Nick Saban is a brilliant college football coach and widely heralded as a football genius. At time of writing, his coaching record stands at 261 wins, 65 losses and 1 tie. He’s won 17 Bowl games along with 7 national titles (the most ever) and counting.
If you’re a fan of the Alabama Crimson Tide, in a state where college football is king, Saban is arguably more popular than Jesus. Fanatics think he is Jesus.
When asked about his unrivaled success, Saban offers a counter-intuitive philosophy that’s guided his coaching career, anchored on two fundamental principles:
- Don’t focus on the outcome
- Trust the process
Incredibly, Saban doesn’t focus on the result – which, for college football – like most sports – is whether you win or lose. Sure, make no mistake about it, Saban wants to win, it’s just he believes that the path to long term success is by trusting the coaching process.
His belief is that by coaching his team, repeatedly and without fail, so that they can execute their designed plays on offense and coaching schemes on defense, he’ll maximize his odds of winning. Losses are important to this process as it provides the team the opportunity to review the film and see where the process failed – either in execution or, sometimes, in coaching and design. Which leads to more coaching, practice and trust in the process.
Trusting the process is a philosophy that has served Saban and the Crimson Tide incredibly well.
We can learn a lot from Nick’s nuggets of wisdom.
Given that we’re often referred to as the Salty Old Sea Dogs of Flowcasting, it’s fair to say that we’ve been around the block a few times and, over time, changed our thinking often. No more than so than in developing, managing and measuring the retail forecasting/demand planning process.
For most retailers, the number of item/store products that sell less than 26 units a year (about 1 every two weeks) can be pretty significant – often comprising 30-50% of a retailer’s assortment. You wouldn’t expect to be as accurate in determining a forecast for these types of products, since there is a fairly large element of probability involved – as an example, based on history, you can feel confident that 1 unit sells every month but you’re not sure when it will be.
While it’s tempting to aggregate the lower level item/store forecasts up to a higher level and assess forecast performance, that’s of little use to anyone – after all, customers buy products in stores, or online, to be acquired or delivered at their preferred location. They don’t buy them at some aggregate level. Not to mention that item/store replenishment plans are driven from these forecasts and the dependent demand is cascaded throughout the supply network.
Like Saban, we work with our clients to help them understand and hopefully instill the idea of assessing the forecasting process by determining something called forecast reasonableness.
So then, what’s a reasonable forecast?
The following diagram outlines, conceptually, what we’re talking about:
If the item/store forecast is within tolerance then the process/solution is producing a reasonable forecast. The beautiful thing is that the planner spends no time chasing these forecasts since, in all likelihood very little can be done to improve the process/outcome for these.
In practice, forecast reasonableness is an exception condition for the demand planners to action. For the item/store forecasts that are outside of tolerance, the planner can investigate these, see if the same item is outside of tolerance for a number of stores and determine if anything is systemically happening or could be improved in the process to bring them within tolerance.
To determine how well the forecasting process is working is simple. What percentage of the item/store forecasts is within tolerance?
We believe that, in retail, forecast reasonableness should replace traditional measures of forecast accuracy (like MAPE, WMAPE, etc., that were developed for more continuous demand streams in manufacturing and distribution).
Now before you think we’re completely mental, here’s something to ponder on. One of our retail clients, who plan using the Flowcasting process, does not measure baseline forecast accuracy.
Instead, they use a forecast reasonableness exception to evaluate the process, honing in on any forecasts that are not within tolerance to see if there is anything that can explain this and, potentially, how they might “improve the process”.
During the time they’ve not measured forecast accuracy they improved daily in-stock from an average of 91.7% to an average of 97.7%, while also improving inventory performance.
For a customer, in-stock is everything. They couldn’t care less about forecast accuracy.
Maybe you shouldn’t either.