There is no consensus on the supply chain management definition of resilience. To aid in evaluating the dynamic behaviour of such systems we need to establish clearly elucidated performance criteria that encapsulate the attributes of resilience. A literature review establishes the latter as readiness, responsiveness and recovery. We also identify robustness as a necessary condition that would complement resilience. We find that the Integral of the Time Absolute Error (ITAE) is an appropriate control engineering measure of resilience when it is applied to inventory levels and shipment rates. We use the ITAE to evaluate an often used benchmark model of make-to-stock supply chains consisting of three decision parameters. We use both linear and nonlinear forms of the model in our evaluation. Our findings suggest that optimum solutions for resilience do not yield a system that is robust to uncertainties in lead-time. Hence supply chains will experience drastic changes in their resilience performance when lead-time changes.

We propose an efficient evolutionary multi-objective optimization approach to the capacitated facility location–allocation problem (CFLP) for solving large instances that considers flexibility at the allocation level, where financial costs and CO2 emissions are considered simultaneously. Our approach utilizes suitably adapted Lagrangian Relaxation models for dealing with costs and CO2 emissions at the allocation level, within a multi-objective evolutionary framework at the location level. Thus our method assesses the robustness of each location solution with respect to our two objectives for customer allocation. We extend our exploration of selected solutions by considering a range of trade-offs for customer allocation

Purpose: – This study aims to aid theory building, the use of decision theory (DT) concepts in sustainable supply chain management (SSCM) research is examined. Design/methodology/approach: – An abductive approach considers two DT concepts, Snowden’s Cynefin framework for sense-making and Keeney’s value-focussed decision analysis, in a systematic literature review of 160 peer-reviewed papers in English. Findings: – Around 60 per cent of the papers on decision-making in SSCM come from operational research (OR), which makes explicit use of DT. These are almost all normative and rationalist and focussed on structured decision contexts. Some exceptions seek to address unstructured decision contexts via Complex Adaptive Systems or Soft Systems Methodology. Meanwhile, a second set, around 16 per cent, comes from business ethics and are empirical, behavioural decision research. Although this set does not explicitly refer to DT, the empirical evidence here supports Keeney’s value-focussed analysis. Research limitations/implications: – There is potential for theory building in SSCM using DT, but the research only addresses SSCM research (including corporate responsibility and ethics) and not DT in SCM or wider sustainable development research. Practical implications: – Use of particular decision analysis methods for SSCM may be improved by better understanding different decision contexts. Social implications: – The research shows potential synthesis with ethical DT absent from DT and SCM research. Originality/value: – Empirical behavioural decision analysis for SSCM is considered alongside normative, rational analysis for the first time. Value-focussed DT appears useful for unstructured decision contexts found in SSCM. Originality/value: – Empirical, behavioural decision analysis for SSCM is considered alongside normative rational analysis for the first time. Value-focussed DT appears useful for unstructured decision contexts found in SSCM.

Purpose: Flexibility is a fundamental performance objective for manufacturing operations, allowing them to respond to changing requirements in uncertain and competitive global markets. Additive manufacturing machines are often described as “flexible,” but there is no detailed understanding of such flexibility in an operations management context. The purpose of this paper is to examine flexibility from a manufacturing systems perspective, demonstrating the different competencies that can be achieved and the factors that can inhibit these in commercial practice.Design/methodology/approach: This study extends existing flexibility theory in the context of an industrial additive manufacturing system through an investigation of 12 case studies, covering a range of sectors, product volumes, and technologies. Drawing upon multiple sources, this research takes a manufacturing systems perspective that recognizes the multitude of different resources that, together with individual industrial additive manufacturing machines, contribute to the satisfaction of demand.Findings: The results show that the manufacturing system can achieve seven distinct internal flexibility competencies. This ability was shown to enable six out of seven external flexibility capabilities identified in the literature. Through a categorical assessment the extent to which each competency can be achieved is identified, supported by a detailed explanation of the enablers and inhibitors of flexibility for industrial additive manufacturing systems.Originality/value: Additive manufacturing is widely expected to make an important contribution to future manufacturing, yet relevant management research is scant and the flexibility term is often ambiguously used. This research contributes the first detailed examination of flexibility for industrial additive manufacturing systems.

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems.