Many companies are excited about the possibility that artificial intelligence could improve the accuracy of their demand plans. However, there are several significant hurdles that they must overcome first.
Artificial intelligence (AI) continues to draw a lot of attention as companies and technology vendors look at how machine learning could improve supply chain operations. In particular demand planning, understood here as the process of developing forecasts that will drive operational supply chain decisions, is being touted as the next potential field for innovation. Technology giants like Amazon and Microsoft have announced 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 and Planning (IBF) identified AI as the technology that will have the largest impact on demand planning in the next seven years.1
It’s not hard to see the fit between AI and demand planning. Demand planning involves lots of number crunching and data analytics, and it is repeated cycle after cycle. Given the nature of the activity, it is tempting to imagine that a self-learning AI application could do at least as good a job as a human planner at forecasting demand.
A closer look, however, reveals that there are some serious challenges to AI successfully penetrating the demand planning market. These challenges are not so much technical as they are managerial. 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.
The need for data and digital savviness
The most striking challenge that companies face as they apply AI to demand planning is the availability and accuracy of data. The more data that is provided to an AI application, the more robust the resulting conclusions are, making data availability an essential foundation to a successful AI implementation. Internally, companies already struggle to maintain accurate data, even for the most basic of elements such as product code. Ever-accelerating product launches 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 (where one product transitions into replacing another) before thinking about AI.”
In addition to internal data, a good demand plan also requires external data in the form of market intelligence, such as competitor actions, customer behaviors, and trade disruptions like price changes and sell-out data.
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About the Author:
Dr. Richard Markoff has spent the last 25 years as a supply chain executive, academic, consultant and coach. With his broad supply chain and operations expertise, he leads Innovobot’s Advisory Services practice and provides valuable insights to the firm’s partner companies.