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

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

Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling

© 2020, Springer Nature Switzerland AG. Dynamic flexible job shop scheduling (DFJSS) has been widely studied in both academia and industry. Both machine assignment and operation sequencing decisions need to be made simultaneously as an operation can be processed by a set of machines in DFJSS. Using scheduling heuristics to solve the DFJSS problems becomes an effective way due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, the subtrees of the selected parents are randomly chosen in traditional GP for crossover and mutation, which may not be sufficiently effective, especially in a huge search space. This paper proposes new strategies to guide the subtree selection rather than picking them randomly. To be specific, the occurrences of features are used to measure the importance of each subtree of the selected parents. The probability to select a subtree is based on its importance and the type of genetic operators. This paper examines the proposed algorithm on six DFJSS scenarios. The results show that the proposed GP algorithm with the guided subtree selection for crossover can converge faster and achieve significantly better performance than its counterpart in half of the scenarios while no worse in all other scenarios without increasing the computational time