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30th CIRP Life Cycle engineering Conference, LCE 2023

作者： Scheller, Christian Kishita, Yusuke Blömeke, Steffen Thies, Christian Schmidt, Kerstin Mennenga, Mark Herrmann, Christoph Spengler, Thomas S. Institute of Automotive Management and Industrial Production Technische Universität Braunschweig Braunschweig38106 Germany Institute of Machine Tools and Production Technology Chair of Sustainable Manufacturing and Life Cycle Engineering Technische Universität Braunschweig Germany Technische Universität Braunschweig Germany Department of Precision Engineering The University of Tokyo Bunkyo-ku Tokyo113-8656 Japan Hamburg University of Technology Resilient and Sustainable Operations and Supply Chain Management Group Hamburg21073 Germany

To achieve CO2 neutrality in 2050, internal combustion engine vehicles will be gradually substituted by electric vehicles since they enable an emission-free use phase if powered with renewable energy. However, producing lithium-ion batteries for electric vehicles is associated with high environmental impacts and economic challenge such as supply bottlenecks. Seeking to tackle these challenges by integrating recovery activities, car and battery manufacturers are increasingly transforming their production systems to circular production. Yet, transforming the entire production system poses several risks and uncertainties, e.g., technological developments and regional political instabilities. Therefore, a robust transformation toward a sustainable circular battery production is needed. Using a scenario design approach, we envision sustainable circular battery production in 2050 and the correlating transformation with minimum total CO2 emissions throughout the transformation process. To consider the potential enablers, inhibitors, and feasible (counter)measures, we conducted a workshop with experts from life cycle engineering, mechanical engineering, and business economics. Based on the results, both technological enablers and fundamental challenges of sustainable circular battery production were derived, which have to be addressed in the context of life cycle engineering. © 2023 The Authors. Published by Elsevier B.V.

关键词： Supply chains

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Aging Workforce and Learning: State-of-the-art

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IFAC-PapersOnLine 2023年第2期56卷 725-730页

作者： Thilini Ranasinghe Eric H. Grosse Christoph H. Glock Mohamad Y. Jaber Chair of Digital Transformation in Operations Management Saarland University Saarbrücken Germany Institute of Production and Supply Chain Management Technical University of Darmstadt Darmstadt Germany Department of Mechanical and Industrial Engineering Toronto Metropolitan University Canada

The population of most developed countries is aging; thus, the median age of the global workforce continues to rise. Human aging often results in a decline in physical and cognitive abilities, which may adversely affect the performance of labor-intensive manufacturing systems. Older workers embody profound experience and refined skills, which are success factors for manufacturing companies. Therefore, it is important for manufacturing companies to ensure that older workers remain active and productive. Identifying the potential of an aging workforce, employing technical assistance systems to meet their needs, customizing work flow processes, imparting proper training, and utilizing their experience and skills may provide a competitive advantage for the company. This paper reviews the relevant literature to understand how aging influences workers’ learning in the manufacturing and service industries and identifies management concepts and technologies suitable to support an active aging workforce. We report preliminary insights and discuss selected papers on how aging influences learning-by-doing, life-long learning, training, and experiential knowledge retention. Finally, we propose some future research directions.

关键词： Ageing workforce learning review manufacturing

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Industry 5.0 and Supply Chain management: Coevolution and Future Research Directions

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IFAC-PapersOnLine 2024年第19期58卷 958-963页

作者： Amila Bandara Amila Thibbotuwawa H. Niles Perera Peter Nielsen Department of Operations Management University of Peradeniya Peradeniya Sri Lanka and Center for Supply Chain Operations and Logistics Optimization University of Moratuwa Moratuwa Sri Lanka Department of Transport Management and Logistics Engineering University of Moratuwa Moratuwa Sri Lanka and Center for Supply Chain Operations and Logistics Optimization University of Moratuwa Moratuwa Sri Lanka Department of Materials and Production Aalborg University 9220 Aalborg Denmark

Subsequent to the wider adoption of Industry 4.0, the European Union initiated a movement towards the fifth industrial revolution (I5.0). The nature of global supply networks makes the conventional organizational-level viewpoint insufficient to fully realize the advantages of I5.0. This paper provides a broader perspective to the research on I5.0 by studying the dynamic interplay between I5.0 and supply chain (SC). A systematic literature review has been conducted by selecting a refined sample of 80 papers from SCOPUS and the Web of Science databases. Analysis revealed three dominant research clusters namely, SC Digitalization and Sustainability, SC Technologies, and SC Resilience. Based on the gaps in the literature, several future research directions were identified, including Human-centered SCs, Interplays between the pillars of I5.0, I5.0 Evaluation/ Scoring Systems, Value 5.0, and SC 5.0 Ecosystems and Governance. We conclude the paper by highlighting the necessity of having more empirical research.

关键词： Industry 5.0 Supply Chain 5.0 Sustainability Resilience Human Centricity Value 5.0

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Reconfigurable Manufacturing: An Investigation of Diagnosability Requirements, Enabling Technologies and Applications in Industry 8th

Reconfigurable Manufacturing: An Investigation of Diagnosabi...

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8th Changeable, Agile, Reconfigurable and Virtual production Conference, CARV 2021 and 10th World Mass Customization and Personalization Conference, MCPC 2021

作者： Napoleone, Alessia Sullivan, Brendan P. Arias-Nava, Elias Andersen, Ann-Louise Department of Materials and Production Aalborg University Aalborg Denmark Department of Management Economics and Industrial Engineering Politecnico Di Milano Milan Italy Department of Industrial and Operations Engineering Instituto Tecnologico Autonomo de Mexico Mexico City Mexico

ISBN: (纸本)9783030906993

In the dynamic environment of today's manufacturing industry, companies need to be changeable, i.e. capable of adapting to changes quickly and cost-effectively. In this context, the diagnosability characteristic, allowing fast and economic ramp-ups of new manufacturing settings, becomes particularly relevant. Depending on their diagnosability requirements, companies can exploit different technologies and applications. In this study, five diagnosability requirements have been identified. Through a literature review, the five requirements have been further investigated;thus, the extent to which these five requirements can be fulfilled, and their enabling technologies and applications has been specified. Finally, a case study has been conducted to show how diagnosability requirements are fulfilled differently in three manufacturing contexts. © 2022, Springer Nature Switzerland AG.

关键词： Changeable manufacturing Diagnosability Industry 4.0 Reconfigurable manufacturing system

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Impact of Machine Learning on Applying the Best Worst Method for Social Sustainability in Manufacturing Supply Chains

Impact of Machine Learning on Applying the Best Worst Method...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Kottala Sri Yogi Dankan Gowda V Atul Kumar Sahu Madan Mohanrao Jagtap Ved Srinivas Ibrahim Abdullah Department of Operations Symbiosis Institute of Business Management Hyderabad Symbiosis International University Pune India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of Industrial and Production Engineering School of Studies in Engineering & Technology Guru Ghasidas Vishwavidyalaya (A Central University) Koni Bilaspur Chattisgarh India Department of Operations Management Symbiosis Institute of Operations Management Nashik Campus Constituent of Symbiosis International (Deemed University) Pune Maharashtra India Department of Human Resources Thiagarajar School of Management Madurai Tamil Nadu India Department of Computer Science Al-Turath University College Baghdad Iraq

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

This research is carried out merging ML with BWM to intensify decision making in manufacturing supply chain using social sustainability under light. As companies become more focused on environmental sustainability, the capability of machine learning to effectively assess and prioritize sustainability criteria should be highly valued. Through a team of domain experts, the research clearly identifies the most vital social sustainability factors and, afterwards, deploys ML algorithms to analyze and optimize the provided decision-making framework by BWM. The selected approach gives possibility to dynamic weighting of the criteria which is based on real-time data. This way we are able to come up with the more precise and effective sustainability strategies. The results show that the hybrid ML-BWM model is not only an effective tool to enhance the validity and accuracy of evaluations but also one that provides the prudent directions that help the manufacturing sector shape its choice and responses. This paper analyzes the possible outcome of the integration of AI and traditional approach to decision-making process on social aspects of sustainability within supply chain.

关键词： Adaptation models Supply chain management Shape Supply chains Decision making Machine learning Real-time systems

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Inventory Decisions Under Stochastic Demand Scenario with High Inflation Rate – Machine Learning Approach

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IFAC-PapersOnLine 2024年第19期58卷 133-138页

作者： Vinu Siriwardena Dilina Kosgoda H. Niles Perera Izabela Nielsen Center for Supply Chain Operations & Logistics Optimization University of Moratuwa Katubedda 10400 Sri Lanka Department of Transport Management and Logistics Engineering University of Moratuwa Katubedda 10400 Sri Lanka Department of Materials and Production Aalborg University Denmark

This study examined the effects of hyperinflation on inventory decisions using a real-world dataset obtained from a leading retail company in Sri Lanka during the hyperinflation period of 2022 and developed a Machine Learning model to forecast optimal order quantities with the intention of lowering inventory holding costs. Six distinct ML techniques were selected to identify the best ML model. Root Mean Squared Error and R Squared values were used to rigorously evaluate the performance of the six ML techniques. The results suggest Random Forest as the most appropriate ML model to forecast optimal order quantities during a high inflation situation.

关键词： Inventory Control Dynamic Pricing Demand Forecasting Supply Chain management (SCM) High Inflation Machine Learning Models

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Modeling the Impact of Leadership on Change management Decisions Using System Dynamics Simulation 1

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4th International Conference on Intelligent Systems in production engineering and Maintenance , ISPEM 2023

作者： Lakdinu, Manuka De Silva, M. Mavin Balachandra, Kavith Thibbotuwawa, Amila Perera, H. Niles Nakayama, Tadachika Nielsen, Peter Department of Transport Management and Logistics Engineering University of Moratuwa Moratuwa Sri Lanka Extreme Energy-Density Research Institute Nagaoka University of Technology Nagaoka Japan Center for Supply Chain Operations and Logistics Optimization University of Moratuwa Moratuwa Sri Lanka Department of Materials and Production Aalborg University Aalborg Denmark

ISBN: (数字)9783031450211

ISBN: (纸本)9783031450204

Many firms are shifting from legacy systems to Enterprise Resource Planning (ERP) system solutions. ERP is a project requiring extensive change management to expect implementation success. It entails severe technological changes, and significant organizational behavior and business process changes. Despite this, the research has not thoroughly investigated the relationship between change management challenges and ERP deployment. This research examines and assesses how leadership factors will aid transformational decision-making in implementing ERP. Empirical research reveals that leadership responsibilities like proper communication between leaders and employees, enhancing core-team productivity, improving re-engineering process quality, and top management commitment will promote implementation success. The research problem is defined using the Relative Importance Index (RII). The dynamic behavior of ERP implementation was then analyzed and validated using the System Dynamics (SD) technique. The findings emphasize the importance of numerous leadership elements in change management decisions and how they will influence the long-term effectiveness of ERP deployment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Enterprise resource planning

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Machine-Learning-Based Soft Sensors for LPG Distillation Columns Quality Monitoring: Comparing Strategies for Zero-Inflated Data

Machine-Learning-Based Soft Sensors for LPG Distillation Col...

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Rožanec, Jože M. Šircelj, Beno Onat, Melike K. Sarantinoudis, Nikolaos Arampatzis, George Fortuna, Blaž Mladenić, Dunja Jožef Stefan International Postgraduate School Ljubljana Slovenia Jožef Stefan Institute Ljubljana Slovenia Process Optimization and Operations Research Tüpras Körfez Turkey School of Production Engineering and Management Technical University of Crete Chania Greece Qlector D.o.o. Ljubljana Slovenia

ISBN: (纸本)9798350327816

Refineries execute a series of interlinked processes. Given the output of a certain unit serves as the input of another one, the failures within these processes affect the quality of the end products and the revenue of the entire refinery. The implementation of realtime quality monitoring helps with accurate decision-making regarding process operations. In this paper, multiple machine-learning models aimed at forecasting the amount of ethylene (C2) and pentane (C5) content in the final product mixture are compared. In particular, strategies to deal with zero-inflated data are explored. The presence of zeroes in the dependent variable usually affects the models' learning and may result in poor performance. Furthermore, the presence of zeroes also affects the metrics used to compute the regressor's prediction quality, making it hard to assess their quality. The research results show a two-fold machine learning model leads to the best outcomes. The approach was validated with a real-world use case from a refinery. © 2023 IEEE.

关键词： Artificial Intelligence Crude Distillation Unit Petroleum Refinery Smart Manufacturing Soft Sensors

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The Setup Carryover Assignment Problem

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operations Research Forum 2023年第2期4卷 25页

作者： Chowdhury, Nusrat T. Baki, Fazle Azab, A. Technology Art and Design Bemidji State University Bemidji 56601 MN United States Odette School of Business University of Windsor Windsor N9B 3P4 ON Canada Production & Operations Management Research Laboratory Faculty of Engineering University of Windsor Windsor N9B 3P4 ON Canada

In this paper, we introduce the Setup Carryover Assignment Problem (SCAP), which consists of determining the setup carryover plan of multiple items for a given lot size over a finite planning horizon with the objective of maximizing setup cost savings. This problem is an extension of the standard Capacitated Lot Sizing Problem (CLSP). We show that the straightforward linear programming (LP) formulation of the problem provides a fractional solution. However, we propose an alternative integer linear program (ILP) and show that it has a totally unimodular property and thus yields an integer solution. Therefore, SCAP is solvable in polynomial time. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Integer linear programming Linear program relaxation Lot size Setup carryover

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Using an ISO 9001 Based Framework as a Benchmark to Identify Best Practices Used by Sri Lankan Practitioners when Selecting Suppliers 1

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4th International Conference on Intelligent Systems in production engineering and Maintenance , ISPEM 2023

作者： Dharmadasa, Nikila De Silva, M. Mavin Thibbotuwawa, Amila Nakayama, Tadachika Nielsen, Izabela Iwa Department of Transport Management and Logistics Engineering Faculty of Engineering University of Moratuwa Katubedda10400 Sri Lanka Extreme Energy-Density Research Institute Nagaoka University of Technology Nagaoka Niigata940-2188 Japan Center for Supply Chain Operations and Logistics Optimization University of Moratuwa Katubedda10400 Sri Lanka Department of Materials and Production Aalborg University Aalborg Denmark

ISBN: (数字)9783031450211

ISBN: (纸本)9783031450204

Supplier selection is highly industry-specific, with industry-specific regulations and requirements, making gauging the quality of the process difficult. This raises the requirement for industry practitioners and quality institutes to benchmark the Supplier Selection Process (SSP) to identify where the process adapted currently stands and what the good practices within the industry are. Benchmarking is an ideal method of identifying the best practices in a process, but the usage of benchmarking to assess and identify best practices in a SSP lacks scholarly and practitioner literature. This study intends to combine benchmarking practices and ISO Standards to identify best practices in SSP by developing a structured benchmarking framework. This is achieved through a two-phase methodology. The first phase will focus on framework derivation and the second phase will focus on framework validation. The recommended Framework was applied to two organizations operating in the Consumer Goods industry, and the results show that adaptation of a structured benchmarking framework aids in identifying best practices in a structured and quantifiable manner. The authors recommend using this Framework as a base framework and a decision support tool to identify best practices within an industry or across industries and to improve the supplier selection processes further on the results achieved from the Framework. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Benchmarking

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