Demand Forecast Accuracy 101

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Demand forecasting is the process of predicting customer demand by extrapolating historical sales data. In the supply chain, knowing and understanding the demand forecast accuracy 101, can help anyone make more efficient decisions. From there, you apply the insights of knowledge workers to that data. Then you can tap into other information, such as live-streaming market data or social data, to further refine your essential time-series forecasts.

The demand forecast, or sales forecast, reveals the likely demand (sales) that your organization will experience, irrespective of your ability to meet that demand. A demand forecast is often referred to as an unconstrained forecast. The primary purpose is to indicate the level and direction of future business activity, so that all teams and functions in your company have time to respond to changes. In essence, you are trying to accurately predict and plan for real market demand, and to understand what drives that demand. This is how you make informed decisions about internal operations, and how you plan marketplace maneuvers. Where do you set inventory levels? What about pricing, promotions, new product releases, and more? If not for demand forecasting, you’d lose all sense of what to prepare for, as well as how, when, and where.

Demand Forecasting 101: Benefits

Done well, the benefits of accurate demand forecasting are numerous. The ability to foresee demand with relative accuracy enables decision makers to improve both operations planning (units) and financial planning (dollars). These include business activities such as:

  • Optimizing inventory levels
  • Increasing inventory turns
  • Raising service levels
  • Lowering carrying costs
  • Improving cash flows
  • Allocating budgets
  • Setting staffing and resource levels
  • Maximizing the bottom line

Demand Forecast Accuracy 101: Fundamentals

There are three essential ingredients to demand forecasting:

  1. The statistical, time-series forecast: This is where an automated forecast engine analyzes your historical sales data, and extrapolates your demand history into the future, factoring seasonal patterns, trends, and drivers.
  2. Your organization’s marketplace knowledge: These are the events that you know are going to happen but that are not yet reflected in the historical data. It might include the sales team’s knowledge about a new account, a recently lost account, a new competitor, or a forthcoming product launch. With this information, you augment the historical, time-series forecast and substantially increase accuracy.
  3. Live streaming market data: This can include point-of-sale (POS) transactions, economic indicators, social media activity, weather patterns, and much more. For example, an appliance maker can use causal models to analyze and factor housing starts, Facebook mentions, wage increases, or employment rates. There is a lot of data out there, and it’s growing all the time.