Nintendo's Demand Forecasting and Shelf Availability

Recently Nintendo has been facing a problem many consumer electronics manufacturers envy– consumers love their game consoles! But now the iconic company has a brand image problem, ie. why can’t it accurately forecast the demand, and ensure its new products are available at its retail partners. See this and this.

In a nutshell, there are two problems in play here: a) Demand Forecasting, and b) On Shelf Availability. In this case, Nintendo is struggling with both. Firstly, they under-forecasted initial demand for the new product at the launch date that they chose. Secondly, subsequent to the actual launch, they didn’t coordinate their supply chain with the retailers’s data (point of sale and distribution network) to ensure On Shelf Availability for the product so its eager customers can buy it.

On Shelf Availability is a sub problem of demand forecasting that deals with optimizing the likelihood that a customer who walks into a store (physical or online) searching for a product X, will find that product both in-stock and, in case of physical stores, actually on the shelf where its supposed to be to complete the purchase.

It is clearly the one metric that ensures no sales are lost. If a customer intends to buy, they will find the product available at the time and location of their choice. 

This problem is a key supply chain problem that’s been around for decades. It requires close real-time collaboration between the manufacturers and the retail channels with daily near-real-time updates of on-hand inventory, distribution network, logistics, and Point of Sale (PoS) transactions. But most importantly, it requires deep learning to solve it at scale!

Unfortunately, traditional machine learning techniques deliver only ~55–65% accuracy which costs millions in lost sales. They are unable to ingest the numerous external signals that impact demand, and they are unable to handle the massive volume of data the tier1 manufacturers and tier1 retailers deal with. So they’re left with building numerous disparate models instead of one large monolithic deep learning engine that can learn from all the patterns and transfer learnings from one store’s patterns to a sister store etc.

A deep learning solution is able to handle all kinds of data feeds that impact the customers’ demand patterns, including economic and demographic data which vary by vertical and product category. It also ingests the PoS and inventory data along with daily feedback from the manufacturer and retailer in order to adapt and self-tune its predictions in real-time. That’s what Nintendo and comparable major enterprise manufacturers need in order to address these key supply chain challenges that could make or break the company.

#ai #deeplearning #supplychain #demandForecasting #shelfavailability #OSA