Devadrita Nair and Maria Jesus Saenz of the MIT Digital Supply Chain Transformation Lab recently published an aritcle in MIT Sloan Management Review. Here's a brief excerpt:
Hot pink made a comeback this past summer. But as numerous fashion brands eagerly jumped on the Barbie-driven trend, many struggled to get their numbers right. Aldo, an official brand collaborator, saw its Barbie-based platform shoes fly off the shelves within 24 hours, thanks to the viral hashtag #TikTokMadeMeBuyIt.
The Barbie phenomenon highlights a modern dilemma: how to accurately forecast product demand when the commercial landscape is constantly changing. This has always been a challenge, but the retail environment is now more volatile and fad-driven than it was in the past, requiring companies to take demand forecasting to a higher level.
Many companies have implemented next-generation tools like artificial intelligence to meet the challenge. While algorithms are improving forecasting performance, human interventions are still needed to contextualize market changes and bring other attributes, like responsiveness, to the table. Because there’s no standard template yet for such relationships, we have developed a framework to help companies marry human expertise with AI-driven forecasting solutions, based on a product’s characteristics.