As consumer trends evolve rapidly, relying solely on legacy methods and historical data is no longer enough to predict demand, drive business value, and improve forecast accuracy.
To add to the challenge, consumer needs, buying patterns, and channel behaviors continue to shift, creating an ever-growing mismatch between historical trends and expected future demand.
As a result, traditional and reliable improvement levers that leaders have at their disposal have stalled, resulting in organizational-wide impact, including elevated end-to-end excess and obsolete inventory penetration, decreased customer service levels, increased environmental, social, and governance (ESG) impact, and weakened supply chain agility.
Recently, there has been an overall acceptance that using predictive analytics like machine learning (ML), artificial intelligence (AI), and generative AI (GenAI) is the next step in driving forecast accuracy improvement, with nearly 50% of supply chain organizations planning to implement AI technologies within the next 12 months2.
However, organizations are quickly realizing that simply implementing the desired technology is not a “magic bullet” to solve their forecasting problems, as teams are stuck not knowing where to start, which models to use, or how to properly leverage available data to drive the right insights and decisions within the Demand Planning process.
Therefore, organizations are caught trying to balance the investment needed to use best-in-class processes and methods with the ability to ensure cross-functional teams are ready to effectively apply leading techniques and deliver the desired value.
1Source: Supply & Demand Chain Executive, “Best Practice Demand Planning Meets Unprecedented Demand Volatility” (December 2023). 2Source: Gartner, “Gartner Survey Shows Half of Supply Chain Organizations Plan to Implement GenAI in the Next 12 Months” (January 2024).
