Freshness Management in the Retail Business

Freshness Management in the Retail Business

Hisashi Kurata (University of Tsukuba, Japan) and Seong-Hyun Nam (University of North Dakota, USA)
Copyright: © 2014 |Pages: 18
DOI: 10.4018/978-1-4666-5202-6.ch089
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Introduction

In a shopping occasion, customers expect something more than just buying products they want. They expect shopping to be a good experience. Hence, a big challenge for retail business is to keep store attractiveness in an intensively competitive market. For example, emerging apparel firms, such as Zara, H&M, and Forever21, keep on introducing new products in a short span but do not reorder the same items once stock out occurs to attract customers’ attention. Another example of such efforts is the excellent assortment planning system, which always introduces new and eye-catching products to sustain store freshness, contributing in the success of Seven-Eleven Japan, Co., Ltd., the largest convenience store chain in Japan (Matsuo & Ogawa, 2007). Here, the role of new products is to raise the sales of the top sellers by attracting customer awareness to the subcategory as well as to give a fresh impression of both the category and the store. In a sense, such new products are not expected to earn profit by themselves but to increase a storewide profit by increasing customer traffic and attention.

At first glance, our discussion might be considered similar to existing work on store assortment and traffic building. For example, category management sets its goal as increasing store traffic and/or raising profits generated by purchases of the items in the store (Dhar, Hoch, & Kumar, 2001). Also, it is common to use a loss leader strategy: A highly discounted price, even lower than its wholesale price, is set not to gain profitability but to increase customer traffic (Hess & Gerstner, 1987). However, our research actually differs in several aspects. First, we clearly separated items that generate profit for a retailer from items that generate freshness for an attractive store image. Category management research often categorizes items with respect to penetration and frequency (e.g., Fader & Lodish, 1990; Dhar, Hoch, & Kumar, 2001). However, our categorization is based on the retailer’s purpose in displaying the item on its shelf. Because our focus is on the assortment of attractive new products that give a store a fresh image, we call this research issue “freshness management.” Second, we took into account the trajectory of the optimal shelf-assortment plan over time. We explored the dynamic behavior of the shelf-space decision for a traffic builder. Third, our model analytically provides the optimal assortment policy in business, while majority of the papers on category and assortment management are empirical based on sales data. In fact, we applied to store freshness management an idea of a simple optimal control model that was used in the machine maintenance model.

In this chapter, we propose freshness management, which determines the most adequate shelf assortment and new product development for retail business to keep an adequate level of store attractiveness to customers, to maximize customer traffic, and to maintain competitiveness and an advantage over rivals. In particular, the goal of this chapter is twofold: First, we present a conceptual framework of freshness management. The other objective is to formulate analytical models that can assist in the research on freshness management. One unique point of our model formulation is to apply optimal control approaches to freshness management. Historically, optimal control theory has been applied to inventory management, repair/maintenance, advertising/pricing (Thompson & Sethi, 2005; Kamien & Schwartz, 1991), and marketing communication mix optimization problem (Raman et al., 2012). However, using optimal control theory to analyze a retailer’s attractiveness and corresponding traffic control is new.

The remainder of the chapter is organized as follows. Background section reviews existing research on store attractiveness and optimal control application. Model section presents a base model. Several extensions of the base model are developed in extension of the model. Finally, concluding remarks concludes the chapter.

Key Terms in this Chapter

Stock Keeping Units (SKU): A SKU is a number or string of alpha and numeric characters that uniquely identify a product. For this reason, SKUs are often called part numbers, product numbers, and product identifiers. SKUs may be a universal number such as a UPC code or supplier part number or may be a unique identifier used by a specific a store or online retailer.

Freshness Management: It deals with how a store can keep the items exhibited on the shelves appealing enough to customers.

Assortment Planning: The process to determine what and how much should be carried in a merchandise category. Assortment plan is a tradeoff between the breadth and depth of products that a retailer wishes to carry.

Category Management: Marketing strategy in which a full line of products (instead of the individual products or brands) is managed as a strategic business unit (SBU). It is based on the concept that a marketing manager is better able to judge consumer buying patterns and market trends by focusing on the entire product category.

Loss Leader Strategy: A business strategy in which a business offers a product or service at a price that is not profitable for the sake of offering another product/service at a greater profit or to attract new customers. This is a common practice when a business first enters a market; a loss leader introduces new customers to a service or product in the hope of building a customer base and securing future recurring revenue.

Assortment: Refers to the number of SKUs within a merchandise category, group or department (depending on the retailers’ reference).

Optimal Control Theory: An extension of the calculus of variations, is a mathematical optimization method for deriving control policies. Optimal control deals with the problem of finding a control law for a given system such that a certain optimality criterion is achieved. A control problem includes a cost functional that is a function of state and control variables. An optimal control is a set of differential equations describing the paths of the control variables that minimize the cost functional. The optimal control can be derived using Pontryagin's maximum principle (a necessary condition also known as Pontryagin's minimum principle or simply Pontryagin's Principle), or by solving the Hamilton-Jacobi-Bellman equation (a sufficient condition).

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