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Surviving Supply Chain Optimization: Intelligent Demand Segmentation Is Key

By Sean Elliffe October 17, 2016 by Kelly Kuhlman

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.

There are several dimensions that make up effective optimization, including:

  • Forecast accuracy providing the demand that reflects the market requirements in terms of quantity and product mix.
  • Material availability of the right stuff at the right time in the right quantity.
  • Reduced or increased inventory as is appropriate for the organization – sometimes we have too much of the wrong stuff or too little of the right stuff.
  • Increased production efficiency and capacity utilization.
  • Streamlined business processes.
  • Better workforce utilization.
  • Improved utilization of functionality that you have already paid for – standard functionality already available to you.
  • Reduced customization requiring more and more support from IT, the result of which reduces the time they have to continue looking for innovative solutions for the business and so on.

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.

Intelligent Demand Segmentation

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 up turns, 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:

  • The patterns probably show the same monthly usage month on month, suggesting VMI items or bulk stocking programs with suppliers.
  • A CV less than 1.0 can indicate the use of pull or flow approaches.
  • One less than 0.5 might best be handled with rate-based replenishment methods.

A high CV shows little to no predictability:

  • These SKUs might make more sense as make to order (MTO), to be purchased as and when needed.
  • A CV greater than 1.0 could potentially be an assemble-to-order SKU.
  • A very high CV suggests products may need to be phased out.
  • The bottom line is that we do not want to hold inventory for SKUs that have a high CV because the unpredictability of demand increases the risk that we hold something in stock we may not be able to sell.
  • Remember that the salespeople might get some heartburn with this approach. Work with them to secure understanding that as they sell more, the CV will lower, the SKU become more predictable and the stocking strategy will be revised.

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:

  • Common parts that have a high rate-based stocking process that could be VMI stock. This would release procurement from daily attention to these items, allowing them instead to give more attention to strategic items.
  • Excess stock, because considering CV and days of supply (DOS) together provides insight into SKUs that may hold excess stock.
  • SKUs with a very low CV oftentimes point to those materials with insufficient inventories to meet demand requirements.

So clearly, embracing intelligent demand segmentation supports the strategic direction to help us in surviving supply chain optimization.

Reveal excels at supply chain optimization. Contact us to learn more about how we help companies transform their supply chains successfully and sustainably.

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