Forecasting and planning are some of the very oldest use cases of modern statistics — businesses as far back as the 1950s used computer-based modeling to anticipate risks and make decisions. But in the age of AI and algorithms, older modeling techniques fail to incorporate the wide variety of data sources needed to produce results precise enough for the modern enterprise.
In addition, traditional forecasting and planning methods can be wrought with manual processes and, therefore, unintended bias. For example, when it comes to forecasting, over-forecasting is a safer choice for a business because it ensures sufficient supply. In order to be more exact, these manual processes and decisions need to be removed entirely to make way for truly data-driven decisions.
Keys to Success
The Institute of Business Forecasting (IBF) ran a survey where 70% of respondents said AI will be the dominant technological element in demand planning. But how can companies get there?
EyeOn, a consulting firm specializing in planning and forecasting (specifically for large customers across four industries with complex global supply chains), focuses on the following three elements to advance and innovate on their approach to planning and forecasting:
- Emphasize data quality.
- Know the importance of bringing business knowledge to data projects.
- Focus not just on delivering accurate predictions, but better decisions.
Case Study
In order to bring efficiency for their clients, EyeOn first had to bring efficiency to their own data projects and larger data team. They ultimately are aiming to half the time necessary to onboard new employees, and they have already increased productivity with project templates and automation — read their full story to see how they did it.