Number Of Stock Keeping Units Research Articles

Travel distance estimation of relation-based storage assignment policies in picker-to-part warehouses

<p>Relation-Based Storage Assignment (RBSA), also called the “family grouping method,” has garnered significant interest among researchers and practitioners looking for an intuitive means by which to arrange related Stock Keeping Units (SKUs) in nearby regions for simultaneous picking. However, there is no analytic model for picking distance estimation for RBSA. Most of the related studies have targeted specific cases and applied simulation or mathematical programming to validate the family grouping method’s superiority over traditional storage assignment methods including the random assignment policy. To explore the effects of RBSA, this current study develops a preliminary analytical model of travel distance estimation for low- level, picker-to-part, and pick-by-order warehouses. We use Frequency-Based Storage Assignment (FBSA) as a baseline for comparison. We consider both single-aisle warehouse layouts and two-block, multi-aisle warehouse layouts, with return and traversal picking policies. The skewed ABC distribution curve of SKUs, the number of picks (N) per picking tour, the number of SKUs (n1) in a family, and the number of families (n2) are also taken into account. Our results show that, in comparison with the FBSA method, the RBSA method decreases the picking distance by as much as 32%.</p> <p> </p>

PREDICTION OF SHARE OF VISUAL INVENTORY USING STORE DISPLAY, CHANNEL, AND PRODUCT VARIABLES

The purpose of this study is to predict the share of visual inventory (SOVI), which is defined as the number of stock-keeping units (SKUs) of a company’s products, calculated as a percentage of the total SKUs on the display of all products. Research studies in the past have focused mainly on the impact of inventory, which includes back end and visual inventory, on sales but less attention has been given to the impact of SOVI on sales. To address this research gap, this study attempted to create an analytics model to predict SOVI at the category of soft drinks level using four predictor variables namely point of purchase display, channel/sub-channel, package group, product category, and derived variable gross national income (GNI). The results were encouraging confirming the effectiveness of such a model. The researchers utilized a data set collected over a period of 18 months (February 2016 to July 2017) by a soft drink firm headquartered in the United States. Based on the findings, it is suggested that this prediction model can be utilized by other researchers and practitioners to predict SOVI of other soft drinks, fast-moving consumer goods (FMCG), and food and beverage companies.

Measuring the Efficiency of Category-Level Sales Response to Promotions

In this study, we focus on measuring the efficiency of category-level sales response to promotions across various categories and stores. Our heterogeneous stochastic frontier model allows us to attribute portions of this efficiency to specific characteristics of the stores and categories. Using our full PEM (promotional efficiency frontier) model, we analyze the efficiency of 20 frequently bought categories of a supermarket retailer and apply it to store-category-level data. We find that the average efficiency of category and store sales response across all categories and stores is 84.34%, with low values in categories such as spreads and fresh seafood and high values in categories such as frozen entrées and meat. We find that the variation in efficiency of this sales response can be attributed to specific store and category characteristics such as selling area of store, distance to competition, number of stock-keeping units in the category, and average interpurchase time. Unobserved heterogeneity is captured by the latent class approach that provides support for the existence of three segments. An understanding of the roles played by these characteristics in the efficiency of sales response can aid managers in devising a strategy that maximizes sales. This paper was accepted by Pradeep Chintagunta, marketing.

Managing Product Variety and Collocation in a Competitive Environment: An Empirical Investigation of Consumer Electronics Retailing

Product variety is an important strategic tool that firms can use to attract customers and respond to competition. This study focuses on the retail industry and investigates how stores manage their product variety, contingent on the presence of competition and their actual distance from rivals. Using a unique data set that contains all Best Buy and Circuit City stores in the United States, the authors find that a store's product variety (i.e., number of stock-keeping units) increases if a rival store exists in its market but, in the presence of such competition, decreases when the rival store is collocated (within one mile of the focal store). Moreover, collocated rival stores tend to differentiate themselves by overlapping less in product range than do noncollocated rivals. This smaller and more differentiated product variety may be because of coordinated interactions between collocated stores. In summary, this paper presents evidence of both coordination and competition in retailers' use of product variety. This paper was accepted by Bruno Cassiman, business strategy.

Brand-Level Effects of Stockkeeping Unit Reductions

When retailers make product assortment changes by eliminating certain stockkeeping units (SKUs), how does this affect sales of individual brands? This is the main question the authors address in this article. Using data from an online retailer that implemented a permanent systemwide SKU reduction (SR) program, the authors investigate how the program affected various components of purchase behavior for individual brands. They find substantial variations in the SR effects across brands, categories, and consumers. They explore possible drivers for these differences and find that higher-market-share, higher-priced, and more frequently promoted brands tend to gain share and that reduction in the number of sizes, reduction in the number of SKUs, and change in SKU share in the category are important in affecting change in a brand's purchase share after the SR. They also find that SRs lead to an increase in category purchase incidence and quantity for highly state-dependent consumers and frequent buyers but a decrease in category purchase and quantity for mildly state-dependent consumers and infrequent buyers. In addition, SRs tend to cause more changes in brand choice probabilities among consumers of lower state dependence and higher price and promotion sensitivity. These findings are of importance both to retailers wanting to make product assortment changes and to manufacturers affected by them.

The Role of Logistics in the Formulation and Deployment of Strategy: An Empirical Analysis of Australian Packaged Consumer Products Manufacturers

ABSTRACT For a number of organisations the practice of logistics management has changed from an operational concern with the elemental activities to a strategic interest in how effective logistics management can help achieve corporate objectives. A detailed investigation of six Australian packaged consumer goods manufacturers was carried out to test this link. First a framework was used to determine the level of sophistication of the logistics operations of these companies. This framework examines six process capabilities and five support capabilities. Sophisticated logistics operations employ advanced technology in the physical and communication activities, focus on effective interrelationships along the supply chain and use leading edge logistics management practices The characteristics of the logistics operations of these companies were then linked to logistics of strategy to external business environmental drivers and of change projects, that these companies hadnderway, to logistics strategy and to corporate strategy, was examined. It was found that these companies had relatively unsophisticated logistics operations which did relate to the basic nature of their logistics environment—few customers, large shipments, a small number of Stock-Keeping Units (SKUs) and few suppliers. However, over the next two years all six companies had plans to increase substantially the sophistication levels of their logistics operations. All had substantial change projects underway, but none had a logistics strategy to guide and prioritise these projects. And all had vague or difficult-to-operationalise corporate strategies. For these companies logistics was not being employed strategically. As a result of using our framework these companies would be advised to: more clearly specih their corporate strategies in operational terms; develop a logistics strategy and ensure that it links with the corporate strategy; avoid investing resources to increase the sophistication of their logistics operalions; and work on improving the processes and management of their existing relatively unsophisticated logistics systems.This is a signij2antly different course of action to that actually planned. The next phases of the overall research study-looking at other industry groups in Australia and comparing thcm with similar groupings in Japan, South Korea, Taiwan and Singapore-will detennine whether this situation is widespread or just applicable to one AustraIian indust y group.