Carl Fransman - Tangent Works
Retail forecast errors reach on average more than 30%, with forecast accuracy impaired due to ever-changing conditions. This makes for a challenging use case, but one with much potential for improvement.
Datasets in this use case vary depending on product, sector or even geographic location, resulting in a cumbersome and complex model building process. TIM not only avoids this pitfall through automated selection of the right input variables, but will even explain the impact of each predictor, allowing for further refinement or data sourcing.
Furthermore, TIM brings responsiveness through automated model tuning in reaction to internal and external changes. The increased responsiveness leads to higher accuracy. This results in less waste due to inventory scrapping (especially for perishable goods) as well as less lost sales due to inventory shortages.
Typical data in this use case includes past sales volumes, supplemented with data regarding commercial actions and external factors impacting sales volumes.
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