CASE: Bram-Wear Lenny Bram, owner and manager of Bram-Wear, was analyzing performance data for the men's clothing

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CASE: Bram-Wear

Lenny Bram, owner and manager of Bram-Wear, was analyzing performance data for the men's clothing retailer. He was concerned that inventories were high for certain clothing items, meaning that the company would potentially incur losses due to the need for significant markdowns. At the same time, it had run out of stock for other items early in the season. Some customers appeared frustrated by not finding the items they were looking for and needed to go elsewhere. Lenny knew that the problem, though not yet serious, needed to be addressed immediately.

Background

Bram-Wear was a retailer that sold clothing catering to young, urban, professional men. It primarily carried upscale, casual attire, as well as a small quantity of outerwear and footwear. Its success did not come from carrying a large product variety, but from a very focused style with an abundance of sizes and colors.

Bram-Wear had extremely good financial performance over the past five years. Lenny had attributed the company's success to a group of excellent buyers. The buyers seemed able to accurately target the style preferences of their customers and correctly forecast product quantities. One challenge was keeping up with customer buying patterns and trends.

The Data

To determine the source of the problem, Lenny had requested forecast and sales data by product category. Looking at the sheets of data, it appeared that the problem was not with the specific styles or items carried in stock; rather, the problem appeared to be with the quantities ordered by the buyers. Specifically, the problem centered on two items: an athletic shoe called Urban Run and the five-pocket cargo jeans.

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Urban Run was a popular athletic shoe that had been carried by Bram-Wear for the past four years. Quarterly data for the past four years are shown in the table. The company seemed to always be out of stock of this athletic shoe. The model used by buyers to forecast sales for this item had been seasonal exponential smoothing. Looking at the data, Lenny wondered if this was the best method to use. It seemed to work well in the beginning, but now he was not so sure.

The data for the five-pocket cargo jean seemed also to point to a forecasting problem. When the product was introduced last year, it was expected to have a large upward trend. The buyers believed the trend would continue and used an exponential smoothing model with trend to forecast sales. However, they seemed to have too much inventory of this product. As with the Urban Run athletic shoe, Lenny wondered if the right forecasting model was being applied to the data. It seemed he would have to dig out his old operations management text to solve this problem.

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Case Questions

1. Is seasonal exponential smoothing the best model for forecasting Urban Run athletic wear? Why?

2. Explain what has happened to the data for Urban Run. What are the consequences of continuing to use seasonal exponential smoothing? What model would you use? Generate a forecast for the four quarters of the fourth year using your model. Determine your forecast error and the inventory consequences.

3. Is exponential smoothing with trend the best model for forecasting five-pocket cargo jeans? Why?

4. What method would you use to forecast monthly cargo jean demand for the second year given the previous year's monthly demand? Explain why you selected your approach. Generate the forecasts for each month of the second year with your method. Determine your forecast error and the inventory consequences.