August 30, 2010

Shortest Path To Recovery

For every batch of service spares that go out the door, a portion of them is bound to return, usually under less than ideal conditions.

No matter how well managed the forward-facing segment of a service parts supply chain may be, it’s often hard to predict exactly what will come back from the field. Will it test as good? If yes, will the organization still have demand for that tested good component? If it does not test as good, will it be covered by warranty, or will it simply be labeled as scrap? If it’s covered in-warranty, how long will it take for the organization to recover credit back from the device manufacturer?

All good questions that seem to be frequenting the desk of any supply chain director in almost perpetual fashion. It’s relatively easy to outsource the forecasting, planning and fulfillment of a service spares inventory, but what to do about this unpredictable returns stream? It represents an element of risk that is quite difficult to quantify until tough questions concerning the end-of-fiscal-period inventory reports are pushed out to inventory control teams by CFOs.

The answer lies within a complete turnkey service parts management solution that includes a buy-back model. The buy-back model has the potential to reduce the time it takes to recover all credits tied to field return product streams from months down to mere weeks or days. It’s the recovery equivalent of just-in-time access to service spares.

In fact, in today’s complex supply chain, no spare parts management solution is complete without a buy-back component. The buy-back model is based on sophisticated analysis of mountains of data concerning the field returns. The data is used by the inventory manager to build a profile of what the returns stream looks like for the organization in question. What is the product mix? What is the error margin within the product mix assumptions? What have the historical yields been for each SKU? How much has the organization recovered from device manufacturers as part of RMA claims concerning in-warranty failures? Does the scrap product carry an intrinsic value of any kind?

Once these questions are answered, a mathematical model is built that predicts the value of field return streams with manageable error margins. Our organization currently builds models that are sustainable for up to five months ahead of analysis date – with some margin of error, of course. The model is utilized to buy back inventory from our partners as soon as that inventory hits their dock doors – condition unknown.

As the underlying data evolves, the model evolves with it. The upside is near-instant gratification for our partners when it comes to recovering value from their field returns instead of managing accounts receivable reports for extended periods of time. And often, the buy-back model prevents good inventory from going stale and bad inventory from ending up in a dumpster somewhere.

While evaluating a buy-back model for your organization, the most important point to consider is picking the right partner for the right commodity mix. Once successfully on-boarded, a properly executed buy-back model will reduce total spend on inventory and will contribute positively to your organization’s lean planning practices.

August 16, 2010

Site balancing for a supply-chain advantage

I was working with one of Converge’s customers a few weeks ago, analyzing service parts inventory levels across half a dozen of their global sites. The exercise was a valuable reminder of the importance of service parts inventory planning and brought to mind a broader supply-chain management problem concerning dynamic sharing of inventory across multiple sites.

Dynamic sharing is a relatively new concept that allows multiple sites to transfer inventory back and forth as needed. To put this into perspective, let’s take the computing industry as an example and focus on notebook computers. There are many common components across multiple brands of notebooks. These common components are typically categorized under CPU modules, memory modules, hard disk drives, optical drives and liquid crystal displays.

Now, let’s consider a service provider that refurbishes customer-returned notebook computers for more than one manufacturer within the same site or at multiple service locations.

The service provider will typically receive consumption or failure forecasts from each manufacturer, and in turn will procure service spares utilizing the notebook manufacturer’s internal part number for each service part.

While this system works well for meeting service-level requirements that the supplier must adhere to for each notebook manufacturer, it also introduces the risk of overprocurement of the same core part under various internal part numbers – either for the supplier or for the owner of the spare-parts inventory.

When this occurs, Converge is approached by our customers, at the end of each fiscal quarter or fiscal year, to evaluate cross-utilization opportunities of their “internal part number” inventory across multiple sites in an effort to drive down their inventory levels by reducing new procurement.

While periodic site balancing through many-to-many part number translation is a good step in the right direction, continuous linkage among internal part numbers that boil down to the same core part number across all service sites can improve supply-chain efficiencies by inherently altering procurement behavior.

Clearly, this is easier said than done. It requires clean data and a fairly sophisticated application logic that is deeply embedded in the supply-chain DNA of the service provider. However, it also represents a competitive advantage if executed properly. In today’s supply-chain services arena, where inventory ownership is the major source of financial liability, a quantifiable competitive edge such as real-time site balancing can be the winning ingredient.

Does your organization practice site balancing? Let me know what you think in comments or by sending me an email.