An environmental uncertainty-based diagnostic reference tool for evaluating the performance of supply chain value streams

Abstract

This research has responded to the need for diagnostic reference tools explicitly linking the influence of environmental uncertainty and performance within the supply chain. Uncertainty is a key factor influencing performance and an important measure of the operating environment. We develop and demonstrate a novel reference methodology based on the data envelopment analysis (DEA) for examining the performance of value streams within the supply chain with specific reference to the level of environmental uncertainty they face. In this paper, using real industrial data, 20 product supply value streams within the European automotive industry sector are evaluated. Two are found to be efficient. The peer reference groups for the underperforming value streams are identified and numerical improvement targets are derived. The paper demonstrates how DEA can be used to guide supply chain improvement efforts through role-model identification and target setting, in a way that recognises the multiple dimensions/outcomes of the supply chain process and the influence of its environmental conditions. We have facilitated the contextualisation of environmental uncertainty and its incorporation into a specific diagnostic reference tool.

Critical indices and model of uncertainty perception for regional supply chains: insights from a Delphi-based study

“…Such questions are indices (or measures) of perceived uncertainty that raise a myriad of supply chain management (SCM) challenges [1], and there is no panacea for confronting variability, volatility and vulnerability due to these questions. In light of these challenges, evaluations of supply chain uncertainty have a propensity to apply multi-criterion decisionmaking (MCDM) methods that inform option selection (Gao et al, 2018;Mitra et al, 2009), assess the relative impact (Gallear et al, 2014;Koh and Gunasekaran, 2006) and adopt mitigation strategies (Farooquie et al, 2017).…”

“…In the area of managerial decision-making for supply chain uncertainty, researchers apply MCDM methods such as analytical hierarchical process and theory of constraint (Koh and Gunasekaran, 2006), data envelopment analysis (Gallear et al, 2014), grey relational analysis (Farooquie et al, 2017), interpretive structural modelling (Chand et al, 2015) and fuzzy techniques (Mitra et al, 2009;Wang and Durugbo, 2013). However, Delphi offers an appropriate MCDM approach for this study because it uniquely supports an iterative process allowing participants to revise choices, allows anonymity for true opinions to emerge, and supports high conflict situations involving multiple decision makers (Mukherjee et al, 2018).…”

Enterprise-wide diagnostic in the UK SME: focus beyond tools and techniques

“…Literature has limited evidence on how any changes (e.g. to operations) can be combined with enterprise-wide diagnostic tools to identify gaps across the enterprise and processes linked to its supply chain (Gallear et al 2014;Böhme et al 2013Böhme et al , 2016Kumar and Harris 2016;Filho et al 2016). Enterprise-wide diagnostics tools can aid an organisation implementing specific change initiatives to identify gaps across the functional boundaries and provide better understanding on how constituent parts of the organisation interact and constrain one another (Salama et al 2009).…”

Special Issue – Applications of reference models for supply-chain integration

“…In another study, Gallear et al (2013) recognise the major impact that environmental uncertainties on the performance of the supply chain and managerial decisions. Hence, the ability to align supply chain product delivery systems with the demands of the external environment is an important source of competiveness.…”

“…Their paper addresses the need for managers to be better equipped with diagnostic reference tools that explicitly link environmental uncertainty to supply chain performance. Accordingly, Gallear et al (2013) contribute to the theme of this special issue by demonstrating a novel application of data envelopment analysis (DEA) to facilitate the development of a diagnostic reference tool to analyse the supply chain performance of product value streams (business units) operating within a supply chain network. Using real industrial data across the linkages of product value streams, the diagnostic reference methodology can be used by managers to guide their efforts to improve their value streams' performance through role-model identification and target setting.…”