Whether your organization is facing rapid growth or another round of belt-tightening, the need for supply chain optimization remains a business imperative. Maybe there already is an initiative planned or started within the company, or it is possible that you have run out of steam or are not achieving expected results. The reality is that 75% of all transformational initiatives fail to deliver. To begin to understand why let’s consider what optimization might be.
Optimization means all of these things need to happen and at the same time improving fill rates that increase customer service levels. Even then, for optimization to be truly successful, it must also be sustainable over the longer term, continuing to deliver improved results into the future.
One of the key dynamics important to optimization is intelligent demand segmentation. Too often, organizations are managing too many parts with quantities that are all over the board. Looking to predict demand changes and spikes – and at the same time processes to negotiate the best price from suppliers by way of win/win volumes – becomes a daunting task.
We look to Kanban options, re-order point planning or vendor-managed inventory (VMI) strategies, or we negotiate consignment agreements or introduce schedule agreements with suppliers without really getting the results we are chasing. Adding demand segmentation to the mix can help make these strategies all the more effective in an optimization process.
What then is demand segmentation? It is an approach to group varying demand types into categories with similar features and characteristics. Considerations include showing consumption volume or sales volume of products against demand consistency. Following our Reveal plan-for-every-part philosophy, the principle is that each part or product is treated uniquely because each has its own distinctive demand attributes. By way of example, when we set up purchase or re-order strategies, we treat those products with high-volume and low-demand variability differently to those with low-volume and high-demand variability. Simply looking at high volumes in product quantity does not recognize that lower-volume items might well be the predictable ones. One option we consider is what we call “movers and shakers.” Those are materials with high average values, the reduction of which drives upturns, against those with high consumption figures. In all of this, understanding demand variation is also key.
In building the segmentation model, historical consumption/sales data is required. The average and standard deviation of historical periods enable the calculation of the coefficient of variation (CV), which provides the risk factor and directs us to see variability patterns that, generally speaking, show:
A lower CV shows consistency/predictability:
A high CV shows little to no predictability:
The final piece of the puzzle is to consider DOS (days of supply of inventory on hand). DOS next to historical usage and CV provides a solid method to identify distinct categories, such as:
So clearly, embracing intelligent demand segmentation supports the strategic direction to help us in surviving supply chain optimization.Inventory Optimization, Supply Chain Optimization