Product Support Financial Value Drivers. 3/10 – Product Failure

Oct 11

This post is the third of ten entries that will discuss product support financial value drivers for solutions supplied by a commercial or military focused capital good Product Support Enterprise [PSE]. The 10 topics that will be discussed are the following:

  1. # of products employed by end-users
  2. End-user product utilization rate
  3. Product failure
  4. Environment in which end users engage the product
  5. Preventive maintenance processes employed
  6. Volatility of product technology
  7. Regulatory requirements
  8. Chronological age of the product installed base
  9. Life cycle stage of the product
  10. Manufacturer’s warranty coverage

Product Support Value Drivers – Product Failure Physics Envy

This area is one of the most “abused” areas in Product Support life cycle financial planning. Operation Research [OR] analysts, design engineers and logistics professionals have what is affectionately called “physics envy” when it comes to estimating the product failure rates of end-items and their components. The elite group of professionals in the business of predicting product failures tend to have a universally low success rate…

The marketplace has defined the acceptable average level of unplanned failures for a capital good/end-item at about once every 5-7 years. This product failure rate is applicable primarily for Commercial Off The Shelf [COTS] items, with Developmental/Design-To-Order items incurring product failure rates anywhere from 50-100% higher than that of COTS items.

The source of the aforementioned failure data is the Security Exchange Commission [SEC] mandatory filings by OEMs detailing their actual expenses incurred to support their warranty programs. There is over 10 years of reliability/failure rate data sets. Note that product failure rates have dropped by almost 50% over this 10+ year period. Why the “failure analysis” community does not employ this treasure trove of data in all their cost calculations is always amazing to me.

Product Support Value Drivers – Product Failure

Recently Giuntini & Co. developed a scenario-based Product Support life cycle financial plan that included the target cost for the correct-failure process throughout the twenty life of a product. We employed a series of SEC filing data sets and estimated $10 million per year in costs associated with the correct-failure process for an installed base of $200 million end-items. We also employed another method to calculate the cost and it still resulted in approximately the same number.

Product Support Value Drivers – Product Failure

While we had been calculating the correct-failure process costs, a team of OR brains were also calculating the same cost; we were both aware that we were working to the same goal. We both agreed to compare our estimated costs and there was a 4-fold difference in our costs; the OR guys were the higher number. After I examined their methodology, which was quite eloquent, I must say (disclosure; I once was an OR geek myself), I found their results to be totally bogus.

If the higher product failure rates were to have occurred, the product would never have been acquired by any end-user. Our common client accepted the Giuntini & Co. cost estimate as the one to be included in his Total Ownership Cost [TOC] calculation. To this day the OR brains have remained convinced that their methodology was the right way to go, even after being proven decidedly inaccurate.

Lesson learned – be extremely careful of ”physics envy” professionals providing you with product failure rate estimates. There is a high probably that they are materially off from the real world and if you accept their costs without an alternative opinion, you have only yourself to blame when an estimated TOC is way, way off.

Hypatia©, a Giuntini & Company financial software tool, provides a highly automated means of calculating the above and other product support financial value drivers, as well as an effortless way of being able to change any utilization assumption and immediately understand its impact upon total ownership costs.

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