Maintenance Decision Making, Supported By Computerized Maintenance Management System

Transcription

1 Western New England University From the SelectedWorks of Mohammadsadegh Mobin Spring January 24, 216 Maintenance Decision Making, Supported By Computerized Maintenance Management System Mohammadsadegh Mobin Ali Rastegari Available at:

2 Maintenance Decision Making, Supported By Computerized Maintenance Management System Ali Rastegari, Maintenance Engineer & Industrial PhD Candidate, Volvo GTO Mohammadsadegh Mobin, PhD Fellow, Western New England University Key Words: Computerized Maintenance Management System (CMMS), Maintenance Decision Making, Multiple Criteria Decision Making (MCDM), Data Clustering. SUMMARY & CONCLUSIONS This paper is written based on the need for Computerized Maintenance Management System s (CMMS) decision analysis capability to achieve world class status in maintenance management. Investigations indicate that decision analysis capability is often missing in existing CMMSs and collected data in the systems are not completely utilized. How to utilize the gathered data to provide guidelines for maintenance engineers and managers to make proper maintenance decisions has always been a crucial question. In order to provide decision support capability, the aim of this paper is to provide and examine three different decision making techniques which can be linked to CMMS and add value to collected data. This research has been conducted within a global project in a large manufacturing site in Sweden to provide a new maintenance management system for the company. The data from the main studies were collected through document analysis complemented by discussions with maintenance engineers and managers at the case company to verify the data. Methods including a Multiple Criteria Decision Making (MCDM) technique called TOPSIS, k-means clustering technique, and one decision making model borrowed from the literature were used. The results indicate the most appropriate maintenance decision for each of the selected machines/parts according to factors such as frequency of breakdowns, downtime, and cost of repairing. The paper concludes with a comparison of results obtained from the different decision making techniques and also a discussion on possible improvements needed to increase the capability of the maintenance decision making models. 1. MAINTENANCE TYPES Maintenance may be performed through various actions, and there are various classifications of maintenance types [1]. One classification of maintenance types and their relationships is indicated in the Swedish standard [2] in which maintenance is divided into two main actions, corrective and preventive. In various studies in the maintenance literature, such as [1], [3] and [4], the term type has been used similarly to other terms, such as approach, action, strategy and policy. Corrective maintenance is also known as run-to-failure or reactive maintenance and is a strategy that is used to restore (repair or replace) equipment to its required function after it has failed [4]. It is sometimes used synonymously with Breakdown Maintenance (BM), Failure-Based Maintenance (FBM) or Operation-To-Failure (OTF) [1], [5] and [6]. Preventive maintenance can be predetermined (periodic) maintenance or Condition-Based Maintenance (CBM) [2]. The Swedish standard defines predetermined maintenance as follows: Preventive maintenance carried out in accordance with established intervals of time or number of units of use such as scheduled maintenance but without previous item condition investigation (p.15). It is sometimes used synonymously with Time-Based Maintenance (TBM) or Fixed-Time Maintenance (FTM), [3] and [6]. The Swedish standard defines CBM as preventive maintenance based on performance and/or parameter monitoring and the subsequent actions (p.15). According to Kobbacy et al. [7], Design-Out Maintenance (DOM) can be considered as another maintenance policy in which the focus is to improve the design of production equipment to make maintenance easier or even eliminate it. Ergonomic and reliability aspects are important in this policy. Labib [6] considers Skill-Level Upgrade (SLU) as another policy for maintenance that is used to improve competence of operators. In addition, various concepts have been developed to increase the effectiveness of maintenance and focus on the maintenance activities. The two more common concepts are Reliability-Centered Maintenance (RCM), and Total Productive Maintenance (TPM). Moubray [8] defines RCM as a process used to determine what must be done to ensure that any physical asset continues to do what its user wants it to do in its present operating context (p.7). A

3 Japanese concept for maintenance, according to Nakajima [9] who introduced the concept; TPM may be defined as Productive maintenance involving total participation (p.1). 2. MAINTENANCE DECISION MAKING Maintenance decision making involves assessing and selecting the most efficient maintenance approach (i.e., strategies, policies, methodology or philosophy) [5]. It involves determining the most appropriate maintenance policy to take, such as BM, TBM or CBM. The consequences of an inefficient maintenance policy go far beyond the direct costs of maintenance. BM is a strategy that leads to high levels of machine downtime (production loss) and maintenance (repair or replacement) costs due to unexpected breakdown [1]. A preventive maintenance strategy contributes to minimizing failure costs and machine downtime (production loss) and increasing product quality [11]. However, the TBM practice is not usually applicable when attempting to minimize operation costs and maximize machine performance [3]. Marquez [12] also states the reason that the maintenance plans provided by the equipment manufacturer are not completely reliable is that they are not aware of business-related consequences of failure, safety considerations, regulatory requirements, the use of condition monitoring techniques, availability of resources and unique environmental conditions (p.16). This statement is supported by Tam et al. [13], who note that preventive maintenance intervals based on Original Equipment Manufacturer (OEM) recommendations may not be optimal because actual operating conditions may be very different from those considered by the OEM. As such, actual outcomes may not satisfy company requirements. In addition to BM and TBM, according to Gupta and Lawsirirat [14], the main goal of CBM is to perform a real-time assessment of equipment conditions to make maintenance decisions, consequently reducing unnecessary maintenance and related costs. Rastegari and Bengtsson [15] emphasize that reducing the probability of maximal damage in production equipment and reducing production losses, particularly in high production volumes, are two potentially significant benefits of a proper CBM implementation. The achievement of more efficient maintenance depends on the capability of the implemented maintenance policy to effectively provide and employ relevant information concerning the factors that affect the life of the component/equipment in question [5]. Providing more relevant information on component condition increases the ability (effectiveness) of a maintenance solution to avoid failures and makes the best possible use of the equipment/component s effective life by performing replacements just before failure; thus, this information improves the maintenance policy s accuracy [5]. Jantunen et al. [16] propose a guide for maintenance decision making based on component failure models. According to Figure 1, in case of wear models D, E and F, the use of CBM is not possible or sensible as failures can take place without a warning being registered by the measuring signals. In such a case, the best solution is to run the component until failure occurs; hence, the optimal maintenance type is BM. When infant mortality is high (A and F), TBM is not an appropriate option. Cases A, B and C can be monitored, but is not sensible to monitor the remaining three (D, E and F). Figure 1 - Failure models, adopted from [16] 3. COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEM (CMMS) The increased amount of information available and a growing need to have this information at hand and in real time for decision making necessitates a CMMS to aid maintenance management [6]. Legacy maintenance systems with large batch reports in which the focus was on data throughput are being replaced by dynamic, on-line queries created on-the-fly with answers in seconds rather than days [6]. The CMMS can provide the following items [6]: Support CBM Track the movement of spare parts Allow operators to report faults faster Improve communication between operations and maintenance personnel Historical information necessary for developing preventive maintenance schedules Provide maintenance managers with information to have better control over their departments Offer accountants information on machines to enable capital expenditure decisions to be made Data collection and data analysis are more or less offered by commercially available CMMS packages, but CMMS packages have always been lacking decision analysis. This lack of decision support is a definite problem; because the key to systematic and effective maintenance is managerial decision making that is appropriate to the particular circumstances of the machine, plant or organization [6], (p.193). 4. RESEARCH METHODOLOGY The purpose of this paper is to provide models that can be linked to CMMS in order to add value to data collected in the system by providing decision making capability. The empirical basis for the study was a single case study of a major manufacturing site in Sweden. The case company s products are gearboxes, with a production volume of 95 products per year. This research has been conducted within a global project at the company to provide a new CMMS. The data was collected through document analysis complemented by discussions with maintenance engineers and managers at the case company to verify the data. Various documents, including Emergency Work Orders (EWO) database, maintenance audits, maintenance strategies and maintenance

4 activities at the case company were studied. The focus of the research was on physical assets at the company (machines, motors, pumps...) and buildings, services and software were excluded. The findings, presented below, are focused on the maintenance decision making according to key factors found in the case study, followed by a discussion on the applied decision making models. 5. MAINTENANCE DECISION MAKING MODEL FROM THE CASE COMPANY Two decision making models found at the case company are considered in this study. One is illustrated in Figure 2, which is based on production output. relatively long Breakdowns often take place because of deterioration The progress of deterioration is relatively slow 6. APPLICATION OF MAINTENANCE DECISION MAKING GRID IN THE CASE COMPANY The model proposed by Labib [6] is called the Decision Making Grid (DMG) and is used in this section. The model is based on identifying the criteria of importance including downtime, frequency of failures and cost. The DMG then proposes different maintenance policies based on the state in the grid, which is indicated in Figure 4. Figure 2 - Maintenance decision making flow chart based on production output The other model is based on the technical viewpoint, which is indicated in Figure 3. Figure 3 - Maintenance decision making flow chart based on technical viewpoint In addition, there are guidelines for selecting appropriate maintenance policies at the company. Here, for example, are points to choose CBM: When a breakdown takes place, it will considerably influence production output Irregular breakdowns often take place Breakdowns cannot be prevented by a normal inspection and servicing The time interval between two sequential breakdowns is Figure 4 - DMG, adopted from [6] The first step that must be taken to make a decision making grid is criteria analysis [6]. The aim of this phase is to establish a Pareto analysis of two important criteria: downtime and frequency of breakdowns. Downtime and frequency can be substituted by Mean Time To Repair (MTTR), and Mean Time Between Failures (MTBF). The purpose is to assess how bad the worst performing machines are for a certain period of time. The machines at the case company are classified in AA, A, B and C levels, which is called the machine ledger. In this classification, AA machines are the most critical machines. Appendix A.1 indicates criteria analysis on data collected from EWO database and maintenance engineers for AA machines. It shows that 53 percent of the total downtimes of the company during the six months of the year in addition to 54 percent of the number of breakdowns are due to AA machines failures. The worst performing machines in both criteria are sorted and grouped into High, Medium, and Low sub-groups. It is obvious that machine number and are the worst machines by having highest downtime and frequency of breakdowns. However, doing criteria analysis needs to have consideration on different aspects such as analytic hierarchy process of faults related to the machine system and components, as well as performing more mathematical analysis to reach an accurate scope for each level. The next step is to place the machines in the DMG (Figure 4) to recommend asset management decisions [6]. By locating machines in to the decision making grid (decision mapping), the model indicates which maintenance policy should be selected for each machine (Figure 5). For instance, machine number 87843, which has the highest downtime and the highest frequency of failure, in the topright region, is the worst performing machine and the action

5 to implement, or the rule that applies is DOM, accordingly the machine design should be modified. On the other side, in the bottom-left region, machine number that has the lowest downtime and frequency of failures can work until a failure happens (BM). In the top-left region, SLU is the most appropriate policy, high frequency of breakdowns with low downtime show that a machine has been stopped many times for limited periods of time. Therefore, maintaining this machine is a rather easy task that can be performed by operators after upgrading their skill levels. Machines for which their performances are located in the bottom-right region are problematic machines. The low frequency of breakdowns shows that the machines don t breakdown often, but their high downtime presents that each breakdown can last for long time due to a big failure. Therefore, the appropriate action for these machines is CBM to analyze the breakdowns and monitor the machines conditions. If a machine is located in a region with a medium downtime or a medium frequency the appropriate policy to take is TBM. However, sometimes two machines are exactly the same and they are doing the same work in the same environment but they are located in different grids. Maintenance engineers should therefore do more analysis according to maintenance concepts such as TPM and RCM in order to select the appropriate maintenance policy. In addition, in some cases two machines are located near or in the border of two different grids. Maintenance concepts can be helpful in these cases to decide which decision to make. For example, when downtime is high but the frequency of the failure is low, performing some analysis such as RCM can be considered to decrease downtime. Figure 5 - Decision making grid for AA machines at the case company The model proposed in this section can be a solution to provide decision making analysis capability for the system by adding value to data collected in CMMS. For having a more logical model and using data for selecting maintenance policy, cost function must be considered. For this reason, the company needs to have historical cost data such as cost of failures for production and cost of maintenance. By considering cost and performing criteria analysis, the model will be in three dimensions in a fuzzy surface and each region indicates which maintenance policy should be selected (Figure 6). In this model the assumption is that the cost function of maintenance policies is linear and trails the relationship as follows: DOM > CBM > SLU > TBM > BM. Figure 6 - The fuzzy decision surface showing the regions of different strategies, adopted from [6] In addition to this, the criteria analysis that has been performed in this section is at the machine level. In order to make a more accurate decision making model, this criteria analysis should be performed at the component level. And, the level of faults in the Analytical Hierarchy Process (AHP) must be prioritized and analyzed according to the components. 7. APPLICATION OF MCDM AND CLUSTERING TECHNIQUE IN THE CASE COMPANY In this section, the logic of the DMG (in Figure 4) is borrowed from Labib [6] and is used in combination with two mathematical methods to rank and categorize the case company s machines and parts in their relevant maintenance policy groups. 7.1 TOPSIS The most widely used MCDM tool called TOPSIS [17], is used in this section to rank the machines and parts. The basic mechanism to this approach is to calculate the distance from each alternative to a Positive-Ideal Solution (PIS) and a Negative-Ideal Solution (NIS) that are defined in n- dimensional space, where n represents the number of criterion in the decision problem. The chosen alternative should have the smallest vector distance from the PIS and the greatest from the NIS. The TOPSIS algorithm presented in [18] and [19] are utilized in this paper. Using TOPSIS method, machines are ranked based on three criteria including downtime, cost and frequency. For the sake of simplicity, the weights of criteria are considered as equal. Based on maintenance experts opinions, the ranked machines are divided into five categories and are presented in Figure 7 for visualization purposes. According to these categories, five different maintenance policies can be considered.

6 Cost Cost Figure 7 - Ranked machines based on TOPSIS using 3 criteria The configurations of each category are summarized as Appendix A.2. The total number of machines is equal to 54. In order to have more investigation on data, we also ranked the parts based on three criteria as mentioned above. Applying TOPSIS method with equal weights for criteria, the ranks of parts are obtained (summarized in Appendix A.3). All parts are divided into five categories as presented in Figure 8: x 1 4 x Frequency 15 1 Rank:1-5 Rank:51-11 Rank:11-15 Rank:151-2 Rank: Ranked parts Downtime Rank:1-1 Rank:11-2 Rank:21-3 Rank:31-4 Rank: of the cluster. The representatives (Centroids) are obtained from the data based on two criteria of different maintenance policy (Figure 4) [6]. Base on Figure 4, the data for machines are divided into five categories as presented in Table 1. Clusters DT Fr 1 (BM) Low (-1) Low (1-5) 2 (CBM) High (1-5) Low (1-5) 3 (TBM) Medium (1-2) Medium (5-15) 4 (SLU) Low (-1) High (6-23) 5 (DOM) High (1-5) High (6-3) Table 1 - Five clusters based on 5 downtime and frequency The cost criterion is also considered in the clustering algorithm, which is assumed as the average cost of machines in each cluster. The representatives of each cluster are presented in Table 2. Clusters DT Fr Cost 1 (BM) (CBM) (TBM) (SLU) (DOM) Table 2 - K-means clusters representatives (Centroids) The result of the clustering technique is presented in Figure 9. This figure indicates that each cluster can be assigned to a specific maintenance policy Frequency Downtime 4 Figure 8 - Ranked parts based on TOPSIS using 3 criteria Based on the category of the machines and parts, maintenance engineers or managers can more easily decide which decision is the most appropriate. For example a machine in the first rank which has the highest downtime with the highest frequency and cost, is a problematic machine. Therefore, CBM can be a good action to take. However, it still needs performing more analysis such as failure analysis before implementation. 7.2 K-means clustering technique The k-means clustering technique [2] is used to cluster the machines based on three criteria. The algorithm was run for a number of different means and the optimal number of clusters was determined to be five according to the calculated Silhouette Index [21]. Each cluster is represented by a single representative, which reflects the characteristics Figure 9 - Clustered machines based on k-means clustering technique 8. CONCLUSIONS The aim of this paper was to provide and examine three different decision making techniques that can be linked to CMMS and add value to collected data. Methods including DMG, TOPSIS and clustering techniques borrowed from the literature were used in the case company. As a result of this study, the necessary information for proper maintenance decision making and the decision making models are identified, and the details of utilizing them are described. The results indicate the most appropriate maintenance decision that suits each of the selected machines according to factors including frequency of breakdowns, downtime, and

7 cost of repairing. Preparing the decision making models for the case company by use of collected data from CMMS indicated that the models can be feasibly and practically applied. Comparing the results with maintenance engineers ideas revealed that utilizing various mathematical decision making tools increases the decision analysis capability, by helping maintenance engineers and managers to have more appropriate maintenance decisions. However, the suggested tools require further practical testing in different potential applications. Based on the empirical findings, it can also be concluded that the results of the decision making models cannot be autonomously used, rather they should be further analyzed in terms of prioritizations and characterization of different failure types and main contributing components. 9. ACKNOWLEDGEMENTS This research work has been funded by the KKfoundation (the INNOFACTURE research school), VINNOVA through the FFI Hållbar produktionsteknik research programme, and Mälardalen University. The research work is also a part of the initiative for Excellence in Production Research (XPRES) which is a cooperation between Mälardalen University, the Royal Institute of Technology, and Swerea. XPRES is one of two governmentally funded Swedish strategic initiatives for research excellence within Production Engineering. 1. REFERENCES 1. S. Hess, W. Biter, S. Hollingsworth, An Evaluation Method for Application of Condition Based Maintenance Technologies, Annual Reliability and Maintainability Symposium, USA, Philadelphia, pp , J. H. Shin, H. B. Jun, On condition based maintenance policy, Journal of Computational Design and Engineering, 2(2), pp , Swedish Standards Institute, Maintenance Terminology, SS-EN 1336, A. Rosmaini, Sh. Kamaruddin, "An overview of timebased and condition-based maintenance in industrial application", Computers & Industrial Engineering, 63(1), pp , B. S. Blanchard, D. Verm, E. L. Peterson, Maintainability: A key to effective and maintenance management, NY: John Wiley & Sons, B. Al-Najjar, I. Alsyouf, "Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making", International journal of production economics, 84(1), pp. 85-1, A. Labib, "A decision analysis model for maintenance policy selection using a CMMS", Journal of Quality in Maintenance Engineering, 1(3), pp , K. A. H. Kobbacy, D. P. Murthy, Complex System Maintenance Handbook, Springer, J. Moubray, Reliability-centered Maintenance. Industrial Press Inc., Second Edition, New York, S. Nakajima, Introduction to TPM Total Productive Maintenance, Productivity Press, Cambridge, A. H. C. Tsang, Condition-based maintenance tools and decision making, Journal of Quality in Maintenance Engineering, 1(3), pp. 3 17, S. J. H. Usher, A. Kamal, W. H. Syed, Cost optimal preventive maintenance and replacement scheduling IEEE Transactions, 3, , A. Marquez, The Maintenance Management Framework: Models and Methods for Complex Systems Maintenance, Springer, A. S. B. Tam, W. M. Chan, J. W. H. Price, Optimal maintenance intervals for a multi-component system Production Planning & Control, 1 11, A. Gupta, C. Lawsirirat, Strategically optimum maintenance of monitoring-enabled multi-component systems using continuous-time jump deterioration models Journal of Quality in Maintenance Engineering, 12(3), pp , A. Rastegari, M. Bengtsson, Cost Effectiveness of Condition Based Maintenance in Manufacturing, IEEE 61 st Annual Reliability and Maintainability Symposium, Florida, USA, E. Jantunen, A. Arnaiz, D. Baglee, L. Fumagalli, Identification of wear statistics to determine the need for a new approach to maintenance, Euromaintenance, 5-8, C. L. Hwang, A. S. M. Masud, "Multiple objective decision making-methods and applications", Berlin: Springer-Verlag, vol. 164, M. Mobin, M. Dehghanimohammadabadi, C. Salmon, Food Product Target Market Prioritization Using MCDM Approaches, Industrial and Systems Engineering Research Conference (ISERC), Montreal, Canada, C. Salmon, M. Mobin, A. Roshani, "TOPSIS as a Method to Populate Risk Matrix Axes" Proceedings of the 215 Industrial and Systems Engineering Research Conference, B. Dash, M. Debahuti, A. Rath, M. Acharya, "A hybridized K-means clustering approach for high dimensional dataset" International Journal of Engineering, Science and Technology, 2(2), pp , B. Mokhtarpour, J. Stracener, "Application of a clustering technique in identifying the best System of Systems (SoS) during development." Systems, Man and Cybernetics (SMC), 214 IEEE International Conference on. IEEE, 214. BIOGRAPHIES Ali Rastegari, Maintenance Eng. & Industrial PhD Candidate Operational Technical Support Volvo Group Trucks Operations Powertrain Production Dept BE62143, Köping SE-731 8, Sweden

8 Ali Rastegari is an industrial PhD candidate at Mälardalen University since September 212. He is employed as a maintenance engineer at Volvo Group Trucks Operation. Ali received his M.Sc. from Mälardalen University in the area of Product and Process Development and his B.Sc. from Tehran Azad University in Mechanical Engineering. His background includes work as a mechanical and maintenance engineer in manufacturing industries. His research interest is strategic maintenance development focusing on use of condition based maintenance in the manufacturing industry. Results of his studies have been published as international journal and conference papers. He is a member in IEEE Reliability Society. Mohammadsadegh Mobin, PhD Fellow Western New England University Springfield, MA, mm33776@wne.edu Mohammadsadegh Mobin is a PhD fellow in Industrial Engineering and Engineering Management at Western New England University. He holds a Master degree in Operations Research (211) and a bachelor degree in Industrial Engineering (29). He served as a quality engineer (26-212) in different manufacturing and service industries. Currently, he is the instructor of Design of Experiment, and Probability and Statistic courses in Western New England University. His research interests lie in the areas of reliability engineering and operations research.

9 Low Low Medium Medium High High Appendix A.1 - Criteria analysis on data collected from EWO database AA Machines' Name Downtime (hrs) AA Machines' Name Frequency (No. of breakdowns) , , , , , , , , , , , , , , Sum of AA Sum of AA 963,48 Machines Machines 113 Sum of All 1818,13 Sum of All 28 Percentage 53% Percentage 54% Appendix A.3 - The summary of categories of parts min All Max All Average All Appendix A.2 - The summary of categories of machines min All Max All Average All