As companies are trying to build more resilient supply chains using digital twins created by smart manufacturing technologies, it is imperative that senior executives and technology providers understand the crucial role of process simulation and AI in quantifying the uncertainties of these complex systems. The resulting digital twins enable users to replay history, gain predictive visibility into the future, and identify corrective actions to optimize future performance. In this article, we define process digital twins and their four foundational elements. We discuss how key digital twin functions and enabling AI and simulation technologies integrate to describe, predict, and optimize supply chains for Industry 4.0 implementations.

Digital twins have become an important element in smart manufacturing. As any other product, digital twins also have a lifecycle, starting from specifying the requirements of the digital twins until their decommissioning. As part of the Manufacturing and Industry 4.0 track of the Winter Simulation Conference (WSC), the purpose of this panel is to discuss the state of the art in digital twins with a special emphasis on the operations and maintenance of manufacturing digital twins during their lifecycles. The panelists come from academia, industry, and government with experience in the digital-twin landscape of the manufacturing industry in the United States, Europe, and Asia. This paper provides a collection of the statements from each panelist with the objective of initiating a deeper discussion during the panel session and inspiring researchers in the simulation community with their perspectives on the use of digital twins for smart manufacturing.

This tutorial defines what a digital twin is and outlines its four required characteristics. Digital twins are developed to derive insights to control entities and processes in the digital world with simulation as one of the key technologies lying at the heart of this development. The resulting insights are used to prescribe actions in the physical world to fix future problems before they happen. This tutorial describes the key digital twin development functions together with the digital twin enabling technologies with focus on the use of simulation for process twin development. The corresponding functions and technologies are displayed on several different digital twin development frameworks with the potential to serve as guides for practitioners interested in developing digital twin solutions. We conclude with an example of a supply chain digital twin use case and the role of simulation and AI in the twin development.

AboutSectionsRequest Access ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to Section HomeINFORMS TutORials in Operations ResearchTutorials in Operations Research: Advancing the Frontiers of OR/MS: From Methodologies to Applications A Practitioner's Guide to Digital Twin DevelopmentBahar Biller, Jinxin Yi, Stephan BillerBahar Biller, Jinxin Yi, Stephan BillerPublished Online:13 Oct 2023https://doi.org/10.1287/educ.2023.0263AbstractThis tutorial describes industrial digital twin development including advanced analytics. Using factory and supply chain digital twins as example applications, we present two different digital twin frameworks that serve as a guide for practitioners interested in developing digital twin solutions. The resulting digital twins are expected to help understand what did happen, predict what may happen and prescribe actions to address future problems before they happen. We conclude with examples of digital twin use cases and challenges of their implementations. Your Access Options Login Options INFORMS Member Login Nonmember Login Purchase Options Save for later Item saved, go to cart Tutorials in OR, TutorialsNew $20.00 Add to cart Tutorials in OR, TutorialsNew Checkout Other Options Token Access Insert token number Claim access using a token Restore guest access Applies for purchases made as a guest Previous Back to Top Next FiguresReferencesRelatedInformation Tutorials in Operations Research: Advancing the Frontiers of OR/MS: From Methodologies to ApplicationsOctober 2023 Article Information Metrics Information Published Online:October 13, 2023 Copyright © 2023, INFORMSCite asBahar Biller, Jinxin Yi, Stephan Biller (2023) A Practitioner's Guide to Digital Twin Development. INFORMS TutORials in Operations Research null(null):198-227. https://doi.org/10.1287/educ.2023.0263 Keywordsdigital twinmachine learningartificial intelligencesmart manufacturing optimizationsimulationrisk managementsupply chain managementPDF download

Digital twins are virtual representations of physical entities and processes. They aid in deriving insights to control entities and processes in the digital world and use those insights to drive actions in the physical world. Simulation is one of the key enabling technologies that lie at the heart of digital twin development, as it provides enhanced visibility into future performance and the ability to identify profit-optimal decisions. This tutorial describes how we envision the digital twins developed in industry and the pivotal role simulation plays in their development. Using supply chain digital twins as an example application, we introduce our digital twin framework that simulation practitioners might find useful when developing their digital twin solutions to understand what did happen, predict what may happen, and determine solutions to fix future problems before they happen. We conclude with simulation research streams that contribute to the use of simulation in digital twin development.

We provide guidance for decision makers to quantify risk/value trade-offs under uncertainty in complex systems. We develop a framework integrating market, operations, and financial modules. The market module includes price, demand and variable cost, the operations module considers production constraints, manufacturing yields and schedules, while the financial module provides proforma income and profit statements considering fixed cost. Special emphasis is given to the use of stochastic simulation, optimization, and real options valuation as the technology choices while the key challenges of integrating financial risk management with operational modeling and IoT-driven execution are discussed. We utilize the integrated models in a real-world setting where process improvements continuously improve return on investment. We determine the cost of uncertainty and conclude with highly promising results obtained from a prior industrial project where building on the integration of financial risk management, operational modeling and IoT-driven execution was the key determinant of success.

The challenges in industrial settings have remained the same for the last 50 years. Operations leaders are being asked to deliver more across KPIs—such as throughput, costs, efficiency, quality, and sustainability—and asked to deliver incremental improvement every year.The Fourth Industrial Revolution (4IR) has brought much promise: more sensors, more connectivity, more data, better controls, more tools and methods to make sense of the data, and a greater understanding of consequences. But for all its promise, results have been stubbornly slow. Assets are increasingly complex, inter-dependencies are difficult to understand and real-time externalities are impacting these systems--often with unforeseen consequences.The confluence of AI and advanced analytics, however, can get business leaders the transformational results they desire. These technologies can break through data silos and analyze your operations holistically to provide deeper insights that humans and advanced controls can act on. And it can all happen in real-time. AI and advanced Analytics can help you better visualize the butterfly effect, understand how decisions about maintenance of one asset will impact another, and help business leaders to obtain the agility their businesses so desperately need.

Problem definition: Autonomous sensors connected through the internet of things (IoT) are deployed by different firms in the same environment. The sensors measure an important operating-condition state variable, but their measurements are noisy, so estimates are imperfect. Sensors can improve their own estimates by soliciting estimates from other sensors. The choice of which sensors to communicate with (target) is challenging because sensors (1) are constrained in the number of sensors they can target and (2) only have partial knowledge of how other sensors operate—that is, they do not know others’ underlying inference algorithms/models. We study the targeting problem, examine the evolution of interfirm sensor communication patterns, and explore what drives the patterns. Academic/practical relevance: Many industries are increasingly using sensors to drive improvements in key performance metrics (e.g., asset uptime) through better information on operating conditions. Sensors will communicate among themselves to improve estimation. This IoT vision will have a major impact on operations management (OM), and OM scholars need to develop and examine models and frameworks to better understand sensor interactions. Methodology: Analytic modeling combining decision-making, estimation, optimization, and learning is used. Results: We show that when selecting its target(s), each sensor needs to consider both the measurement quality of the other sensors and its level of familiarity with their inference models. We establish that the state of the environment plays a key role in mediating quality and familiarity. When sensor qualities are public, we show that each sensor eventually settles on a constant target set, but this long-run target set is sample-path dependent (i.e., dependent on past states) and varies by sensor. The long-run network, however, can be fully defined at time zero as a random directed graph, and hence, one can probabilistically predict it. This prediction can be made perfect (i.e., the network can be identified in a deterministic way) after observing the state values for a limited number of periods. When sensor qualities are private, our results reveal that sensors may not settle on a constant target set but the subset among which it cycles can still be stochastically predicted. Managerial implications: Our work allows managers to predict (and influence) the set of other firms with which their sensors will form information links. Analogous to a manufacturer mapping its supplier base to help manage supply continuity, our work enables a firm to map its sensor-based-information suppliers to help manage information continuity.

The capability of modeling real-world system operations has turned simulation into an indispensable problem-solving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose.

A system for generating platform-specific control logic implementation code for execution on a programmable logic controller (PLC) platform includes a plurality of processing layers. A first layer models generic control requirements as a unitary mathematical model (UMM). A second layer trans lates the UMM into generic control code describing a plat form-independent set of generic control functions following an open structured language. A third layer automatically transforms the generic control functions into the platform specific implementation code executable on different PLC platforms. A method of generating the implementation code includes modeling control requirements as a mathematical model, transforming the model into platform-independent control code describing a predetermined set of generic con trol functions using Extensible Markup Language (XML) schema, and automatically transforming the generic control functions into the implementation code.

Sustainable production automation, as an effective way to enable and expedite transitions to sustainability and enhance resource utilizations, attracts substantial efforts from researchers in both academy and industry. This book presents the recent development of innovative algorithms, models, heuristics, hardware and software in broad areas of sustainable production systems. It focuses on design, analysis and management of the processes involved in the product life cycle (from design to delivery to return) to have the minimal negative impacts on society (including environmental, economic and social). The contributors are experts from both universities and industrial research centers.

An integrated modeling and analysis approach is much needed that can relate the sensor data with the dynamic battery manufacturing and evaluate the system performance for continuous improvement. This chapter presents such a method for sensor-enabled battery manufacturing system modeling and analysis. Distributed sensing, a system-wide deployment of sensing devices, has resulted in a data-rich environment with opportunities and challenges in manufacturing systems. On the basis of the system description and assumptions, an event-based modeling (EBM) approach for virtual multilayer sensor structure is introduced to reflect the market demand-driven battery manufacturing systems. The EBM method does not solely focus on physical model nor it is based on long-term steady state, but it closely communicates with distributed sensor networks for smart monitoring and diagnosis. The cost in manufacturing system is categorized into two categories: resources that are supplied as used (and needed) and resources that are supplied in advance of usage.

The General Electric Company (GE) is a global technology company involved in a broad range of businesses relying heavily on advanced materials and manufacturing technologies. This chapter focuses on three advanced technologies: (i) ceramic matrix composites (CMCs), a revolutionary materials technology for aircraft engines and industrial gas turbines, (ii) polymer matrix composite (PMC) fan blades for aircraft engines, primarily for weight reduction, and (iii) NaMx batteries that rely very heavily on ceramics for their efficient operation.

This article focuses on the transformation and dimming lines between digital and physical worlds. Industrial Internet tools and applications also help people collaborate in a faster and smarter way – making jobs not just more efficient but more rewarding. Technological progress and economic growth are contributing to a seismic shift in the role that human beings play in the production process. Technological progress, notably in high-performance computing, robotics, and artificial intelligence, is extending the range of tasks that machines can perform better than humans can. The Future of Work is being shaped by a profound transformation, driven by the meshing of the digital and the physical worlds, the emergence of new design and production techniques, and a seismic shift in the role that human beings play in the production process. Technological progress is expected to push a growing share of the workforce toward creativity and entrepreneurship, where humans have a clear comparative advantage over machines.

Automotive battery manufacturing has become more and more important due to the need of alternative energy storage device to replace gasoline powered engines. The increasing market of hybrid and electric vehicles has intrigued the demand for large volume battery manufacturing. In this paper, we present a summary of recent advances in the area, review some commonly used battery technologies, their manufacturing processes, and related recycling and environmental issues.

In this paper, a market demand driven modeling framework is developed. The market demand is modeled as an end-of-line virtual machine based on which the market demand dissatisfaction (MDD) can be measured as production loss using event-based analysis. A general Markovian continuous-flow model is developed for market demand-driven systems with multistage production networks combining manual and automatic processes. Machine failure bottlenecks (MF-BNs) and machine capacity bottlenecks (MC-BNs) are defined and identified based on event-based indicators. A supervisory control algorithm is integrated in the framework to reduce MDD and improve system productivity through identification and mitigation of MF-BNs and MC-BNs. Simulation-based analysis will also be utilized, and case studies are performed to validate the effectiveness of the modeling framework and the supervisory control policies.

Battery manufacturing systems are characterised by their complex dynamics subject to constant changes caused by technology insertion, engineering modifications, as well as disruption events. To support daily operation, distributed sensors are used to provide real-time data describing the status of each process. Despite the big potential in improving productivity, the advantages of distributed sensor networks are not fully realised for overall system efficiency due to a lack of system-level modelling. Motivated by this need, we develop an event-based modelling (EBM) approach to quantify the systematic impacts of stations and supporting activities with an index called permanent production loss. EBM instantaneously captures the system dynamics using distributed sensor information and provides a severity ranking of stations and supporting activities. We also study the system dynamics of serial production lines with multiple slowest stations and quantify the impacts of disruption events to the production system. A case study is conducted to demonstrate the application of EBM in a battery production system and its ability to facilitate decision-making at plant floor on resources and budget allocation.

Improving quality in large volume battery manufacturing systems for hybrid and electric vehicles is of significant importance. In this paper, we present a flow model to analyze and improve product quality in electrical vehicle battery assembly lines with 100% inspections and repairs for defective parts. Specifically, a battery assembly line consisting of multiple inspection stations is considered. After each inspection, defective parts will be repaired and sent back to the line. A quality flow model is introduced to analyze quality propagations along the battery production line. Analytical expressions of final product quality are derived and structural properties, such as monotonicity and sensitivities, are investigated. A bottleneck identification and mitigation method is introduced to improve quality performance. Finally, a case study is presented to illustrate the applicability of the method.