Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

An Empirical Analysis to Control Product Counterfeiting in the Automotive Industry\u27s Supply Chains in Pakistan

The counterfeits pose significant health and safety threat to consumers. The quality image of firms is vulnerable to the damage caused by the expanding flow of counterfeit products in today’s global supply chains. The counterfeiting markets are swelling due to globalization and customers’ willingness to buy counterfeits, fueling illicit activities to explode further. Buyers look for the original parts are deceived by the false (deceptive) signals’ communication. The counterfeiting market has become a multi-billion industry but lacks detailed insights into the supply side of counterfeiting (deceptive side). The study aims to investigate and assess the relationship between the anti-counterfeiting strategies and improvement in the firm’s supply performance within the internal and external supply chain quality management context in the auto-parts industry’s supply chains in Pakistan

Traceability systems in the manufacturing industry: A systematic literature review

Traceability, the ability to generate knowledge about where, when, how, and of what materials a product was made, is a basic requirement in manufacturing and important to all stake-holders of a supply chain. Thus, traceability systems are needed to enable traceability in the manufacturing industry. The goal of this work is to map existing knowledge on traceability systems by understanding the technology, requirements and benefits associated with these systems. For this work, academic literature discussing traceability and traceability systems in the manufacturing industry was examined using the Systematic Literature Review process. Out of 561 analysed sources, 62 were accepted into the full review. To verify the results of the litera-ture review, a survey to Finnish industry practitioners was conducted using Elomatic Oy cus-tomer contacts. The results show that the most common traceability system benefits discussed in academic literature were increased production efficiency, ability to handle production errors, increased product and production safety, higher customer trust, more efficient recalls, and improved quality assurance. The survey results showed high support for each of these benefits, although seemingly with slightly different prioritization. The most common technologies associated with traceability systems discussed in the academic literature were RFID, blockchain, IoT, QR codes, and barcodes. Additionally, cloud services were often also discussed in literature. The survey results showed support for the use of barcodes and cloud services in enabling traceability. Other surveyed technologies were not widely used in the participants’ companies. The most common requirements associated with traceability systems discussed in the academic literature were the ability to trace and track traceable resource units and the ability to identify them, the ability to share traceability information, the ability to integrate data from different sources, and the ability of maintaining a production history. An important non-functional requirement was the compliance with necessary requirements. The survey results showed high support for each of these requirements. Further research is required to better understand the current market of traceability systems, the prevalent systems used and the economics of traceability systems in general. The literature review conducted for this work did not find enough information on these aspects, and they were not addressed in the survey

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical