It goes without saying that for any retail business, it is highly essential to maintain the balance between demand and supply to hold an appropriate stock that provides the maximization of sales and profit. At the same time, in inventory management, forecasting sales for seasonal products is notoriously challenging as “retailing lives on the ability to quickly react to ever-shifting customer demand patterns” (Davis, 2013, p. 31). Demand may be affected by unpredictable factors, such as natural disasters, terrorist attacks, disease outbreaks, and weather conditions, and generally predictable ones, including general economic conditions and holidays (Roni et al., 2016). Traditionally, retail businesses deal with independent demand on the basis of the current market situation (Wee, 2011).
As a matter of fact, supermarkets traditionally increase their inventory levels before Christmas and related winter holidays expecting higher demand in comparison with other parts of the year (BBC Worldwide Learning, 2014). For instance, according to Gregg Wallace’s film, the sales of wine in grocery stores rise up to 40% every year before Christmas (BBC Worldwide Learning, 2014). At the same time, supermarkets use previous years’ sales data related to the volumes and types of bought alcohol in order to define the stock. In general, the data collection and the analysis of sales history is an intrinsic part of the demand forecasting process (Baudin, 2004).
In turn, incorrect forecasts for products with increased seasonal demand will inevitably lead either to additional expenditures or loss of profit and customers. In other words, if supply exceeds demand, it will lead to overstocked inventory and expired products. This situation may happen when unpredictable events that may impact demand occur (Babongo et al., 2018). For instance, during the pandemic, consumer demand decreased even before Christmas due to the challenging economic situation. At the same time, if demand is higher than supply, businesses will lose profit and customers who will choose a rival company that may satisfy their needs.
References
Babongo, F., Appelqvist, P., Chavez-Demoulin, V., Hameri. A.-P., & Niemi, T. (2018). Using weather data to improve demand forecasting for seasonal products. International Journal of Services and Operations Management, 31(1).
Baudin, M. (2004). Lean logistics: The nuts and bolts of delivering materials and goods. Productivity Press.
BBC Worldwide Learning. (2014). Winter: Supermarket secrets [Video]. Web.
Davis, R. A. (2013). Demand-driven inventory optimization and replenishment: Creating a more efficient supply chain. John Wiley & Sons, Inc.
Roni, M. S., Eksioglu, S. D., Jin, M., & Mamun, S. (2016). A hybrid inventory policy with split delivery under regular and surge demand. International Journal of Production Economics, 172, 126-136.
Wee, H.-M. (2011). Inventory systems modeling and research. Nova Science Publishers, Inc.