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Design of Supply Chain Network to Reduce Impacts of Damages during Shipping

Abushaega Mastoor M., Daehy Yahya H., Alghamdi Saleh Y., Krishnan Krishna K., Khamaj Abdulrahman

Management and Production Engineering Review

2021

Vol. 12, No. 2

17--26

Recently, the expand of industrial market has led to have long supply chain network. During the long shipment, the probability of having damaged products is likely to occur. The probability of having damaged products is different between stages and that could lead to higher percentage of damaged products when arrived at retailers. Many companies have rejected the entire shipment because the damaged product percentage was higher than that agreed on. Decision-makers have tried to reduce the percentage of damaged products that happened because the transit, loading unloading the shipment, and natural disasters. Companies started to implement recovery centers in the supply chain network in order to return their system steady statues. Recovery models have been developed in this paper to reduce the damaged percentage at minimum costs to do so. Results show that the possibility of implementing an inspection unit and a recovery centers in the system before sending the entire shipment to the retailer based on examining a sample size that has been selected randomly from the shipment and the minimum cost of committing type I and type II errors. Designing a methodology to minimize the total cost associated with the supply chain system when there is a possibility of damage occurring during shipping is the objective of this research.

Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS

Kumar D. T., Soleimani H., Kannan G.

International Journal of Applied Mathematics and Computer Science

2014

Vol. 24, no. 3

669--682

Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network.