Joint optimisation of tracking capability and price in a supply chain with endogenous pricing

Abstract

Tracking systems have been widely used to resolve the issues of product recall and food safety. Thus far, few researches have been done on designing the tracking capability from the perspective of supply chain. In this paper, using the traceable unit size at the manufacturer level to measure the tracking capability, we propose a non-convex non-linear programming to jointly optimise the tracking capability and price considering the tracking cost and recall cost in a supply chain with endogenous pricing. Results show that, in both centralised and decentralised supply chains, there is a unique tracking capability and retailing/wholesale price with closed-form solutions to optimise the supply chain profit. When the cost ratio (unit tracking cost/unit recall cost) is sufficiently large and small, the optimal tracking strategy is barcode tracking and unit tracking, respectively, and otherwise, the optimal tracking strategy is batch tracking with an economic traceable unit size which depends on the cost ratio, quality inspection threshold, supply defection rate and the supplier’s tracking capability. Furthermore, in the context of large and small cost ratio, we find that improving tracking capability will enlarge and mitigate the effect of double marginalisation, respectively. In particular, we find that the strict tracking regulation policy is more robust than the subsidy policy to improve the supply chain tracking capability.

Keywords:

• design
• optimisation
• pricing theory
• tracking capability
• product recall

Acknowledgements

The authors would like to thank editors and referees for their valuable suggestions which can significantly improve our manuscript.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 71671133], [grant number 71301122], [grant number 71571079]; Soft Science of Hubei Province of China [grant number 2016ADC074]; National Social Science Foundation of China [grant number 15ZDA061]; Research Fund for Academic Team of Young Scholars at Wuhan University [grant number Whu2016013].