Facing market disruptions: values of elastic logistics in service supply chains

Abstract

Market disruptions are commonly seen nowadays which directly affect demand. However, in logistics service supply chains, service capacity suppliers and service providers usually have to prepare logistics-service-capacity before demand is known. In this paper, we explore a logistics service supply chain with which the logistics-service provider (LP) has to decide the quantity of capacity to reserve to satisfy future demand in the upcoming season, which depends on whether market disruption occurs or not. The optimal capacity planning policy is determined and the impacts brought by the chance of market disruption are uncovered. Then, we consider the scenario with ‘elastic logistics’ in which capacity can be adjusted after the market state is known. We analytically establish the corresponding optimal dynamic policy and prove that it helps to stop the ripple effect from appearing. We explore the value of elastic logistics and propose conditions and measures to achieve Pareto improvement in the supply chain upon the adoption of elastic logistics. We extend the analysis to the case with the risk-averse LP and uncover that our qualitative findings remain robust, irrespective of the LP’s risk attitude.

Keywords:

• market disruptions
• elastic logistics
• ripple effect
• service supply chain systems
• service capacity
• supply chain management
• risk aversion

Acknowledgements

The author sincerely expresses his hearty thanks to the editors and reviewers for their helpful comments. He also thanks Professor Dmitry Ivanov for his kind invitation to develop this paper and contribute to the special issue.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.bbc.com/news/world-latin-america-50315106 (accessed 20 November 2019).

2 https://www.bbc.com/news/world-asia-50037907# (accessed 20 November 2019).

3 https://logisticsofthings.dhl/story/elastic-logistics/ (accessed 20 November 2019).

4 In this paper, we take this ‘wholesale price’ $c_0$ as a given parameter which is decided by the bargaining (Shi, Chan, and Dong 2018) between the LP and LS. To be specific, following Taylor and Plambeck (2007), if the LP and LS bargain non-cooperatively, the ‘subgame perfect equilibrium’ wholesale price $c_0=\lambda p$, where $0<\lambda <1$ reflects the relative bargaining power between the LP and LS.

5 Note that in this paper, we simply consider the case when there is a discrete chance for market disruption to occur, and the corresponding market state will become low. Otherwise, it is normal (and the market state is high). This model well-captures market disruptions for our problem.

6 http://www.igalogistics.com.hk/home.html (accessed 21 November 2019).

7 Note that we consider the selling price and buying price of the per unit logistics-service-capacity in the spot market under the elastic logistics alliance platform are equal because: (i) Since our purpose of this study is to highlight how elastic logistics can help dampen the ripple effect and deal with market disruptions, including different selling and buying prices will create another revenue source from this elastic logistics alliance platform which dilutes the focus of this study. (ii) This arrangement is ‘fair’ in the sense that the elastic logistics alliance platform has to pay a fee to join and can enjoy the benefit of acquiring or trading extra logistics-service-capacity at Time 1. (iii) For analytical tractability, including more variety of cases will complicate the analysis and yield findings which are not analytically tractable.

8 In the logistics service supply chain, for the LP, setting up the logistics-service-capacity itself requires an investment which is a service capacity setup cost $K(\cdot )$. In this case, the per unit logistics-service-capacity cost is lower. However, if the LP gets the logistics-service-capacity from the ‘spot market’ in the elastic logistics alliance, the setup cost is paid by other members of the alliance while the per unit logistics-service-capacity cost is higher. This is the tradeoff between getting the logistics-service-capacity from the two sources considered in this paper.