompasses smart manufacturing’s evolving p Figure 1. Confusion around smart manufacturing is due to the many nature, enabling systems (e.g., digital solutions such as the equivalent terms, new IT terms, and new underlying tools and technologies IoT and AI), improvement drivers (e.g., connectivity, analyt- that are being applied in virtually every functional area.

based on subsystems, including data collection and storage used to host the IoT/IIoT. A small-scale IoT may be imple- (e.g., smart measuring devices, databases), internet/intranet mented via conventional servers. connectivity for data exchange (e.g., IoT, cloud), data The popularity of commercially available clouds can analysis and decision-making (e.g., big data, analytics, AI), be attributed to their pay-per-usage scheme. Application decision implementation mechanisms (e.g., control, auto developers that utilize a cloud do not need to invest upfront mation), and human-machine interfaces (e.g., workstations, in computing infrastructure or maintain and update hardware mobile phones). and software, and it allows developers to easily scale their Industrial internet of things. A primary characteristic of applications. Generic clouds from companies like Microsoft smart manufacturing is that its subsystems are connected and Amazon enable software as a service (SaaS), platform as and can exchange information in real time via the industrial a service (PaaS), and infrastructure as a service (IaaS). Pre- internet of things (IIoT). The various “things” might include dix from GE, Sentience from Honeywell, and MindSphere manufacturing equipment (such as process units, controllers, from Siemens are examples of industrial clouds specifically and machines), computers, software, human experts, and designed for the manufacturing IIoT. end-users. The term internet of things (IoT) is more general Clouds have been recently used by process technology and extends beyond the realm of manufacturing. licensors to provide connected services using near-real-time To complicate things further, IoT and IIoT are sometimes operational data that is continuously collected and transmit- referred to as cyber-physical systems (CPS), because things ted to a panel of remote technology models and experts. in an IoT/IIoT can be physical equipment or be computa- These experts analyze the data regularly and provide solu- tional- or web-based (cyber). Moreover, services provided tions for troubleshooting, asset reliability, and operational via IoT/IIoT are sometimes called connected services. optimization (Figure 2). (UOP/Honeywell, for example, Cloud. A cloud is a platform that provides data storage, provides such systems and services under the brand name computing, and communication hardware and software Connected Performance Services [CPS] for plants that lever- in place of conventional physical servers. The difference age UOP technologies.) between a cloud and a server is that a server is usually Other tools. The IIoT and clouds are primarily mecha- owned by and dedicated to a single company, while a cloud nisms for data storage, data transfer, and connectivity. can serve large numbers of companies and individuals, Predictive analytics, AI, and digital twins are another class usually on a pay-per-usage basis. A cloud can be used for of smart manufacturing tools that use all of the data gathered general-purpose storage and computing needs, or it can be to make timely inferences and decisions, which helps to optimize operation in real time. The data gathered is dubbed big data. These huge volumes (terabytes) of data can be processed and analyzed to gain insight into systems. Predictive analytics use big data to predict system behavior and trends. Artificial intelligence is software-based cognitive capa- bility that allows computers or machines to infer decisions based on rules and data. Some of the systems that apply AI use the term smart as a qualifier or prefix in their names or descriptions. Machine learning is one of the most relevant branches of AI in the context of smart manufacturing. It is a branch of AI that gives computers the ability to learn from data and can be incorporated in predictive analytics. A digital twin of equipment is an adaptive model that is continuously updated to reflect the changing features of the physical object. Usually, a 3D visualization of a physical object is part of a digital twin model.

Digitalization: A means to achieve smart manufacturing In an ideal IIoT, every entity would have up-to-date p Figure 2. Connected services are enabled by data historians and laboratory information management systems (LIMS) that send real-time knowledge of properties and capabilities of other connected data via a cloud to remote experts. This gives these remote teams a view entities and would be able to manipulate their properties. into plant operation and enables them to make recommendations. Any entity in a manufacturing enterprise, technical or

44 www.aiche.org/cep June 2019 CEP Copyright © 2019 American Institute of Chemical Engineers (AIChE). Not for distribution without prior written permission. nontechnical, that can generate and communicate data can Big data either benefit from digitalization or provide a benefit to other For decades, chemical plants have been collecting and connected entities. archiving large amounts of structured operational data. A The question is now: What is digitalization and what is distributed control system (DCS) gathers and uses this data its relationship to smart manufacturing? to monitor and control equipment and processes in real time. Digitalize means to convert a continuous (analog) signal Process engineers routinely analyze data obtained through into a discrete signal of binary digits. In automation, the term the DCS or plant historians via spreadsheets for plant or prefix digital has been used to describe controllers and troubleshooting and improvement. And, for more than two related instrumentation that use discrete signals, unlike older decades, plant data has also been used to develop artificial analog instrumentation based on analog (continuous) signals. neural network (ANN) models for APC. (Read “Introduction The term digitalization is now applied in a much broader to Deep Learning: Part 1,” in the June 2018 issue of CEP for and more generic sense. It is used to describe the implemen- more on ANNs.) tation of any solution that involves computer hardware or However, much plant data that is archived is seldom software. Digitalization is an essential enabler of smart man- revisited. Availability of technical data is not the problem; ufacturing, but it is sometimes used as a synonym. In such rather, the problem is finding the time, tools, and expertise to cases, it is used to describe a solution that connects system analyze it. Nearly two-thirds of chemical executives rank big components (e.g., equipment, processes, instruments, ana- data as one of their top three concerns with respect to digital lyzers, models, people) by enabling them to communicate transformation (1). via streams of digital data and to utilize that information. Big data itself is nothing more than huge amounts of Plant digitalization is not totally new to the chemical real-time and archived data. Distributed file systems (e.g., process industries (CPI). CPI facilities were among the early Hadoop HDFS) and distributed/parallel computing environ- implementers of digital technologies for automation, safety, ments (e.g., Hadoop MapReduce) are specially developed and optimization. The scope of digitalization, however, has to process and utilize data in large volumes (thousands of expanded and involves cutting-edge technologies, including terabytes) and at high velocities (several terabytes per day). connectivity, mobility, predictive analytics, AI, and robotics. Smart devices and additional instrumentation are adding Digitalization in the CPI is enabled by: to the already large volumes of data gathered by the CPI. • data connectivity through clouds. Plant operational This data has grown in volume, velocity (speed of accumula- data shared via a cloud can be used for various objectives, tion), variety (structured, semi-structured, non-structured), including near-real-time troubleshooting by remote tech- and veracity (spectrum of quality, certainty, and correctness). nology experts. Additionally, companies now continuously gather and • advanced process control (APC) and real-time opti- archive big data about their customers, suppliers, competi- mization (RTO). APC and RTO help optimize plant operators, and relevant ecosystems. This has triggered a seem- tion, maintaining it within applicable constraints. APC uses ingly unending wave of platforms, tools, and solutions that dynamic models of individual units (e.g., reactors, distilla- promise insights from companies’ big data. Now, the term tion columns), while RTO uses rigorous steady-state models big data refers not only to the data itself, but also to the asso- to perform a full plant flowsheet optimization. ciated data-processing solutions, including data extraction, • big data and predictive analytics. Operational data can data storage, data cleaning, data mining, data analysis, data be analyzed to predict imminent process upset or equipment visualization, and machine learning. faults. For example, analysis of vibration data can help to Although this trend itself is not a bad thing, it has created predict a fault in a compressor. expectations of big data as a solution to all problems. How- • robotics, smart sensors, radio-frequency identification (RFID), augmented and virtual reality, etc. Smart sensors not only measure the intended Asset Availability Operational Visibility Operational Optimization phenomenon but also report their own accuracy. Asset Performance Equipment, Process, Advanced Process Control Management and Energy Monitoring Moreover, smart sensors themselves perform Real-Time Optimization Predictive Fault Real-Time Material and Production Planning situation-specific preprocessing of measured signals. Detection and Isolation Energy Balances and Yields and Scheduling This improves measurement reliability and system Predictive Maintenance Data Reconciliation Exchange of Feedstock, uptime. Robotics can be used in difficult or danger- Rule-Based Insights from Smart Product, and Utilities Process Diagnostics Instruments Between Sites ous jobs in various R&D or plant operations. Digital Twins A CPI company can leverage digitalization to improve asset availability, operation visibility, and p Figure 3. Digitalization can improve asset availability, operational visibility, and operational optimization (Figure 3). operational optimization.

ever, it is not all hype. Big data tools can be used effectively Benefits for the CPI to detect anomalies, diagnose faults, and perform predic- Smart manufacturing will expand digitalization beyond tive and preventive maintenance, as well as to determine plant automation to plant operation, R&D, and even non- customer preferences, predict market forecasts, and analyze technical functions. supply chain risks. Benefits in plant operation include: Big data is not suitable for developing models of • lower operating costs chemical processes, such as distillation, reactors, and heat • reliable availability of assets via early fault detection exchangers, for which rigorous first-principles models either based on predictive analytics already exist or can be easily developed. Big data models are • lower maintenance costs as preventive maintenance is based on correlation rather than causation, so they cannot replaced with predictive maintenance be extrapolated outside the range of the data. Moreover, it is • optimized supply chains difficult to separate data error, uncertainty, and measurement • less inventory by implementing production planning noise from actual phenomena. Plant data can be and has solutions been used to tune first-principles models. A properly tuned • enhanced reliability via early detection of upsets first-principles (causation) model is always better than a cor- and faults relation model developed from big data analytics. • less waste • on-spec quality using APC. Rigorous Modeling Benefits in R&D include: Real-Time Optimization Automation and Robotics • accelerated lab experiments Advanced Process Control Benchmarking • improved research collaboration and open innovation Linear Programming (LP) Production and networks Model Development Maintenance Dashboards • enhanced technology and market intelligence Computational Technologies Process Monitoring Big Data and Analytics Equipment and • reduced time to market. Artificial Intelligence Instrumentation Maintenance Faster experiments could be a boon to R&D. When developing new technologies, CPI companies must conduct lab experiments to develop and evaluate various catalysts R&D Manufacturing and generate kinetic reaction rate parameters. These experiments are typically conducted on a few single-reactor setups. Many experiments must be run to test applicable ranges of Business Units IT temperature, pressure, and species compositions and to build reliable kinetic models. This can take several months to more than a year to complete. High-throughput experimenta- tion (HTE) systems incorporate robotics to conduct up to LP-Based Production Planning Cloud and Server Management 50 parallel experiments. HTE enables several months of Supply Chain Risk Management Computational Resources work to be accomplished in several weeks. Technology Insights Connectivity and Mobility Benefits in nontechnical areas include: Technology-Specific Modeling Cybersecurity Technology Reliability • improved customer relationships Technology-Specific • enhanced supplier and distributor experience Operational Optimization • improved talent acquisition p Figure 4. Each department has a role to play in a company’s • improved business development. digital journey. It is important to track customers’ preferences, comments, and purchase pat- Strategy Phase Planning Phase Execution Phase terns to improve customer relationships. A company’s internal customer-relationship Choose a Consultant Define an Approach Perform Engage Management Develop a Roadmap Implementation Tasks management (CRM) data holds limited Develop a Strategy and Vision Specify Discipline-Specific Estimate Baselines information. However, the internet hosts Perform a SWOT Analysis Scopes Evaulate KPIs many platforms where customers express Determine Project Scope Define the Order Perform Change their view of products directly or indirectly, of Implementation Managment Develop a Business Case including social media, review sites, and Perform a Risk Assessment Evaluate and Select Vendors Establish Milestones discussion forums. Big data analytics can extract useful information about customer p Figure 5. Achieving smart manufacturing is a three-phase journey. preferences from these sources, enabling

46 www.aiche.org/cep June 2019 CEP Copyright © 2019 American Institute of Chemical Engineers (AIChE). Not for distribution without prior written permission. personalized customer interactions and services. expected outcomes of the project, as well as determines The benefits to plant operations, R&D, and nontechni- a planning phase team. These boundaries will be deter- cal areas are clear, and the CPI will need to embrace this mined from a detailed strengths, weaknesses, opportuni- new wave of digital technologies as a necessity. Some ties, and threats (SWOT) analysis and a high-level quanti- decision-makers in the CPI view digitalization as nice to tative analysis. have but not necessary. Digitalization can be expensive and The strategy phase should be led by the corporate strat- time-intensive, but with the proper knowledge, a realistic egy department or an equivalent corporate-level department, strategy, and comprehensive planning, cost and time can and may benefit from the services of a relevant consultant be minimized. company. This phase may take two to six months, depending Reluctance to transition to digital solutions might also on the size, commitment, and efficiency of the company. be based on past experiences with legacy software platforms During the planning phase, each department should that were proprietary and expensive. Modern digitalization solutions are mostly based on open and free platforms. In addition, pay-per-usage services can minimize hardware Literature Cited costs. In most cases, CPI facilities are already collecting and 1. Deloitte Consulting LLP, “Digital Transformation: Are Chemi- storing the necessary data. cal Enterprises Ready?,” www2.deloitte.com/global/en/pages/ consumer-industrial-products/articles/digital-transformation- The primary hurdle is to start using this data and con- chemical-enterprises-prepare.html (Jan. 2017). necting entities to form an IIoT. Data can then be trans- 2. Sarathy, V., et al., “2017 Chemical Trends,” Strategy&, PwC, formed into trackable key performance indicators (KPIs) that London, U.K., www.strategyand.pwc.com/trend/2017-chemicals- can inform and support decisions. Connectivity will bring industry-trends (2017). departments closer and facilitate collaboration. AI-based CRM will enhance customer relationships, and increased MUNAWAR SAUDAGAR, PhD, is a staff scientist at SABIC Technology & mobility will enable remote access. This digital transfor- Innovation (14100 Southwest Freeway, Sugar Land, TX 77478; Email: [email protected]), where he provides linear programming model- mation will make companies more knowledgeable, agile, ing support for a crude-oil-to-chemicals project. He has over 25 years and competitive. of experience in chemical R&D, with expertise in modeling, simulation, optimization, and advanced process control (APC). He also has experi- Despite the clear advantages, many decision-makers are ence in developing and accessing software and other technologies. He still doubtful, indifferent, or maybe a little confused. While developed a fixed-bed reactor model for SABIC’s acetic acid technology and was also involved in various APC projects. Saudagar has BE and some remain skeptical, others believe that they have already MS degrees in chemical engineering from NED Univ. of Engineering and digitalized their enterprise because they implemented enter- Technology (Pakistan) and King Fahad Univ. of Petroleum and Minerals (Saudi Arabia), respectively, as well as a PhD in process control from prise resource planning (ERP), APC, and other conventional the Univ. of Alberta. solutions. Others are reticent to share data, even among their MINGHUA YE, PhD, is a staff scientist at SABIC Technology & Innovation own departments and people. In addition, many have genuine (14100 Southwest Freeway, Sugar Land, TX 77478; Email: mye@sabic. com), where he develops process design packages and applies central- concerns about data security and cyberattacks. ized information technology to process flow diagrams, equipment data- Although confusion and concerns abound, many CPI sheets, piping and instrumentation diagrams, and other documenta- tion. He has more than 30 years of experience in R&D and engineering companies have already started their digital journey. Chemi- tool development, with expertise in modeling, simulation, optimization, cal companies are planning to spend 5% of their annual innovative technology commercialization, and process design. Minghua has BS, MS, and PhD degrees in chemical engineering from Nanjing revenue on digitization, and approximately one-third claim Institute of Chemical Technology, East China Univ. of Technology and they have already achieved a high level of digitization (2). Sciences, and Qinghua Univ., respectively.

SULTAN AL-OTAIBI is a senior manager of the Process Technology Dept. at Achieving smart manufacturing SABIC Technology & Innovation (Qordoba District Global Headquarters, Riyadh 11422, Saudi Arabia; Email: [email protected]). His depart- The most expedient and logical way to start a digital ment develops and supports process simulations, advanced modeling, journey is to use an industry-specific platform available conceptual process design, and process design packages (PDP). He has 21 years of experience in the petrochemical industry, with expertise from a known vendor. Each department should be involved in operations of chemical plants, research, technology development, in a unified implementation of digitalization to avoid silos project management, and process improvement. Al-Otaibi earned a BS and duplicate work. Each department has a role to play in and MS in chemical engineering from King Saud Univ. company-wide digitalization endeavors (Figure 4). KHALID AL-JARBA, PhD, is the head of the Process Technology and Licens- ing Dept. at SABIC Technology & Innovation (Qordoba District Global To manage this huge undertaking, companies should Headquarters, Riyadh 11422, Saudi Arabia, Email:Jarbaka@SABIC. approach digitalization as a series of phases that include com). He oversees more than 80 experts and researchers in areas including technology licensing, plant support, process improvement strategy, planning, and execution (Figure 5). A strategy is and optimization, process design package (PDP) development, and key to avoid chaos and disorder in the planning and execu- growth projects. He has an MS in metallurgy from the Univ. of Sheffield, U.K., and a PhD in materials science and engineering from the Univ. of tion phases. A well-formed digitalization strategy establishes Florida, as well as an MBA from Arizona State Univ. limits and ranges for cost, a time period, and the extent and

Many digital solutions use technical, to be adopted. As digital solutions will most likely change commercial, and financial data that require various work processes, the change management may be quite challenging. Therefore, it is crucial to educate relevant a good data-stewardship program. staff about the impact of digitalization from the beginning of project execution. separately develop a detailed business case or benefits study. These studies can be used to inform priorities, scope, Looking forward and budget for target departments. At this point, the planning It is important to have a balanced view of smart manu- team needs to develop digitalization roadmaps that include facturing and digitalization. Do not consider it magic or approximate timelines for individual departments and the simply all hype. Like any other tools, the benefits of digital overall enterprise. Consultants can be helpful in determining solutions depend on how they are selected, implemented, preliminary milestones for each roadmap, as well as select- and utilized. ing systems, services, and solution providers. They can also Because digitalization is applicable to many areas of an assist in setting high-level success criteria for the projects enterprise, multiple departments may independently initi- that are planned for the subsequent execution phase. ate their own digital projects. If various departments in your A detailed plan makes the execution phase much easier. organization have already started digital initiatives, you may During the execution phase, multiple department-specific want to ensure they are coordinated under a company-wide projects will be simultaneously implemented. The projects strategy. Change management is also critical. Finally, remem- will mostly be executed by the digital solutions providers in ber that many digital solutions extensively utilize technical, coordination with company project teams. commercial, and financial data that requires a good data- Standard best practices for project management need stewardship program with dedicated data engineers. CEP

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