Mapping the supply chain: Why, what and how?

There is now widespread appreciation of the critical role played by supply chains in the global economy. Supply chains are dominant concerns for many organisations, governments, policy makers, and consumers. A primary requirement in addressing many contemporary supply chain challenges is the need to ‘map’ a supply system. With notable exceptions, much of the supply chain management literature has shied away from providing guidance on the mapping process. In this paper, we stress the reasons for the increased emphasis on mapping. We review the academic literature, highlighting the diversity of mapping exercises conducted by researchers and the lack of clarity about the different types of maps developed. Supply chain mapping has been used as an umbrella term for studies at very different aggregation levels. We define the fundamental elements needed to create a supply chain map and develop a formal hierarchy of supply systems for mapping at different levels of analysis. The hierarchy provides a structured way to consider the diversity of mapping exercises in the literature and to define the unit of analysis for a mapping study. We illustrate the hierarchy with a range of examples from the textile and apparel industry. We identify the primary and secondary data sources that can underpin mapping studies, highlighting the significant challenges in using them. We discuss the emerging commercial solutions to capture, map, and analyse supply systems for different purposes. In an increasingly data rich world, there are many opportunities to develop the supply chain mapping process further

Digital supply chain surveillance using artificial intelligence: definitions, opportunities and risks

Digital Supply Chain Surveillance (DSCS) is the proactive monitoring and analysis of digital data that allows firms to extract information related to a supply network, without the explicit consent of firms involved in the supply chain. AI has made DSCS to become easier and larger-scale, posing significant opportunities for automated detection of actors and dependencies involved in a supply chain, which in turn, can help firms to detect risky, unethical and environmentally unsustainable practices. Here, we define DSCS, review priority areas using a survey conducted in the UK. Visibility, sustainability, resilience are significant areas that DSCS can support, through a number of machine-learning approaches and predictive algorithms. Despite anecdotal narrative on the importance of explainability of algorithmic results, practitioners often prefer accuracy over explainability; however, there are significant differences between industrial sectors and application areas. Using a case study, we highlight a number of concerns on the unchecked use of AI in DSCS, such as bias or misinterpretation resulting in erroneous conclusions, which may lead to suboptimal decisions or relationship damage. Building on this, we develop and discuss a number of illustrative cases to highlight risks that practitioners should be aware of, proposing key areas of further research

Identifying dynamical instabilities in supply networks using generalized modeling

Abstract Supply networks need to exhibit stability in order to remain functional. Here, we apply a generalized modeling (GM) approach, which has a strong pedigree in the analysis of dynamical systems, to study the stability of real-world supply networks. It goes beyond purely structural network analysis approaches by incorporating material flows, which are defining characteristics of supply networks. The analysis focuses on the network of interactions between material flows, providing new conceptualizations to capture key aspects of production and inventory policies. We provide stability analyses of two contrasting real-world networks?that of an industrial engine manufacturer and an industry-level network in the luxury goods sector. We highlight the criticality of links with suppliers that involve the dispatch, processing, and return of parts or sub-assemblies, cyclic motifs that involve separate paths from a common supplier to a common firm downstream, and competing demands of different end products at specific nodes. Based on a critical discussion of our findings in the context of the supply chain management literature, we generate five propositions to advance knowledge and understanding of supply network stability. We discuss the implications of the propositions for the effective management, control, and development of supply networks. The GM approach enables fast screening to identify hidden vulnerabilities in extensive supply networks

Proposing a Tool for Supply Chain Configuration: An Application to Customised Production

The full implementation of collaborative production networks is crucial for companies willing to respond to consumer demand strongly focused on product customisation. This chapter proposes an approach to evaluate the performance of different Supply Chain (SC) configurations in a customised production context. The model is based on discrete-event simulation and is applied to the case of supply chain in the fashion sector to support the comparison between mass and customised production. A prototype web-based interface is also developed and proposed to facilitate the use of the model not only for experts in simulation but for any user in the SC management field

The classification of FMS scheduling problems

This paper proposes a classification scheme for scheduling problems in flexible manufacturing systems (FMSs) based on an analysis and discussion of scheduling decisions in an FMS. The classification scheme attempts for the first time to identify and describe all the major factors which affect the modelling of, and the solution to, FMS scheduling problems. It provides a systematic framework for the description and the analysis of FMS scheduling problems and for the development, evaluation and comparison of FMS scheduling approaches. Examples are given to demonstrate the usefulness of the proposed scheme

General heuristic procedures and solution strategies for FMS scheduling

This paper presents two new heuristic procedures for FMS scheduling. The heuristics decompose the very complex scheduling problem into a series of relatively easily handled subproblems, and solve them using MILP models and heuristics. Unlike traditional `routing then sequencing' methods, both procedures consider constraints not only on machines but also on other critical resources, ensuring practical feasibility of the resulting schedules while reducing complexity in the solution process. The first heuristic, SEDEC, adopts an improved `routing then sequencing' method with a further step for allocating other resources. The second procedure, CODEC, represents a new scheduling strategy which emphasizes interconnections between subproblems in addition to the solution of the individual subproblems themselves. Computational experiments are carried out in various scheduling environments to compare the performance of the two heuristic procedures with a large MILP model which considers the whole FMS scheduling problem. The results show that the heuristic procedures can generate schedules with optimality close to the solution of the MILP method, in a much shorter time. Comparing the two heuristic approaches, CODEC, representing a new solution strategy, performs significantly better on average than SEDEC, which is based on the traditional strategy. This is observed in all the FMS scheduling environments tested, especially when the problem is large, complex or with tight resource constraints. Analysis of the results also suggests that CODEC may be improved further by investigating the way the subproblems are solved and the way the routing subproblem is reformulated. The CODEC solution strategy provides a general framework for scheduling in a wide range of FMS environments

An Investigation of Production Workers’ Performance Variations and the Potential Impact of Attitudes

In most manufacturing systems the contribution of human labour remains a vital element that affects overall performance and output. Workers’ individual performance is known to be a product of personal attitudes towards work. However, in current system design processes, worker performance variability is assumed to be largely insignificant and the potential impact of worker attitudes is ignored. This paper describes a field study that investigated the extent to which workers’ production task cycle times vary and the degree to which such variations are associated with attitude differences. Results show that worker performance varies significantly, much more than is assumed by contemporary manufacturing system designers and that this appears to be due to production task characteristics. The findings of this research and their implications are discussed