The high cost of outdated, inefficient forecasting methods
Despite the error-prone nature of creating forecasts via manual analysis, static monthly planning cycles and consumer-grade spreadsheets, many demand planning teams still stick to their traditional methods. Why? Because change is difficult. It requires an investment in new solutions and new processes, as well as significant employee education and cultural transformation efforts.
But when market volatility and disruptions cause planning teams to produce an inaccurate forecast due to their outdated, inefficient forecasting methods, the costs are incredibly high. The consequences include:
- Lost sales and decreased consumer loyalty due to product shortages
- The financial costs of markdowns, waste and excess inventory
- Damage to crucial retailer relationships
The only comprehensive solution? Artificial intelligence
The truth is that most planning teams aren’t equipped to consider hundreds of relevant factors and arrive at an accurate forecast in a dynamic daily or intraday fashion. The scope, depth and pace of the required analysis exceed human cognition and consumer-grade tools — and historic sales data has become almost meaningless in today’s fast-changing landscape.
Today successful organizations are empowering their demand planning teams with advanced, forward-looking, predictive technology solutions that are powered by artificial intelligence (AI). How is this accomplished?
- Fueled by AI, modern decision engines can ingest huge volumes of real-time data from across the supply chain and external sources like news, weather, and social media and arrive at optimal forecasts in mere seconds.
- As conditions change, these “always on” engines dynamically adjust their predictions in real time to reflect new influencing factors. Invisibly and behind the scenes, they’re constantly creating more and more accurate predictions, enabled by AI capabilities that continually learn and improve.
- In addition to making recommendations, AI-enabled decision engines further support decision-making by autonomously creating and analyzing hundreds of scenarios — then providing insights to planners.
AI represents the best way for demand planning teams to keep pace with the dynamic nature of today’s markets and make optimal decisions. For example, because new tariffs may follow the U.S. presidential election on November 5, many American companies are stalling their offshore production efforts, while other companies are speeding imported products across the border. Probabilistic capabilities in advanced demand planning software can show the likely outcomes of both strategies. In addition, the analysis conducted by demand planning engines can have big implications upstream, downstream and across the supply chain, if the planning team shares these kinds of predictions.
