In this paper, an INLP model is presented to optimize EWDN from the perspective of a third party for supporting multi-indenture products. The Product is a multi-component system, which is sold by the manufacturer to a series of customers along with the base warranty. After the expiration of base warranty, third party optimizes the extended warranty policies according to the applied strategies by the manufacturer. To do so, a two-echelon extended warranty network based on the METRIC model is designed, which contains a depot repair center in the first echelon and a number of operational repair centers in the second echelon. In order to reduce repair costs, a novel imperfectpreventivemaintenance approach is established regarding virtual age concept in the proposed EWDN. In such conditions, the purpose of the model is to determine the optimum level of spare parts for every single component of the product in each of the operational and depot repair centers in a way that: (1) total expected backorders is minimized; (2) total maintenance and retrieval cost of product components are controlled. Optimization of the proposed model is a challenging act. Since for medium and large scale models of INLP attaining the global optimum is practically impossible due to the presence of numerous local optima. To overcome the mentioned challenge an exact hybrid solution approach is developed based on Branch and Bound (B&B) algorithm and Variable Neighborhood Search (VNS) approach. This research could add the following contributions to the warranty literature:
A production process consists of set of components (machines) that arranged according to a given configuration is considered. We suppose the production system is a generic multi-state system (MSS) with general configuration (such as series, parallel, series-parallel, network and etc).The system has different levels of performance from complete total performance to complete failure since each component j has a binary state (i.e. either good or failed) and its own nominal performance rate G . This system should produce set of P items (products) during a given planning horizon H including T periods. Each production time period t has a fixed and equal length L that could be divided into several maintenance sub-period m with fixed length for performing the preventivemaintenance. The maintenance policy is based on implementing the corrective and preventivemaintenance on each component. Corrective maintenance is performed as a minimal repair whenever an unplanned failure occurs to change the state of a component to "as bad as old". Preventivemaintenance is carried out noncyclical and divided into two type of perfect and imperfect. The imperfectpreventivemaintenance is performed as a planned repair to change the state of a component to "part of as good as new"; whereas the perfect preventivemaintenance is performed as a planned overhaul to change the state of a component to "as good as new".
Abstract — Effective maintenance management is essential to reduce the adverse effect of equipment failure to operation. This is accomplished by accurately predicting the equipment failure such that appropriate actions can be planned and taken in order to minimize the impact of equipment failure to operation. This paper presents a development of model based on Markov process for a degraded multi-state system to evaluate the system performance. The system degradation was quantified by five distinct level of system’s production output ranging from perfect functioning state to complete failure with zero output. At any point in time, the system can experience Poisson failure from any state upon which an imperfect repair will be performed while imperfectpreventivemaintenance will be performed at the last acceptable state as indicated by minimum acceptable production output. This research explored a method of estimating of transition matrix for the five state Markov process by utilizing production output data. The results indicate the applicability of Markov where comparison with traditionally binary model is presented.
Bupe 3 considered the safety issues in maintenance with the freedom from danger, protection from the risk and injury during the process of carrying out maintenance procedures. Cheng and Chen 4 the periodic preventivemaintenance Policy for deteriorating systems by using improvement factor model has been focused. Dekker 5 gave detail maintenance analysis and it’s optimal application. Gupta et al. 6 The relationship between preventivemaintenance and manufacturing system performance relationship between maintenance and production surveyed. Jaturonnatee et al 7 an optimal preventivemaintenance through corrective minimal repair of leased equipments have been discussed. Lee and Cha 9 considered periodic preventivemaintenance policies for a deteriorating repairable system. Levitin and Lisnianski 10 optimization of imperfectpreventivemaintenance for multi-state systems policies have been discussed. Nakagawa and Mizutani 12 gave a detail summary of maintenance policies for a ﬁnite interval. Poppe 13
This paper highlights the review and critiques of RCM for the hydraulic systems. On the basis of literature review, the findings show the importance of reliability centred maintenance in terms of cost effectiveness. The RCM not only improve the responsibility of a system but also significantly reduce the needed maintenance in today’s highly competitive world, and thereby reducing concerned cost, saved, both from reduced failures and reduced work. It also focuses on the safety of the system by assigning criticality index to the various subsystems and further selecting maintenance activities based on the risk of failure involved. Therefore, it can be said that RCM introduces a maintenance plan designed for maximum safety in an economical manner and making the system more reliable.
Preventivemaintenance strategies (both time and condition based) are widely used for infrastructure life-cycle management decision making. These strategies can be planned and scheduled and their costs are typically lower than those for CM approaches. However early preventivemaintenance intervention adds little to the reliability of the system and can lead to unnecessary costs, hence maintenance strategies often comprise a combination of preventive and corrective approaches.
The Japanese, based on the planned approach to preventivemaintenance (PM), evolved the concept of total productive maintenance (TPM). Nakajima (1986) outlines how, in 1953, 20 Japanese companies formed a PM research group and, after a mission to the USA in 1962 to study equipment maintenance, the Japan Institute of Plant Engineers (JIPE) was formed in 1969, which was the predecessor to the Japan Institute of Plant Maintenance (JIPM). In 1969, JIPE started working closely with the automotive component manufacturer Nippondenso on the issue of PM, and when the company decided to change roles of operators to allow them to carry out routine maintenance this was the beginning of TPM. Tajiri and Gotah (1992) point out that whilst TPM was communicated throughout Japan only a small number of factories took up the challenge. It was the severe economic situation in the early 1970s that accelerated the adaptation of TPM, propagated by the seven-step programme developed by the Tokai Rubber Industries (see Nakajima, 1989). In the early 1990s, Western organisations started to show interest in TPM following on from their total quality management (TQM) interventions. The more academic papers focus on the relationship of TPM with other productivity.This paper examines how TPM was implemented at automobile manufacturing companies to improve overall equipment effectiveness .
To achieve a high level of NPP performance it is important to ensure a high quality of the operational management programmes. The root cause of the performance decline at nuclear power plant is usually related with deficiencies at the low management level, the level of specific operational management programmes, such as maintenance, training and qualification, provision of industrial safety, radiation protection, and so on. It is obvious that the maintenance programme affects such WANO performance indicators as unity capability factor and unplanned capability loss factor. The safety system performance indicator can be used to monitor the effectiveness of maintenance practices in managing the unavailability of safety system components. The high level of thermal performance indicator reflects emphasis on thermal efficiency and attention to detail in maintenance of balance-of- plant systems. On the other hand it is clear that the WANO performance indicators are not appropriate for use as the sole indicators of maintenance-effectiveness because of the number of non- maintenance related factors included in them. The other reason why WANO indicators are not very useful in measuring the effectiveness of the activities at the lower hierarchical level is that they are lagging indicators, which reflect actual plant performance and do not capture lower level problems that affect the plant performance processes 1 . The overall NPP performance is dependent on the performance of several operational management processes established at the plant. WANO performance indicators are in such a way dependent on the performance of separate operational management programmes, such as maintenance, conduct of operations, technical support, radiation protection, training and qualification, fuel management, etc. Variation of WANO performance indicators is the reflection of the changes in the performance of specific management programmes contributing into the overall plant performance. Increased value of the unplanned capability loss factor indicates important plant equipment is maintained inadequately, with low reliability and may be there are many outage extensions. A low trend of these indicators may be attributed to the poor maintenance performance. On the other hand low values for the WANO indicators may also be the result of inadequate operations or mishaps in the training and qualification programme.
Maintenance: As discussed in the introductory part in the past maintenance practices both in private and Government sectors would imply that maintenance is the actions associated with equipment repair after it is broken. No Sector in developing nation can afford this thinking or practice in the present scenario of globalization and competitiveness. The dictionary defines maintenance as follows “the work of keeping something in proper condition; upkeep.”This would imply that maintenance should be actions taken to prevent a device or component from failing or to repair normal equipment degradation experienced with the operation of the device to keep it proper working condition .Maintenance is the combination of all technical and associated administrative actions intended to retain an item in or restore it to, a state in which it can perform its required function. Maintenance function as defined by Maintenance Engineering Society of Australia (MESA) is “The Engineering decisions and associated actions necessary and sufficient for optimization of specified capability”. Where capability is the ability to perform a specified function within a range of performance levels that may relate to capacity, rate, quality and responsiveness. Maintenance concept is set of various maintenance interventions (Corrective, preventive, condition based, etc) and the general structure in which these interventions are brought together. Maintenance Management is “Activities of Management that determine the maintenance strategy, objectives, and responsibilities and implement them by means such as maintenance planning, maintenance control, and supervision, improvement of methods in the organization including economic aspects. Maintenance is not merely preventivemaintenance, although this aspect is an important ingredient .Maintenance is not lubrication, although lubrication is one of the primary functions. Nor is maintenance simply a frenetic rush to repair a broken machine part or a building segment, although this is more often than not the dominant maintenance activity.
A non-trivial problem in the degradation-based maintenance need be solved: the treatment to imperfectmaintenance. Widely used assumptions on maintenance actions are good-as-new (or, perfect) and bad-as-old (or, minimal). It is more realistic in true experience that main- tenance actions merely restore a product’s condition to somewhere between good-as-new and bad-as-old. This type of maintenance is known as the imperfectmaintenance. Extensive re- search on imperfectmaintenance has been documented (Lindqvist, 2006; Nicolai et al., 2009; Zhang et al., 2013). One of the most popular treatments is the improvement-factor method. The improvement-factor method assumes that each maintenance action changes the time of the failure rate curve to some newer time but not all the way to zero; see Pham and Wang (1996). A general framework of the improvement-factor method is given as follows. Let h(t) (t ≥ 0) denote the hazard rate function of the target product. Given that a maintenance action is per- formed at time t 1 (> 0), right after the maintenance action the hazard rate function assumes a new expression: β h(t −t 1 +α t 1 ). Here, t −t 1 represents the time elapsed from the maintenance
As further proof of its commitment to the personal touch, San Jose also fields a special, mobile team of six fleet technicians who provide vehicle maintenance services to scattered-site workers around the city. The technicians are equipped with specially outfitted trucks that allow them to provide oil changes, waste oil recovery, and minor preventivemaintenance services anywhere in the city. And, while garage-based fleet maintenance services are available only during the tra- ditional workday, services from these mobile mechanics are also available after- hours and on weekends.
The B2W Operational Suite provides categories that you can use to organize and classify certain types of data structures. Many of these categories apply directly to B2W Maintain. For example, there are certain categories that you need to work with when you set up maintenance programs. In this exercise we’ll introduce some of these categories and go through the process of setting up some categories.
Abstract- In the present work, maintenance planning based on computer-aided preventivemaintenance policy are introduced. The focus of this paper is on preventivemaintenance activities. Preventivemaintenance involves the repair, replacement, and maintenance of equipment in order to avoid unexpected failure during use. The aim of this study is to build the preventivemaintenance program and is to improve system availability and maintenance resources. The preventivemaintenance program results indicate that the availability and reliability have increased for three specifications M/C under investigation. For first Longitudinal Seaming machine, the result shows that the machine availability increases from 75.6 % to 90.34 %. While, machine reliability improves around 3.97 % for the proposed preventivemaintenance. In case of second Longitudinal Seaming machine, as global results, about 14.4% and 4.31% of the machine availability and reliability are increased for the proposed preventivemaintenance, respectively. In addition, the Rotary machine availability improves from 86.287 to 92.21 % and the machine reliability improves from 3.95% for the proposed preventivemaintenance. Moreover, obtained results showed that using such preventivemaintenance program will eliminate the six big losses; time losses, setup and adjustment losses, idling and minor stoppages losses, lowering machine operational speed losses, scrap & rework losses and production start up losses.
In this paper, a sequential failure limit maintenance policy for a repairable system is studied. The system is assumed to have states, including one working state and failure states, and the multiple failure states are classified by, e.g., failure severity or failure cause. The system will be replaced at the th failure and corrective maintenance is conducted immediately at each of the first failures. A reliability-centered preventivemaintenance schedule is proposed in which, between two adjacent failures, a preventivemaintenance action is taken as soon as the system reliability drops to a critical reliability . Both preventivemaintenance and corrective maintenance are assumed to be imperfect. Increasing and decreasing geometric processes are introduced to characterize the efficiency of these two types of maintenance. The objective is to derive an optimal maintenance policy such that the long-run expected cost per unit time
Estimated costs based on previous work expenditure in the field were allocated to each of the 16,329 activities. These cost estimates were then converted to specific costs per year, Fig. 3. It can be seen from the diagram that the most expensive maintenance activities are those performed several times each year. The costs for activities with a periodicity of ≤ 1 are on average five times as high as the mean costs for all activities, see figure 3 (rightest column). It is moreover clear that no more than 40 % of the annually incurred maintenance costs are the result of work in the field. The remainder, in other words 60 % of the overall costs, is expended on work preparation and on coordinating/evaluating the results. This leads us to conclude that it would be worthwhile examining the steps that go to make up each activity again.
Today's companies face the challenge of modeling their production processes in a flexible, versatile, and customer- oriented manner to remain competitive. This leads to challenges such as increased installation complexity, decreased lead time, increased variation in production and assembly processes, growing quality and cost pressure complaints, especially shortened lead times causing increased capacity demands and production flexibility . Corrective maintenance is reactionary and may occur in an unscheduled manner, still observed as repair or replacement of an asset after a defective failure has occurred. Corrective maintenance is usually performed after the failure of an aircraft asset, and the objective is to restore it to its operating state as soon as possible by repair or replacement .
Before the outsourcing of maintenance is considered, the current performance of maintenance should be understood in terms of quality, quantity, cost, and effectiveness. This will allow for a benchmark to which a future contractor can be measured. 79 However, this might not always be possible, for example when the contract is accompanied by the acquisition of new production equipment, or when the maintenance contract concerns equipment that can only be maintained by the manufacturer. A possibility to accurately measure the performance of outsourced maintenance is measuring the number of failures, the amount of usable time between maintenance and the tested condition of an unit. A problem with these parameters is that it will fail to detect the scenario where maintenance policies of the contractor, for example under threat of high fines in case of production failure or to increase its profits, result in unnecessarily high maintenance costs that could otherwise be avoided. For example, when an office has a contract for its printer, but if the contractor decides to change the cartridge when its half full to avoid ink shortages, the associated costs will be unnecessarily high, but when just the number of
In contrast to previous SPC applications, where the measurement is output for the purpose of controlling the input or transformation, the developed design measures outcomes for the purpose of controlling output. This means essentially that the developed measure here does not utilize SPC of the service supplier process but rather SPC of the customer process. This answers to a call of research to measure value-in-use (Nudurupati et al., 2011), motivated by the increasing importance of product related services (Lovelock and Gummesson, 2004) and the transition from product- to service-dominant thinking (Vargo and Lusch, 2004; Vargo et al., 2008). Further, from a quality control perspective, this application of SPC is not only (or even primarily) intended to limit the costs incurred by special causes, but rather to limit the costs incurred by their absence, thus providing a concrete measure for the holistic measurement of the cost of quality and the identification of over-service or over-maintenance. The strong theoretical foundation of the developed measure secures a good generalizability, but it also dictates the limitations. While this general SPC-based measurement method is basically applicable to any population within the limits of statistical significance, certain requirements on data availability arise from the reliability theory. Depending on factors influencing the failure rate, we could expect that applying this measure would require being able to at least control for technology, equipment usage and environment. However, the bottom line is that the service information required for the measure is quite simple and could be expected to be available to any maintenance service provider, providing no decisive advantage for the servitized original equipment manufacturer (OEM) (cf. Oliva and Kallenberg, 2003; Ulaga and Reinartz, 2011).
HPLC is the sensitive and high cost equipment also. So, we need to care about regular maintenance of HPLC. It leads to reduce the cost due to routine problems. The equipment should be inspected weekly for signs of leaks. Prior to any analysis, a system suitability test, which closely resembles the intended assay, should be performed to ensure that the system is operating within establish criteria. Before using mobile phase solvents should be thoroughly familiar with all hazards and safe handling practices. Observe the manufacturer recommendations for use, storage and disposal. These recommendations are normally provided in material safety data sheets supplied with the solvents. The storage condition is one of the important parameter to maintain the HPLC. Should not operate the system in a cold room or refrigerated area. The ambient temperature is 10-35 ◦ C,
This structured approach in Autonomous Maintenance implementation ensures that every activity is critically analyzed before being undertaken and this reduces the chances and probability of overlooking very important detail . Within Autonomous Maintenance, improvement goals need to be closely integrated with the corporate objectives and should be considered separate to new capital intensive projects , . Plans for improvement of product service, process quality, safety, environmental impact dependability and customer satisfaction are needed at all levels of any process. The performance measure for Autonomous Maintenance is Overall Equipment Effectiveness (OEE). Overall Equipment Effectiveness is an important performance measure metric for Equipment Effectiveness . While Autonomous Maintenance requires increased commitments to training, resources and integration, there is also the promise to improve availability performance  as well as Maintenance