Production systems and supplier selection: a multi-phase process model

Purpose: This paper presents a combined multi-phase supplier selection model. The process repeatedly revisits the criteria and sourcing decision as the development process continues. This enables a structured adoption of product and production system innovation from strategic suppliers, where previously the literature purely focuses on product innovation or cost reduction. Design/methodology/approach: The authors adopted an embedded researcher style, inductive, qualitative case study of an industrial supply cluster comprising a focal automotive company and its interaction with three different strategic stamping suppliers. Findings: Our contribution is the multi-phased production and product innovation process. This is an advance from traditional supplier selection and also an extension of ideas of supplier-located product development as it includes production system development, and complements the literature on working with strategic suppliers. Specifically, we explicitly articulate the previously unreported issue of whether a supplier chosen for its innovation capabilities at the start of the new product development process will also be the most appropriate supplier during the production system development phase, when an ability to work collaboratively may be the most important attribute, or in the large-scale production phase when an ability to manufacture at low unit cost may be most important. Originality/value: The paper identifies a multi-phase approach to tendering within a fixed body of strategic suppliers which seeks to identify the optimum technological and process decisions as well as the traditional supplier sourcing choice. These areas have not been combined before and generate a valuable approach for firms to adopt as well as for researchers to extend our understanding of a highly complex process.

Agent-Based Modeling of Deshopping Behaviour: A Single Shop Model with Multiple Deshoppers

Deshopping is rapidly turning into a modern day scourge for the retailers worldwide due to its prevalence and regularity. The presence of flexible return policies have made retail return management a real challenging issue for both the present and the future. In this study, we propose and develop a multi-agent simulation model for deshopper behavior in a single shop context. The background, theoretical underpinning, logical and computational model, experiment design and simulation results are reported and discussed in the paper.

A multi-agent-based evolution model of innovation networks in dynamic environments

An innovation network can be considered as a complex adaptive system with evolution affected by dynamic environments. This paper establishes a multi-agent-based evolution model of innovation networks under dynamic settings through computational and logical modeling, and a multi-agent system paradigm. This evolution model is composed of several sub-models of agents' knowledge production by independent innovations in dynamic situations, knowledge learning by cooperative innovations covering agents' heterogeneities, decision-making for innovation selections, and knowledge update considering decay factors. On the basis of above-mentioned sub-models, an evolution rule for multi-agent based innovation network system is given. The proposed evolution model can be utilized to simulate and analyze different scenarios of innovation networks in various dynamic environments and support decision-making for innovation network optimization.