Logistics of the conversion from "brick-and-mortar" to "click-and-mortar" retailing model.
详细信息
文摘
The conversion from brick-and-mortar retailing to the hybrid click-and-mortar business model is studied from the perspective of logistics. The retailer addressed in this study runs chain stores—designated as brick-and-mortar stores—and warehouses to meet the demand of walk-in customers. When the retailer decides to go online, its brick-and-mortar stores are converted to click-and-mortar stores with the capability of processing online orders. Such facilities both appeal to walk-in customers and sell goods on the Web to online customers with different service requirements. While the distance between home and the nearest open store is assumed as the primary preference metric for walk-in customers, a quality of service guarantee for online customers is mostly about the delivery time of orders to their residences.;This conversion primarily affects logistics and distribution operations of a retailing business. A new service system has to be designed and integrated into the ongoing operations. In doing this, the retailer will be confronted with competing cost and distance considerations. We propose an integrated mathematical optimization model to determine a strategy for the retailer's logistics and distributions. The model unifies several objective functions into a single comprehensive objective function in which each component is expressed in dollars and cents. A composite Lagrangian relaxation approach is followed to bracket the model's true optimal objective value between a lower and upper bound. The best feasible solution that gives an upper bound reveals the best mix of bricks and clicks as well as the best delivery plan for online orders. This solution represents a favorable location of stores and an allocation of both types of customers to them. Quality of service guarantees of online customers are strictly satisfied. However, a walk-in customer's demand is considered lost if there is no open store within a maximum distance from her residence. The performance of our Lagrangian-based approach has been tested on a number of randomly generated problems. We also obtain an efficient frontier of competing cost objectives, on which each point represents a heuristically nondominated (efficient) mix of bricks and clicks in service. Several methods employed in the Lagrangian relaxation to improve feasible solutions are also discussed.