Demand forecasting is one of the most crucial factors that determine the success of every business, online or offline, retail or wholesale. Being able to predict future customer behavior is essential for optimal purchase planning, supply chain management, reducing potential risks and improving profit margins. In some form, demand prediction has existed since the dawn of civilization, just as long as commerce itself.
Yet, even nowadays, when businesses have much more historical data available for analysis and a broad range of statistical methods to crunch it, demand forecasting is still not hard science, often relying on expert decisions based on intuition alone. With all the hype surrounding artificial intelligence’s potential applications in just about any line of business, it’s no wonder then that many experts believe it will have the biggest impact on demand planning as well.
Benefits of AI applications in demand forecasting
But what exactly are the potential benefits of this new approach as opposed to traditional methods? Well, the most obvious one is efficiency due to the elimination of the human factor. Instead of relying on serendipity, machine learning-based methods operate on quantifiable data, both from the business's own operational history and on various market intelligence than may influence demand fluctuations (like competitor activities, price changes or even weather).
On the other hand, most traditional statistical demand prediction methods were designed to better approximate specific use cases: quick vs. slow fluctuations, large vs. small businesses and so on. Selecting the right combination of those methods requires you to be able to deal with a lot of questions you currently might not even anticipate, not to mention know the right answers. Machine learning-based business analytics solutions are known for helping companies to discover previously unknown patterns in their historical data and thus for removing a substantial part of guesswork from predictions.
Last but not least, the market already has quite a few ready-made solutions to offer, either as standalone platforms or as a part of bigger business intelligence solutions. You don’t need to reinvent the wheel anymore. Just connect one of those solutions to your historical data, the rest, including multiple sources of external market intelligence, will be at your fingertips instantly.
What about challenges and limitations?
Of course, one has to consider the potential challenges of this approach as well. The biggest one has even nothing to do with AI: it’s all about the availability and quality of your own data. Machine learning models require lots of input to deliver quality results, and by far not every company has all this information in a form ready for sharing yet. For many, the journey towards AI-powered future has to start with breaking the silos and making historical data unified and consistent.
This does not apply just to sales operations, by the way. Efficient demand prediction can only work when data across all business units can be correlated: including logistics, marketing, and others. If your (or your suppliers’, for that matter) primary analytics tool is still Excel, thinking about artificial intelligence is probably a bit premature.
A major inherent problem of many AI applications is explainability. For many, not being able to understand how exactly a particular prediction has been reached might be a major cause of distrust. Of course, this is primarily an organizational and cultural challenge, but a major challenge, nevertheless.
However, these challenges should not be seen as an excuse to ignore the new AI-based solutions completely. Artificial intelligence for demand forecasting is no longer just a theory. Businesses across various verticals are already using it with various, but undeniably positive results. Researchers claim that machine learning methods can achieve up to 50% better accuracy over purely statistical approaches, to say nothing about human intuition.
If your company is not ready for embracing AI yet, make sure you start addressing your shortcomings before your competitors. In the age of digital transformation, having business processes and business data agile and available for new technologies is a matter of survival, after all. More efficient demand forecasting is just one of the benefits you’ll be able to reap afterward.
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