2nd Edition of Cost Modeling Book Published

This book provides an introduction to cost modeling for electronic systems that is suitable for professionals involved with electronic systems development, management and sustainment, and advanced undergraduate and graduate students in electrical, mechanical and industrial engineering.

This book melds elements of traditional engineering economics with manufacturing process and life-cycle cost management concepts to form a practical foundation for predicting the cost of electronic products and systems. Various manufacturing cost analysis methods are addressed including: process-flow, parametric, cost of ownership, and activity based costing. The effects of learning curves, data uncertainty, test and rework processes, and defects are considered. Aspects of system sustainment and life-cycle cost modeling including: reliability (warranty, burn-in), maintenance (sparing and availability), and obsolescence are treated. Finally, the total cost of ownership of systems, return on investment, cost-benefit analysis, and real options analysis are addressed.

The book includes a large number of  quantitative examples solved in the text, over 230 problems (detailed solutions to over 90% of the problems are available to practitioners, researchers and instructors using the book in classes), and over 300 references.

http://www.worldscientific.com/worldscibooks/10.1142/10241

Table of Contents (571 pages)

Chapter 1 Introduction

Part I Manufacturing Cost Modeling
Chapter 2 Process-Flow Analysis
Chapter 3 Yield
Chapter 4 Equipment/Facilities Cost of Ownership (COO)
Chapter 5 Activity-Based Costing (ABC)
Chapter 6 Parametric Cost Modeling
Chapter 7 Test Economics
Chapter 8 Diagnosis and Rework
Chapter 9 Uncertainty Modeling - Monte Carlo Analysis
Chapter 10 Learning Curves

Part II Life-Cycle Cost Modeling
Chapter 11 Reliability
Chapter 12 Sparing
Chapter 13 Warranty Cost Analysis
Chapter 14 Burn-In Cost Modeling
Chapter 15 Availability
Chapter 16 The Cost Ramifications of Obsolescence
Chapter 17 Return on Investment (ROI)
Chapter 18 The Cost of Service
Chapter 19 Software Development and Support Costs
Chapter 20 Total Cost of Ownership Examples
Chapter 21 Cost, Benefit and Risk Tradeoffs
Chapter 22 Real Options Analysis

Appendices
Appendix A Notation
Appendix B Weighted Average Cost of Capital (WACC)
Appendix C Discrete-Event Simulation (DES)

Index (over 900 entries)

More Than Acquisition Cost – F-35

Aircraft are “sustainment-dominated” systems.  These are systems for which the lifetime footprint significantly exceeds the footprint associated with making it [1].  In the case of aircraft, the footprint we are talking about includes cost.   Lockheed Martin’s official response to President-elect Trump’s recent tweet about out-of-control costs for the F-35 included the following statement [2]:

“The cost doesn't just include the acquisition price. Lockheed Martin and its industry partners are also investing in reducing the sustainment costs of the aircraft recognizing that much of the cost of owning and operating an aircraft is after it's delivered. We're investing hundreds of millions of dollars to reduce the cost of sustaining the airplane over its 30-40 year lifespan. We understand the importance of affordability and that's what the F-35 has been about.”

It is not uncommon for 70% of more of the life-cycle cost of a sustainment-dominated system (e.g., commercial and military aircraft, ships, power plants, and other high-cost, long-life items), to be incurred after the design, development, and procurement of the system.  These life-cycle costs can include: operation, maintenance, upgrade, spare parts, testing, training, documentation, unplanned life extensions, obsolescence management, and many more things that contribute to the logistics footprint of a complex system.  As an example, consider obsolescence management [3].  The majority of the electronic systems in the aircraft are not constructed from “custom” parts, but rather from the same parts that are in consumer products (phones, computers, etc.).  Most of these parts have a procurement life of a few years at best, but an airplane has to be supported for 30+ years.  Sourcing these parts after they are discontinued (obsoleted) by their original manufacturer can be expensive and risky.  The problem is that aircraft are safety-critical systems that are highly qualified and certified, replacing obsolete parts with newer versions of parts may be a very expensive proposition (may require re-qualification of critical subsystems or even the entire aircraft); alternatively using aftermarket suppliers exposes systems to the risk of counterfeit parts [4]. Obsolescence is only one example of how high procurement cost systems can become even more (much more) expensive to sustain.

[1] Sandborn, P. and Myers, J. (2008). Designing engineering systems for sustainability, in Handbook of Performability Engineering, K.B. Misra, Editor, pp. 81-103 (Springer, London).
[2] http://www.zerohedge.com/news/2016-12-12/lockheed-responds-trump-tweet
[3] Sandborn, P. (2008). Trapped on technology’s trailing edge. IEEE Spectrum, 45(1), pp. 42-45.
[4] Pecht, M. and Tiku, S. (2006). Electronic manufacturing and consumers confront a rising tide of counterfeit electronics. IEEE Spectrum 43(5), pp. 37-46.

Engineering Economics vs. Cost Modeling

Engineering economics treats the analysis of the economic effects of engineering decisions and is often identified with capital allocation problems.  Engineering economics provides a rigorous methodology for comparing investment or disinvestment alternatives that include the time value of money, equivalence, present and future value, rate of return, depreciation, break-even analysis, cash flow, inflation, taxes, and so forth. Cost modeling is a different beast.

While traditional engineering economics is focused on the financial aspects of cost, cost modeling deals with modeling the processes and activities associated with the manufacturing and support of products and systems, i.e., determining the actual costs that engineering economics uses within its cash flow oriented decision making processes.

Cost modeling is one of the most common business activities performed in an organization.  But what is cost modeling, or maybe more importantly, what is it not?  The goal of cost modeling is to enable the estimation of product or system life-cycle costs.  Cost analyses generally take one of two forms:

•       Ex post facto (after the event) – Cost is often computed after expenditures have been made.  Accounting represents the use of cost as an objective measure for recording and assessing the financial performance of an organization and deals with what either has been done or what is currently being done within an organization, not what will be done in the future.  The accountant’s cost is a financial snapshot of the organization at one particular moment in time.

•       A priori (prior to) – These cost estimations are made before manufacturing, operation and support activities take place.

Cost modeling is an a priori analysis.  It is the imposition of structure, incorporation of knowledge, and inclusion of technology in order to map the description of a product (geometry, materials, design rules, and architecture), conditions for its manufacture (processes, resources, etc.), and conditions for its use (usage environment, lifetime expectation, training and support requirements) into a forecast of the required monetary expenditures.  Note, this definition does not specify from whom the monetary resources will be required--that is, they may be required from the manufacturer, the customer, or a combination of both.

Absolute vs. Relative Accuracy in Cost Modeling

Cost modeling, like all other modeling activities, is fraught with weaknesses.  A well-known quote from George Box, “Essentially, all models are wrong, but some are useful,” [1] is appropriate for describing cost modeling.  First, cost modeling is a “garbage in, garbage out” activity – if the input data is inaccurate, the values predicted by the model will be inaccurate. That said, cost modeling is generally combined with various uncertainty analysis techniques that allow inputs to be expressed as ranges and distributions rather than point values.  Obtaining absolute accuracy from cost models depends on having some sort of real-world data to use for calibration.  To this end, the essence of cost modeling is summed up by the following observation from Norm Augustine [2]:

“Much cost estimation seems to use an approach descended from the technique widely used to weigh hogs in Texas.  It is alleged that in this process, after catching the hog and tying it to one end of a teeter-totter arrangement, everyone searches for a stone which, when placed on the other end of the apparatus, exactly balances the weight of the hog.  When such a stone is eventually found, everyone gathers around and tries to guess the weight of the stone.  Such is the science of cost estimating.”

Nonetheless, when absolute accuracy is impossible, relatively accurate costs models can often be very useful.  Relatively accurate cost models produce cost predictions that have limited (or unknown) absolute accuracy, but the differences between model predictions can be extremely accurate if the cost of the effects omitted from the model are a “wash” between the cases considered--that is, when errors are systematic and identical in magnitude between the cases considered.  While an absolute prediction of cost is necessary to support the quoting or bidding process, an accurate relative cost can be successfully used to support making a business case for selecting one alternative over another.

As an example for a relatively accurate cost model (with little or no absolute accuracy), consider a model for selecting the best way to maintain a system.  In this case the question to be answered is not whether or not to maintain a system, nor how much to budget to maintain a system, but rather which of several alternative approaches one should use.  Presumably once an approach is chosen, more accurate modeling can be performed for budgeting purposes.  The alternatives might be preventative, predictive, and corrective maintenance, and combinations thereof.

[1] Box, G. E. P. and Draper, N. R. (1987). Empirical Model-Building and Response Surfaces (Wiley, Hoboken, NJ).

[2] Augustine, N. R. (1997). Augustine’s Laws, 6^th Edition (AIAA, Reston, VA).