As Industry 4.0 and Supply Chain Digitalization continue to draw attention from companies and suppliers, it comes as no surprise that the field of demand planning is evolving as the next potential field for innovation.
Tech giants like Amazon and Microsoft have announced Artificial Intelligence (AI) tools for improving demand planning, and several consulting companies are promoting their skills to bring AI to companies’ demand planning processes. In fact, a recent survey by the Institute of Business Forecasting (IBF) identified AI as the technology that will have the largest impact on demand planning in the next seven years.
It’s not hard to see the fit between AI and demand planning. Demand planning involves lots of number crunching, data analytics and is repeated cycle after cycle. It is tempting to imagine that a self-learning AI application could do at least as good a job as a planner.
Taking a closer look reveals that there are serious challenges to AI successfully penetrating the demand planning market. These challenges are not so much technical as they are managerial. However, even if AI does not become a significant contributor to demand planning accuracy, addressing these challenges can only improve a company’s demand planning performance.
AI needs data
The most striking challenge to applying AI to demand planning is in the availability and accuracy of data. Internally, companies already struggle to maintain accurate data, starting with the most basic element of all, the product code. Ever-accelerating launch programs and shrinking product lifecycles mean more product churn than ever. One corporate head of planning that we spoke to said: “Let’s show we can correctly link product codes in substitutions before thinking about AI.”
In order to build a correct demand plan, one-off events have to be identified and accounted for, such as service issues and one-time promotions. For an AI application to learn from these events, they would need to be fully understood and coded, which is no small effort. There is also external data in the form of market intelligence that would need to be acquired and leveraged, such as competitor actions, customer behaviors, and trade disruptions like price changes and sell-out data.
Yet many companies today struggle with their digital culture and level of savviness. In speaking to large multinationals that have made serious investments in demand planning tools, almost all of them face the same struggle: their planners prefer to build demand plans in Excel and upload them into the expensive, integrated tools they must use to propagate their demand plans. The usual explanation for this resistance is that the tools don’t have enough of the internal and external contextual data to build pertinent statistical plans.
A recent survey from Supply Chain Quarterly revealed that Excel is by far the most common analytical tool used by supply chain planners, with advanced tools like supply chain control towers used by about 60% of companies. This matches our anecdotal observation that about half of companies use an advanced planning system (APS).
The absence of data, resistance to using the existing suite of statistical tools, and level of digital savvy represent non-negligible challenges to the deployment of AI-enabled demand planning.
