19 Sep Bad Forecasting – The Perils of Using Last Year as Your Baseline
Bad Forecasting – The Perils of Using
Last Year as Your Baseline
Written by Jeff Coull, Director of Professional Services at Retalon
We’ve all heard the retail cliche, “we need to do it differently.”
Yet year after year, organizations plan to do the exact same thing as before.
Slight hyperbole, I know, but using last year (LY) actuals as your baseline for creating this year (TY) plans is doing just that.
Most organizations are creating financial plans that mirror their successes and, more importantly, their failures from the prior year. The worst part about this is how easily it can be avoided through a modest technology investment. A predictive analytic solution can provide LY’s level of demand, rather than simply actual sales, allowing planning teams to consistently get better and do things differently.
Predictive analytics help answer complex questions.
From a retail perspective, predictive analytics can tell you what you’re going to sell by calculating a unified demand forecast or, specific to retail planning, also tell you what you would have sold had the conditions resulting in missed opportunities not occurred.
Lost sales resulting from out-of-stock situations are a good example.
If you were out of stock for several weeks last year in key products, then using LY as your baseline will result in a low sales plan for those weeks TY, which drives lower purchases and therefore less inventory. You’re planning to repeat your poor inventory position, which will most likely lead to lost sales again. Why not use a baseline that incorporates lost sales?
Getting Your Head Out of the Sand
Simply put, lost sales isn’t easy to calculate and it’s a politically charged metric, so people naturally avoid using it.
Lost sales isn’t as simple as looking at zero inventory situations and assuming lost sales have occurred. It requires a forecasted level of demand that incorporates cannibalization factors, size range distribution, elasticity of demand, lead times and repeat availability, past promotional uplift relative to current/future promo activity, daily weights, nonannualized sales spikes, and so much more to deliver the accuracy required.
Therefore, the level of demand has to be compared to what actually happened and determine the probability of a lost sale occurring and the magnitude of those lost sales.
For example, just because a store was out of stock, with a forecasted level of demand, should a lost sale be calculated if all stores, even those with inventory, had zero sales as well?
Different people are going to have very different opinions on the definition of a lost sale. I’d argue that it’s impossible for your average planning manager to do this type of heavy lifting using basic software like Microsoft Excel, especially given today’s omnichannel requirements. Technology is the only answer, specifically a fully integrated predictive analytic solution.
Doing Things Differently
Ultimately, there’s a lot of intense math and computer science needed to deliver an effective predictive analytic solution that supports retail planning and all its nuances.
The best option is to choose a comprehensive, integrated solution, one that incorporates an extensive list of factors to deliver an accurate demand forecast — bonus points if the provider has a planning solution as well! This may seem daunting, but these software solutions do exist and the benefits of using level of demand as your planning baseline, rather than LY actuals, will deliver a high return on investment year after year as you consistently get better and “do things differently.”
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