Merchandise Management Case Study
As one of the core elements of retail industry, merchandise management is critical for a retailer’s success and it is an important aspect of enterprise management and enterprise operation (Zeng, Li and Ding, 2013; Levy and Weitz, 2001). Park and Park (2003) describe merchandise management as the process of selecting, ordering and distributing desirable merchandise across channels, disposing of slow-moving goods, choosing the overall best supplier who can provide the selected merchandise and negotiating with them regarding orders and purchases. Also, merchandise management encompasses functions such as the size planning of the inventories, pricing, promotion and sale of merchandise (Levy and Weitz, 2001).
Today’s retailing fierce competition
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Measuring the performance of merchandise is important in order to interpret which product has performed well and which has not achieved the predetermined targets. Retailers nowadays can use powerful business intelligence systems that depend on structured data and can be statistically analysed (Işık, Jones and Sidorova, 2013). Moreover, through business intelligence in merchandise management retailers can compare product performance and assess the effectiveness of promotion efforts across individual items, categories, region and vendors (Chen and Lin, …show more content…
The formula to calculate the turnover at cost is: Cost of Goods Sold ÷ Average inventory (Levy and Weitz, 2001). Therefore, it is always better to have a high turnover rate as opposed to a lower one, as this could reveal unsuccessful efforts of turning stock into profit.
In-Stock Percentage
In-stock percentage is a very important KPI in the merchandise business process, since it enables managers and retailers to meet sales expectations and avoid out-of-stock situations. At the same time it could be a safety net against overstock while keeping inventory investment at acceptable level.
Seasonal Buying
Seasonal Buying is one of the most important key indicators for forecasting planning as it suggests the use of known variables to predict future (Olszak and Ziemba, 2006). For example, a predicting model based on seasonal buying historical data could support revenue estimations from particular groups of products. Managers and retailers can use past knowledge and create effective forecasts based on historical data and consumers’ needs. Then, assortment strategies can be deployed by considering price, customer segmentation and profiling (Levy and Weitz,
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