Advancing Demand Planning Decisions Through Causal AI
The primary goal of this initiative is to harness the power of Causal AI to overhaul and enhance the demand planning operations for companies in the apparel and consumer goods industries. This endeavor aims to accomplish three pivotal objectives:
- To cultivate a deep and systematic comprehension of the causal chains affecting demand, thereby facilitating superior decision-making in real-time.
- To minimize the over-reliance on crucial personnel, whose expertise currently serves as the linchpin of demand forecasting.
- To transition from complex, antiquated spreadsheet models to a dynamic, sophisticated Causal AI-driven platform.
Achieving these goals is expected to bolster forecast precision, curtail instances of stockouts and surplus inventory, and ensure a supply chain that is both nimble and attuned to market dynamics.
Our client is a multinational consumer goods manufacturer with a highly complex supply chain. The demand planning department oversees thousands of SKUs and places production orders 12 months in advance, based on customer indications and historical data. The stakes are incredibly high, with the manufacturing process initiating 18 months before products actually reach retail shelves. At this stage, there is a risk-laden gamble of committing to large-scale orders before actual demand is fully understood.
The complications don't end there. Once orders are placed, goods are manufactured in the Far East and shipped, usually by sea, to warehouses in the US and UK. The shipping process itself is fraught with its own set of variables like weather conditions, port closures, and capacity issues. Furthermore, fluctuating costs of raw materials, fuel and containers can drastically affect the cost of goods sold.
Currently, these multi-faceted causal relationships are not adequately captured or understood by the existing spreadsheet-based system. As a result, forecasting inaccuracies are common, leading to financial setbacks, delayed shipments, and stock imbalances. To make matters worse, a significant portion of this complex decision-making resides in the minds of a couple of key staff members, making the entire process vulnerable to the proverbial 'key person risk.'