The value of VMI beyond information sharing in a single supplier multiple retailers supply chain under a non-stationary (Rn, Sn) policy☆
Introduction
A VMI initiative encompasses two distinct components – information sharing and a shift in decision making responsibility from a downstream retailer to the upstream supplier [5]. In other words, under VMI, inventory is managed at both echelons by the supplier. The academic literature and industry reports have shown mixed results from the implementation of VMI and related programs. For example, Spartan Stores had to shut down its VMI initiative due to higher inventory levels and planning inefficiencies [23]. The study of Blackhurst et al. [1] also suggests that the implementation of the VMI initiative at a large electronics manufacturer actually resulted in increased inventory levels at the downstream partners. Recently, companies like Toyota and Honda have moved away from VMI, and are focusing on information sharing as well as locating suppliers as close as possible to their facilities [10]. Nowadays, in spite of these unsuccessful VMI adoption occurrences, there has been growing interest in implementing VMI in many supply chains after successful execution by several world-class businesses such as Wal-Mart, Sara Lee, Nabisco, etc. ([17]). Several studies also provide analytical validation for economic benefits offered by VMI by comparing it to a traditional system with no information sharing (e.g., [34], [2], [11]). However, these industry reports and studies do not conclude whether benefits could have been achieved mostly through the information sharing or the decision transfer component of VMI.
In practice, adoption of VMI over retailer-managed inventory with information sharing (IS) leads to greater implementation difficulties and may increase operational costs. Thereby, switching from IS to VMI can be justified if the decision transfer component generates significantly higher value above information sharing. Distinguishing the incremental value that could be achieved from VMI over IS is a difficult task, and there have been a few attempts to do so under stationary stochastic demand (e.g., [6], [22], [20]) and non-stationary deterministic demand (e.g., [4]). Furthermore, the complexity is substantially greater when demand is non-stationary and stochastic, which is nowadays quite common because of short product life cycles, seasonality, customer buying patterns, etc. [14]. For example, while the product life cycle of a Hewlett-Packard (HP) personal computer could be as low as only three months, HP digital cameras have an average life cycle of less than 12 months. Even through the launch, ramp, peak, and end-of-life phases of the product life cycle, not only is demand non-stationary, but also the uncertainty changes (Fig. 1). The demand for many products has a seasonal component or experiences monthly/quarterly “hockey stick” patterns because of sales-force incentives and customer buying behavior. For example, Microsoft experiences about 66% of the annual demand for its Xbox video game consoles during the last 13 weeks before Christmas. Similarly, a peak in demand is observed for Dell׳s enterprise products in the last week of every month.
The extent and intensity of competitive advantage gained from VMI above and beyond IS varies from company to company depending on the demand process and business environment in which a supply chain operates. Realizing these facts, this study aims to determine the incremental value offered by VMI beyond that of IS alone under non-stationary stochastic demand with service-level constraints. For this purpose, we first model a serial supply chain consisting of a single upstream supplier and multiple downstream retailers under both the IS and VMI initiatives. Then, a comprehensive numerical study is carried out considering a large number of business settings to figure out the conditions where the value of VMI over IS is significant. In the IS initiative, we consider that the supplier and retailers manage their inventory independently. The retailers determine their own replenishment schedules and place orders with the supplier. Moreover, the retailers provide full information to the supplier via information technology tools such as electronic data interchange (EDI) or through internet [6]. In other words, the supplier gets an access to the retailers׳ replenishment-up-to levels, expected inventory levels, and timings of planned orders as well as demand distribution data. Based on this information, the supplier decides its production schedule to meet orders of the retailers. On the other hand, under the VMI initiative, the supplier also manages inventory at the retailers along with full information sharing. Earlier studies compare VMI with IS mainly in terms of the economic benefits. It is less clear whether the benefits have been achieved through a decrease in inventory levels or consolidation of shipments at the expense of increased inventory levels. Also, there is a lack of clarity on whether shipment sizes from the supplier to retailers increase or decrease using VMI [32], [33], [12]. In this paper, we assess the incremental benefits offered by VMI above and beyond IS on various supply chain performance measures such as expected cost savings, reduction in inventory levels and increase in replenishment deliveries at the retailers. A comparison of VMI with IS based on these performance measures helps in clarifying situations where the economic benefits can be realized either through increased frequency of shipments and decrease in inventory levels, or through consolidation of shipments.
Section snippets
Problem statement and background
We consider a two-echelon serial supply chain in which a product is delivered from a common supplier to a set r={1,…, R} of retailers over a time horizon T. Each discrete time period t={1,…, T} is of the same duration. We assume that the retailers serve geographically dispersed (thus independent) retail markets. The end-user demand at each retailer in each period is normally distributed with a known probability density function. In addition, for each retailer, different periods have mutually
Analytical model development
This section details mixed-integer programming formulations used in this study to determine optimal schedules of the retailers and production schedule of the supplier under both the IS and VMI initiatives.
The mixed-integer programming formulation of Tarim and Kingsman [25] allows simultaneous determination of the review intervals and order-up-to levels with some mild assumptions. This model aims to minimize the expected costs of meeting non-stationary stochastic demand over some finite planning
Computational experiments
In this section, we conduct numerical experiments to explore the value of VMI beyond IS, the allocation of benefits across supply chain partners, and the impact of system parameters on the supply chain performance measures such as reduction in expected costs and inventory levels, and increase in replenishment frequencies at the retailer(s). We consider two types of serial supply chains, one with a single retailer, and other with multiple retailers.
Conclusions, limitations, and directions for future research
The incremental value of VMI over IS varies greatly depending on the structure of the supply chain being investigated, the inventory control policy employed, and the values of system parameters used in the analytical or simulation study. Prior research investigates the value of VMI beyond IS under stationary stochastic demand using (s, S) and (R, nQ) inventory control policies. In this paper, we assess the benefits offered by VMI over IS for a supplier, multiple retailers and the system as a
Acknowledgment
We are grateful to the anonymous referees and editorial board for suggesting a number of improvements to an earlier draft of this paper. The authors would also like to acknowledge the valuable feedback received from Prof. S. Armagan Tarim at the initial stage of preparing this research paper.
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