Demand Driven Performance - DDPSC06.2

06.2 Configure And Manage Supply Chains To More Effectively Address The Challenges Associated With Inventory Management (Part 2 of 3) (DDPSC06.2)
06d. How would this approach work when sizing buffers for a new product launch, for which there is no history?

If the product you are launching is a totally new product, and not one that is replacing an existing product, or one that is targeting a competitor's product, then the question of having no demand history is a truly valid one. However, when a new product is replacing an existing product then, at a minimum, you do know the demand history of the product being replaced. If you are targeting a competitor's product then it is likely that you know something about the demand for their product; otherwise, why would you target it with one of your own? All three cases do have one thing in common: there is a greater level of risk when sizing inventory buffers given the limited and uncertain product demand history.

For a totally new product there really is no Point Of Sale (POS) demand data that can be used, which makes forecasting quantity and timing of demand, no matter how it is done, mission impossible.

When replacing an existing product with a new product, one for which you have POS demand data, the risk is usually much less then when launching a totally new product, but there is still risk nevertheless. Here the risk is more in terms of the level and rate of acceptance, by the existing customers, of the new product as a replacement for the old.

Targeting a competitor's product with POS demand data carries a risk similar to that of replacing an existing product of your own with a new product, whereas doing the same without POS demand data presents risks that more closely match those of launching a new product.

For all three cases, it is clear that we will not be able to reliably predict, or forecast, what POS demand will look like in terms of quantity and timing. Hence, the only effective response we have resides in our ability to effectively position and size inventory buffers to meet customer demand without cross shipments and expedited deliveries, while minimizing our exposure to slow moving and obsolete inventory. As such, risk management resides in our ability to effectively accommodate the variability in demand, in terms of both quantity and timing.

To better address this aspect of risk management, we need to re-examine what we discussed previously (DDPSC06, Part 1) where we said,

The size of an item (i.e., Part Number (PN) or Stock Keeping Unit (SKU)) specific inventory buffer is a result of determining the cumulative demand in windows of TRR along a timeline.

The real risk resides in our inability to adequately forecast the quantity and timing of POS demand. However, a slight shift in our forecasting efforts towards one of forecasting the cumulative demand in windows of TRR might very well simplify the entire forecasting problem, while significantly reducing risks of sizing inventory buffers.

We would suggest, that forecasting the cumulative demand for an item (i.e., Part Number (PN) or Stock Keeping Unit (SKU)) in windows of TRR is much easier than forecasting the detailed quantity and timing of POS demand.

06e.    How does TOC use inventory positioning to address the uncertainties of customer demand?

When forward positioning inventory in an uncertain market we run the risk of being overstocked or under stocked depending on how much inventory we initially forward position and how much Time it takes to Reliably Replenish (TRR) the inventory buffer. Having additional inventory positioned further upstream can help alleviate the risk of overstocking, while shortening the TRR can make us more responsive to the risk of under stocking.

When it comes to identifying the best upstream inventory positions, the key work flow interdependencies, discussed in DDPSC03, Configuring and Managing Supply Chains for Effective and Efficient Performance, would point to the divergent points (DDPSC03 Figure 3) as being the best candidates since they enable demand to be aggregated, which increases both the likelihood and frequency of its use. As a result, inventory turns at divergent points tend to be higher then at non-divergent points. While DDPSC03 Figure 3 highlights divergent points in material moving from raw material to finished goods, inventory moving from manufacturing to wholesale to retail inventories also follows a divergent flow.

Knowing how the aggregated demand for an item stacks up on a time line is the other aspect that affects inventory turns at divergent points. Does all of the supported downstream demand occur at the same time, or is it more complimentary? Aggregating downstream demand that is occurring at the same time from different inventory buffers simply produces an additive quantity upstream at the aggregation point. Whereas, aggregating downstream demand that is not occurring at the same point in time from different inventory buffers results in a more uniform non-additive quantity upstream (DDPSC06, Part 1, Figures 6-9).

For example, an aggregated upstream buffer, with a 10-day TRR, supporting 10 downstream inventory buffers each having a single one day demand of 5 units in that 10-day TRR window would require a maximum inventory level of 50.

If the demand for all 10 downstream buffers were on the same day, then the maximum daily demand at the upstream buffer during a 10-day TRR would be 50 units.

If the demand for each downstream buffer were on a different day during the 10-day Time to Reliably Replenish (TRR) the upstream buffer, then the maximum daily demand at the upstream buffer during a 10-day TRR would be 5 units.

A more uniform recurring demand generally makes for a better choice of an upstream inventory buffer, in that the workload associated with processing incoming and outgoing inventory is more constant. The result is a more uniform workload, which usually translates into a lower overall cost to operate. Any reductions in the Time to Reliably Replenish (TRR) at the upstream buffer would translate into a reduced inventory level.

With that said, it is important to keep in mind how uniformity versus non-uniformity and regularity versus irregularity in Point Of Sale (POS) demand come together within TRRs to produce Time Based Demand Patterns. And it is those Time based Demand Patterns (DDPSC06, Part 1, Figure 5) that enable the tradeoffs between time, inventory, investment and risk to be easily processed at the item (i.e., Part Number (PN) or Stock Keeping Unit (SKU)) level.

The above discussion is as equally applicable to the launch of a new product or the launch of a replacement product, whether the new product is intended to replace one of your own or one that your competitor offers. And, if you stop and think about it for a moment, you will see that this same approach is just as effective when we think about entering emerging markets. The only thing that might be different is the physical location of the divergent point inventory buffers, given the implications of country boundary's, but the application of the concepts and the approach for containing supply-side and demand-side variability is the same.

In the end, being able to effectively size and manage inventory levels at the item (i.e., Part Number (PN) or Stock Keeping Unit (SKU)) level can provide a company with a fairly significant competitive advantage.

To see a working example of the TOC Inventory Management Solution, where properly located and sized inventory buffers are managed using TOC Replenishment Principles to provide a closed loop inventory management process that can reduce out-of-stock conditions, while improving inventory turns, please view: TOC for Inventory Management - Buffer Sizing Exercise.

Next: Configure and Manage supply chains to more effectively address the challenges associated with Inventory Management

Who defines the required service level and how should it be defined?
What kind of policies, measures and information exchange issues can cause replenishment based inventory buffers to fail to perform?

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