Manufacturing Operations Management Research Articles

Digital twin-enabled robust production scheduling for equipment in degraded state

Technological advancements are leading to a world where digital twins will become integral to manufacturing operations management. While wide-ranging applications of digital twins are being researched, robust production scheduling remains an enduring challenge, especially considering the numerous sources of uncertainty in a complex manufacturing system that can affect the validity of the obtained solution. Thus, the article proposes a Prognostics and Health Management (PHM)-enabled digital twin framework to perform job scheduling for a flow shop scheduling problem considering real-time equipment health state. The framework combines the adoption of a Genetic Algorithm optimizer with data-driven modelling leveraging algorithms like Principal Component Analysis and models like Discrete Event Simulation with the purpose to solve the engineering task of scheduling at hand. Building on such a technological blend, the framework incorporates the degradation and fault detection and diagnosis of failure modes across multiple components. The effect of degradation and faults on job processing times is learned as a distribution from the field data. The proposed framework is then validated in a laboratory environment where degradation is produced by inducing degradation and faults in the equipment. By means of various experiments, the optimized makespan is compared for the output schedules in different configurations. When equipment is degraded, production scheduling with PHM-enabled field-synchronized digital twin results in better makespan estimation, if compared to the digital twin without PHM. This shows the superiority of the framework in terms of more realistic makespan estimations, which finally corresponds to improved production schedule optimization, sensitive also to degraded states.

Workshop digitalisation and its effect on manufacturing operations management: a new analysis method from a data quality perspective

ABSTRACT Workshop digitalisation (WD) provides a rich supply of real-time production data to achieve efficient manufacturing operations management (MOM). However, if data quality (DQ) cannot be ensured, manufacturing enterprises will not benefit from WD despite the resources invested. This study proposes a new method to evaluate the effect of WD from a data quality (DQ) perspective and identifies the root causes of WD failures at the operational level by information stream analysis based on an extended value stream mapping 4.0 (VSM4.0). The method is developed through a Design Science Research (DSR) approach and tested in an automobile parts manufacturer in China.

Assessing the performance of state-of-the-art machine learning algorithms for predicting electro-erosion wear in cryogenic treated electrodes of mold steels

In manufacturing, predicting and reducing electro-erosion wear during the electric discharge machining (EDM) process is critical to minimize delays, financial losses and product defects. Achieving this requires developing and evaluating accurate machine learning models. In our study, we focus on cryogenically treated mold steel electrodes to investigate the potential of different machine learning algorithms to predict EDM wear. We considered five machine learning algorithms—artificial neural networks, ensemble learning, boosting algorithms, tree-based algorithms, and k-nearest neighbors—to evaluate their ability to predict wear patterns accurately. Each algorithm was trained and tested using actual experimental data from EDM processes. Our results show that the machine learning models demonstrated exceptional accuracy, accurately predicting EDM wear in training and testing datasets with almost 99% accuracy. In addition, we identified the most influential characteristics that affect wear patterns, including operating current, cryogenic process parameters, and electrode composition. Based on these findings, manufacturers can gain valuable insight into the factors that cause EDM wear and optimize their EDM processes accordingly to improve productivity, reduce wear-related costs, and increase production quality across multiple manufacturing industries. Furthermore, this research provides insights into the possibilities of implementing these models in real manufacturing contexts and motivates future research on this topic. Ultimately, integrating advanced computing techniques and prudent decision-making strategies will shape the future of manufacturing operations management and promote sustainable and profitable business growth.

Quantum-inspired computing technology in operations and logistics management

PurposeThe purpose of this paper is to explore and disseminate knowledge about quantum-inspired computing technology's potential to solve complex challenges faced by the operational agility capability in Industry 4.0 manufacturing and logistics operations.Design/methodology/approachA multi-case study approach is used to determine the impact of quantum-inspired computing technology in manufacturing and logistics processes from the supplier perspective. A literature review provides the basis for a framework to identify a set of flexibility and agility operational capabilities enabled by Industry 4.0 Information and Digital Technologies. The use cases are analyzed in depth, first individually and then jointly.FindingsStudy results suggest that quantum-inspired computing technology has the potential to harness and boost companies' operational flexibility to enhance operational agility in manufacturing and logistics operations management, particularly in the Industry 4.0 context. An exploratory model is proposed to explain the relationships between quantum-inspired computing technology and the deployment of operational agility capabilities.Originality/valueThis is study explores the use of quantum-inspired computing technology in Industry 4.0 operations management and contributes to understanding its potential to enable operational agility capability in manufacturing and logistics operations.

Applications of IoT and Advanced Analytics for manufacturing operations: a systematic literature review

IoT is driving the digital transformation of companies into smart factories: data collected through IoT technologies can provide powerful support to manufacturing operations management, making the processes more efficient and leading to improved productivity. To maximize IoT value, turning raw data into actionable insights, Advanced Analytics algorithms can be the key to opening novel opportunities for operations and discovering hidden patterns in data collected through IoT sensors. This review presents an overview of the current state-of-the-art of integrated applications of IoT and Advanced Analytics in the fields of Quality, Maintenance, and Production Planning & Control. Through a Systematic Literature Review, twenty-six articles were retrieved and analyzed to highlight the key points in terms of goals, employed techniques, and use of data. The results show that only a few articles apply the combination of these techniques in Production & Planning Control while on the other hand Maintenance area presents the highest maturity in terms of data analytics techniques since more advanced data-related aspects are discussed in the selected studies. This paper provides insights for researchers and operations managers in the manufacturing sector interested in unlocking the value of IoT data through the use and implementation of Advanced Analytics in one of these areas.

Application of MES/MOM for Industry 4.0 supply chains: A cross-case analysis

Manufacturing is facing challenges in integrating information technology (IT) with operational technology (OT) and implementing Industrial Internet of Things (IIoT) concepts in the industry to increase manufacturing flexibility. This paper addresses the research gap in designing and using next-generation manufacturing execution systems (MES)/manufacturing operations management (MOM) in IIoT to improve manufacturing flexibility through reconfigurability. For this, we follow an abductive research design and build on the literature on Industry 4.0′s information architectures and models to propose a framework for building smart factory capabilities. Using the framework, we collect empirical data on MES/MOM implementation objectives for smart factories for six case studies conducted over a 4-year research project in Denmark (2018–2021), primarily through semi-structured interviews.Through cross-case analysis, we identify seven dominant themes that capture focus areas for MES/MOM implementation for IT/OT integration. We use these findings to present generalized design recommendations for IIoT-connected MES/MOM to support reconfigurability for Industry 4.0 supply chains. Our findings indicate that despite considerable investments from many companies in Industry 4.0 initiatives such as artificial intelligence-based analytics and digital twins, the industry is not yet in a state to extract the data from all its legacy production equipment. Therefore, we present design recommendations to enable Industry 4.0 supply chains with IIoT-connected MES/MOM by using the data from OT devices. Our analysis helps us conclude that open standards and open application programming interfaces (APIs) are key requirements for enhancing IIoT interconnectivity and interoperability to achieve end-to-end integration in supply chains.

IEC 62264 standard-based manufacturing operations management resource modelling for electron beam welding

The development and deployment of a Manufacturing Operations Management (MOM) system for electron beam welding aims the integration and organization of the accumulated knowledge for the process, and the application of this knowledge for the improvement of the modelling and control capabilities, their efficiency, adaptability, flexibility and re-configurability, as well as integration with other technological processes. The paper presents the Unified Modelling Language (UML) information modelling of electron beam welding installation as a part of an IEC 62264 standard-based manufacturing operations management system development.

Sustainable Supplier Selection using Neutrosophic Sets and MCDM Framework

Because of stricter rules from the government and growing awareness among the general public, sustainable supply chain management (SSCM) performs a significant role in the management of firm manufacturing operations. Companies that want to promote sustainable supply chain management (SSCM) must first choose the most suitable sustainable supplier, which is a MCDM dilemma, as highlighted in a number of research studies. In addition, because of their limited expertise, those who make decisions have a propensity to convey their opinions via the use of language phrases. The purpose of this work is to report on a unique MCDM model for the choice of sustainable suppliers. This approach integrates the MCDM MABAC method inside an uncertain language situation. With the assistance of uncertain linguistic sets, the neutrosophic sets used to overcome these uncertainty. When it comes to generating the ranking of possible suppliers, the MABAC is dependable and easy to understand. In conclusion, an iron maker is used as an example to illustrate the practicability and efficacy of the suggested strategy for the selection of sustainable suppliers.

Enterprise IT Architecture Greenfield Design Combining IEC 62264 and TOGAF by Example of Battery Manufacturing

IT-systems supporting business operations are a cornerstone of industry 4.0 and smart manufacturing. IT-systems must not be considered as an end in itself. The enterprise IT architecture must be aligned with the business goals and strategy. Current approaches for designing enterprise IT architectures provide little guidance for greenfield environments in which there are little given boundaries, such as operation procedures. In smart manufacturing this is challenging due to the complexity of the shop floor involving OT and IT. This paper addresses this gap by combining the TOGAF architecture development method with the manufacturing operations management standard IEC 62264. The paper explores this combination by the example of designing an enterprise IT architecture for the Fraunhofer battery manufacturing research line involving the TOGAF phases architecture vision, business architecture, information systems architecture and technology architecture for manufacturing.

Introduction to the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management

AboutSectionsRequest Access ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to Section HomeManufacturing & Service Operations ManagementVol. 24, No. 6 Introduction to the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations ManagementSerguei Netessine , Christopher Tang , Michael Toffel Serguei Netessine , Christopher Tang , Michael Toffel Published Online:7 Oct 2022https://doi.org/10.1287/msom.2022.1155"Introduction to the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management." Manufacturing & Service Operations Management, 24(6), pp. 2797–2798 Back to Top Next FiguresReferencesRelatedInformation Volume 24, Issue 6November-December 2022Pages 2797-3306, C2 Article Information Metrics Information Received:September 14, 2022Accepted:September 14, 2022Published Online:October 07, 2022 Copyright © 2022, INFORMSCite asSerguei Netessine, Christopher Tang, Michael Toffel (2022) Introduction to the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Manufacturing & Service Operations Management 24(6):2797-2798. https://doi.org/10.1287/msom.2022.1155 PDF download

Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0

The Industry 4.0 initiative has been showing the way for industrial production to optimize operations based on collecting, processing, and sharing data. There are new requirements on the production floor: flexible but ultra-reliable, low latency wireless communications through interoperable systems can share data. Further challenges of data sharing and storage arise when diverse systems come into play at the Manufacturing Operations Management and Business Planning & Logistics levels. The emerging complex cyber-physical systems of systems need to be engineered with care. Regarding industrial requirements, the telecommunication industry has many similarities to production—including ultra-reliability, high complexity, and having humans “in-the-loop”. The current paper aims to provide an overview of converging telco-grade solutions that can be successfully applied in the wide sense of industrial production. These toolsets range from model-driven engineering through system interoperability frameworks, 5G- and 6G-supported manufacturing, and the telco-cloud to speech recognition in noisy environments.

Overcoming the barriers of effective implementation of manufacturing execution system in pursuit of smart manufacturing in SMEs

Manufacturers, especially SMEs, are increasingly adopting digitalization to improve their agility to respond to the fluctuating market dynamics and to be transparent. To achieve sustained competitiveness, SMEs are shedding their reluctance to invest in technologies like Internet of Things (IoT) and data analytics. Further, Manufacturing Execution System (MES) can serve as the intelligence hub, integrating product lifecycle with manufacturing, augmenting the IoT investments to orchestrate their facilities to be aligned to deliver what the customer needs.MES is a pivotal function integrating virtual and physical worlds and that makes it worthy of deeper examination. Integrated Manufacturing Operations Management (MOM) is the evolved state of standalone MES, and it consolidates production processes to facilitate quality management, scheduling, manufacturing execution and more. The motivation of this research is to study the potential barriers of implementing MOM/MES that reduces its effectiveness and it has three goals. Firstly, the current knowledge associated with MOM/MES needs to be examined to derive its relevance, functions and benefits. Secondly, potential barriers of MOM/MES are to be identified that can reduce its effectiveness. Thirdly, approaches need to be established to overcome the barriers and improve the overall value of the initiative.SMEs cannot afford delayed returns on their digital investments; therefore, it is crucial to mitigate the barriers of implementing MOM/MES. This study resulted in calling attention to five barriers and its corresponding mitigations - constructive closed-loop, cross-functional integration, flexible operations orchestrations, high-availability cloud connection and insightful data contextualization. Most barriers discussed in the past studies can be classified as readiness related, i.e. funding, expertise, compatibility, priority, capacity, security, whereas the barriers discussed in this research paper are directly related to implementation. Knowing the barriers and its mitigation approaches, manufacturers can now pre-empt measures to neutralize the challenges and make MOM/MES adoptions successful.

Security in the Internet of Things Application Layer: Requirements, Threats, and Solutions

Communication systems and networks are evolving as an integral part of not only of our everyday life but also as a part of the industry, fundamental infrastructures, companies, etc. Current directions and concepts, such as the Internet of Things (IoT), promise enhanced quality of life, greater business opportunities, cost-effective manufacturing, and efficient operation management through ubiquitous connectivity and deployment of smart physical objects. IoT networks are able to collect, pre-process, and transmit a huge amount of data. A considerable portion of this data is security- and privacy-critical data, which makes IoT networks a tempting option for attackers. Given that these networks deal with the actual aspects of our lives and fundamental infrastructures (e.g. smart grids), cybersecurity in such networks is crucial. The large scale of these networks and their unique characteristics and complexity bring further vulnerabilities. In this study we give an introduction to IoT, focusing on the IoT application layer, security requirements, threats, and countermeasures in this layer, and some of the open issues and future research lines.

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Physical Internet (PI) is an open global logistics system of which components are hyperconnected for increased efficiency and sustainability. Digital twin (DT), referring to the virtual representation of a physical object, is well-perceived as a key driver in the development of PI-based Supply Chain Management (SCM). Due to the capabilities of real-time monitoring and evaluation of large-scale complex systems, significant research efforts have been made to exploit values of PI/DT in SCM. Despite this, the current literature remained largely unstructured and scattered due to a lack of systematic literature reviews to synergise research findings, analyse the evolution of research fronts and extract emerging trends in the field. To address this issue, the paper deploys a bibliometric knowledge mapping approach to provide a bird's eye view of the current research status in the PI/DT-SCM area. Using CiteSpace's keyword co-occurrence network, 518 journal articles are clustered into 10 key research streams on PI/DT applications in: job shop scheduling, smart manufacturing design, PI-based SCM, manufacturing virtualisation, information management, sustainability development, data analytics, manufacturing operations management, simulation and optimisation, and assembly process planning. Based on citation burst rate, keywords representing research frontiers of the PI/DT are detected and their temporal evolutions are discussed. Likewise, some identified emerging research trends are production process and system, robotics, computer architecture, and cost. Finally, seven future research directions are suggested, which emphasise on several PI/DT-related issues, including business ecosystem, sustainability development, SC downstream management, cognitive thinking in Industry 5.0, citizen twin in digital society, and SC resilience.

OM Forum—A Vision of Responsible Research in Operations Management

Are we contributing positively to the society at large by research that we conduct in the field of Operations Management? Currently, the answer is probably closer to “no” than to “yes.” We often do not realize it, but there is very real cost of conducting our research nonresponsibly. In this invited OM Forum contribution, which is based on my Manufacturing and Service Operations Management Distinguished Fellow lecture given at the University of Texas at Dallas in 2018, I discuss what I believe it means to conduct research responsibly, I offer some specific steps to improve the relevance of what we do, and I argue that this approach gives our field a new opportunity to make broader impact by expanding the horizon of our work. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management.

Introduction to the Manufacturing & Service Operations Management Special Issue on People-Centric Operations

AboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to Section HomeManufacturing & Service Operations ManagementVol. 23, No. 4 Introduction to the Manufacturing & Service Operations Management Special Issue on People-Centric Operations Guillaume Roels , Bradley R. Staats Guillaume Roels , Bradley R. Staats Published Online:12 Aug 2021https://doi.org/10.1287/msom.2021.1021 Previous Back to Top Next FiguresReferencesRelatedInformation Volume 23, Issue 4July–August 2021Pages 745-1004, C2 Article Information Metrics Information Received:May 27, 2021Accepted:May 27, 2021Published Online:August 12, 2021 Copyright © 2021, INFORMSCite as Guillaume Roels , Bradley R. Staats (2021) Introduction to the Manufacturing & Service Operations Management Special Issue on People-Centric Operations. Manufacturing & Service Operations Management 23(4):758-760. https://doi.org/10.1287/msom.2021.1021 PDF download

Statement of the Manufacturing & Service Operations Management Journal

Statement of the <i>Manufacturing &amp; Service Operations Management</i> Journal

A Vision of Responsible Research in Operations Management

Are we contributing positively to the society at large by research that we conduct in the field of Operations Management? Currently, the answer is probably closer to “no” than to “yes”. We often do not realize it but there is very real cost of conducting our research non-responsibly. In this invited OM Forum contribution, which is based on my Manufacturing and Service Operations Management Distinguished Fellow lecture given at UT Dallas in 2018, I discuss what I believe it means to conduct research responsibly and I offer some specific steps to improve the relevance of what we do, and I argue that this approach gives our field a new opportunity to make broader impact by expanding the horizon of our work.

Website and pricing strategy development: a business coaching at local SME

&lt;em&gt;Small and medium-sized enterprises (SME) make a very large contribution to Indonesia's gross domestic product (GDP) and absorb a lot of labor from the entire workforce. Unfortunately, many SMEs face so many problems in their business operation. Whether the issues arising from internal factors such as management or external factors such as the COVID-19 pandemic, therefore they need support and insights from the experts on how to solve their problems. The current business coaching was conducted in PT Bumi Perkasa Internasional, a small business located in Bekasi, West Java and is engaged in the manufacturing industry with the scope of expertise in the Dust Collector System. The Business Coaching used a qualitative approach that mapping the real conditions and problems of SMEs, then propose solutions, and implement them for SMEs improvement. this method requires two types of data, primary data and secondary data Primary data collection technique is obtained by interview, observation and documentation. Secondary data was obtained by conducting a literature study related to information on the manufacturing industry, SMEs itself, finance, marketing, operations management and human resource management The result shows that the company did not have an effective website as its marketing channel and an unfavorable pricing strategy, indicated by no leads originating from the company's website and inability to profit from the project. The business coaching suggests the management to develop their website and pricing strategy to improve their conditions.&lt;/em&gt;

Design of condition-based maintenance framework for process operations management in pharmaceutical continuous manufacturing.

Continuous manufacturing, an emerging technology in the pharmaceutical industry, has the potential to increase the efficiency, and agility of pharmaceutical manufacturing processes. To realize these potential benefits of continuous operations, effectively managing materials, equipment, analyzers, and data is vital. Developments for continuous pharmaceutical manufacturing have led to novel technologies and methods for processing material, designing and configuring individual equipment and process analyzers, as well as implementing strategies for active process control. However, limited work has been reported on managing abnormal conditions during operations to prevent unplanned deviations and downtime and sustain system capabilities. Moreover, although the sourcing, analysis, and management of real-time data have received growing attention, limited discussion exists on the continued verification of the infrastructure for ensuring reliable operations. Hence, this work introduces condition-based maintenance (CBM) as a general strategy for continually verifying and sustaining advanced pharmaceutical manufacturing systems, with a focus on the continuous manufacture of oral solid drug products (OSD-CM). Frameworks, such as CBM, benefit unified efforts towards continued verification and operational excellence by leveraging process knowledge and the availability of real-time data. A vital implementation consideration for manufacturing operations management applications, such as CBM, is a systems architecture and an enabling infrastructure. This work outlines the systems architecture design for CBM in OSD-CM and highlights sample fault scenarios involving equipment and process analyzers. For illustrative purposes, this work also describes the infrastructure implemented on an OSD-CM testbed, which uses commercially available automation systems and leverages enterprise architecture standards. With the increasing digitalization of manufacturing operations in the pharmaceutical industry, proactively using process data towards modernizing maintenance practices is relevant to a single unit operation as well as to a series of physically integrated unit operations.