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Statistical Forecasting : Right or Wrong?

Statistical forecasting for demand planning

Reasons why statistical methods may not suit every situation:

  1. Do the peaks/troughs happen at the same time each year? If not, how can a statistical algorithm predict them? Characteristic of Sales and Marketing activity is ‘never the same thing twice’.
  2. Statistical algorithms are typically slow to react to recent trend changes.
  3. Statistical algorithms are typically poor at forecasting ‘lumpy demand’ in small customers.
  4. Statistical forecast is possibly good for long term trend. But how much of the variation in month on month is attributable to this and how much to promos, competitor activity etc.?
  5. Once you have your initial forecast, how much of the ongoing forecast process is likely to be statistically based? Isn’t it the worst thing potentially overwriting a forecast that has been carefully amended for promotions etc. previously with a new statistical forecast? 
  6. How much more accurate is one more month of actuals going to make the statistical forecast? (Versus the improved accuracy that applying new management knowledge to the existing forecast will bestow)?
  7. In reality the main activity each month is not the generation of a new statistical forecast. It is a review of the existing sales plan in the light of new information – qualitative on events plus what was sold last month.
  8. How can a statistical forecast accurately react to forecast errors? Does an oversale last month mean the start of an increasing rate of sale or merely business brought forward from the next month? Does an undersale last month mean the start of a decline, or merely that the stock pipeline was reduced and that actually there will be larger than expected orders this month? It is surely impossible for the software to know. Yet actually the forecasts 1, 2, or at most 3 periods out are often considered the most business critical!
  9. Sales are the result of a complex cocktail of behavioural and synergistic factors which cannot easily be quantified. For example, if your last promotion was very successful in a customer, their buyer is likely to buy big and early on the next one. Whereas if the previous promotion was unsuccessful they are likely to adopt the opposite buying strategy. Two alternate perceptions of the same promotion! How do you reflect that in ‘clean’ and ‘dirty’ or however you wish to ‘clean up’ the data? (Is a stock-outalways a lost sale? Probably not – maybe the customer will order more next time, or more of a different size/product this time. Again, judgement is required.)
  10. Statistical approaches like ‘cleaning past promos out of the history’ and adding them back to a statistical forecast are naïve, overly-mechanistic and do not remove the need for judgement. (They replace judgement and resource at the forecasting end with judgement and resource at the cleaning up history end. Plus, how do you accurately quantify the ‘standard’ rate of sale without significant judgemental resource?)
  11. If you think statistical forecasting is suitable, prove it to yourself with a subset of your data. Don’t just assume!
  12. Apply alternatives to statistical forecasting – forecast steady / low sellers less frequently, or review by exception based on accuracy analysis. Focus time on big / highly variable items.
  13. Apply the 80:20 rule to what you forecast.
    1. Forecasting aggregate groups of small customers is easier because there are fewer numbers and the individual peaks and troughs tend to even each other out.
    2. Use 80:20 to identify the skus to apply the strongest forecasting focus to. Small or steady selling lines should need much less forecasting time and their inventory is much less costly to support.
    3. It is remarkable how many businesses do follow the 80:20 rule on both products and customers. If you haven’t analysed your data in this way it is definitely worth a go!
  14. Don’t forecast volumes in isolation of the wider business plan. Manage volumes, revenues and margins within a single, integrated forecasting process. So that all parts of the business buy into the same vision of the future and can work in a coordinated way to deliver it.

Conclusion:

Unless you are happy to produce daily, weekly or monthly forecasts by product exclusively using statistics and without manual adjustment, then the activity of generating more accurate forecasts is potentially and unavoidably a time-intensive process. The more time you spend on it, the more likely you are to generate the most accurate possible forecasts.

Please see our article on 'forecastability' - a way of identifying which combinations are most and least suited to statistical forecasting / machine learning.