Disruption tails and revival policies: A simulation analysis of supply chain design and production-ordering systems in the recovery and post-disruption periods

Highlights

• Matching supply chain design and production-ordering analysis in terms of ripple effect.

• Post-disruption production-ordering instability in the supply chain has been observed and analysed.

• Consequences of the production-ordering behavior during the disruption period are studied.

• Formulation of revival control policy notion in the supply chain.

• Insights on the recovery policies with consideration of post-disruption period.

Abstract

We study production-ordering behaviour in a supply chain (SC) with disruption risks in recovery and post-disruption periods and the influence of severe disruptions on production and distribution network design. A real-life case-study of a disruption in a SC is considered and investigated with the help of discrete-event simulation blended with network optimisation in anyLogistix. Two novel findings are presented. First, disruption-driven changes in SC behaviour may result in backlog and delayed orders, the accumulation of which in the post-disruption period we call “disruption tails”. The transition of these residues into the post-disruption period causes post-disruption SC instability, resulting in further delivery delays and non-recovery of SC performance. Second, a smooth transition from the contingency policy through a special “revival policy” to normal operations mode partially mitigates the negative effects of disruption tails. The results show that isolated production and distribution network design optimisation can lead to severe decreases in performance in the event of SC disruptions. Contingent recovery policies need to be applied during the disruption period along with a revival policy in the post-disruption period to avoid disruption tails. These revival policies must be developed for the transition from the recovery to the disruption-free operations mode. A revival policy is meant to mitigate the negative impact of disruption tails and stabilise the ordering control policies and performance in the SC. Thus, recovery policies should not be limited to the disruption period only. They should also consider the post-disruption period and be included in SC design decisions. The revival policy should be included in the SC resilience framework.

Introduction

Design of production and distribution networks has been a prominent research avenue over the past three decades. In the supply chain (SC) framework, the tasks of production and distribution networks have been integrated. These have formed the SC design research domain (Chopra and Meindl, 2015, Dolgui and Proth, 2010). In the SC design domain, recurrent operational risks and uncertainty of inventory and demand have been typically analysed with the help of robust/stochastic/fuzzy optimisation or simulation models.

Tang, 2006, Chopra et al., 2007, Klibi et al., 2010, Kumar and Tiwari, 2013, Simchi-Levi et al., 2015, Sokolov et al., 2016, Choi et al., 2017, Ivanov, 2018a, Ivanov, 2018b, Dolgui et al., 2018, Lücker et al., 2018, Ivanov et al., 2017 suggest differentiating disruption risks and operational risks in the SC. Disruption risks can be caused by natural or man-made catastrophes, political crises, strikes, or legal disputes.

In regard to disruption risks, resilient production and distribution network design has become an active research avenue over the last decade (Gunasekaran et al., 2015, He et al., 2018, Ho et al., 2015, Jain et al., 2017, Kamalahmadi and Mellat-Parast, 2016, Losada et al., 2012, Macdonald et al., 2018, Namdar et al., 2018, Raj et al., 2015, Rezapour et al., 2017, Sawik, 2018, Simangunsong et al., 2012, Spiegler et al., 2016, Tukamuhabwa et al., 2015). Since trends of globalisation, outsourcing, efficiency principles, and specialisation have been on the rise in SC management, SC vulnerabilities, and the risks that a SC will be affected by a disturbance have correspondingly increased.

Moreover, the ripple effect has been identified in literature as a specific phenomenon in the disruption risk management framework (Ivanov, 2018a, Ivanov, 2018b, Dolgui et al., 2018, Ivanov, 2017, Levner and Ptuskin, 2018, Liberatore et al., 2012, Mizgier, 2017, Pavlov et al., 2018, Scheibe and Blackhurst, 2018). The ripple effect describes the disruption-based scope of changes in structural SC design and planning parameters and the impact of disruption propagation on SC performance. As a consequence of disruption propagation, Ivanov and Rozhkov (2017) observed post-disruption instability in the SC called ‘postponed redundancy’. ‘Postponed redundancy’ describes the SC’s delayed reaction to disruption and recovery actions as a consequence of production-ordering behaviour that occurs during the disruption period. For example, disruption-driven changes in SC behaviour may result in accumulated backlog and delayed orders. The transition of these residues into the post-disruption period destabilizes normal operations, resulting in further delivery delays and non-recovery of SC performance (Ivanov and Rozhkov, 2017).

Even if considerable advancements have been achieved in the given area, the resilient production and distribution network designs and the production-ordering systems in the SC have been mostly considered in isolation from each other. At the same time, decisions in each of these two areas are interconnected. Closing this research gap, this study considers production and distribution network design subject to disruption risk at both the proactive and reactive control stages. We study production-ordering behaviour in a SC with disruption risks in the recovery and post-disruption periods and the influence of the ripple effect on production and distribution network design. The methodology of this paper is based on a real-life case-study with real company data that is used for quantitative analysis of decisions on matching the production and distribution network design with ripple effect considerations from a disruption risk perspective. The objectives of the analysis are twofold. First, it aims to show how production and distribution network design decisions influence each other. Second, it aims to provide insights about how SC managers can enhance SC resilience by implementing proactive and reactive policies with integrated consideration of production-ordering decisions in both recovery and post-disruption periods.

The rest of this paper is organized as follows. In Section 2, a literature analysis is presented. Section 3 is devoted to the problem statement and research methodology. In Section 4, a simulation model is described. Experimental results are considered in Section 5, followed by a discussion on managerial insights in Section 6. The paper is concluded by summarizing the most important findings and outlining future research avenues in Section 7.