PRODUCT FULFILLMENT IN SUPPLY CHAINS SUPPORTING INTERNET-RETAILING OPERATIONS

Journal of Business Logistics, 2003 by Rabinovich, Elliot, Evers, Philip T

The use of the Internet in transactions between retailers and consumers has altered exchange activities involving products and information in a number of supply chains. This phenomenon is high-lighted in the research on information-exchange activities in supply chains that fulfill consumer orders placed over the Internet (Bailey 1998; Bakos 1997). However, little research attention has been given to mechanisms leading to product-fulfillment improvements in these settings (Cooke 2000; Leon 2000). This paper addresses that gap by studying mechanisms that enable the improvement of fulfillment operations in supply chain firms that directly or indirectly participate in filling online orders.

Consistent with Ballou (1992) and Bowersox and Closs (1996), two dimensions of distribution performance in the fulfillment of online consumer orders are considered: inventory performance and product-release performance. Both dimensions are defined in terms of supply chain responsiveness (Chopra and Meindl 2001). Inventory performance refers to measures of inventory throughput in the cost-efficient fulfillment of end consumer orders from in-stock inventories across the supply chain. Product-release (or release) performance refers to wait times associated with the transport of cost-efficient product volumes bound to and from inventory locations across different supply chain echelons.

Two mechanisms, inventory location consolidation and market demand growth are assessed, that are theorized to facilitate improvements in both performance dimensions. These mechanisms are within the control of Internet retailers and arc also part of the decision-making portfolio of firms located upstream in the supply chain. Inventory location consolidation stems from inventory centralization within and across supply chain echelons. Market growth, while dependent on macroeconomic forces affecting demand, can be leveraged at the firm level through market share expansion resulting from customer service improvements (Innis and La Londe 1994) and/or lower prices (Porter 1974). Furthermore, market share expansion may result from retailers offering a wider assortment of products across multiple levels of popularity (Emmelhainz, Emmelhainz, and Stock 1991).

As explained in the next section, the effects of inventory location consolidation and market demand growth on inventory and release performance hinge on Internet retailers and their suppliers accessing orders that arrive one at a time through a unified point of contact with shoppers, i.e., the website. The effects also depend on the disintermediation of echelons and location postponement of inventories across the supply chain once location and timing attributes of customer orders and inventory are decoupled.

The next section also details how inventory location consolidation and market demand growth impact performance through statistical, scale, and scope economies. Statistical economies stem from the pooling of uncertainty within and across echelons (Evers 1993) and are reflected in improvements in safety stock utilization. Scale economies are defined by advantages in release and inventory performance that result from increases in the size or capacity (Nahmias 1997) of Internet retailing operations when product demand or market share increase. Finally, scope economies generate gains in release performance from diversity in material transfer activities (Zipkin 2000) bundled across multiple product origins and destinations (Christensen and Huston 1987; Nahmias 1997; Xu et al. 1994).

Subsequently, the third section applies the theoretical model to a supply chain supporting Internet retailing operations. The fourth and fifth sections present empirical methods and results. The final section discusses conclusions, implications, and further research opportunities stemming from this research.

INVENTORY MANAGEMENT IN INTERNET SUPPLY CHAINS

The optimization of inventory and release performance at the supply chain level depends on a coordinated fulfillment of consumer orders among all supply chain entities (van Hoek 2001). In supply chains supporting the fulfillment of Internet orders, coordination is key in addressing high levels of market price elasticity (Stiglitz 1989) and low profit margins (Bailey and Rabinovich 2002; Bakos 1997; Brynjolfsson and Smith 2000) stemming from consumers' low search costs (Bailey 1998; Stiglitz 1989).

However, difficulties in synchronizing inventory decisions within each firm with accurate and timely demand data present special challenges to inventory optimization in these supply chains. This is exacerbated by capacity limitations at the suppliers' inventory locations, by customer service requirements, and by market competition driving Internet sellers to unnecessarily accumulate safety stocks and absorb costs from unexpected demand variations prior to the arrival of end consumer orders (Bailey and Rabinovich 2001). To reduce these inefficiencies, inventory location consolidation may be adopted to achieve statistical economies. One form of consolidation is the multi-echelon post-ponement of inventory, henceforth referred to as inventory postponement (Alderson 1957; Bucklin 1965; Zinn and Bowersox 1989; Zinn and Levy 1988). Inventory postponement contributes to lowering uncertainty by deferring inventory location and time of shipment until after market demand is received, thereby pooling demand across supply chain echelons.