Abstract
PurposeProject-driven supply chain risks pose a significant threat to the success of complex development projects, in terms of achieving key performances such as quality, time and efficiency. The purpose of this paper is to adopt a supply chain quality perspective in order to explore and better understand the unique attributes of risks associated with project-driven supply chains for continuously improving the quality of both processes and products.Design/methodology/approachTheoretically grounded in the framework of Bayesian Belief Networks and Game theory, this paper develops a structured process for assessing and managing risks in project-driven supply chains. The application of the proposed approach is demonstrated through a simulation case study conducted on the development project of Boeing 787 aircraft.FindingsThe conflicting incentives amongst stakeholders in a supply chain can jeopardise the success of a project and therefore, assessment of this category of risks classified as “Game theoretic risks” needs special consideration. Project-driven supply chain risks pose a significant threat to the success of complex projects. The results of the study clearly revealed that without mitigating the game theoretic risks, the main objective of timely completion of the Boeing 787 project was not materialised. Further, the lack of management expertise was the major factor contributing to the overall project costs including cost of quality.Originality/valueThe proposed process and analyses present a significant and original insight in terms of capturing the key determinants of both product and service quality such as product performance, convenience and reliability of service, timeliness, ease of maintenance, flexibility, and customer satisfaction and comfort. Propositions are developed for ascertaining the significance of information sharing in a project-driven supply chain, and a fair sharing partnership is introduced to help supply chain managers in managing game theoretic risks in order to achieve the goals of quality, time and efficiency.
Highlights
Chains have become more complex and vulnerable due to trends such as globalisation, increased outsourcing, global sourcing, and focus on efficiency (Son and Orchard, 2013)
Non-linear interactions between risks make it a daunting task to understand and properly manage these risks (Ackermann et al, 2014) and a number of frameworks and tools have been introduced to the literature on supply chain risk management (SCRM)
Bayesian Belief Networks (BBNs) facilitate capturing the interdependency between uncertain variables and a number of studies specific to SCRM have substantiated the efficacy of BBNs in modelling supply chain risks (Badurdeen et al, 2014; Garvey et al, 2015)
Summary
Chains have become more complex and vulnerable due to trends such as globalisation, increased outsourcing, global sourcing, and focus on efficiency (Son and Orchard, 2013). (Christopher and Peck, 2004) and supply chain quality management (SCQM) (Foster, 2008) to better address the increasing interdependency among supply chain actors, processes and risks. Amongst these approaches, there are, for example, scenario-based analyses (Das, 2018), interpretive structural modelling (Haleem et al, 2012), fuzzy techniques (Nieto-Morote and Ruz-Vila, 2011), Bayesian Belief Networks (BBNs) (Qazi et al, 2018), and the Analytic Hierarchy Process (AHP) (Gaudenzi and Borghesi, 2006). In the specific case of a new product development (NPD) project, key risks are major delays and cost overruns, which are influenced by significant and complex interdependency between risks, involving different stakeholders and decision-makers (Ackermann et al, 2006, 2014)
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