Dimensionality and relevance of maintenance performance measures DECISION SCIENCES INSTITUTE. (Full Paper Submission)

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1 DECISION SCIENCES INSTITUTE (Full Paper Submission) Carlos F. Gomes University of Coimbra School of Economics ISR Institute of Systems and Robotics Coimbra - Portugal cfgomes@fe.uc.pt Mahmoud M. Yasin East Tennessee State University Department of Management & Marketing Johnson City, TN USA mmyasin@etsu.edu Jorge M. Simões REVIGRES Lda - Department of Maintenance Águeda - Portugal Fab.JSimoes@revigres.pt ABSTRACT This study utilizes cluster and factor analysis to identify the different dimension of the most relevant maintenance. The results obtained appear to indicate that maintenance function has different facets, which are essential to the organizational competitiveness. This was the case regardless of organizational size and industry. KEYWORDS: Performance measurement, Performance management, and maintenance management INTRODUCTION Closed system organizations mainly focused on operational efficiency. As such, these organizations viewed the maintenance function accordingly. The rate of maintenance, in such organizations, was merely focused on maintaining the efficiency of their machinery and equipment. In this context, maintenance performance was viewed from a unidimensional perspective. As organizations shifted slowly towards the open system mode of operation, maintenance began to take on a more significant organizational role. Therefore, the different aspects of the maintenance performance needed to be identified and measured. Using a sample of ninety-five (95) Portuguese maintenance managers, this research utilizes cluster analysis to identify the most relevant maintenance. Factor analysis is utilized to identify the different dimensions of maintenance measures. The identification of these different maintenance dimensions has practical value to managers, as

2 they attempt to improve the different aspects of maintenance activities, resources, and their contribution to the organizational performance. LITERATURE REVIEW Due to the changing organizational role of maintenance, and the increasing complexity of production technologies, maintenance related costs have been on the increase (Parida & Kumar, 2006). In manufacturing organizations, maintenance related costs are estimated to be about twenty-five percent of the overall operating costs (Cross, 1988; Komonen, 2002). In some industries, such as petrochemical, electrical power, and mining, maintenance related costs may, even, surpass operational cost (De Groote, 1995; Eti, Ogaji, & Probert, 2005; Parida & Kumar, 2006; Raouf, 1993). As such, close attention should be paid to maintenance performance measures, measurement and management in order to utilize the scarce maintenance resources more effectively. In order to utilize maintenance performance measurement and management to promote positive and proactive organizational change, the maintenance performance management system should be designed to track and continuously improve the different aspects of the maintenance effort. This process should be guided by the integration of critical success business factors, which are derived from the overall organizational strategy (Tsang, Jardine, & Kolodny, 1999). Therefore, the consistency between overall organizational strategy and maintenance strategy cannot be ignored. Based on an extensive literature review, three relevant themes related to maintenance, measurement, and management emerged (Simões, Gomes, & Yasin, 2011). These themes include effective utilization of maintenance resources, total maintenance and information systems support, measurement, measures, and human factor management. These themes clearly incorporate the critical aspects of an effective maintenance system. The implementation of quality improvement programs, modern information systems, continuous improvement programs, and the evolution of the performance measurement systems tended to promote the proliferation of maintenance and measurement in many organizations (Bamber, Sharp, & Castka, 2004; Cua, Mckone, & Schroeder, 2001; Seth & Tripathi, 2008). Due to the increase in the number, and variety of maintenance measures, new approaches for maintenance and measurement are needed (Kumar, 2006). The literature has presented several approaches to a better systematization and utilization of maintenance and measurement. Traditional approaches tended to establish a hierarchy with two sets of indicators, namely (i) key indicators to be evaluated periodically, and (ii) detailed indicators, which are only used for searching for the causes of deviations observed in the key indicators (Martorell et al., 1999). However, relatively new innovative approaches tended to emphasize a more balanced view of maintenance, namely, equipment related performance, task related performance, cost related performance, immediate customer impact related performance, and learning and growth related performance (Garg & Deshmukh, 2012; Kutucuoglu, Hamali, Irani, & Sharp, 2001). The human resources aspect of maintenance has been playing an increasingly important role in relation to the operational environment safety (Patankar & Taylor, 2000; Rankin, Hibit, Allen, &

3 Sargent, 2000). Maintenance resources management deals with the issues related to organization, communication, problem solving, and decision-making (Taylor, 2000). Maintenance and safety are, sometimes, treated as separate and independent sets of activities. However, part of the accidents in manufacturing environments is caused by poor maintenance (Raouf, 2004). Therefore, an integrated approach is the appropriate approach for optimizing plant capacity, as safety and maintenance are not mutually exclusive functions (Liyanage, 2007; Raouf, 2004). There appears to be a shift away from viewing the maintenance performance measurement effort based on a mere budget reporting perspective, to viewing it based on a systematic, organizational perspective. The evolution of the organizational role of the maintenance function shows a clear path toward the integration of maintenance resources and activities into a total management system. This change appears to have evolved from a reactive, preventive, and predictive mode, to a more holistic/process-oriented, complete, systematic organizational mode (Alsyouf, 2007). Such evolution path was marked by different generations of maintenance milestones (Arunraj & Maiti, 2007). In this context the different facets of maintenance activities, resources, measures, and measurement in business organizations should be examined. The changing role of maintenance in today s organizations calls for closer, practical investigation of current maintenance activities, measures, and measurement. Such research has direct, practical implications to organizations, as they attempt to utilize maintenance competencies to support their customer-orientation strategy. METHODS Sample and Procedure For the purpose of this exploratory study, sixteen-hundred and five (1605) maintenance managers of Portuguese business organizations were invited to participate on an online questionnaire. Ninety-five (95) completed responses were received. This resulted in a response rate of approximately 6%. While the response rate is relatively low, it compares favorably with similar operations surveys (Huan, Brooksbank, Taylor, & Babbis, 2008; Scannell, Calantone, & Melnyk, 2012; Shah & Ward, 2002; Wu, Melnyk, & Swink, 2012). Given the fact that the survey instrument was quite time-consuming to complete, the response rate is considered reasonable. According to Table 1, the sample includes business organizations from different industries. These business organizations represent different sizes, both in terms of the number of employees, as well as in terms of the number of machines requiring regular maintenance. Instrument The research instrument used in this study was designed based on an extensive literature review (Simões et al., 2011). The first phase of the instrument development included translation and adaptation to the realities of the Portuguese maintenance environment. In the second phase, the instrument was presented to a panel of experts, including both practitioners and academicians. In the process, special attention was paid to using terminologies and vocabulary

4 consistent with the background of the participants. This objective was achieved after few iterations. The final version of the research instrument was composed of one hundred and twenty-four (124) maintenance. However, four measures were dropped during the data validation process. Therefore, only 120 were used in this study. For each of the measures included in the instrument, maintenance managers were asked to classify the characteristics and nature of the measure used based on a 1 to 5 Likert-type scale. Table 1 Sample Profile Item Frequency Percentage Number of employers Less than From 10 to From 50 to More than Total: Machines with regular maintenance Less than From 10 to From 50 to From 150 to From 250 to More than No response Total: Industry Basic metals, and metal products Electricity, gas and water supply Food products, beverages and tobacco Pulp, paper, and paper products Chemical products Car vehicles, and motorcycles Ceramic products Construction Electronic products, and semiconductors Logistics Mining / Extraction and processing stone Plastic products Transportation Miscellaneous (with less than three occurrences) Total:

5 Models, Variables, and Data Analysis The data obtained from the participants was analyzed using several statistical methodologies in order to assess the profiles of maintenance managers with regard to the different maintenance measures utilized. In the first phase, clusters analysis was used to evaluate the responses. The predictive value that maintenance managers associated with the 120 maintenance was examined. In the second phase of the data analysis, factor analysis was used to extract the underlying dimensions of maintenance performance related with the measures identified by maintenance managers as the more relevant. In the third phase of data analyses the ANOVA was used to identify differences between the perceptions of maintenance mangers relating the performance dimensions obtained by the factor analysis. RESULTS Based on the cluster analysis results, fifty-six (56) measures were included in clusters one and two (Table 2). According to the maintenance managers perception, these are the performance measures with more relevance. Table 2 Cluster Analysis Results Relative to Predictive Value Measures Cluster Measure Mean Stand. Devia. Coeffic. Variat. Maintenance budget Repair cost for each machine Production quantity (output) for each machine Percentage of maintenance type for each machine Utilization rate of each machine Rate maintenance plan execution Flexibility of the maintenance team Preventive maintenance corrective maintenance (machine) Reliability for each machine Training of maintenance personnel (hours) Preventive maintenance cost total maintenance cost Labour costs of maintenance team ( /hour) Future investment needs for maintenance 3,61 1,08 0,30 Rate of utilization of the maintenance capacity (persons) 3,60 1,05 0,29 Percentage of machine downtime 3,59 1,34 0,37 No. of maintenance occurrences 3,59 1,12 0,31 Available maintenance capacity (hours) 3,57 1,05 0,29 Machine availability Planned production time for that machine 3,57 1,21 0,34 2 Percentage of machines with full documented technical specifications 3,57 1,19 0,33 Maintenance planned unplanned maintenance (machine) 3,57 1,23 0,34 Unplanned maintenance cost 3,57 1,24 0,35 Units produced given time unit 3,56 1,35 0,38 Planned maintenance hours total maintenance hours 3,56 1,20 0,34 Note: Clusters were predefined to 5 to provide an analogy with the scale used on the questionnaire

6 Table 2 (cont.) Percentage of availability of each machine 3,55 1,23 0,35 Preventive maintenance hours Corrective maintenance hours 3,55 1,27 0,36 Total cost of spare parts 3,54 1,22 0,34 Total cost of spare parts in stock 3,53 1,17 0,33 Energy consumption per machine 3,50 1,17 0,33 Level of satisfaction of the maintenance technicians 3,49 1,13 0,32 Safety record 3,49 1,48 0,42 Overall Equipment Effectiveness (OEE) 3,49 1,27 0,36 Percentage of documented maintenance procedures 3,48 1,24 0,36 Energy consumption per unit produced 3,48 1,33 0,38 Age of plant(s) and Machine(s) 3,47 1,13 0,33 Machine age 3,47 1,20 0,35 Percentage of conform products produced by each machine 3,47 1,29 0,37 Actual environmental policy implemented targeted environmental policy 3,47 1,18 0,34 Mean time between failure (MTBF) for each machine 3,46 1,26 0,36 2 Percentage of machine subject to regular analysis of condition based 3,46 1,23 0,36 maintenance and to inspections Percentage of critical machines 3,45 1,32 0,38 No. of overtime hours worked by the maintenance team 3,44 1,31 0,38 Relations between managers and maintenance technicians 3,43 1,17 0,34 Actual services performed services planned 3,43 1,20 0,35 Failure rate for each machine 3,39 1,26 0,37 Preventive maintenance cost reactive maintenance cost 3,39 1,23 0,36 Rate of utilization of maintenance budget 3,39 1,24 0,37 Maintenance procedure quality 3,39 1,21 0,36 Acquisition cost of machines. 3,38 1,26 0,37 Average response time of the maintenance team 3,38 1,25 0,37 Cost of maintenance personnel total personnel cost 3,37 1,17 0,35 Percentage of machines with a documented functional diagn. checklist 3,37 1,38 0,41 Mean time to repair (MTTR) for each machine 3,37 1,23 0,36 No. of efficiency/quality/safety improvements undertaken by maint. team 3,36 1,28 0,38 Mean time to failure (MTTF) for each machine 3,34 1,30 0,39 Replacement cost of all machines 3,33 1,24 0,37 Percentage of downtime of the entire production system 3,32 1,45 0,44 In the next phase of data analysis, the exploratory factor analysis (EFA) procedure was used. Using the Kaiser-Meyer-Olkin test, sample adequacy for all variables was analyzed. A sample adequacy overall value of 0.82 was obtained. This value reached the value considered good in the literature for this type of analysis (Hair, Black, Babin, & Anderson, 2009). The principal component method with Varimax rotation was used to extract relevant factors. The results of the Bartlett test confirmed the appropriateness of the factor analysis procedure as used. Based on the factor analysis procedure, a twelve-factor solution was obtained (Table 3). This solution explained 76.5 per cent of the total variance. Anova procedure was used to test the existence of differences between large and small organizations. The differences between industries were also tested. Finally, the differences between companies different number of machines with regular maintenance were also tested. Based on the results, no significant differences were found. Therefore, it seems that the maintenance performance dimensions found are the same across different organizations and industries.

7 Table 3 Factor Analysis Results FACTORS F 1 F 2 F 3 Comm Cronbach s alpha value (0.954) (0.890) (0.867) F1- Reliability and Planned Prevention Preventive maintenance corrective maintenance (machine) Preventive maintenance hours Corrective maintenance hours Maintenance planned unplanned maintenance (machine) Preventive maintenance cost total maintenance cost Planned maintenance hours total maintenance hours Mean time between failure (MTBF) for each machine Percentage of maintenance type for each machine Preventive maintenance cost reactive maintenance cost Mean time to repair (MTTR) for each machine Mean time to failure (MTTF) for each machine Reliability for each machine Overall Equipment Effectiveness (OEE) F2 - Service Production quantity (output) for each machine Machine availability Planned production time for that machine Utilization rate of each machine Units produced given time unit Percentage of availability of each machine Percentage of conform products produced by each machine Percentage of critical machines F3 Quality and Effectiveness Maintenance procedure quality Rate maintenance plan execution Actual services performed services planned Average response time of the maintenance team Eingvalues 8,10 4,94 3,82 Percent of total variance explained 16,07 8,82 6,82 Cumulative percent of variance explained 16,07 24,89 31,71

8 Table 3 (cont.) (Continued) FACTORS F 4 F 5 F 6 F 7 F 8 Comm Cronbach s alpha value (0.855) (0.775) (0.814) (0.914) (0.803) F4 - Safety and Downtime Safety record Percentage of machine downtime Training of maintenance personnel (hours) Percentage of downtime of the entire production system Failure rate for each machine F5 Environment and Energy Concerns Actual environmental policy implemented targeted environmental policy Acquisition cost of machines Energy consumption per unit produced Replacement cost of all machines F6 Team Efficiency Cost of maintenance personnel total personnel cost Labour costs of maintenance team ( /hour) No. of overtime hours worked by the maintenance team F7 Durability Machine age Age of plant(s) and Machine(s) F8 Procedural Concerns Percentage of documented maintenance procedures Percentage of machines with full documented technical specifications Percentage of machines with a documented functional diagnostic checklist Eingvalues 3,76 3,59 3,27 2,75 2,65 Percent of total variance explained 6,71 6,41 5,83 4,91 4,74 Cumulative percent of variance explained 38,42 44,83 50,66 55,57 60,31 (Continued)

9 Table 3 (cont.) FACTORS F 9 F 10 F 11 F 12 Comm Cronbach s alpha value (0.862) (0.849) (0.802) (----) F9 Manpower Available maintenance capacity (hours) Rate of utilization of the maintenance capacity (persons) F10 Budget control Total cost of spare parts in stock Total cost of spare parts Maintenance budget F11 Satisfaction Relations between managers and maintenance technicians Level of satisfaction of the maintenance technicians F12 Frequency No. of maintenance occurrences Eingvalues 2,63 2,49 2,10 1,86 Percent of total variance explained 4,70 4,44 3,74 3,32 Cumulative percent of variance explained 65,01 69,45 73,19 76,51 DISCUSSION AND CONCLUSIONS Closed system organizations of the past tended to emphasize the dimension of maintenance performance, as if it is the only determinant of effective maintenance performance. This study uncovered several facets of the maintenance function. The different dimensions of maintenance performance identified in this study have organization wide implications. Based on the results of this study, the most prominent maintenance pertain to reliability. The identified dimensions tend to have an impact on the organizational customer orientation and overall competitive organizational performance. The identification of the different dimensions can aid managers as they attempt to improve different aspects of their organizational performance. The identified maintenance performance measures appear to be consistent across different organizations and industries. REFERENCES Alsyouf, I. (2007). The role of maintenance in improving companies productivity and profitability. International Journal of Production Economics, 105(1),

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