Merchandise testing

The remainder of this chapter is organized as follows. In Sect. 2 we discuss merchandise testing. In Sect. 3, we analyze replenishment planning. In the concluding chapter, we suggest provide some important conclusions and suggest areas for possible future research for each of these aspeets. 

Despite the practical relevance and complexity of this problem, we found nothing in the academic or managerial literature that describes how to design an effective merchandise test. There is extensive academic literature on test marketing (e.g., see Urban and Hauser (1980)) that would appear to be relevant, but turns out not to be directly applicable since they involve longer duration and observation of trial and assume repeat purchases. 

A number of articles that review current retail practice (Doyle and Gidengil (1977), Fox (1995), Hollander (1986), Pollack (1994) and Wilson et al. (1995)) emphasize the importance of merchandise testing and highlight a need for more effective procedures, but do not themselves describe how to conduct an effective merchandise test. Doyle and Gidengil (1977) review merchandise testing as part of the broader topic of retail experimentation and conclude that these methods, despite enormous potential, have thus far made little contribution to retailing because of problems both practical and theoretical. 

We describe a methodology for resolving the key decisions in designing a merchandise test how many and which specific stores in which to conduct the test and how to create a season forecast for the entire chain based on test store sales. We assume the retailer has (1) identified a set of products within a classification that they would like to test, (2) specified the time interval within which the products will be sold (the sales season), and (3) determined a test period during which the products will be offered for sale in selected stores to test their sales potential. 

Since the purpose of merchandise testing is to create a forecast used to determine purchase quantities, it is important to understand the costs that result from forecast errors. If Sp is the actual demand for a product p during the primary season. Up the per unit cost of buying less than demanded and Op the per unit cost of buying more than demanded, then the cost associated... 

In the final analysis, the real test of our method is its ability to improve the accuracy of the merchandise testing process in an actual retail environment, a question we consider next. 

Gloss is an important merchandising factor and this test makes it possible to specify and control this surface characteristic so that the desired effect will be ensured. ]... 

The key factors that can influence the decision-making process are product specihcation, production capacity, quality, cost and time. Based on the product specihcation, the buyer will determine which product will be purchased, will select the supplier and will negotiate on the delivery terms and price. The production capacity, sales forecasting, preseason style testing, sampling, merchandise line planning and development will ultimately govern the overall procurement process. 

Next, we present the basie ideas behind om methodology. Seetion 2.3 presents the optimization models used to form elusters and select a test store within each cluster and predict total season sales across all the stores of the chain from test store sales. Section 2.4 reports an apphcation of these ideas to a retailer speciahzing in women s fashion apparel, with over 1,000 stores nationwide. Compared to the current testing process in place at the retailer, our method would reduce the cost of stockouts and of merchandise left over at the end of the season by enough to increase profit by 100%. 

In developing merchandise plans, the retailer divides the year into two seasons fall/winter and spring/summer. The first week of the fall/winter season is the first full week of September, while the first week of the spring/summer season is the first full week in April. In their current methodology, they select 25 test stores whose total dollar sales are close to average store sales for the chain. Product tests are conducted at these stores over a 3-week period. To develop a season forecast and supply plan for the entire chain, total sales during the test period are divided by two factors estimated from past sales history. The first factor equals the proportion of season sales that are historically observed during these 3 weeks, while the second factor equals the proportion of total sales that are observed at these 25 stores. 

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