Product Support Financial Value Drivers. 8/10 – Chronological Age of the Product Installed Base

Jan 08

This post is the eighth 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 requirement
  8. Chronological age of the product installed base
  9. Life cycle stage of the product
  10. Manufacturer’s warranty coverage

The basic premise underlying this Product Support financial value driver is that as an item that is continuously employed in a process gets older, “stuff” may or may not happen to it. The analysis of this area is primarily dominated by the product design/reliability engineering community; this may be good or bad as we delve further in our discussion.








For my calculations, all the primary subassemblies of an end-item need to be identified and codified as to falling into one of the six age-related reliability curves. These subassembly categories can be the following, though they are not exhaustive:

  • Sensors (i.e. lasers)
  • Electronics (i.e. computer processor)
  • Electrical (i.e. generator)
  • Mechanical (i.e. gearbox)
  • Hydraulic (i.e. actuator)
  • Hard/soft goods (i.e. filters)
  • Software (i.e. application)
  • Structure (i.e. housing)
  • Others (i.e. outfits, tools)

Once the reliability life cycle curves are identifies as well as the subassembly categories that are part of the end-item configuration, I can then identify how each reliability curve matches-up with the subassembly category.









Then, the costs of correcting or preventing failure can be estimated using the following methodology:

  • Identify a variable that is closely aligned with the cost of correcting or preventing unplanned failures. The correlation selected for our discussion is that between the value of a subassembly and the cost of its repair; the higher the value of the subassembly, the higher the cost of a repair event
  • Obtain a Cost Estimating Relation [CER] with that of the repair cost of a subassembly type and the value of the item. The use of warranty financial data, filed with the Securities and Exchange Commission [SEC] by every publically-held OEM (i.e. Caterpillar, United Technologies) and their key suppliers, provides a means to establish a CER

    For example, direct and indirect repair costs for electronic components, as a percent of their value, is 2% per year. If the cost of repair is 10% of the component value, then the annual failure rate is 20% of fielded items (2%/10%) based upon a “normal” utilization

Areas such as modifications and remanufacturing/overhaul can reset some of these aging factors. Also the source of design, design-to-order versus off-the-shelf, can impact reliability factors.

Now, for most reliability engineers, my calculations are most likely “foreign”, but I can tell you that leadership can understand my “simple” method of establishing the cost of correcting or preventing failure. Most reliability engineers have “Physics Envy”; they develop formulas that demonstrate that the reliability world is an “orderly place”, just as that is found in the realm of the sciences. But for anyone who has been in the field of reliability, it is at best an inexact science in which one is happy to be accurate in one’s prediction by +/-50% in anyone year, and over the life of an item +/-20% accurate.

Note that reliability engineers struggle to obtain credibility with leadership because they “get into the weeds” almost immediately when discussing reliability; one recommendation to engineers is to translate all that is calculated into financial terms; not always easy to do. Annual costs to prevent or correct failure should always be within the range of 3-6% of the value of an item that is being analyzed; anything above these percentages should be suspect.

From my experience I have seen highly rigorous quantitative analysis performed by an engineer, when converted into financial terms, to be in the 20-25% of the value of the item. Almost always after further analysis, the underlying assumptions of the engineer’s calculation were incorrect and indeed the ultimate outputs resulted in a 3-6% range of the value of the item.

Product Support life cycle financial planning should include scenario-based tools that can analyze the impact of different factors upon reliability in any one period, as well as upon the entire life cycle.

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. Hypatia is also a proven, trusted and highly effective tool for assisting in the development of product support business case analysis.

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