Structural quantification of the ripple effect in the supply chain

Abstract

In recent years, remarkable advancements have been achieved in quantitative analysis methods for supply chain design (SCD). Typically, cost or service level optimisation has been included in the objective functions. At the same time, supply chain managers face the ripple effect that arises from vulnerability, instability and disruptions in supply chains. This research aimed to quantify the ripple effect in the supply chain from the structural perspective. The research agenda of this study includes issues of integrating operability objectives as new key performance indicators, e.g. resilience, stability, robustness into SCD decisions. The research is based on a simultaneous consideration of both static structural properties of SCD and execution dynamics subject to uncertainty and disruptions. Due to high dimensionality of real SCD problems, such integration can hardly be implemented in only one model. In this study, an original two-model multi-criteria approach is proposed in order to assess the potential ability of an SCD to remain stable and resilient. This modelling approach is based on a combined application of a static and a dynamic model. A multi-criteria approach relies on the analytic hierarchy process method. The results of this research can be used as an additional quantitative analysis tool in order to select an SCD. An additional application of the developed method is that it can be used at the control stage in order to adapt supply chain execution subject to the achievement of desired economic performance.

Keywords:

• supply chain design
• ripple effect
• uncertainty
• performance
• disruptions
• robustness
• resilience
• complexity
• analytic hierarchy process (AHP)

Acknowledgement

The authors thank the anonymous referees for their valuable comments and improvement suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research described in this study is partially supported by the Russian Foundation for Basic Research [grant number 15-07-08391], [grant number 15-08-08459], [grant number 13-07-00279], [grant number 13-08-01250], [grant number 13-07-12120], [grant number 13-07-00279]; [grant number 074-U01] supported by Government of Russian Federation; Department of nanotechnologies and information technologies of the RAS (project 2.11), and project ‘5-100-2020’ (arrangement 6.1.1) supported by NRU St. Petersburg SPU.