#demandforecasting ai

Navigating today's supply chain planning landscape is more challenging than ever, riddled with unforeseen external factors like climate changes, geopolitical tensions, and labor-related disruptions.

Despite these shocks, manufacturers must maintain accurate demand planning to optimize production capacity, efficiently allocate inventory to DCs, and meet customer service expectations and OTIF delivery targets.

So what's a planner to do?

Enter autonomous resilient planning!

We can take a customer's demand forecast as an input, which maybe manual i.e. simply aggregated from human sales reps estimating which orders are coming by when, or it maybe a simple traditional ML based forecast. Then we add our own external signals data from our VuGraph knowledge graph and perform the shock simulations in our digital twins along with the shock's impact on your own supply chain dynamics. Consequently, the AI Agents are trained to recommend the production quantity levels that optimize for the customers business KPIs for both the nornal scenarion, as well as the shock scenarios.

It's important to note: while AI agents offer recommendations, the human planner retains the ultimate decision-making authority. Informed by the agent's insights and the recommended actions, the planner can make informed choices, carefully weighing their impact on business KPIs.

In summary, the integration of AI-driven decisioning agents into the demand planning process empowers companies to proactively anticipate and respond to disruptions, ensuring a more resilient and adaptive supply chain.

By Moataz Rashad

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 selected. 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 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 updates of on-hand inventory and point-of-sale 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.