The problem of a multi-period supplier selection and order quantity allocation in the presence of supply chain disruption and delay risks is considered. Given a set of customer orders for finished products, the decision-maker needs to decide from which supplier and when to deliver product-specific parts required for each customer order to meet customer requested due date at a low cost or a high service level and to mitigate the impact of supply chain risks. For the selection of risk-neutral or risk-averse dynamic supply portfolio, a scenario-based stochastic mixed integer programming approach is developed. In the scenario analysis, the low probability and high impact supply disruptions are combined with the high probability and low impact supply delays. The risk-neutral portfolio is optimised by minimising expected cost or maximising expected service level. The risk-averse portfolio is optimised by calculating cost- or service-at-risk and minimising conditional cost-at risk or maximising conditional service at risk. The proposed dynamic portfolio approach leads to a time-indexed stochastic MIP formulation with a strong LP relaxation, which has proven to be computationally very efficient. The findings indicate that neglecting potential delay risks in supplier selection may lead to greater supply fluctuations and manufacturing delays.

Selection of a dynamic supply portfolio under delay and disruption risks

ABSTRACT Supply chains (SCs) operate in a highly disruptive environment, where they face a variety of disruptions in product and cash flows. In such an environment, determining suitable inventory and cash replenishment policies ensures that cash and inventory are at the right place at the right time and provides a productive SC with high customer service levels. In this study, we first examine the impact of the disruptions in physical and financial flows on SC performance. We then, investigate the potential of a SC digital twin framework to help decision-makers in managing inventory and cash throughout the SC during disruption, currently absent from the literature. The proposed SC digital twin framework integrates machine learning (ML) and simulation to identify the inventory and cash replenishment policies that minimise the impact of the disruptions on SC performance. This approach proves effective in a SC disrupted by demand increase, capacity reduction, and credit purchase increase. Results show that employing the SC digital twin leads to a noticeable reduction in the cash conversion cycle for upstream members of the SCs. We observe that the cash conversion cycle for the upstream SC members is greatly impacted by the inventory policy employed by their immediate downstream members.

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2022.2093682?needAccess=true

Applying digital twins for inventory and cash management in supply chains under physical and financial disruptions

The COVID-19 pandemic has triggered new research areas in supply chain resilience. One of these new areas is viability. Viability extends the resilience understanding from performance-based assessment of firm’s responses to disruptions towards survivability of both supply chains and associated ecosystems not only during some short-term disruptions but also under conditions of long-term crises. To explore the state-of-the-art knowledge on methods, models, capabilities, and technologies of supply chain viability, we edited this important IJPR special issue. To introduce the special issue, we review the existing literature on supply chain viability, conceptualise seven major pillars of supply chain viability theory (i.e. viable supply chain design, viability in process planning and control, ripple effect, intertwined and reconfigurable supply networks, ecosystems, digital supply chain, and Industry 5.0), and establish some associated future research directions. The findings of this editorial paper, as well as the articles in the special issue, can be used by researchers and practitioners alike to consolidate recent advances and practices of viability in supply chain networks and lay the solid foundation for further developments in this area.

Toward supply chain viability theory: from lessons learned through COVID-19 pandemic to viable ecosystems

Supply chain managers are coming under pressure to accommodate multiple demands in their decision-making processes, including consideration of environmental-friendly, resilient, and digitally integrated supply chains. And a supplier selection and order allocation (SS/OA) solution that integrates such demands has not yet been identified. Thus, this article proposes a framework that integrates digitalization, economic, green, and resilient SS/OA criteria and tests this framework by means of multi-attribute decision-making (MADM) algorithms and multi-objective optimization approach methodologies. The applied methodologies can help purchasing managers to better evaluate suppliers based on multiple criteria and take direct action towards improving priority areas and order allocation. This article advances the current debate on supplier selection by proposing and testing an integrated framework, called ‘digitalized econogresilience’. 

The sourcing decision-making process in the era of digitalization: A new quantitative methodology

Covid-19 has posed difficult and challenging situations to the supply chains and companies are in fix how to choose the vendors under the uncertainty and complexity in recent years. Therefore, this research aims to incorporate structural transformation of the fuzzy analytical hierarchy process (FAHP) that is most appropriate for the uncertainty and disruption caused by Covid-19 like situation for ensuring supplies from vendors. The conventional approaches for vendor selection and evaluation use numerous multi-criteria decision-making tools that may not ensure reliability in a dynamic situation caused due to Covid-19. In this research, Fleiss’ Kappa method ensures the reliability of responses from eight respondents by using pairwise comparisons and assigning weights as envisaged in FAHP. In addition to determine the reliability of responses, a step under FAHP has been altered. This alteration is demonstrated in the vendor selection case in the Covid-19 scenario. The research suggests a plausible system required to address the uncertainties associated with Covid-19 to select and evaluate vendors by modifying a FAHP. The proposed altered mechanism can be incorporated in a similar type of other decision-making circumstances such as Covid-19, where the decision-makers are more than one, and the situation is very dynamic. The study is likely to facilitate information management, algorithmic development in decision making, or machine-driven decisions in uncertain conditions. The study offers managerial implications to purchase managers to accommodate and combine multiple factors and responses concerning the vendor performances for their evaluation, thus making a process more reliable. 

/pdf/structural-transformation-of-fuzzy-analytical-hierarchy-2wzxfy3u.pdf

Structural transformation of fuzzy analytical hierarchy process: a relevant case for Covid-19

This paper develops an integrated methodology aimed at diagnosing supply chain resilience in terms of (1) internal dynamic capabilities of an enterprise, and (2) resilience of its suppliers. In addition, unlike other research, it integrates the suppliers’ resilience evaluation into the order size allocation plan. Multi-attribute decision making (MADM) algorithms were employed to quantify the relative importance to evaluate the internal and external resilience of an enterprise. Furthermore, the MADM output was combined with a multi-objective programming model formulated to solve the order size problem considering economic and resilience objectives. The applicability of the developed methodology is demonstrated via a dairy manufacturing enterprise that suffered from disruptions attributed to COVID-19. The results translate the enterprise’s non-viable manufacturing due to its poor external and internal resilience profiles. It is emphasized that if an enterprise fails to develop internal capabilities such as readiness and sensing, the enterprise could also fail in managing external resilience. A resilient supply chain requires a blend of internal and external resilience. This work represents the first quantitative attempt to provide a unified methodology for identifying and measuring internal and external resilience. Furthermore, the integration between suppliers’ resilience performance and the order allocation process into decision makers’ opinions for order size assignment is another unique contribution of this work.

Ahmed Mohammed

Papers

COVID-19 pandemic disruption: a matter of building companies’ internal and external resilience