Common Forecasting Myths Debunked – Part 3 – How Much History is Enough History?

If you were to take a guess, how many years of sales history do you think you need to create a forecast? Five years? Ten years? The answer may surprise you.

Myth #3: You need several years of data to create a forecast.

For most levels of management within an organization, aggregated demand history acts as a baseline for effectively product demand and is a great predictor of future performance. But, how much history is enough history?

Reality: Product SKUs with at least one year of sales history offer sufficient information to incorporate a seasonal profile into the projected trend.

Creating a forecast for new product introductions, seasonal products and end-of-life products continues to be a major challenge for today’s planners. However, there are techniques to overcome these challenges. For example, an attribute-based model looks at a wide variety of demand profiles that planners can use to characterize the new product and quickly adjust the product’s plan based off early demand signals. This method will analyze historical sell-in and/or sell-through data to develop a wide variety of demand and seasonal profiles. These profiles are assigned to individual planning records. Then, as actual demand information is captured, the current profile is validated to dynamically adjust the product’s plan.

Attribute-based modeling consists of four unique processes.

  1. Creation of Demand Profiles – The demand planner selects products to be included based on attributes such as color, fabric type, region of the country and more. Multiple attributes can be used at once.
  2. Assigning Demand Profiles – Advanced attribute-based models offer ‘user-defined attribute’ matching capabilities, allowing the planner to set criteria for how a new product’s attributes must match the attributes of a demand profile.
  3. Automatic Revision of the Forecast Based on Demand Signals – Forecast accuracy must be monitored continually using data such as Point-of-Sale (POS) to accurately monitor customer buying patterns.
  4. Assess Accuracy of Demand Profile Based on Demand Signals – New products never sell exactly the same way as other products with similar attributes. But by using point-of-sale or other demand signals, the accuracy of the assigned curve can be checked against other demand profiles that have similar attributes.

There are many solutions available for planners that struggle to create forecasts for products with limited history. What types of products are you forecasting? What kind of demand history do they have? As always, your feedback is always welcomed at

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