Abstract

In recent years, third-party platforms (3PPs), such as Amazon, have attracted considerable interest from omnichannel retailers as a sales channel option. Even though omnichannel retailers have their own offline and online channels, they have participated in the fulfillment service of 3PPs to absorb additional demand from the 3PP channel. To the best of our knowledge, no existing study has addressed the robust omnichannel retail operations utilizing the channel of 3PPs as one of a retailer’s sales channels. To fill these research gaps, this paper formulates omnichannel retail operations with the 3PP channel into a multi-period stochastic optimization model. The proposed model involves the supply chain networks of the retailer and 3PP and also the production capacity constraint, which restricts replenishment quantity depending on the production capacity of each supplier. Unfortunately, the existence of the 3PP channel and the production capacity constraint increases the computational complexity; thus, the problem cannot be solved within an acceptable computational time by using the existing approach (i.e., a two-phase approach (TPA) based on robust optimization). To overcome these challenges, we propose a novel decomposition approach called DECOM. DECOM has a distinct advantage in that it can decompose the proposed problem into two small problems, one for the retailer’s supply chain and the supply chain of the 3PP. We evaluate the performance of DECOM by comparing it with the TPA on a set of experiments carried out in various experimental settings. Both DECOM and TPA could provide high-quality solutions, but DECOM outperformed TPA in terms of computational efficiency. In particular, we observed that DECOM was scalable to large-scale instances. Furthermore, we explored the advantages of utilizing omnichannel retail operations and the 3PP channel by performing a sensitivity analysis. In particular, we showed the cost-saving effect resulting from the introduction of the 3PP channel in omnichannel retailing.

Full Text
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