A.I-driven forecasting for demand in retail and manufacturing looks for more than the most likely outcome.
Rather than just attempting to foretell the future, the new generation of AI-driven forecasting is more focused on foreseeing all the possible outcomes, so you're better prepared for anything.
There's no fool-proof crystal ball when it comes to seeing into the future, which is why forecasters tend to talk in probabilities rather than certainties. Traditionally, they map out the possible outcomes on a bell curve and then plan for the most likely outcome – calculated from the top of the curve.
More often than not, planning for the most likely event will serve your business well. But if that's your sole focus, there's always the small but significant risk that an unlikely event can catch you totally unprepared.
The new generation of AI-driven forecasting paints a more holistic view of the future. It uses sampling simulation technology to consider every possible outcome, rather than placing too much emphasis on the most likely outcome.
The result is a forecast which takes into account those less likely eventualities, and their potential impact on the business, to ensure you're better prepared for the unexpected.
This change of approach can present a challenge for some businesses. It requires forecasters and planners to think more about the breadth of uncertainty in their forecasts, rather than just trying to make their forecasts more accurate and more accurate. There’s a classic Winston Churchill quote that’s become a trope amongst the walls of the Remi office: Plans are of little importance, but planning is essential.
In much the same way, planners need to become comfortable with the fact that the only certainty is uncertainty. Businesses are better-served by agile forecasting which embraces this and considers the wide range of possibilities, rather than locking in a single static forecast for the coming year.
There are similarities to the way software development has moved away from big bang implementations to a more agile approach, which can be honed to allow for changing circumstances. Regular, subtle adjustments in course require greater diligence, but they are easier to manage than infrequent, dramatic adjustments that occur late enough to mitigate impact, not avoid it entirely.
Part of this change in forecasting requires learning how to better weight risks. Put simply, there might be a 50 per cent chance of things going according to plan in the next 12 months. Yet, there could also be 10 different potential incidents which could throw the plan into disarray, each with a 5 per cent chance of occurring.
In this scenario, traditional forecasting would plan for everything to go according to plan. The likelihood of any one of those incidents occurring is quite low at 5 per cent.
That doesn't mean you can breathe easy. When AI looks at the big picture, there's actually a 50 per cent chance of something going wrong. Considering the likelihood, consequences and severity of each potential outcome, AI can create a more holistic forecast which better mitigates those risks.
AI-based forecasting isn't merely a rules-based engine.
Instead, you set your priorities and required outcomes – along with your appetite for risk – and it determines the best course of action to achieve your business goals.
You can also proactively wade into the uncertainty: creating scenarios to simulate the impact of different unlikely events and then plan your response. This trains the AI to know how to respond in unlikely events, such as extreme fluctuations in supply or demand. The decision engine is now better prepared to cope with uncertainty and even black swan events, for example a global pandemic (we’re based in Sydney. We couldn’t not mention it.)
AI doesn't claim to be more accurate than traditional forecasting, in some ways it's the opposite. It acknowledges that the future has not been written and events don't always go as expected.
AI-based forecasting aims to study the realm of possibilities and better manage that uncertainty, rather than working on the risky assumption that we can accurately predict the future.
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