Understanding the role of obsolescence in PPP/PFI

In 2013 the Guardian newspaper reported that the UK Government had acquired £300 billion worth of capital costs and unitary payments within the formally known Private Finance Initiatives – now Public Private Partnerships. This paper is not about the economics or moral debate upon the success and failures of PPP’s within the UK, but rather the untold story of the impact of obsolescence upon the integral asset systems which support the service delivery. Prisons require supportable and maintainable security systems, the same can be said for government/defence buildings, not to the mention the life critical systems within hospitals and clinics across the country. However, there is an untold story, which is impacting the through life or lifecycle costs to support and maintain key asset systems, driving additional lifecycle expenditures that may be unforeseen. This paper contains evidence of the scale of the financial impact of obsolescence through obsolescence driven investments, not least to mention the potential operational impacts if systems become unsupportable. This paper begins to create a foundation for future research focusing on obsolescence and how best to monitor and mitigate its effects

Forecasting obsolescence risk and product lifecycle with machine learning

Rapid changes in technology have led to an increasingly fast pace of product introductions. New components offering added functionality, improved performance and quality are routinely available to a growing number of industry sectors (e.g., electronics, automotive, and defense industries). For long-life systems such as planes, ships, nuclear power plants, and more, these rapid changes help sustain the useful life, but at the same time, present significant challenges associated with managing change. Obsolescence of components and/or subsystems can be technical, functional, related to style, etc., and occur in nearly any industry. Over the years, many approaches for forecasting obsolescence have been developed. Inputs to such methods have been based on manual inputs and best estimates from product planners, or have been based on market analysis of parts, components, or assemblies that have been identified as higher risk for obsolescence on bill of materials. Gathering inputs required for forecasting is often subjective and laborious, causing inconsistencies in predictions. To address this issue, the objective of this research is to develop a new framework and methodology capable of identifying and forecasting obsolescence with a high degree of accuracy while minimizing maintenance and upkeep. To accomplish this objective, current obsolescence forecasting methods were categorized by output type and assessed in terms of pros and cons. A machine learning methodology capable of predicting obsolescence risk level and estimating the date of obsolescence was developed. The machine learning methodology is used to classify parts as active (in production) or obsolete (discontinued) and can be used during the design stage to guide part selection. Estimates of the date parts will cease production can be used to more efficiently time redesigns of multiple obsolete parts from a product or system. A case study of the cell phone market is presented to demonstrate how the methodology can forecast product obsolescence with a high degree of accuracy. For example, results of obsolescence forecasting in the case study predict parts as active or obsolete with a 98.3% accuracy and regularly predicts obsolescence dates within a few months

Selection of obsolescence resolution strategy based on a multi criteria decision model

A component becomes obsolete when it is no longer available from its original manufacturer in its original form. Component obsolescence is a significant problem in the electronics industry. There are different strategies employed to address this problem, for example, using an alternative part, life time buy, redesign etc. Often, techniques used in industry select one of these options based on the most economical solution as determined by minimizing direct costs. However, there are factors other than cost, such as the number of suppliers, time constraints, reliability of the solution etc., which may play a crucial role in determining an overall best decision. In addition, there are multiple stakeholders like design, operations, manufacturing, sales, service etc., who might have different opinions when it comes to obsolescence management. This research provides a multi criteria decision model that will consider the trade-offs among multiple factors and provide the decision maker solution that will be acceptable to a wide variety of stakeholders as well as being viable from the company\u27s perspective. The model is based on multi attribute utility theory. It will provide the stakeholders a platform to express their preferences and experience in the decision process. And, based on the overall utility value, the most suitable obsolescence resolution strategy for a specific application will be provided. The research provides a hypothetical case study in order to illustrate the application and usage of the model

Electronic Part Total Cost Of Ownership And Sourcing Decisions For Long Life Cycle Products

The manufacture and support of long life cycle products rely on the availability of suitable parts from competent suppliers which, over long periods of time, leaves parts susceptible to a number of possible long-term supply chain disruptions. Potential supply chain failures can be supplier-related (e.g., bankruptcy, changes in manufacturing process, non-compliance), parts-related (e.g., obsolescence, reliability, design changes), logistical (e.g., transportation mishaps, natural disasters, accidental occurrences) and political/legislative (e.g., trade regulations, embargo, national conflict). Solutions to mitigating the risk of supply chain failure include the strategic formulation of suitable part sourcing strategies. Sourcing strategies refer to the selection of a set of suppliers from which to purchase parts; sourcing strategies include sole, single, dual, second and multi-sourcing. Utilizing various sourcing strategies offer one way of offsetting or avoiding the risk of part unavailability (and its associated penalties) as well as possible benefits from competitive pricing. Although supply chain risks and sourcing strategies have been extensively studied for high-volume, short life cycle products, the applicability of existing work to long life cycle products is unknown. Existing methods used to study part sourcing decisions in high-volume consumer oriented applications are procurement-centric where cost tradeoffs on the part level focus on part pricing, negotiation practices and purchase volumes. These studies are commonplace for strategic part management for short life cycle products; however, conventional procurement approaches offer only a limited view for parts used in long life cycle products. Procurement-driven decision making provides little to no insight into the accumulation of life cycle cost (attributed to the adoption, use and support of the part), which can be significantly larger than procurement costs in long life cycle products. This dissertation defines the sourcing constraints imposed by the shortage of suppliers as a part becomes obsolete or is subject to other long-term supply chain disruptions. A life cycle approach is presented to compare the total cost of ownership of introducing and supporting a set of suppliers, for electronic parts in long life cycle products, against the benefit of reduced long-term supply chain disruption risk. The estimation of risk combines the likelihood or probability of long-term supply chain disruptions (throughout the part's procurement and support life within an OEM's product portfolio) with the consequence of the disruption (impact on the part's total cost of ownership) to determine the "expected cost" associated with a particular sourcing strategy. This dissertation focuses on comparing sourcing strategies used in long life cycle systems and provides application-specific insight into the cost benefits of sourcing strategies towards proactively mitigating DMSMS type part obsolescence

CONCURRENT MULTI-PART MULTI-EVENT DESIGN REFRESH PLANNING MODELS INCORPORATING SOLUTION REQUIREMENTS AND PART-UNIQUE TEMPORAL CONSTRAINTS

Technology obsolescence, also known as DMSMS (Diminishing Manufacturing Sources and Material Shortages), is a significant problem for systems whose operational life is much longer than the procurement lifetimes of their constitute components. The most severely affected systems are sustainment-dominated, which means their long-term sustainment (life-cycle) costs significantly exceed the procurement cost for the system. Unlike high-volume commercial products, these sustainment-dominated systems may require design refreshes to simply remain manufacturable and supportable. A strategic method for reducing the life-cycle cost impact of DMSMS is called refresh planning. The goal of refresh planning is to determine when design refreshes should occur (or what the frequency of refreshes should be) and how to manage the system components that are obsolete or soon to be obsolete at the design refreshes. Existing strategic management approaches focus on methods for determining design refresh dates. While creating a set of feasible design refresh plans is achievable using existing design refresh planning methodologies, the generated refresh plans may not satisfy the needs of the designers (sustainers and customers) because they do not conform to the constraints imposed on the system. This dissertation develops a new refresh planning model that satisfies refresh structure requirements (i.e., requirements that constrain the form of the refresh plan to be periodic) and develops and presents the definition, generalization, synthesis and application of part-unique temporal constraints in the design refresh planning process for systems impacted by DMSMS-type obsolescence. Periodic refresh plans are required by applications that are refresh deployment constrained such as ships and submarines (e.g., only a finite number of dry docks are available to refresh systems). The new refresh planning model developed in this dissertation requires 50% less data and runs 50% faster than the existing state-of-the-art discrete event simulation solutions for problems where a periodic refresh solution is required

Stochastic modeling of responsiveness, schedule risk and obsolescence of space systems, and implications for design choices

The U.S Department of Defense and the National Aeronautics and Space Administration continue to face common challenges in the development and acquisition of their space systems. In particular, space programs repeatedly experience significant schedule slippages, and spacecraft are often delivered on-orbit several months, sometimes years, after the initially planned delivery date. The repeated pattern of these schedule slippages suggests deep-seated flaws in managing spacecraft delivery and schedule risk, and an inadequate understanding of the drivers of schedule slippages. Furthermore, due to their long development time and physical inaccessibility after launch, space systems are exposed to a particular and acute risk of obsolescence, resulting in loss of value or competitive advantage over time. The perception of this particular risk has driven some government agencies to promote design choices that may ultimately be contributing to these schedule slippages, and jeopardizing what is increasingly recognized as critical, namely space responsiveness. The overall research objective of this work is twofold: (1) to identify and develop a thorough understanding of the fundamental causes of the risk of schedule slippage and obsolescence of space systems; and in so doing, (2) to guide spacecraft design choices that would result in better control of spacecraft delivery schedule and mitigate the impact of these "temporal risks" (schedule and obsolescence risks). To lay the groundwork for this thesis, first, the levers of responsiveness, or means to influence schedule slippage and impact space responsiveness are identified and analyzed, including design, organizational, and launch levers. Second, a multidisciplinary review of obsolescence is conducted, and main drivers of system obsolescence are identified. This thesis then adapts the concept of a technology portfolio from the macro- or company level to the micro-level of a single complex engineering system, and it analyzes a space system as a portfolio of technologies and instruments, each technology with its distinct stochastic maturation path and exposure to obsolescence. The selection of the spacecraft portfolio is captured by parameters such as the number of instruments, the initial technology maturity of each technology/instrument, the resulting heterogeneity of the technology maturity of the whole system, and the spacecraft design lifetime. Building on the abstraction of a spacecraft as a portfolio of technologies, this thesis then develops a stochastic framework that provides a powerful capability to simultaneously explore the impact of design decisions on spacecraft schedule, on-orbit obsolescence, and cumulative utility delivered by the spacecraft. Specifically, this thesis shows how the choice of the portfolio size and the instruments Technology Readiness Levels (TRLs) impact the Mean-Time-To-Delivery (MTTD) of the spacecraft and mitigate (or exacerbate) schedule risk. This work also demonstrates that specific combinations/choices of the spacecraft design lifetime and the TRLs can reduce the risk of on-orbit obsolescence. This thesis then advocates for a paradigm shift towards a calendar-based design mindset, in which the delivery time of the spacecraft is accounted for, as opposed to the traditional clock-based design mindset. The calendar-based paradigm is shown to lead to different design choices, which are more likely to prevent schedule slippage and/or enhance responsiveness and ultimately result in a larger cumulative utility delivered. Finally, missions scenarios are presented to illustrate how the framework and analyses here proposed can help identify system design choices that satisfy various mission objectives and constraints (temporal as well as utility-based).PhDCommittee Chair: Saleh Joseph; Committee Member: Brown Owen; Committee Member: Erwin R. Scott; Committee Member: Feron Eric; Committee Member: Mavris Dimitr

INTEGRATION OF TECHNOLOGY ROADMAPPING INFORMATION INTO DMSMS-DRIVEN DESIGN REFRESH PLANNING OF THE V-22 ADVANCED MISSION COMPUTER

As the pace of technological progress increases, technology obsolescence problems will have a greater effect on traditionally sustainment-dominated industries. Many organizations rely solely on reactive approaches to manage obsolescence events as they occur, often employing lifetime buys, aftermarket sources and other mitigation approaches to ensure that they have enough parts to last through the system's lifecycle. Strategically planned design refreshes coupled with various mitigation approaches can, in many cases, lead to greater cost avoidance than reactive mitigation alone. Design refresh planning is performed by organizations that wish to avoid the high costs of purely reactive obsolescence solutions. Planning to phase-out specific parts at certain times lessens the reliance on reactive solutions (and the resulting quest for obsolete parts) and, in turn, lessens the total cost of sustaining a system. However, design refreshing solely to manage obsolescence is not practical for many systems, and therefore, obsolescence management refresh activities need to be coordinated with the technology insertion roadmap. Technology insertion roadmaps are developed to dictate how the system's functionality and performance must be changed over time. Technology roadmaps reflect an organization's internal technology goals and budget cycles, and give insight into the organization's inherent modus operandi. The MOCA (Mitigation of Obsolescence Cost Analysis) software tool has been designed to generate and select an optimum design refresh plan for a system. This thesis describes an extension to MOCA that allows information from technology roadmaps to be used as constraints in MOCA. The integration of technology roadmap information into MOCA's decision analysis ensures that selected refresh plans meet roadmap imposed timing constraints, and that the costs of roadmap specified actions are included within relevant refreshes. These new developments in MOCA are discussed in the context of the V-22 Advanced Mission Computer (AMC) system. The mechanics of the MOCA tool's optimization analysis with roadmapping considerations are described and the cost avoidance resulting from the optimum refresh plan is articulated in business case terms

Optimizing Lifetime Buy Quantities to Minimize Lifecycle Cost

Mismatches between electronic part procurement lifecycles and the lifecycles of the products they are used in causes products with long manufacturing and/or support lives to suffer from significant obsolescence management costs. Lifetime buy is a prevalent mitigation approach employed for electronic part obsolescence management. Making lifetime purchases of parts upon obsolescence involves managing interacting influences and concurrent buys for multiple parts in a sequential manner. This thesis is focused on optimizing lifetime buy quantities by minimizing lifecycle cost. The Life of Type Evaluation (LOTE) tool was created to optimize lifetime buy quantities. LOTE requires component and system data and expected demand information. With the given data, LOTE uses stochastic analysis to determine the lifetime buy quantity per part that minimizes the lifecycle cost for the system. Results from a LOTE analysis of a Motorola communication system indicate that organizations may be systematically overbuying at lifetime buys giving inventory shortage penalties a greater emphasis than other hidden costs