Lenny Bram, owner and manager of Bram-Wear, was analysing 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.
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 colours.
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.
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 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 centred on two items: an athletic shoe called Urban Run and the five-pocket cargo jeans.
Urban Run was a popular athletic shoe that had been carried by Bram-Wear for the past six years. Quarterly data for the past six years are shown in the Table 1 below. When the product was introduced six years ago, it was expected to have a seasonal demand pattern. The buyers believed this pattern would continue and used a seasonal forecasting method to forecast sales. However, they seem to have be constantly running out of inventory of this product. Looking at the data, Lenny wondered if this forecasting method 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 three years ago, it was expected to have an increasing demand pattern. The buyers believed that this increasing trend pattern would continue and used a trend adjusted exponential smoothing method to forecast sales. However, they seem to have be constantly overstocked with this product. The monthly demand data for the last three years is shown in Table 2. As with the Urban Run athletic shoe, Lenny wondered if the right forecasting model was being applied to the data. He has asked you to look at the data with a view to improving the forecasting results.
Prepare a short report on the forecasts for both products for L. Bram. Your report must contain at least the following:
- An explanation of what has happened to the demand for both products. You should include graph/graphs of the data to substantiate your explanations.
- The consequences of continuing to use the current forecasting models for both products?
- What forecasting methodologies would you recommend for each of the products? Clearly explain your reasons for choosing these recommendations.
- Use two of your recommended forecasting methodologies to generate relatively accurate demand forecast for each quarter of the 2013, 2014 and the first three quarters of 2015 for the Urban Run Athletic Shoe. Calculate at least one forecast accuracy measure for your forecasts and recommend one of these forecast methods based on your calculations.
- Use two different forecasting methodologies to generate relatively accurate demand forecasts for each of the last six months of 2014 and the first eleven months of 2015 for the five-pocket cargo jeans. Calculate at least one forecast accuracy measure for your forecasts and recommend one of the methods based on your calculations.
- Briefly discuss the inventory consequences of using your recommended forecasting models for both products.
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