One of the major challenges all retailers face is how to manage their operations to maintain efficient stock control. Retailers need to balance meeting the demands of their customers whilst minimising waste from perishable goods and the costs of holding surplus stock. Having too many items in stock also takes up space and reduces capital that could be used elsewhere. However, having too little stock to satisfy customer demand can create delivery delays and potentially lost custom. Retailers therefore need to accurately forecast demand in order to decide what quantities to purchase to maintain optimum stock levels.
Historically, stock ordering has been the responsibility of individual store managers or regional buyers, for example, using their local knowledge and perhaps store- or regional-level data on recent sales to come to a decision. However, this simple approach is vulnerable to human error such as over-reacting to random fluctuations in sales figures, seeing false patterns in noisy data and failing to account for external factors. This process can also take up a lot of time, preventing managers from focussing on other important areas of the business.
Using a statistical approach, historic sales figures combined with data on relevant external drivers can provide accurate forecasts of future demand that can be used to generate order values that optimise stock levels. Unlike judgemental forecasters, statistical models are able to filter real patterns from noisy data. By utilising all of the data available, the precision of forecasts can also be increased, as information on factors common to multiple regions or stores can be borrowed from across the business. For example, it may be possible to incorporate dates of relevant events such as school holidays and national promotions.
Statistical models, including time series approaches that take account of the temporal structure of the data, can identify patterns and trends that allow us to describe historic sales patterns and both account for and estimate the effects of other important variables. Working with business experts, we can leverage their knowledge to understand what factors are likely to affect sales and incorporate these into the model.
By identifying the key factors involved, demand can be broken down by store and product, and predictions can be made under different scenarios, e.g., accounting for advertising events. If weather plays a role, historical forecasts could even be used to predict the change in demand for each additional degree of temperature rise. Similar models could be used to relate sales of other products to oil price or exchange rate fluctuations or, by incorporating seasonal variations, they could determine when shops should start stocking Christmas or summer goods, for example.
However, any system is only as good as the data upon which it is built. Therefore, as much accurate data as possible are needed to inform the statistical models. As more, (and better quality) data are collected, models can continue to learn and provide more accurate forecasts.
Statistical models of customer demand eliminate human error and inexperience from the ordering process and help maximise sales whilst maintaining lean stock control. This can greatly improve cash flow and allow more effective use of limited space. By utilising these models, businesses can better manage their inventory control and operate more efficiently than their competitors who are using more traditional techniques. A more automated process is also more cost effective as it allows managers to spend more time concentrating on other key areas of the business. Furthermore, by avoiding running out of stock, the need for emergency deliveries will be reduced and customers will get better service as they will always be able to buy what they want when they want.