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: