Demand forecasting could be the foundation of all data-driven decisions that drive value in daily operations. Think of decisions in your distribution planning, production scheduling, inventory management, and strategic staffing. Having accurate forecasts allows you to provide better customer service, lower costs, and even improve employee happiness. Learn more about what demand forecasting exactly is, how it works and why you should have it.
What is demand forecasting?
Demand forecasting is the type of data analytics that uses algorithms to predict future customer demand. This estimated demand can take any shape or form depending on your business. Next to generic demand drivers like expected revenue, sales and transactions, retailers would for example want to know the estimated footfall, call centers want to know the number of calls to expect, and food delivery companies the number of orders.
How to forecast demand
The basis of demand forecasting lies in recognizing patterns in historical data and continuing that pattern to give an estimation of future demand. And the more historical data you have, the more accurate your forecast will be. Now, there is a vast set of techniques to forecast demand. Forecasts could be made in excel using Exponential Smoothing and Linear Regression which could lead to accurate results in certain situations. Situations, however, change. Think for example of events, trends, weather, and seasonality which all have different impacts on demand and change the context of data sets. It is proven that some methods work for one context, but not necessarily for another. To calculate the impact of those factors and create accurate demand forecasts you might be better off with different, more advanced forecasting methods which also include AI-driven ones. Another reason to add AI-driven forecasting methods is that more traditional forecasting methods, like Historical Averaging, often require manual tweaks or new procedures to be introduced, which can result in a tuning hell when making forecasts for multiple demand drivers. It’s truly the case that the more forecasting methods you have, the merrier.
The benefits of demand forecasting in your operations
One of the implementations of demand forecasting is inventory management. Accurate demand forecasting gives powerful insights on how much, when, and which products should be stocked in inventory. Forecasting can then be utilized to better align sales and marketing efforts and reduce the risk of stockouts, resulting in lower holding costs and increased turnover rates.
Simultaneously, demand forecasting also allows operational alignment in terms of logistics and workforce. Knowing exactly what demand will be, allows planners to coordinate their logistics and distribution with the right amount of vehicles, optimize routes and streamline their warehouse activities. This lowers logistic costs and maximizes asset efficiency.
Another area where demand forecasting can be of great help is workforce management. Demand forms the perfect base to build schedules on. Labor demand forecasting, as we call it, allows you to pinpoint peak hours and slower periods and to translate demand into required headcount using the correct labor standards which opens up doors to optimized shift creation and shift filling opportunities. An example is a slightly undercover demand when costs have to be reduced and overcovering demand when high service to customers must be guaranteed. Accurately staffing against demand also takes the pressure off your employees in peak hours and overtime. With labor demand forecasting, costs will decline, service levels will increase and employee happiness will improve.
Demand forecasting has been around for a long time, but with the newest technologies, demand forecastings are more accurate than ever. It has also been easier to generate these forecasts more frequently and to calculate the impact of external and internal factors. Demand forecasting gives a deeper understanding of the available demand data and forms a powerful tool to start optimizing operations across the entire supply chain.
