Consequently, Shell experienced service level hits, resulting in firefighting. The block chart in Figure
18 tracks the relationship between the stable, forecastable product, the variable product, and the
unpredictable product.
As Shell’s sales volume, excess stock, the number of SKUs sold grew, and the re venue over a long
enough period, there was a disturbing picture: the areas for growth in the company’s business were
the hardest to forecast. Nick recognized that he was running out of levers to drive improvement. The
regions running the business were finding it harder and harder to stay on the projects. He needed to
find new solutions. This quest led to the consideration of the adoption of Demand-Driven MRP.
Figure 18. Product Portfolio Analysis
By definition, in traditional MRP, the forecast translates into supply chain requirements. In the
process, the initial forecast number first becomes a finished goods requirement, then a planned order,
and finally a materials requirement – all based on the initial forecast. The problem is that a forecast is
not an absolute number. Instead, it is a set of probabilities. As demand error increases, a focus on
inventory buffers and push/pull decoupling methods increases in importance. Previously, Shell was
only looking at safety stock levels and not the form and function of inventory. The adoption of DDMRP
enabled the building of buffer inventories to reduce the ‘nervousness’ of the system.
In early 2015, three senior regional planning managers discussed the concept of demand-driven
planning. To prove the concept, Shell, with help from consultants, built a simulation model and tested
the potential benefits for the North America market. In Figure 19, we show the results of the
simulation. The red line of DDMRP was a substantial improvement to traditional MRP output shown
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