Modeling a Four Echelon Omni-Channel Supply Chain for Seasonal Product under Stochastic Demand

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Industrial Engineering, School of Industrial Engineering, Alborz Campus, University of Tehran, Tehran, Iran.

2 Prof., Department of Industrial Engineering, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

3 Associate Prof., Department of Industrial Engineering, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

Abstract

Objective: The present work proposes a mathematical model for a four-echelon supply chain network for a seasonal product with stochastic demand. The supply chain structure includes a supplier, a producer, a distributor, and a retailer with three sale channels.
Methods: The methodology introduced in this paper is a fundamental yet practical one that expands the knowledge of modeling for omnichannel supply chain and examines the outcomes in a real case study using a quantitative approach. In this case study, the retailer and the distributor are decision-makers, who decide the optimum order quantity. First, the centralized and decentralized models are identified. Second, the optimum order quantity for each model is determined, and finally, the results are verified using a numerical approach. Further, a sensitivity analysis is performed on the parameters that affect the profits obtained by members and the supply chain.
Results: Numerical examples show that the supply chain profit in the centralized model is more than the decentralized model, which correctly predicts the models and the estimated optimum order quantities.
Conclusion: The results of the sensitivity analysis show that the expected value of the members and the supply chain profit will increase when the final selling price and production capacity are increased. However, the increasing rate of profit obtained by increasing the selling price is higher than that of production capacity.

Keywords

References

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Industrial Management Journal
Volume 12, Issue 2
2020
Pages 206-235

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