Resumo

Integration across channels is no longer a decision issue about whether or not to do it, but how to do it in the most effective and efficient way (GALLINO & MORENO, 2014; HÜBNER et al, 2016b). The challenge for retailers today is to cope with new customer shopping behavior, however, without sacrificing their results due to the logistical complexity (HÜBNER et al, 2015). For this reason, issues such as availability of products stock are so relevant. (DAUGHERTY et al, 2019).

For store-based retailers, aggregating online sales means redefining their distribution channels. Consequently, they need to reorganize their internal technology, organizational, and managerial processes for competitive advantage, making their migration to the omnichannel a complex process (ISHFAQ et al, 2016). With this complexity in mind, uncertainty regarding distribution risks related to product availability in response to customer demand variation become a problem for retailers. In order to respond to this problem, this study simulates a set of logistic variables for a grocery company.

Several studies focused on omnichannel, but it was not found studies that established a direct relationship between product availability behavior in response to customer demand variations. The most of studies in omnichannel focuses on order fulfillment strategies (BENDOLY, 2004; CHIANG & MONAHAN, 2005; MAHAR & WRIGHT, 2009; BRETTHAUER et al, 2010) or distribution design (for exemplo, HÜBNER et al, 2016a; HÜBNER et al, 2016b; CHOPRA, 2016; MELACINI et al, 2018; WOLLENBURG et al, 2018). Modak (2017) analysed the uncertainty of demand, but related to the sales price and delivery lead time.

A literature review was performed in order to identify similar studies. A structure of omnichannel logistics distribution was developed based on the distribution typology and the archetypes of dispatching locations developed by Hubner et al (2016a). At last, the operation of this previously developed structure was simulated using the modeling in dynamic systems with two scenarios, omnichannel and multichannel, which subsequently had their results compared. The model was calibrated and subsequently submitted to a demand variation of 50% and 100%, with pulse variations and sine wave variations.

Regarding stockout facing a demand variation, there was occurrence in both scenarios, however with different characteristics. Up to 50% of pulse demand variation, multichannel performance is better, however from 100% the omnichannel had best results. Actually, it was the stockout in the multichannel scenario that increased, not the stockout in the omnichannel scenario that decreased. On the other hand, on the sine wave demand variation simulation, the multichannel reached better results than omnichannel in all variation indices.

Even though that previous studies have evidenced that a physical and online channel integration in an appropriate way leads to a competitive advantage due to the synergy of channels (MARCHET et al, 2018), with regards to stockout generated, channels operating separately showed better results of stock availability than operating in an integrated way.

Hübner, A., Kuhn, H., & Wollenburg, J. (2016a). Last mile fulfilment and distribution in omni-channel grocery retailing: a strategic planning framework. International Journal of Retail & Distribution Management, 44(3), 228-247. Gonçalves, P., Hines, J., & Sterman, J. (2005). The impact of endogenous demand on push–pull production systems. System Dynamics Review: The Journal of the System Dynamics Society, 21(3), 187-216.