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International audience; In this article, we consider a single-unit system that operates in a dynamic environment and is subject to shocks. Shocks only affect the system (nonlethal shock) and do not cause sudden failure, and arrive according to a counting process. Both homogeneous and nonhomogeneous Poisson processes are considered for shocks arrival modeling. In order to model the dynamic environment and consider shock effects, a multiplicative failure rate model is proposed. Both corrective maintenance and shock-based preventive maintenance are considered, and two policies are proposed. In the first proposed policy, the system is replaced by a new one upon a failure or based on the predetermined number of shocks, whichever comes first, while the second proposed policy extends the first one by considering an imperfect preventive repair at each inspection time. The inspection times are periodic and the interinspection interval is considered as a decision variable. The proposed policies are optimized according to long-run cost rate criteria. Numerical examples illustrate the applicability and efficiency of the proposed policies.

Studying the influence of imperfect prognostics information on maintenance decisions is an underexplored area. To bridge this gap, a new comprehensive maintenance support system is proposed. First, a survival theory‐based prognostics module employing the Weibull time‐to‐event recurrent neural network was deployed in which prognostics competence was enhanced by predicting the parameters of failure distribution. In conjunction with this, a new predictive maintenance (PdM) planning model was framed via a trade‐off between corrective maintenance and time lost due to PdM. This optimises maintenance time based on operational and maintenance cost parameters from the historical data. The performance of the proposed framework is demonstrated using an experimental case study on maintenance planning for cutting tools within a manufacturing facility. Systematic sensitivity analysis is provided, and the impact of imperfect prognostics information on maintenance decisions is discussed. Results show that uncertainty about prediction declines as time goes on, and as uncertainty declines, the maintenance timing becomes closer to the remaining useful life. This is expected, as the risk of making a wrong decision decreases over time.

This paper considers the integration of preventive maintenance and tactical production planning in a multiproduct production system, where setup costs and times are sequence dependent. A set of products needs to be produced in lots during a finite planning horizon, where preventive maintenance is conducted periodically at the end of some production periods and corrective maintenance is always performed when there is a failure. The system downtime—as caused by maintenance and setup—affects the system's available production capacity. We use a sequence-oriented method to search for the optimal setup sequence and develop some steps to prune the searching tree. Our objective is to find the optimal preventive maintenance interval, production lot size, and production sequence in every period, by minimizing the sum of maintenance, production, inventory, and setup costs within the planning horizon. Numerical examples are presented to illustrate our model.

This paper presents an adaptive maintenance model for equipment that can be adjusted (minor preventive maintenance, imperfect state) or replaced (major preventive maintenance, as good as new) at specific scheduled times based on degradation measurements. An initial reliability law that uses a degradation-based model is built from the collection of hitting times of a failure threshold. Inspections are performed to update the reliability, the remaining useful life, and the optimum time for preventive maintenance. The case of both as good as new replacements and imperfect adjustments is considered. The proposed maintenance model is based on the optimization of the long-term expected cost per unit of time. The model is then tested on a numerical case study to assess its effectiveness. This results in an improvement for the occurrences of maintenance tasks that minimizes the mean cost per unit of time as well as an optimized number of adjustments that can be considered before replacing an item. The practical application is a decision aid support to answer the 2 following questions: Should we intervene now or wait for the next inspection? For each intervention, should we adjust or replace the item of equipment? The originality is the presence of 2 criteria that help the maintainer to decide to postpone or not the preventive replacement time depending on the measured degradation and to decide whether the item should be adjusted or replaced.

In photovoltaic (PV) projects, it is important to establish a common practice for professional risk assessment, which serves to reduce the risks associated with related investments. The objective of this paper is to present a methodology on how to improve the current understanding of several key aspects of technical risk management during the PV project lifecycle, with the identification of technical risks and their economic impact. To achieve this, available statistical data of failures during a PV project have been collected with the aim to (i) suggest a guideline for the categorisation of failure and (ii) develop a methodology for the assessment of the economic impact of failures occurring during operation but which might have originated in previous phases. The risk analysis has the aim to assess the economic impact of technical risks and how this can influence various business models and the levelised cost of electricity. This paper presents the first attempt to implement cost-based failure modes and effects analysis to the PV sector and to define a methodology for the estimation of economic losses because of planning failures, system downtime and substitution/repair of components. The methodology is based on statistical analysis and can be applied to a single PV plant or to a large portfolio of PV plants in the same market segment. The quality of the analysis depends on the amount of failure data available and on the assumptions taken for the calculation of a cost priority number. The overall results can be linked to the cost of periodic and corrective maintenance and form the basis to estimate the impact of various risk and mitigation scenarios in PV business models. Copyright © 2017 John Wiley & Sons, Ltd.

In this paper, we consider a repairable system whose failures follow a non-homogenous Poisson process with the power law intensity function. The system is subject to corrective and multiple types of preventive maintenance. A corrective maintenance has a minimal effect on the system; however, a preventive maintenance may reduce the system's age. We assume the effects of different preventive maintenance on the system are not identical and derive the likelihood function to estimate the parameters of the failure process as well as the effects of preventive maintenance. Moreover, we derive the conditional reliability and the expected number of failures between two consecutive preventive maintenance types. The proposed methods are applied to a real case study of four trucks used in a mining site in Canada. Copyright © 2016 John Wiley & Sons, Ltd.

This paper proposes a graphical method to easy decision-making in industrial plants operations. The proposed tool ‘Graphical Analysis for Operation Management (GAOM) method’ allows to visualizing and analyzing production-related parameters, integrating assets/systems maintenance aspects. This integration is based on the Total Productive Maintenance model, using its quantitative management techniques for optimal decision-making in day-to-day operations. On the one hand, GAOM monitors possible production target deviations, and on the other, the tool illustrates different aspects to gain control on the production process, such as availability, repair time, cumulative production, or overall equipment effectiveness. Through appropriate information filtering, individual analysis by class of intervention (corrective maintenance, preventive maintenance, or operational intervention) and production level can be developed. Graphical Analysis for Operation Management (GAOM) integrates maintenance information (number of intervention, type of intervention, required/not required stoppage) with production information (cumulative production, cumulative defective products, and cumulative production target) during a certain timeframe (cumulative calendar time, duration of intervention). Then the tool computes basic performance indicators supporting operational decision-making. GAOM provides interesting graphical outputs using scatter diagrams integrating indicators on the same graph. GAOM is inspired in the Graphical Analysis for Maintenance Management method, published by the authors (LB, AC, and PV) in 2012. Copyright © 2015 John Wiley & Sons, Ltd.

A new approach to optimal maintenance of multicomponent systems networks is suggested. It is applied to systems subject to external shock processes. Signatures are used for description of systems structures and for dealing with the corresponding optimal maintenance problems. The number of shocks experienced by a system defines a discrete parameter for minimization of the long-run costs per unit of time and is viewed as an alternative scale. The optimal preventive maintenance is performed either after the shock with the obtained optimal number or the corrective maintenance replacement of the whole failed system occurs, whichever comes first. A numerical example is considered and the comparison with the age-based preventive maintenance is performed. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper generalised proportional intensities models are fitted to gas turbine generator failure and maintenance data. The systems of interest have identical maintenance procedures and information about preventive maintenance (PM) and corrective maintenance (CM) activities is known. Closed-form maximum log-likelihood estimates of the model parameters are evaluated, and the model goodness-of-fit is measured. A simulation study is conducted to determine an optimal cost effective PM schedule. Our analysis and results facilitate decision support for determining the cost-optimal maintenance strategy. Copyright © 2012 John Wiley & Sons, Ltd.

We develop a model for the economic design of a Bayesian control chart for monitoring a process mean. The process may randomly suffer failures that result in a non-operating state, and thus call for an immediate corrective maintenance action, as well as assignable causes that shift the process mean to an undesirable level. Quality shifts, apart from poorer quality outcome and higher operational cost, also result in higher failure rate. Consequently, their removal, besides improving the outcome quality and reducing the quality-related cost, is also a preventive maintenance action since it reduces the probability of a failure. The proposed Bayesian model allows the determination of the design parameters that minimize the total expected quality and maintenance cost per time unit. The effectiveness of the proposed model is evaluated through the comparison of its expected cost against the optimum expected cost of the simpler variable-parameter Shewhart chart. Copyright © 2010 John Wiley & Sons, Ltd.

Periodic maintenance of equipment is essential for its optimum performance, thereby enabling production efficiency. In the past, studies on preventive maintenance of automated manufacturing systems (AMS) determined the optimal preventive maintenance policy under different performance indexes. Generally, most hypotheses indicate that equipment reliability can be restored to 1.0 through preventive and corrective maintenance. However, in practical application, the implementation of preventive maintenance results in partial deterioration of equipment; moreover, the reliability of equipment cannot be restored to as-good-as-new. In addition, the greater the complexity of connections of the equipment, the greater is the difficulty in determining the timing for preventive maintenance. On account of these characteristics, generalized stochastic Petri nets (GSPN) are well-suited for the implementation of preventive maintenance. Therefore, this paper applies GSPN for deciding the optimal maintenance policy and constructing models for different levels of maintenance and renewal for an AMS with a serial-parallel layout. As a result of the application of GSPN, the following optimal maintenance policy for an AMS was obtained in this study: Preventive maintenance conducted at intervals of every 240 hours will reduce cost by 46% as opposed to the practice of replacing defective parts when necessary.

Percy and Alkali presented generalizations of the proportional intensities model introduced by Cox. They identified several features of these models that are particularly relevant for modelling complex repairable systems subject to preventive maintenance (PM). These include the baseline intensity, scaling factors and explanatory variables. We investigate these aspects in detail and apply the models to five sets of reliability data collected from the main pumps at oil refineries. We use likelihood methods to estimate the model parameters and compare how well the models fit the data. Our analyses suggest that a log-linear baseline intensity function performs well and that an exponential deterministic scaling function is useful for corrective maintenance. The inclusion of explanatory variables to represent the quality of last maintenance and time since last maintenance also proves to be beneficial. We develop algorithms for simulating the reliability behaviour of a complex repairable system into the future, in order to schedule appropriate maintenance activities, identifying special cases that simplify the algebra. Applying these methods to the oil pump data, we derive recommendations for PM plans and demonstrate that adopting this strategy can lead to substantial savings.

This paper addresses a predictive condition-based maintenance approach based on monitoring, modeling, and predicting a system's deterioration. The system's deterioration is considered as a stochastic dynamic process with continuous degrading. Structural time series, coupled with state-space modeling and Kalman filtering methods, is adopted for recursively modeling and forecasting the deterioration state at a future time. The probability of a failure is then predicted based on the forecasted deterioration state and a threshold of a failure. Finally, maintenance decisions are made according to the predicted failure probabilities, associated preventive and corrective maintenance cost, and the profit loss due to system performance deterioration. The approach can be applied on-line to provide economic and preventive maintenance solutions in order to maximize the profit of the ownership of a system. Copyright © 2007 John Wiley & Sons, Ltd.

We examine the long-run average availability and cost rate of a maintained system which deteriorates according to a random-shock process. Shocks arrive according to a Poisson process. The system fails whenever the cumulative damage exceeds a given threshold. The system's failures are not self-announcing, hence, failures must be detected via inspections. The system is inspected at periodic or exponentially distributed intervals. Systems are replaced by preventive maintenance or after failure (corrective maintenance), whichever occurs first. When the distribution function of the shock magnitudes belongs to the class of subexponential distributions, we obtain simple approximations for the availability and the cost rate. Copyright © 2007 John Wiley & Sons, Ltd.

A connection between precision and downside expected lost revenue for linear systems was developed in previous work. Having the value of precision and accuracy in economic terms helps justify the upgrade of instrumentation and/or the increase in corrective maintenance repair rate. The connection between accuracy and economic value is extended in this article to the case where biases are present and in the context of existing corrective maintenance capabilities. © 2005 American Institute of Chemical Engineers AIChE J, 2006

The operational tasks for distributing clean water in a city involve monitoring flows, pressures, storage levels, and water quality at different network locations; controlling and moving valves; attending to reports and enquiries from the public; billing and charging for consumption; keeping updated infrastructure maps and consumer records and census; repairing leaks; and replacing or improving pipes, hydrants, pumps, and other water distribution networks components. There is no clear or definite boundary between operational and maintenance tasks, and their distinction is merely the intensity or frequency of a given job. Sometimes, the same employee team performs operational, preventive, or corrective maintenance tasks. In contrast, other rather specialized tasks can require a contract with a private service provider (as analyzing water samples to detect a specific and difficult pollutant or controlling and supervising a sophisticated automated pump). Keywords: distribution; operation; leaks; surge; peak demand; regulation; storage; pressure zones; valves; district metering areas (DMA); complaints; maintenance; surveillance; monitoring; information systems; public relations; disruptions; backflow prevention

Within corrective maintenance, the front-end support mainly assists the customer- and back-end maintenance organizations in the communication of corrective maintenance demands. This implies receivi ...

The paper considers the economic lot scheduling problem (ELSP) where production facility is assumed to deteriorate, owing to aging, with an increasing failure rate. The time to shift from an "in-control" state to an "out-of-control" state is assumed to be normally distributed. The system is scheduled to be inspected at the end of each production lot. If the process is found to be in an "out-of-control" state, then corrective maintenance is performed to restore it to an "in-control" state before the start of the next production run. Otherwise, preventive maintenance is carried out to enhance system reliability. The ELSP is formulated under the capacity constraint taking into account the quality related cost due to possible production of non-conforming items, process inspection, and maintenance costs. In order to find a feasible production schedule, both the common cycle and time-varying lot sizes approaches are utilized. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 650 - 661, 2003.

The Oliver Hazard Perry class of frigates and the Arleigh Burke class of destroyers, the US Navy's newest combatants, share a common design fault despite, or perhaps because of, a 20-year difference in design time. This paper describes the fault and links the underlying cause of the error to a lack of readily available historical knowledge. A solution is proposed to prevent the recurrence of error repetition by increasing the visibility of corrective maintenance actions to designers and of the status of material condition throughout the fleet to the maintenance managers and operations planners. The results of the process include better design, elimination of repetitive design errors, and true depictions of fleet readiness as a systems engineering solution.

CM3: Problem Management is a first detailed descriptive problem management process model to be utilized within corrective maintenance. It is the result of a long-term empirical study of industrial corrective maintenance processes. It has been developed at ABB and evaluated for its industrial relevance within 17 non-ABB organizations.Playing the role of a descriptive model, CM3: Problem Management specifies what a problem management process should look like. It also structures it into three maturity levels, Initial, Defined, and Optimal, where each level offers a different grainedness of process visibility.In this paper, we present the CM3 levels of problem management process maturity within corrective maintenance and match them against the industrial state of practice. Our goal is to establish the current status of problem management maturity using CM3: Problem Management as an evaluation model. Our evaluation results show that the industrial processes today suffice to attend to software problems within corrective maintenance. Very few of them, however, do learn from the past in order to prevent future problems and to improve development or maintenance processes.

Summary Many of today’s software systems evolve through a series of releases that add new functionality and features, in addition to the results of corrective maintenance. As the systems evolve over time it is necessary to keep track of and manage their problematic components. Our focus is to track system evolution and to react before the systems become difficult to maintain. To do the tracking, we use a method based on a selection of statistical techniques. In the case study we report here that had historical data available primarily on corrective maintenance, we apply the method to four releases of a system consisting of 130 components. In each release, components are classified as fault-prone if the number of defect reports written against them are above a certain threshold. The outcome from the case study shows stabilising principal components over the releases, and classification trees with lower thresholds in their decision nodes. Also, the variables used in the classification trees’ decision nodes are related to changes in the same files. The discriminant functions use more variables than the classification trees and are more difficult to interpret. Box plots highlight the findings from the other analyses. The results show that for a context of corrective maintenance, principal components analysis together with classification trees are good descriptors for tracking software evolution. Copyright © 2001 by John Wiley & Sons, Ltd.

The opportunistic maintenance of a k-out-of-n:G system with imperfect preventive maintenance (PM) is studied in this paper, where partial failure is allowed. In many applications, the optimal maintenance actions for one component often depend on the states of the other components and system reliability requirements. Two new (τ, T) opportunistic maintenance models with the consideration of reliability requirements are proposed. In these two models, only minimal repairs are performed on failed components before time τ and the corrective maintenance (CM) of all failed components are combined with PM of all functioning but deteriorated components after τ; if the system survives to time T without perfect maintenance, it will be subject to PM at time T. Considering maintenance time, asymptotic system cost rate and availability are derived. The results obtained generalize and unify some previous research in this area. Application to aircraft engine maintenance is presented. © 2000 John Wiley & Sons;, Inc. Naval Research Logistics 47: 223–239, 2000

The ITC-Irst Reverse Engineering group was charged with analyzing a software application of approximately 4.7 million lines of C code. It was an old legacy system, maintained for a long time, on which several successive adaptive and corrective maintenance interventions had led to the degradation of the original structure. The company decided to re-engineer the software instead of replacing it, because the complexity and costs of re-implementing the application from scratch could not be afforded, and the associated risk could not be run. Several problems were encountered during re-engineering, including identifying dependencies and detecting redundant functions that were not used anymore. To accomplish these goals, we adopted a conservative approach. Before performing any kind of analysis on the whole code, we carefully evaluated the expected costs. To this aim, a small but representative sample of modules was preliminarily analyzed, and the costs and outcomes were extrapolated so as to obtain some indications on the analysis of the whole system. When the results of the sample modules were found to be useful as well as affordable for the entire system, the resources involved were carefully distributed among the different reverse engineering tasks to meet the customer's deadline. This paper summarizes that experience, discussing how we approached the problem, the way we managed the limited resources available to complete the task within the assigned deadlines, and the lessons we learned. Copyright © 2000 John Wiley & Sons, Ltd.

As software systems evolve over a series of releases, it becomes important to know which components are stable compared to components that show repeated need for corrective maintenance. To track these across multiple releases, we adapt a reverse architecting technique to defect reports of a series of releases. Fault relationships among system components are identified based on whether they are involved in the same defect report, and for how many defect reports this occurs. There are degrees of fault-coupling between components depending on how often these components are involved in a defect fix. After these fault-coupling relationships between components are extracted, they are abstracted to the subsystem level. We also identify a measure for fault cohesion (i.e. fault-proneness of components locally.) The resulting fault architecture figures show for each release what its most fault-prone relationships are. Comparing across releases makes it possible to see whether some relationships between components are repeatedly fault-prone, indicating an underlying systemic architecture problem. We illustrate our technique on a large commercial system consisting of over 800 KLOC of C, C++, and microcode. Copyright © 2000 John Wiley & Sons, Ltd.

This paper proposes a technique for identifying areas within system designs that may be prone to future corrective or preventative maintenance activity. From that, this paper presents a set of measures that can be applied at the design stage of software development to support the production of more maintainable software. The measures are founded on the theories of coupling and control flow and have been empirically validated on a medium-sized industrial project. The results have shown that the control flow and export coupling measures are significantly good predictors of the future error rate. Copyright © 1999 John Wiley & Sons, Ltd.

We consider an on–off production system which is subject to failure during on-periods. In case of a failure, the production system is maintained correctively. In addition, preventive maintenance is carried out to prevent failures. The costs of both preventive and corrective maintenance are modelled in terms of the net down-time of the production system, that is, the time that the system is not available when needed for production. Since preventive maintenance can be planned during off-periods, there is a perspective of significant savings if some freedom is built in concerning the starting time of preventive maintenance. Therefore, a two-stage maintenance policy is considered, which—in a first stage—provides the maintenance manager with a finite interval during which preventive maintenance must be carried out, and—in a second stage—determines the optimal starting time for preventive maintenance within this interval. Computational results offer useful insights, and indicate that significant savings can be achieved in comparison with a classical age maintenance policy.

This paper introduces the Voluntary Registration Scheme (VRS) for harmless dyestuffs, an initiative of the Hong Kong government, in response to the introduction of the German regulation stipulating a ban on the production import, and sale of consumer goods containing azo colorants produced from certain amines (4th Amendment Regulation to the Consumer Products Regulations). In presenting reasons for the use of Internet as an effective means of overcoming import restrictions and remaining up–to–date on all issues of importance to the environment, the paper further suggests evaluation and corrective maintenance of the database for optimal utility. Though the VRS is of local origin, the international usefulness of such a service is also highlighted.

Maintenance is performed to restore a system's inherent reliability. However, reliability is not enough: a maintenance task must “pay for itself‘ in dollars or readiness. This applies to all types of maintenance: corrective, preventive, and alterative. We can reduce corrective maintenance costs through preventive and alterative maintenance. We can reduce preventive maintenance costs by eliminating inapplicable and ineffecitve tasks. We must weigh alterative maintenance costs against the value of improved readiness and cost-avoidance of future corrective maintenance. The U. S. Navy is improving alterative maintenance by engineering for reduced maintenance (ERM). This process corrects the root cause of some high-cost items by replacing the system, component, or coating with an improved design or material. It overcomes the limit of preventive maintenance: higher levels of inherent reliability require design improvement. The U. S. Navy is improving preventive maintenance through condition-based maintenance (CBM). The Navy's CBM initiative encompasses three parallel efforts: rules (improving maintenance requirements and plans), tools (using computer and diagnostic technology to facilitate maintenance decision-making), and schools (educating all levels of maintenance decision-makers in reliability and condition-based maintenance engineering principles). This paper addresses how the U. S. Navy surface ship community plans to avoid inapplicable and ineffective maintenance and reduce maintenance costs without sacrificing reliability.

E. Burton Swanson summarizes and explains his typology of software maintenance (corrective, adaptive, and perfective), and describes his research on maintenance. His two major projects have been the large DPMA study done in 1977 with Bennet P. Lientz, and the in-depth intensive study of twelve sites done a bit over a decade later with Cynthia M. Beath. Professor Swanson includes some of his views of the current situation and future in software maintenance.

Budgetary considerations are forcing organizations to become more cost-effective entities. Selected operations research techniques have been integrated into the Risk Constrained Optimized Maintenance Planning (RCOMP) tool to provide decision support for this process. The RCOMP tool allows for the exploration of tradeoffs between life cycle cost and total maintenance time and between net investment and total maintenance time for a system. The tool also facilitates the selection of the optimal mix of preventive and corrective maintenance as well as the most cost-effective maintenance acquisition strategy.

Decades of research on maintenance activities in the procedural paradigm has produced several conclusions. Among these conclusions are recommendations that a reduction in maintenance cost could be achieved by a more controlled design process, by more rigorous testing of potential problem areas earlier in the life cycle. With the increasing emphasis on the object oriented paradigm, the authors performed an empirical study of the maintenance patterns in two commercial object oriented systems. Although this is a preliminary study, intuition is presented as insight into the object oriented maintenance activities.

An empirical study of software maintenance tasks was carried out in a large Norwegian organization in the period 1992–93. More than one hundred randomly selected maintenance tasks were studied through interviews with the maintainers performing the tasks immediately before they started the tasks and immediately after they had completed the tasks. The collected data is used to develop distributions and to test 33 hypotheses about software maintenance. The findings from the study indicate, for example, that Application system documentation plays only a minor role as maintenance information source. The maintenance productivity, measured in LOC/effort, is predicted by the size of the task and type of change in the source code, but seems rather independent of language level, maintainer experience, application age and application size. There is an economy of scale for software maintenance tasks. The type of programming language does not correlate with proportion of unexpected maintenance problems, task complexity or task size. The proportion of corrective maintenance decreases with increasing age of the application.

This statistical study examines management effectiveness and its relation to software maintenance. The findings include that low-level managers in software maintenance who see their organizations as well managed also see their organizations as having a constellation of software maintenance characteristics. Compared to equivalent managers in other organizations, that perception includes doing less corrective maintenance and less new development, but doing more enhancement work on systems. It includes doing software maintenance with staff having a lower average experience level, on programs which commonly are bigger. It includes a lower maintenance backlog per maintenance person. The general picture is that effective management in the organization is more associated with relatively trouble-free systems, more often being, by choice, enhanced, rather than it is associated with meeting new and changed organizational needs by developing new systems.

This paper presents findings from the analysis of data contained in reports and documents maintained by an MIS department of a major pharmaceutical and nutritional manufacturer. To guide data collection, the paper develops a conceptual framework consisting of a descriptive scheme and an explanation model. The descriptive scheme classifies maintenance activities into four categories: adaptive, corrective, perfective, and ongoing support. The explanation model identifies meta-factors that affect maintenance activity. The meta-factors are age, size, programming language, processing environment, structured programming, modularization, analysis and design methodologies, end-user involvement, documentation generation, and maintenance management. The paper analyses three business functions with high, middle, and low average time per repair maintenance project (ATM) measured in person-hours over a period of one year. Each function has several systems, and each system consists of modules. The analysis of available data for all functions maintained shows that maintenance (both repair and update) consumed 49% of data processing (DP) resources. For the three functions, the majority of these resources are devoted to perfective maintenance and corrective maintenance. Guided by the explanation model, several variables belonging to the meta-factors are identified. The following variables have the most significant influence over maintenance: real-time processing, database processing, end-user ongoing support, module size, number of runs and runtime per module, and number of reports and number of copies per function.

Access to large portions of our knowledge is unstable, so that recall fluctuates depending upon momentary context. Laboratory interventions can stabilize access for a time, either by preventing the loss of precarious access (preventive maintenance) or by re-establishing lost access (corrective maintenance). This paper describes two methods of measuring the magnitude and duration of such intervention effects. Preventive maintenance effects are measured by the degree and the persistence of asymmetry of up- versus down-fluctuations on successive attempts to recall. The preventive maintenance effect of a single successful recall trial has a half-life in excess of 1 month. The methods are suitable for comparing the effectiveness of various schedules and types of maintenance interventions for a variety of semantic memory content and for diverse subject populations.

COBOL-implemented systems commonly rely on the PERFORM verb to provide structure and iteration in the software. Because of that key role, shortfalls in the use of PERFORM can lead to challenging difficulties in both enhancive and corrective maintenance. This paper explains the source of many of the symptoms by examining the way the compiler-generated object code executes. Then it classifies the symptoms by their pattern and relates the classes to the normally hidden generated code. In closing, the paper offers six positive actions to improve COBOL software using the PERFORM verb, and points to their contribution in enhancive and corrective maintenance, software quality assurance, systems improvement, and the management of software maintenance.

Current trends in manufacturing have led to reduced levels of work-in-process inventory and increased levels of automation and machine complexity. These changes have two important implications for maintenance management. First, machine breakdowns not only cause a loss of output from the down machine, but also result in lost production on downstream machines. This occurs because of the elimination of buffer stock between workstations. Second, the gap between the technological expertise of the operators and the technological sophistication of their machines has widened. This gap decreases the ability of operators to make adjustments and minor repairs to their own machines. These two factors cause an increase in maintenance costs associated with machine downtime and maintenance labor. Historically, the proportion of preventive and corrective maintenance has attempted to balance these costs. As buffer stock is reduced and more machines are coupled, the costs of downtime will increase, increasing the need for preventive maintenance and decreasing its cost compared to the cost of lost production. Increases in the use of computer-based planning systems have resulted in the development of computerized preventive maintenance systems that have proven to be successful in planning for maintenance labor and materials. Integrating planning methods with corrective maintenance tasks will help reduce breakdowns, make more efficient use of maintenance craft labor, and provide information for inventory control of maintenance, repair, and operating (MRO) materials. This article examines methods of reducing machine downtime costs, maintenance labor costs, and MRO inventory costs in today's changing production environment. Techniques for reducing these costs are explored in the areas of maintenance planning, maintenance methods improvement, and MRO inventory control.

In the philosophy of an automated maintenance system, the system design and development process is geared to making all operating system information, which could be used in system maintainability, available in an orderly meaningful manner. This process means designing the system software to monitor system status and performance data, to use this data along with preventive maintenance scheduling and fault prediction analysis in performing total system evaluation. This evaluation process then results in the definition of recommended preventive testing or the required fault detection and isolation testing. During the latter stages of fault isolation testing and continuing through fault correction, the system is also responsible for making the available reconfiguration recommendations for the failure being corrected.