Which Escalation Rate Should I Use? (LC-1)
Nathan Honsowetz – Senior Consultant, Cobec Consulting Inc.
Conducting life cycle cost estimates requires time frames of 10, 20, even 30 years, and with such long time frames it’s important to use appropriate escalation indices. Escalation can have a significant impact on cost estimates, especially estimates with longer time frames. However, often cost estimators insert a “standard” escalation index into their models without considering whether that index is appropriate for their estimate. In addition, risk is hardly ever applied to escalation as little consideration is applied to the appropriateness of the escalation factors used. This presentation will explain the common escalation indices used in cost estimation, including how they are developed, the purpose they are designed to serve, and how best to use them. Common mistakes will also be covered, such as mistaking nominal rates for real rates and using default escalation factors without further investigation. Escalation factors discussed include OMB, Global Insight labor, FAA labor, escalation in the private sector, and cases of special escalation such as energy.
Ground Vehicle Reliability Analysis Using the Mean Cumulative Function (LC-2)
Caleb Fleming – Cost Analyst, Kalman & Company, Inc.
The primary focus of this paper is to demonstrate the significance of the non-parametric Mean Cumulative Function (MCF) as a comparative and predictive estimating tool for historical and future recurrent maintenance event costs.
Ground vehicle data is presented to demonstrate and address fundamental concepts, algorithmic computations, and potential shortcomings. This paper offers guidelines for identifying recurrent behaviors and outlines the MCF’s value and application to federal government and commercial cost estimating.
The MCF is a non-parametric estimator for determining repair costs and quantities as a function of time or age. Units within a population traditionally follow independent staircase function curves that flat-line between ages and are vertical (x = time) at specific ages. The pointwise average of the cumulative population function at a specific age is the MCF, offering an identifiable cumulative repair cost or quantity up to and at a particular point in time.
In addition to presenting cumulative costs and quantities, MCF plots are valuable tools for developing historical baseline repair rates by component. The cumulative repair rate at a particular age is the derivate of the MCF, calculated through differentiation. The derivatives that increase with age yield increasing repair rates, while derivatives decreasing with age yield decreasing repair rates.
Summative plots obtained from the analysis of MCF outputs have the potential to reveal statistically significant maintenance event tendencies towards constant recurrence, increasing recurrence, decreasing recurrence, and “bathtub effect” recurrence. Further, results generated from the MCF are comparable across analogous systems to reveal potential effects of varying engineering designs and maintenance concepts.
Applied specifically to the cost industry for federal government and commercial clients, the MCF is an alternative to parametric estimating and a slew of loosely defensible assumptions. The MCF generates precise point estimate values for component failure events, enabling the estimator to forecast repair parts demand, manpower requirements, and potential deadlining effects of particular component failures.
This paper surveys and outlines the fundamental MCF methodologies and explanations detailed in-depth in Wayne Nelson’s Recurrent Events Data Analysis for Product Repairs, Disease Recurrences, and Other Applications. Applying the MCF to vehicle maintenance data reveals recurring component failure behaviors, develops new guidelines for interpretation, and assists in data normalization and validation.
Cost Overruns and Their Precursors: An Empirical Examination of Major Department of Defense Acquisition Programs (LC-3)
Alan Gideon – Senior Systems Engineer, Booz Allen Hamilton
Enrique Campos-Nanez – Senior Software Engineer, Epsilon Group
Pavel Fomin – Aerospace Engineer, United States Air Force
James Wasek – Senior Enterprise Architect, Science & Technology Solutions, Inc. (eSTS)
This paper proposes a model of acquisition program future cost for two specific acquisition domains – aircraft and ships – that takes into account the non-recurring developmental costs defined at program approval and each domain’s historic tendencies to exceed planned program cost. Technical and non-technical reasons for these observations are discussed. We begin with an exploratory analysis of trends in cost, schedule, and performance from the 1960s to the present. We use those results, the level of Research and Development funding assigned to each program, and a platform-specific characterization of technical risk as inputs to calculate the likely variance of an acquisition program’s future cost.
The root causes of all acquisition program risks can be categorized as either programmatic/business, cost, schedule, or technical. In turn, these lead to impacts in a program’s final cost, schedule, and/or technical performance. Clearly, inadequate funding of challenging programs increases risk, and providing greater R&D funding for high risk programs reduces the degree of risk attached to a given program. Thus, contracted performance specifications at the limits of current technology and inadequate RDT&E budgets can be seen as underestimated risk that may, in turn, drive a program to deliver late and over the budget established at program commitment. The authors believe that when a program’s technical risks are seen in their historical perspective, program outcome can be better managed. We investigate the part played by initial R&D funding to better improve program risk impact estimates, and extend that effect to examine the effect that these program cost vulnerabilities can have on enterprise portfolio risk profiles.
Department of Defense policy is to calculate a “most probable cost” for each acquisition program at a specified level of confidence, and then fund each program at its most probable cost. Portfolios of programs, either at the service level or the departmental level, are simply the sum of the individual funding levels. This approach is sometimes referred to as percentile funding, and is based on Markowitz’ portfolio theory, where bounding cost envelopes are defined by standard Gaussian marginal cost probabilities of the subject data. If the outcomes of activities within a given program or if the outcomes of programs within a particular portfolio followed Gaussian distributions, Markowitz’ theory would apply. However, Smart (2010) shows that acquisition program outcomes are better described by lognormal distributions, which have “fatter” tails. The authors’ model demonstrates that Smart’s assessment was optimistic for some product lines. The initial results of the authors’ model proposed model is compared to the present policy (Herzberg’s expected value-at-risk) and Smart’s Conditional Tail Expectation for this data.
Data was drawn from the Defense Acquisition Management Information retrieval (DAMIR) database and a number of open sources. The data was stripped of specific identifiers as required to protect proprietary data and normalized to increase the universality of the
System Utilization: An In-depth Method of Modeling and Measuring Military Manpower Costs (LC-4)
Omar Mahmoud – Lead Associate, Booz Allen Hamilton
A major challenge facing Information Technology (IT) based DoD systems is estimating Military Manpower costs. For most DoD programs, military manpower costs are the largest contributor to a programs Total Ownership Costs (TOC), Acquisition Category (ACAT) status, and Economic Analysis (EA) determination. External costs are critical cost components for the Office of the Secretary of Defense.
For a Major Automated Information System ACAT 1A program under the Space and Naval Warfare Enterprise, we have established a proven method to capture military manpower requirements, system utilization, and other relevant cost metrics that avoid the pitfalls of overestimating or double counting costs. This analysis will examine the requirements necessary to independently assess military manpower costs by evaluating various cost estimating factors including: Required billets, manpower utilization, and other relevant metrics.
Utilize System Manpower Description Documents and Conducting Manpower Survey: Data gathering and analysis is a critical part of estimating military manpower costs and several documents were utilized as a basis in establishing cost requirements, such as the Navy Training Systems Plan (NTSP) and Training Planning Process Methodology (TRRPM). However, the NTSP only describes the number of required billets and their associated functions and the TRPPM in most cases does not define the frequency, periodicity, and duration of system usage for each user. Without IT system utilization metrics, a cost estimator is sure to either overestimate or double count military manpower costs.
System utilization metrics measures the time a user is operating, administering, or maintaining a system while underway. As such, a manpower survey was conducted in San Diego, CA and Norfolk, VA to assess system utilization by functional category in order to accurately estimate the IT systems TOC.
Benefits: Accurately estimating military manpower costs has significant implications on DoD programs and can directly influence a program’s TOC, ACAT designation, and EA determination. This manpower survey enabled the cost estimator to employ sound cost estimating methodologies that established defendable military manpower costs and avoided the pitfalls of overestimating or double counting costs.
Disadvantages: Scarcity of adequate documentation, scheduling constraints, and inaccessibility of required resources to conduct the manpower survey are the main impediments to accurately estimate military manpower costs. However, early buy-in from the program office is a necessary first step in conducting such a survey. In addition, engaging with the User community to ensure that resources would be available to participate in the manpower survey is a critical part of the planning process.
Summary: Establishing defendable cost estimating methodologies for capturing military manpower costs is a key component in any Program Life Cycle Cost Estimate. With a proven and systematic approach to estimating military manpower costs, a program can be confident in selecting a proper course of action among competing alternatives when conducting an EA, appropriately designate their ACAT level, avoid the pitfalls that lead to over/under estimating or double counting costs, and above all obtain a high level of confidence from their resource sponsor and Milestone Decision Authority.
Integrating Sustainability into Weapon System Acquisition within the Department of Defense (DoD) (LC-5)
DoD acquisition and logistics professionals use the term sustainment to describe the support needed to operate and maintain a system over its lifetime. In the context of the DoD acquisition process, sustainability involves using resources to minimize mission, human health, and environmental impacts and associated costs during the life cycle. This paper will present a draft version of “DoD Guidance ? Integrating Sustainability into DoD Acquisitions,” initial findings from pilot studies, and the challenges and road ahead.
The DoD acquires weapons systems that must be sustained up to 30 years or more. Resources are at a premium and in many cases dwindling. Acquisition personnel must fully understand life cycle impacts and the costs of systems; otherwise, they could inadvertently “push downstream” significant impacts and associated costs to the operational, logistics, and installation management communities.
While sustainability is not a new topic, it is now, more than ever, an area of emphasis for the DoD. Executive Order 13514?Federal Leadership in Environmental, Energy and Economic Performance (05 Oct 2009) establishes an integrated strategy for sustainability in the federal government. In accordance with the EO, the DoD developed a Strategic Sustainability Performance Plan (SSPP), updated annually. The SSPP includes goals for efficiency and reductions in energy, water, solid waste, and the use of hazardous chemicals and materials. Further, reducing life cycle costs by acquiring more sustainable systems directly supports the Better Buying Power initiative and design for affordability goal established by USD(AT&L).
The Guide introduces the concept of Sustainability Analysis and provides guidance on how to complete such analyses and use the results to better inform tradeoff, design, and supportability decisions. A Sustainability Analysis consists of a Life Cycle Assessment (LCA), which assesses a system’s impacts to human health and the environment, and Life Cycle Costing (LCC), which attempts to capture relevant costs associated with the system throughout its life cycle. While this paper will briefly discuss LCA, the focus will be on LCC and providing additional detailed guidance on cost elements and techniques for identifying and quantifying sustainability-related costs that are often not included in the current acquisition cost structure. It describes how to use existing data from legacy systems or proxy data from similar systems to conduct an SLCA and estimate relevant sustainability-related costs.
Our discussion of findings from pilot studies will focus on the quantification of cost and environmental impacts-related differences between the use of chrome and non-chrome primer on two Navy aircraft. The paper will identify and explain the procedures used to identify specific sustainability-related cost elements for the two aircraft as well as the estimated cost differences between the two scenarios.
This paper will also enumerate the significant challenges associated with the analysis of sustainability-related costs. These challenges include the lack of standardized reporting procedures for and documentations of sustainability costs; the shortage of empirical data to be used as a foundation for developing cost estimating relationships and cost factors; and the requirement to establish and implement procedures to gather necessary sustainability data without creating onerous reporting requirements.
Cost Analysis & Optimization of Repair Concepts Using Marginal Analysis (LC-6)
Justin Woulfe – EVP, Technical Services, WPI
OPRAL is an analytical model for determining the optimal repair locations and spares allocations in a multi-level hierarchical support organization to optimize Life Cycle Cost. With this model, the analyst can either treat repair decisions as fixed and given as input parameters, or using the OPRAL algorithm, evaluate several different repair strategies in order to find the optimal one, considering all aspects of life cycle cost. The dependence between repair and spares allocation decisions is strong,which why it is necessary to integrate the optimization of the two.
We have developed a model for simultaneous spares optimization and optimal location of repair facilities.
Perhaps the most fundamental property of OPRAL is that the model allows a simultaneous treatment of two problems that are central in the design of support or maintenance systems:
• What repair strategy should be used for items of a given type?
• What sparing strategy should be used for items of a given type?
The choice of repair strategy concerns whether to discard or repair faulty items of a given type. Furthermore, if the item is to be repaired, it also concerns where the repair should take place. The sparing strategy concerns the amount of spares to stock at each warehouse within the organization, when to reorder and how much to reorder.
The problem of determining the optimum repair strategy is sometimes called level-of-repair analysis or LORA. Unfortunately, and as the name suggests, a common assumption is that the repair strategy is limited by levels or echelons within the sup- port organization. In some cases, the restriction to levels is not severe, while in some asymmetric cases it is highly questionable. In any case, the limitation is artificial why we suggest it is removed. Thus, we propose that the acronym LORA should instead read Location Of Repair Analysis.