فراتر از هزینه های آن، ارزش تعمیر و نگهداری: چارچوب تحلیلی برای گرفتن ارزش فعلی خالص آن

Maintenance planning and activities have grown dramatically in importance across many industries and are increasingly recognized as drivers of competitiveness if managed appropriately. Correlated with this observation is the proliferation of maintenance optimization techniques in the technical literature. But while all these models deal with the cost of maintenance (as an objective function or a constraint), only a handful addresses the notion of value of maintenance, and seldom in an analytical or quantitative way. In this paper, we propose that maintenance has intrinsic value and argue that existing cost-centric models ignore an important dimension of maintenance, namely its value, and in so doing, they can lead to sub-optimal maintenance strategies. We develop a framework for capturing and quantifying the value of maintenance activities. Our framework is based on four key components. First, we consider systems that deteriorate stochastically and exhibit multi-state failures, and model their state evolution using Markov chains and directed graphs. Second, we consider that the system provides a flow of service per unit time. This flow in turn is “priced” and a discounted cash flow is calculated resulting in a present value (PV) for each branch of the graph—or “value trajectory” of the system. Third as the system ages or deteriorates, it migrates towards lower PV branches of the graph, or lower value trajectories. Fourth, we conceptualize maintenance as an operator (in a mathematical sense) that raises the system to a higher PV branch in the graph. We refer to the value of maintenance as the incremental PV between the pre- and post-maintenance branches of the graphs minus the cost of maintenance. The framework presented here offers rich possibilities for future work in benchmarking existing maintenance strategies based on their value implications, and in deriving new maintenance strategies that are “value-optimized.”

Maintenance planning and activities have grown dramatically in importance across many industries. This importance is manifested by both the significant material resources allocated to maintenance departments as well as by the substantial number of personnel involved in maintenance activities in companies—for example over a quarter of the total workforce in the process industry is said to deal with maintenance work [1]. This situation, coupled with an increasingly competitive environment, creates economic pressures and a heightened need to ensure that these considerable maintenance resources are allocated and used appropriately, as they can be significant drivers of competitiveness—or lack thereof if mismanaged. In response to these pressures, the notion of “optimality” and the mathematical tools of optimization and operations research (OR) have seeped into maintenance planning, and resulted in the proliferation of “optimal” maintenance models (see the reviews by Pham and Wang [2] and Wang [3], for example). In each “optimal” maintenance model developed, an objective function is first posited, then analytical tools are used to derive a maintenance policy that maximizes or minimizes this objective function subject to some constraints. For example, the objective function can be the minimization of cost (cost rate, or life cycle cost) of maintenance given a system reliability and/or availability constraint; conversely, the objective function can be the maximization of reliability or availability, given a cost constraint. In addition to varying the objective function, different “optimal” maintenance models are obtained by: (1) varying for example the system configuration (e.g., single-unit systems versus multi-unit systems, k-out-of-n systems); (2) by including several degrees of maintenance (e.g., minimal, imperfect, perfect); (3) by varying the planning horizon; (4) by using different analytical tools; or (5) by positing different types of dependencies between the various units in a multi-unit system. Yet, while all these models deal with the cost of maintenance (as an objective function or a constraint), only a handful of models touches on the notion of value of maintenance, and seldom in an analytical or quantitative way (e.g., [24]). Wang [3] highlights a critical idea for the development of a value-based perspective on maintenance when he suggests that the cost of maintenance as well as the resulting system reliability should be considered together when developing optimal maintenance strategies. Unfortunately, where the benefits of maintenance are considered, it is usually in the sense of avoiding the costs of failure. Interestingly, it is only within the civil engineering community that the benefits in the sense of service delivery are considered and cost-benefit considerations explicitly taken into account in the development of maintenance strategies (e.g., [24]). The argument for dismissing or not focusing on the value of maintenance, when it is made, goes along these lines: while it is easy to quantify the (direct) cost of maintenance, it is difficult to quantify its benefits. Other authors wishing to consider the value of maintenance lament the difficulties in quantifying the benefits of maintenance. Dekker [4] for example notes “the main question faced by maintenance management, whether maintenance output is produced effectively, in terms of contribution to company profits, […] is very difficult to answer”. Therefore maintenance planning is usually shifted from a value maximization problem formulation to a cost minimization problem (see [5] and [6] for a discussion of why these two problems are not the same and do not lead to similar decisions in system design and operation). Incidentally, in many organizations, maintenance is seen as a cost function, and maintenance departments are considered cost centers whose resources are to be “optimized” or minimized. In short, as noted by Rosqvist et al. [7] cost-centric mindset prevails in the maintenance literature for which “maintenance has no intrinsic value”. In this paper, we argue that maintenance has intrinsic value and argue that existing cost-centric optimizations ignore an important dimension of maintenance, namely its value, and in so doing, they can lead to sub-optimal maintenance strategies. We therefore develop a framework for capturing and quantifying one important aspect of the value of maintenance activities, their impact on revenue-generation capability, by connecting an engineering and OR concept, system state, with a financial and managerial concept, the net present value (NPV).1 Here the system state refers to the condition of the system and hence its ability to perform and thereby provide a flow of service (hence generate revenue, or “quasi-rent”). In order to build this connection, we first explore the impact of a system's state on the flow of service the system can provide over time—for a commercial system, this translates into the system's revenue-generating capability. Next we consider the impact of maintenance on system state evolution and hence value generation capability over time. We then use traditional discounted cash flow techniques to capture the impact of system state evolution with and without maintenance on its financial worth, or NPV. For simplification, we call the results of our calculations the ‘value of maintenance’. Finally, we discuss the advantages and limitations of our framework. This work offers rich possibilities for assessing and benchmarking the value implications of existing maintenance policies, and deriving new policies based on maximizing value, instead of minimizing cost of maintenance.

While maintenance optimization techniques abound in the literature, most of these models focus on minimizing the cost of maintenance or maximizing system availability, and seldom is there mention or discussion of the value of maintenance. In this paper, we argued that maintenance has value and argued that existing cost-centric models ignore an important dimension of maintenance, namely its value, and in so doing, they can lead to sub-optimal maintenance strategies. We argued that the process for determining a maintenance strategy should involve both an assessment of the value of maintenance—how much is it worth to the system's stakeholders—and an assessment of the costs of maintenance. We considered “value” as the net revenue generated by the system over a given planning horizon. We did not include additional dimensions of value such as the potential positive effects of maintenance on environmental or health impacts. Such effects can be incorporated in future work. Further, we did not include factors such as safety or regulatory requirements. Such factors can be easily included as constraints on the optimization of value in future work. We developed a framework for capturing and quantifying the value of maintenance activities by connecting an engineering and OR concept, system state, with a financial and managerial concept, the NPV. We explored the impact of a system's state on the flow of service the system can provide over time. Next we considered the impact of maintenance on system state evolution and hence value generation capability over time. We used traditional discounted cash flow techniques to determine the ‘value of maintenance’, which we defined as the difference between the expected NPV of a system with and without maintenance. By identifying the impact of system state or condition on NPV, the framework and analyses developed in this paper provide (financial) information for decision-makers to support in part the maintenance strategy development. Maintenance, as a consequence, should not be conceived as “just an operational matter” and guided by purely operational matters but by multi-disciplinary considerations involving the marketing, finance, and operations functions within a company. We made several simplifying assumptions in order to advance our main argument. In future work these assumptions will be validated or relaxed. Our assumption that downtime is negligible can also be relaxed to incorporate the impact of downtime on for example production levels. The work can be used as the basis of the development of optimal maintenance policies in the presence of constraints (safety, maintenance resources, etc.). In particular, the work will be expanded to consider multi-unit systems, in which the possibility of opportunistic maintenance arises. For example, airlines perform additional PM once specific panels on aircraft have been opened for corrective maintenance, or when delays result in the aircraft being on the ground for longer periods than originally planned. This work can be used to (1) value opportunistic maintenance interventions and (2) identify optimal approaches to selecting targets for opportunistic maintenance. Is it possible to take limitations in maintenance resources into account (in the context of multi-unit systems this is relevant)? Is opportunistic maintenance possible to be taken into account in the method? How? One important implication of this work is that the maintenance strategy should be tied to market conditions and the expected utility profile (core revenue-generating capability of the system). In other words, a value-optimal maintenance strategy is dynamic and changes not only in response to environmental and system conditions but also in response to market conditions. Finally, we believe that the framework presented here offers rich possibilities for future work in benchmarking existing maintenance strategies based on their value implications, and in deriving new maintenance strategies that are “value-optimized.”