Sustainment Vicious Circle

A vicious circle or cycle is when a bad situation feeds on itself to get worse.  Many “systems” (social, economic, ecological, technological) are comprised of chains of events that form feedback loops that can under some circumstances reinforce the previous cycle (positive feedback). 

It is not uncommon for sustainment-dominated systems (see my January 2017 blog post) to get caught in a “sustainment vicious circle” (or “vicious cycle” [1]).  In this case, more money is going into sustainment (maintaining the systems) at the determent of new investment, which causes the systems to age, which in turn causes more money to be required for sustainment, which leaves less money for new investment, and the cycle continues.  The sustainment vicious circle is a reality for militaries of many of the world’s countries, civil infrastructure, and can also appear for systems owned by individuals.  For example, individuals might face this dilemma with their automobile – fixing your existing car is expensive, but it is less expensive than buying a new car; after several such repairs one is left to wonder if purchasing a new car would have been less expensive, but there is no turning back, too much has been invested in repairing the old car (the funds that could have been used to purchase a new car have been spent).

Setting aside your used car, in this blog we are primarily talking about large, complex, expensive systems, like a fleet of aircraft, a fleet of buses, rail infrastructure, the water/sewer system in cities, etc.  These things are massively expensive to replace or upgrade (and massively expensive to sustain).  Let’s be clear, not every large, complex, expensive system suffers the sustainment vicious circle – many are well managed and correctly funded.  However, the evidence of sustainment vicious circles are all around you – look at the neglected portion of a city’s aging infrastructure.  The cause of the sustainment vicious circle is often the inability or unwillingness of system’s owners (which may be the public) to budget appropriately for sustainment (operation and maintenance), which is exacerbated by unanticipated life extensions of the system requiring it to last longer than it was designed (or funded) to last.  The money to build new things is always easier to get than the money to sustain old things AND old things always end up having to be supported longer than anyone anticipated.

How do you get out of a sustainment vicious circle?  Either the system reaches its breaking point (literally, possibly catastrophically) and simply must be replaced, or the system has to be transitioned to a more “vital cycle” that would reduce the sustainment costs of legacy systems, and provide for modernization. Pursuing the “vital cycle” is not cheap (customers have to be willing to pay) and it usually comes with one key caveat: any action taken cannot adversely affect the customer’s needs in the short term.

Corollary to the Vicious Circle – The Upgrade Trap [3]

Related to the vicious circle is the “upgrade trap”.  A common version of the “upgrade trap” is forced upon consumer electronics users.  How often have you been forced to buy a new device because none of your software would run on your old hardware?   The upgrade trap is indiscriminant, even users that derive no actually benefit from higher-performance hardware or greater functionality software, are forced to “keep up” whether they want to or not.  Even systems that are seemingly disconnected from commercial interests such as military systems are impacted, e.g., if these systems contain COTS (Commercial Off-The-Shelf) application software, the continued support of the application may require hardware upgrades that are not otherwise needed or wanted.  Is the upgrade trap by design – certainly, it is a form for planned obsolescence (see my February 2017 blog post).

The one thing that is worse than being caught in the upgrade trap, is not being caught in the upgrade trap, i.e., being caught in the “sustainment vicious circle”.

[1]  Achieving an Innovative Support Structure for 21st Century, PSB 96 (10-13-97)

[2] Sustainment, Proceedings of the Sustainment Team of the Industry Affordability Task Force, Report Number 98-SS1A, National Center for Advanced Technologies, January 1999  http://www.ncat.com/sustain.pdf

[3] P. Sandborn and J. Myers, "Designing Engineering Systems for Sustainment," Handbook of Performability Engineering, ed. K.B. Misra, Springer, pp. 81-103, London, 2008.

Cost Avoidance Based Return on Investment (ROI)

The determination of the ROI allows managers to include quantitative, readily interpretable results in their decision-making.  While the formulation of an ROI associated with investing money in a financial instrument is straightforward, the calculation of an ROI associated with the generation of an increase in the customer base, cost savings, or a future cost avoidance is less straightforward. 

ROI is a common performance measure used to evaluate the efficiency of an investment or to compare the efficiency of a number of different investments. To calculate ROI, the benefit or gain associated with an investment is divided by the cost of the investment and the result is expressed as a percentage or a ratio:

                (1)

An ROI of 0 represents a break-even situation--that is, the value you get back exactly equals the value you invested.  If the ROI is > 0, then there is a gain; if the ROI is < 0, there is a loss.  Equation (1) is relative to the no-investment case (e.g., burying the cash in the ground).

Cost Avoidance ROI
Cost avoidance is a reduction in costs that have to be paid in the future.  Cost avoidance is commonly used as a metric by organizations that have to support and maintain systems to quantify the value of the services that they provide and the actions that they take.  See my May 31, 2017 Blog post for more about cost avoidance.

Cost avoidance ROIs are a bit more difficult to calculate than the financial ROIs.  As an illustration, consider the determination of an ROI for the addition of some sort of improvement to a system.  This improvement will result in a future cost avoidance (maybe it reduces the future maintenance costs).  Is the investment in the improvement worth it? 

To formulate the ROI for adding this improvement to a system, we first have to decide what we are measuring the ROI relative to.  For our example, we could measure the ROI relative to maintaining the system without the improvement.  The ROI from Equation (1) becomes, [1]:

                             (2)
where

C_wo = the life-cycle cost of the system managed without the improvement

C_w  = the life-cycle cost of the system managed with the improvement

I_w    = the investment in the improvement

If C_wo is larger than C_w then there is a positive ROI.  The nice thing about Equation (2) is that the numerator is the difference of life-cycle costs, i.e., one only has to be able to determine the difference between the two cases (the actual values of C_wo and C_w, which are much harder to determine, are not needed) – see my November 2016 blog post on absolute vs. relative costs.  Note that C_w is a life-cycle cost that therefore includes I_w within it.

Another challenge with this ROI calculation is determining the investment cost (the denominator of Equation (2)).  For cost avoidance cases it is not always clear what costs are the investment and what costs are the result of an investment.  For example, if my improvement converts corrective maintenance actions (due to system failures) to preventative maintenance actions (taken before failure), the improvement could result in more (but less expensive) maintenance actions and the need for more spare parts.   Is the cost of the extra spare parts accounted for in the investment (I_w)?  The extra spare part cost in this case would not be included in the investment cost because the extra spare parts are the result of the improvement (i.e., the result of the investment) and are reflected in the life-cycle cost C_w.

General Comments about Calculating ROIs

1. Constructing a business case for an activity does not necessarily require that the ROI be greater than zero; in many cases, value is not fully quantifiable in monetary terms, or the activity is necessary in order to meet a system requirement that could not otherwise be attained, such as satisfying a system availability requirement.  
2. The calculation of ROI can be modified to suit the situation depending on what you include as returns and investments.  This means that ROIs are easy to calculate but can be deceivingly difficult to get right.  Financial investment ROIs are straightforward, but when evaluating the ROI of a cost savings, market share increase, or cost avoidance, the difference between costs that are investments and those that are returns is blurred.
3. ROIs have to be relative to some clearly defined situation.  Most often ROI for cost avoidance is measured relative to “business as usual”, i.e., the management of the system if no investment was made.
4. ROI is not independent of the cost of money (discount rate).  The discount rate will impact the life-cycle costs in Equation (2).
5. ROI can fool you, be careful.  The largest ROI solution may not represent the best option for an organization – see my February 6, 2020 Blog post.

[1] Feldman, K., Jazouli, T. and Sandborn, P. (2009). A methodology for determining the return on investment associated with prognostics and health management, IEEE Transactions on Reliability, 58(2), pp. 305-316.

Should-Cost

Should-cost is the result of cost modeling done by a customer to determine what it should cost them to purchase a product, service or system support [1].  Should-costing is based on the customer’s accounting and their understanding of the product or service. When a customer knows a product or service’s should-cost, then they know what they should pay for it.

Why is should-cost a thing?  For many common products, market competition protects the customer from significant overpayments (assuming the customer shops around or allows the market compete for their business).  However, for unique products and services (e.g., military programs, contracted services, etc.), where there is no market competition that sets prices at fair levels, knowing the should-cost is critical when a customer wishes to guard against overpayment.

A common alternative to should-costing is strategic sourcing, which is the process of comparing price quotes from different sources.  Alternatively, should-costing would start from an analysis of labor, materials, overhead, profit margin, etc., to estimate what the price should be.  Strategic sourcing is used for commodity items (e.g., if you are going to buy a washing machine, you shop around), while should-costing is used for highly engineered systems and services where there are few suppliers (e.g., a new aircraft carrier).  A commonly used example is the purchase of a car.  A strategic car buyer goes to multiple dealers to see who will give them the best price.  A should-cost buyer researchers the dealer’s invoice price and how dealers determine their markups and then tells the dealer what price they will pay for the car.

The car buying example aside, in the real world should-cost should be thought of as leverage, not a prediction [2].  Any should-cost analysis you perform will be wrong – there are thing you simply don’t know.  Most people have the expectation that the predictive power of should-cost alone will have the strength to cause the negotiated price to equal the price determined from the should-cost estimate - this is a great goal, but it is not typically realistic.  The point of should-cost is that it gives you leverage to push a quoted price toward your should-cost estimate.

[1] Carter, A. B. and Mueller, J. (2011).  Should cost management: Why? How?, Defense AT&L: Better Buying Power, Sept-Oct, pp. 14-18.

[2] Hiller, E. A. (2012), “Your Should-Cost Number is Wrong, But it Doesn’t Matter,” Industry Week, October 21, http://www.industryweek.com/procurement/your-should-cost-number-wrong-it-doesnt-matter  Accessed July 17, 2017.

Cost Avoidance vs. Cost Savings

In the case of manufacturing processes, it is reasonable to characterize the value of process, equipment and yield changes as cost savings.  However, the value of life-cycle management activities (i.e., sustainment) is usually quantified as a cost avoidance.  “Cost avoidance is a cost reduction that results from a spend that is lower than the spend that would have otherwise been required if the cost avoidance exercise had not been undertaken” [1].  A simpler definition of cost avoidance is a reduction in costs that have to be paid in the future to sustain a system [2].  

Cost avoidance is commonly used as a metric by organizations that have to support and maintain systems to quantify the value of the services that they provide and the actions that they take.  These organizations do not like to use the term “cost savings” since a savings implies that there is unspent money, whereas in reality there is no unspent money, only less money that needs to be spent.  Another way to put it is, if you told a customer that you saved 100 dollars, the customer could ask you for the 100 dollars back; if you told a customer you avoided spending 100 dollars there is no 100 dollars to give back.

Unfortunately, making business cases based on a future cost avoidance argument is often more difficult than business cases that are based on cost savings, therefore, there is a greater need to be able to provide detailed quantification of sustainment costs.  Requesting resources to create a cost avoidance is not as persuasive as making a cost savings or a return on investment argument. 

[1] Ashenbaum, B. (2006). Defining Cost Reduction and Cost Avoidance, CAPS Research.

[2] Sandborn, P. (2017). Cost Analysis of Electronic Systems, 2nd edition, World Scientific.

Resilient Systems and Resilient Design

We hear the word "resilience" a lot these days.  It's a word that's ripe for misuse and vagueness.  

There is a general agreement that resilience is the intrinsic ability of a system to resist disturbances.  Another equivalent definition of resilience is the ability to provide required capability in the face of adversity (sometimes referred to as disaster recovery).  Resilient design is about managing the ubiquitous uncertainties that constrain current design practices as well as finding ways to overcome an imperfect understanding of system requirements that lead to fragile and ineffectual system designs.

The definitions above are fine, but the real question is what is the “scope” of the system – and this is where views vary.  In this blog we focus on electronic products and systems, however, the concepts of resilience and resilient design are more prevalent in the building construction, architecture, and communities design space.

When you talk about resilient systems, several disciplines think they have a handle on the problem.  The control systems folks think that this is their domain, the optimization folks, the PHM (system health management) folks, the reliability engineering folks, sustainability folks, the system engineering folks, machine learning, artificial intelligence, … In my experience, none of these disciplines (with the possible exception of some system engineers) really grasps the total scope of resilience.  Every discipline wants to gather resilience under their umbrella and grant themselves ownership of the problem space based on their narrowed definition of what a system is.

In my opinion, designing resilient hardware and software (which is the focus of most resilient system design activities) is necessary but not sufficient for creating resilient systems.  For a system to be resilient requires:

• reliable (or self-managing) hardware and software
• a resilient logistics plan (including supply chain and workforce management)
• a resilient contract structure
• resilient legislation or governance (rules, laws, policy)
• a resilient business model

One might also include within all of the above aspects, a resilience to changes in the end-of-support for the system.  For many systems, the end-of-support is a moving target that is extended ("life extensions") during the use of the system.

This represents a somewhat broader scope than what is generally articulated in the engineering literature, however, in practice, neglecting any of these elements potentially creates a legacy system with substantial (and potentially untenable) life-cycle support costs.

Although the discussion in this blog has an electronic product/system focus (our "scope").  Resilient design can certainly be applied to other things, e.g., buildings, communities, furniture, information technology, websites, health care, etc.  In this broader world one could consider adding the following to the bullets above:

• culturally resilient (does the system transcend cultures and culture changes)
• environmental resilience (sustainability)
• resilience to climate change

Obsolescence Definition

Obsolescence isn’t an uncommon word – a Google search finds more than 5.7 million entries.  However, in the engineering design and product support context it has several different meanings.  First, the dictionary definition of obsolescence is the condition of no longer being used or useful. This definition is not inconsistent with the definitions that follow, but it is also not specific enough to provide much value in a product support context.

Planned Obsolescence – we hear this a lot.  Planned obsolescence refers to products (usually consumer products) that are designed to be rendered obsolete by the introduction of another product that has more functionality, higher performance, smaller size, and/or costs less.  Planned obsolescence is a strategy sometimes followed by companies that design and manufacture consumer electronics.  Planned obsolescence is often confused with “made-to-break” products [1].  Made-to-break products are products that are intentionally designed or manufactured to fail at some point in the future (nominally after the warranty has ended) forcing the customer to purchase a new product.

Sudden or Inventory Obsolescence - Sudden or inventory obsolescence occurs when the product design or system part specifications changes such that existing inventories of components (e.g., spare parts) are no longer required.  This type of obsolescence has been studied in the operations research literature.

  

DMSMS or Procurement Obsolescence – DMSMS (Diminishing Manufacturing Sources and Material Shortages) obsolescence is the loss or impending loss of original manufacturers of items or suppliers of items or raw materials [2].  This type of obsolescence is caused by the unavailability of technologies or parts that are needed to manufacture or sustain a product.  DMSMS means that due to the length of the system’s manufacturing and support life and possibly unforeseen life extensions to the support of the system, the necessary parts and other resources become unavailable (or at least unavailable from their original manufacturer) before the system’s demand for them is exhausted.  DMSMS obsolescence is the opposite of sudden or inventory obsolescence.

Planned obsolescence is a strategy followed by product manufacturers, DMSMS obsolescence is a situation forced upon system sustainers (these two types of obsolescence are not the same).  DMSMS obsolescence may be the result of the planned obsolescence of products that drive the market for specific types of parts, e.g., if your long field life system depends on the same parts that cell phones depend on, then planned obsolescence strategies for cell phones drive the DMSMS obsolescence of the parts you depend on.  The “poster child” for DMSMS type obsolescence is electronic parts.  For some electronic parts the planned obsolescence of particular products is the primary driver behind the part’s obsolescence, but the discontinuance of parts is also simply the result of falling demand that makes it more profitable to dedicate manufacturing resources to other products, or changes in ownership of product lines or companies.

All of the types of obsolescence defined above are relevant for real product and system segments.  While it is easiest to think of these as impacting hardware, obsolescence also has a significant impact on software [3], materials, and even the human workforce [4]. 

[1] Slade, G. (2006). Made to break: Technology and obsolescence in America, Harvard University Press.

[2] Sandborn, P. (2008). Trapped on technology's trailing edge, IEEE Spectrum, 45(1), pp. 42-45, April.

[3] Sandborn, P. (2007).  Software obsolescence - Complicating the part and technology obsolescence management problem, IEEE Transactions on Components and Packaging Technologies, 30(4), pp. 886-888, December.

[4] Sandborn, P. and Prabhakar, V.J. (2015).  Forecasting and impact of the loss of the critical skills necessary for supporting legacy systems, IEEE Transactions on Engineering Management, 62(3), pp. 361-371, August.