Supply Chain Inventory Management and the Value of Shared Information

In traditional supply chain inventory management, orders are the only information firms exchange, but information technology now allows firms to share demand and inventory data quickly and inexpensively. We study the value of sharing these data in a model with one supplier, N identical retailers, and stationary stochastic consumer demand. There are inventory holding costs and back-order penalty costs. We compare a traditional information policy that does not use shared information with a full information policy that does exploit shared information. In a numerical study we find that supply chain costs are 2.2% lower on average with the full information policy than with the traditional information policy, and the maximum difference is 12.1%. We also develop a simulation-based lower bound over all feasible policies. The cost difference between the traditional information policy and the lower bound is an upper bound on the value of information sharing: In the same study, that difference is 3.4% on average, and no more than 13.8%. We contrast the value of information sharing with two other benefits of information technology, faster and cheaper order processing, which lead to shorter lead times and smaller batch sizes, respectively. In our sample, cutting lead times nearly in half reduces costs by 21% on average, and cutting batches in half reduces costs by 22% on average. For the settings we study, we conclude that implementing information technology to accelerate and smooth the physical flow of goods through a supply chain is significantly more valuable than using information technology to expand the flow of information.supply chain, multi-echelon inventory management, periodic review policies, electronic data interchange

Manufacturer Benefits from Information Integration with Retail Customers

Information integration efforts between manufacturers and retailers, in the form of information sharing, synchronized replenishment, and collaborative product design and development, have been cited as major means to improve supply chain performance. This paper develops a conceptual framework that relates information-integration initiatives to manufacturer profitability. The framework allows such initiatives to impact inventory management and revenue-enhancing measures that, in turn, increase manufacturer profit margins, or affect profit margins directly. Through an extensive survey in the food and consumer packaged goods industry, we empirically examine this framework. The analysis reveals that the various integration techniques are differentially associated with manufacturer performance. Collaborative planning on replenishment, in the form of vendor-managed inventory (VMI), is directly and positively related to manufacturer margins, while collaboration on new products and services is positively related to intermediate performance measures. Specifically, this latter form of collaboration allows the manufacturer to charge higher wholesale prices and, interestingly, is associated with lower retailer, and consequently manufacturer, stockouts. In contrast, collaboration on the handling of excess and defective retailer inventory (i.e., reverse logistics) results in higher manufacturer stockout levels, on average. Solely sharing information on either inventory levels or customer needs is associated with higher manufacturer performance measures up to a certain point; sharing this information is prevalent among manufacturers that achieve industry-average profitability relative to those that achieve below industry-average profitability. The paper explains these results in the context of the conceptual framework developed and discusses the managerial implications for effective coordination between supply chain partners.supply chain management, information integration, collaboration

Commercial Use of UPC Scanner Data: Industry and Academic Perspectives

The authors report the findings from an exploratory investigation of the use of UPC scanner data in the consumer packaged goods industry in the U.S. The study examines the practitioner community's view of the use of scanner data and compares these views with academic research. Forty-one executives from ten data suppliers, packaged goods manufacturers, and consulting firms participated in wide-ranging, in-person, interviews conducted by the authors. The interviews sought to uncover key questions practitioners would like to answer with scanner data, how scanner data is applied to these questions, and the industry's perspective regarding the success that the use of scanner data has had in each area. The authors then compare and contrast practitioners' views regarding the resolution of each issue with academic research. This produces a 2 × 2 classification of each question as “resolved” or “unresolved” from the perspectives of industry and academia. Along the diagonal of the 2 × 2, issues viewed as unresolved by both groups are important topics for future research. Issues deemed resolved by both groups are, correspondingly, of lower priority. In the off-diagonal cells, industry and academics disagree. These topics should be given priority for discussion, information exchange, and possible further research. Practitioners reported that scanner data analysis has had the most success and been most widely adopted for decision making in consumer promotions (i.e., coupons), trade promotions, and pricing. For example, logit and regression models applied to scanner data have revealed very low average consumer response to coupons which has directly led to reduced couponing activity. Managers also reported high levels of comfort with and impact from analyses of trade promotions and price elasticities. While industry views most of the issues in these areas to be resolved, academic research raises concerns about a number of practices in common commercial use. These include price threshold analysis and trade promotion evaluation using baseline and incremental sales. In product strategy, advertising, and distribution management, practitioners reported that the use of scanner data has had more limited development, success, and impact. In the case of new product decisions, scanner data use has been slow to develop due to the inherent limitations of historical data for these decisions and a heavy reliance on traditional primary research methods. In advertising, scanner data is widely analyzed with models, but confusion among practitioners is very high due to controversies about methods (e.g., what level of data aggregation is best) and conflicting results. In distribution and retail management, scanner data use has tremendous potential but a mixed track record to date. Thus, practitioners view the use of scanner data as unresolved for most issues in product strategy, advertising, and distribution. This view is largely, though not entirely, consistent with academic research, which has only begun to address many of the key questions raised by practitioners. In light of the large number of unresolved issues and mixed record of scanner data use to date, the authors offer a series of specific recommendations for immediate and long-term research priorities that are likely to have the greatest impact on commercial utilization of UPC scanner data. Topics of immediate priority include price thresholds and gaps, baseline and incremental sales, base price elasticity, competitive reactions, measurement of advertising effects, management of brand equity, rationalization of product assortments, and category management. Long-term priorities include a greater emphasis on profitability versus sales or market share, developing prescriptive models versus descriptive models, and the need for industry standards.Scanner Data, Marketing Research, Marketing Models, Research Priorities