An organization’s forecast accuracy and overall ability to predict demand can have real bottom-line impacts that have ripple effects throughout all levels of the supply chain; from customer service levels, inventory investments, working capital requirements, and overall total cost-to-deliver. Despite the recent buzz around “big data” and analytics in supply chain planning, there has been a surprising lack of intelligent data science being applied to understanding and improving forecast accuracy and subsequent demand planning performance. For example;
- Forecast accuracy targets are still being set by negotiation based on historical planning performance
- Many Forecast Accuracy benchmarks lack actionable insight, using broad peer groups for comparisons without standardization of metrics or taking into account large product portfolio differences.
- Rapidly expanding product portfolios and increasingly volatile consumer behaviors are impacting planning performance without quantification
- Most supply chain organizations lack competitive intelligence into other company’s performance, being largely limited to their own internal performance data.
There’s a better, more cost efficient approach to improving forecast accuracy that also ensures maximum return on investment for a demand planner’s effort. Ben YoKell, Principal at Chainalytics, explores a couple of intelligent, data-driven approaches that organizations can leverage to begin improving forecast accuracy while actually yielding tangible performance results immediately. For those interested in exploring this simple, scientific approach in more detail, you’re in luck. I hosted a webinar on this very topic you can watch. If you’re hitting the sound barrier and looking for new ways to improve forecast accuracy, check out the session on-demand here. 
