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In Search Of Best Practices In Industrial Maintenance: Some Underlying Factors
In Search Of Best Practices In Industrial Maintenance: Some Underlying Factors K. Komonen, S. Kunttu, T. Ahonen, J. Heikkilä, P. Valkokari VTT Technical Research Centre of Finland P.O.Box 1000 FI-02044 VTT Finland (Tel: +358 20 722111; email: { kari.komonen, susanna.kunttu, toni.ahonen, jouko.heikkila, pasi.valkokari}@vtt.fi)
Abstract: Business requirements and characteristics of production systems determine the effective modes of operations in industrial maintenance. In order to identify the best practices the first task is to identify the best performance in various business environments using relevant key performance indicators. The next step is to identify the modes of operations which create the best performance. There are several research strategies to find out the best practices. However, there are a lot of challenges in data collection and therefore a research process requires a long term roadmap in order to boost knowledge on behavior behind peak performance. This paper presents some of the results of a long research process, which form a solid basis for determining the best practices in the area of industrial maintenance. Key words: Maintenance management, maintenance performance, benchmarking, best practices
1. INTRODUCTION Integrated view on the development of companies’ engineering assets has become more important and even vital in the very dynamic business environment which we meet today. Productivity of capital could be increased, which results in more sustainable production. Demands for higher turnover of capital, better return on assets and improved sustainability of asset solutions in the fast changing business environment have led very sophisticated decision making situations where a lack of effective and proper decision making tools is evident. However, various industrial sectors differ from each other as far as the requirements on assets are concerned. Therefore, industry specific requirements should be clearly kept in mind when making decisions concerning engineering assets. One of the most effective tools to achieve better productivity is benchmarking. Since, it is very difficult to find or use cases from the similar business and production environments, more sophisticated methods are needed. One of the main tasks is to find methods to reveal the influence of the business, production and engineering characteristics on the performance of the maintenance function. One of the visions of the research is to find methods which release benchmarking from the prison of the industrywise differences and give opportunity to apply branch-free methods. In this paper we shall present some result of the research programme which enables the present ongoing research project to search for the best practices in the area of industrial maintenance. The objective of the earlier research projects has been to develop methods to evaluate the performance in various environments and to construct a framework to find out the best practices. The objective of the ongoing project is to identify various maintenance environments in order to form a maintenance typology and then to find typical modes of operations in each category of
the typology. The role of the former research results is to determine the best modes of operations to achieve peak performance among the various options. The latter project is to determine various options. In this paper we concentrate on the earlier projects and the final results can be reported in six months from now. 2. THE EFFECTS OF BUSINESS ENVIRONMENT ON MAINTENANCE The structure of the production system and other technological factors has a great influence on the aspired performance level. Later in this section, some empirical evidence of this influence will be presented. Increases in capacity, operating rate or OEE require e.g. investments, improvements, development of maintenance processes, and therefore cost money. High performance figures are not necessarily optimal, because the costs of these actions may be too high. The asset strategy, strategic choices, and optimal performance objectives give a basis for investments, improvement decisions and development processes (Komonen 2008). The four performance factors presented above are not independent, but must be levelled as a whole. The basis for the maintenance oriented examination is presented in the following text. The description of the maintenance system starts with the business requirements of the plant in question. These requirements influence on the production system. The plant asset strategy, customer satisfaction and safety considerations also guide maintenance operations. The requirements for the maintenance function and maintenance objectives originate from the abovementioned business requirements. We should model the effects of these requirements (Komonen et al. 2006).
replacement value" ratio decreases as the amount of machinery grows. The size of the plant offers scale economies. As the utilisation rate (operating rate) of the production system grows the pressure to retain high availability increases. "Maintenance costs / production equipment replacement value" ratio increases as the degree of integration of the production system grows if the amount of production equipment does not change.
The system of the maintenance function consists of five separate sub-processes (Komonen et al. 2006): maintenance planning resource management and development management of maintenance processes execution follow-up and continuous improvement. The above mentioned requirements on the maintenance function influence on the way the maintenance function is organised, modelled and developed.
These kinds of studies are not very common. However, there exist some interesting papers concerning the business and technological determinants of maintenance expenditures e.g. Bhat 2000 and Swanson 1997. Comparing the focus of our study with Bath's and Swanson's studies the following differences can be identified (Komonen 2006): Our focus was at the plant level instead of the company level as in Bath's approach. Our focus was on the technological explanatory factors (as Swanson's) rather than direct financial variables (Bhat). Swanson and Bhat focused on maintenance costs, but we include both the OEE (or availability) and maintenance. We tried to explain economic performance rather than modes of operations (Swanson). In this study our aim was to explain the effects of business factors on the performance of the plant with the aid of technological factors (indirect evidence).
From the maintenance strategy and policy point view the following conclusions or predictions could be drawn. These conclusions are illustrated in Figure 1. In Figure 1, there is 2x2 table to depict various business environments with two dimensions: (1) profitability and (2) operating rate. Profitability stands for contribution margin (%) and operating rate refers to shift work rate or typical operating rate of the production system: e.g. in the paper industry equipment tend to run days and nights for the whole week, instead in the food processing industry equipment are often in use only 8 hours a day for five days. If operating rate is low, down-time does not necessary mean production losses. If profitability is high, it often means that short down time is not crucial from the business success point of view. If profitability rate is low and normal operating rate is high it means that high availability is very important. Instead if profitability level is high and operating level is low a lot of unplanned maintenance could be expected. Expected performance Low
Profitability (%)
We should understand how business pressures in various circumstances determine e.g. required availability or OEE level. In order to estimate business pressures one should have information of many economic variables such as ROA, added value, contribution margin, profit level and some indication concerning competitive environment and market trends. In practice, it is very difficult to collect this kind of data from a large number of companies and from several industrial sectors at the plant level because it is considered as confidential. In this study it was easier to get data which depict e.g. the technical characteristics or the amount of assets and then apply indirect reasoning.
High
Expected performance ?
Expected performance Expected performance
• high OEE and availability • a lot of improvements • medium level OEE and availability • a lot of planned maintenance • a lot of preventive maintenance • a lot of corrective maintenance • a lot of condition based • a lot of unplanned maintenance maintenance Low
According to prior mathematical modelling based on queuing theory and on some other underlying theoretical assumptions presented by Komonen (1998, 2002 and 2006 ) for example the following conclusions could be drawn: The higher the degree of integration of the production system the higher the production losses per unit of time due to unavailability. That is, stronger pressures for higher availability. (Degree of integration measures mechanisation rate, continuity of processes, capital intensity etc. and therefore also the probability of production losses due to down time in any part of the production system). Scale economics exist in the maintenance of production equipment. "Maintenance cost / production equipment
• high OEE and availability • a lot of planned maintenance • a lot of preventive maintenance • a lot of condition based maintenance
High
Operating rate of production equipment
Figure 1. The influence of various business environments on maintenance policies (Komonen 2009) The above assumptions are supported by the data from the Finnish industry. The impact of the utilisation rate on the performance of the plants e.g. OEE (overall equipment effectiveness), availability performance, planned and scheduled maintenance or preventive maintenance has been demonstrated in Table 1. In the table shift work rate approximates the utilisation or operating rate of production equipment. Numbers 1.5, 2.5 and 3.5 refer to situations where a part of the production system is utilised at a higher rate than
Shift work rate (1-5)
1-1.5
2-2.5
3-3.5
4-5
OEE % Availability % Planned and scheduled maintenance % Preventive maintenance %
67,5 85,9 50,6
72,7 86,4 58,6
68,4 88,5 67,7
80,7 91,6 74,8
27,5
34,3
35,1
43,3
The structure of the production system may also have an impact on availability requirements. If a machine is the bottleneck of the production system, it is obvious that availability objectives are higher than in the case of a machine, which is not in the main production line and which has a low operating rate (Komonen 2002). One indicator to model any production system is the degree of integration (plant replacement value divided by the number of front line production operatives in day shift), which is a multipurpose meter to measure mechanisation rate, continuity of processes and scale economies. Generally speaking, we can state that e.g. in the chemical industry and in the pulp and paper industry the integration level is high and in the manufacture of metal products it is lower. The impact of degree of integration on the above mentioned performance indicators has been demonstrated in Table 2: the higher the degree of integration of the production systems the higher availability performance they show. The same applies to the planning rate of the maintenance activities (Komonen 2006). Table 2. Some key performance indicators of production system as a function of integration level in Finland in 2000-2003 (N =261; N= 85-89 in each category) Integration level OEE % Availability % Proportion of planned and scheduled maintenance % Proportion of preventive maintenance %
<0,65 69,5 88,3 55,0
0,65-4,5 72,7 87,3 68,6
>4,5 80,3 93,4 72,2
31,8
36,3
41,3
There exists other empirical evidence of the impact of the business environment and technological determinants e.g. on OEE (overall equipment effectiveness), availability or maintenance costs (e.g. Komonen 1998, 2002, 2006). The structure of the production system has an impact on availability requirements as stated above. If a machine is the bottleneck of the production system, it is obvious that availability objectives are higher than in the case of a
Table 3. The variables having an impact on OEE in the processing of food and liquid (Komonen 2006). Processing of food and liquid (N=12) Dependent variable: OEE Adjusted R²= ,721 F(3,8)=10,498 t(8) Intercept 6,608 Degree of integration 4,265 Production equipment -2,183 replacement value Plant turnover / plant -1,328 replacement value
p<,0037 p-level 0,0002 0,0027 0,061 0,221
The relationship between relative maintenance costs and some exogenous factors follows the same pattern as the relationship between OEE and integration level, scale factor and capital intensity. The statistical significance of various independent variables is also expressed in Table 3. It can be seen that the statistical significance of integration level is very high and scale factor (RPV) almost significant. The result is interesting because the number of cases is quite low (12). Processing of food and liquid De pendent variable: OEE
. .
Table 1. Some key performance indicators of production systems as a function of shift work rate in Finland in 2000-2003 (N =166)
machine, which is not in the main production line and which has a low operating rate (Komonen 2002). The impact of the degree of integration on the above mentioned performance indicators is demonstrated in Table 3 and is illustrated in Figure 2. In Figure 2 predicted and observed values equal on the diagonal. Cases above the diagonal represent better performance than expected.
Observed OEE .
the rest. We can see that the higher the utilisation rate, the higher is OEE, availability or proportion of scheduled maintenance activities. The same tendency was supported by the sample from the year 2000 (Komonen 2002).
Pre dicted OEE .
95% confidence
Figure 2. The predicting power of integration level on OEE in the processing of food (Komonen 2006) 3. THE IMPACT OF EXOGENOUS FACTORS The upper management of the companies should be interested in the structural causes (exogenous factors) for the maintenance performance and cost level in order to develop
the operations of the company. The maintenance management should be interested in the impact of the exogenous factors in order to draw correct conclusion concerning maintenance function when using benchmarking. In the studies mentioned before, multiple regression analyses were used to estimate the causalities between the exogenous factors and the key performance indicators (objectives). Logarithmic forms of the variables were often needed in the analyses, because the model to be estimated was non-linear. Logarithmic transformations have also other statistical and mathematical advantages (e.g. estimated parameters measure the elasticity of the variables). The explanatory power of the models (adjusted R2) was high (mainly between 40% and 90 %) and estimated parameters were statistically highly significant (e.g Komonen 1998, 2002, 2006). As mentioned above different kinds of production units or plants can be made comparable with the aid of exogenous factors. Thus, for example, objectives such as 'overall equipment effectiveness' (OEE) or availability are dependent on the external factors, which cannot be influenced in the shorter time span. Examples of the exogenous factors can be a priori the following variables (the signs + and – indicate the direction of the impact): integration level of production system (+) operating rate and /or shift work rate (+) technical capital intensity (plant turnover / plant replacement value) (+) redundancy (+) severity of the production environment (+) In the case of maintenance costs in relation to equipment replacement value exogenous factors may be the following: Integration level of production system (which has, in practice, often parallel impacts to maintenance costs as replacement value) (-) scale (replacement value of production equipment ) (-) production volume/production equipment replacement value (+) shift work rate, operating rate (+) Industry –dummy variables ((-, +) The ratio 'maintenance costs / plant replacement value' tells us, how much it costs to maintain a certain type and size of equipment or facility. When using this ratio we should make a difference between the total costs of maintenance and the maintenance costs of the machinery. In certain branches the proportion of real estate of the total fixed capital may vary a lot, and therefore this indicator may give misleading results, at least, as far as the maintenance costs of the machinery are concerned. Altogether, the problem with the above ratio is the determination of the replacement value. Very often the fire insurance value is a good estimate of replacement value. The advantage of this ratio is that it makes possible to compare various kinds of plants within or between industrial branches. The disadvantage of this ratio is that it does not take into account the availability of production equipment (Komonen et al.2006). .
There are some other key performance indicators such as the ratio 'maintenance costs/production volume' or 'maintenance costs in relation to production capacity which behave very much in the similar way. 4. INTEGRATED ASSET MANAGEMENT APPROACH ‘Asset management’ emphasizes integrated approach in decision making. It integrates asset development (capacity), operating of asset and upkeep of assets. An important part of asset development is determination of capacity needs and capacity creation which means investment planning and investments. Operating of assets means production and especially the part of production that influences assets and their prevailing production capability. The last dimension, upkeep stands for the maintenance function. The very obvious consequence from the approach presented above is that the company management should follow the development of a combined performance indicator, where the components are unavailability costs, replacement and maintenance investments and maintenance costs. If the total costs are increasing, the management of the plant in question should be worried. In the opposite case the development of the modes of operations has turned to be positive. 5. SOME VIEWS ON PERFORMANCE INDICATORS One of the important tools for the asset management is the system of the performance indicators. It is required at all the levels of asset management. Often the systems of the key indicators are lists, which are grouped according to subject matters, but which are not in any way linked to each other. Surely, there are classifications which aim to organise the performance indicators into various classes such as economic, organisational and technical. The aim of this chapter is to introduce the system of maintenance key indicators, which supports the building of a benchmarking system. This system helps a user to grasp the purpose and significance of various key figures. The simplified system of the performance indicators can be classified in a hierarchical manner. The following structure is the modified version of those ones presented earlier by Komonen.(e.g. 2002b): 1. 2. 3. 4. 5. 6. 7. 8.
business-oriented objectives first level objectives of production second level objectives of production objectives for maintenance exogenous factors for production and maintenance (external factors) intermediate internal objectives for maintenance (followup variables) action variables of maintenance function (means for improvement) internal descriptive (explanatory) variables
Exogenous independent factors are indicators, which help management to evaluate the state of technology or business environment and at the same time are beyond the scope of production or maintenance managers to determine, but which have a considerable impact on the other objectives. Examples
The success of the maintenance function can be evaluated (simplifying) with the aid three factors: performance of production system due to maintenance efforts, costeffectiveness of the maintenance function and the quality of maintenance processes. The performance of production system can be measured e.g. with the aid of overall equipment effectiveness (OEE), availability or reliability of the machinery. Typical measures for the cost-effectiveness of the maintenance function are 'maintenance costs in relation to plant replacement value' or 'maintenance costs in relation to production capacity'. The quality of the maintenance processes can be evaluated with such the indicators as planning rate, customer satisfaction, employee satisfaction, accident rate or hazard rate etc. If we assume that the above mentioned exogenous variables approximate well enough the requirements of business environment and technology, the results presented above offer also an opportunity to judge whether a plant in question has been successful or less successful (see Figure 2). If a plant is located on the diagonal, the performance of the plant is as good as predicted or expected. The deviation from the diagonal can be assumed to result from differences in modes of operations. This result can be used for planning purposes or benchmarking purposes (as the Internet tool in the home pages of Finnish Maintenance Society, in English and Finnish). When focusing on the planning option, it is possible to determine improvements needs for e.g. OEE or maintenance cost efficiency. These options are demonstrated in Figure 3 (Komonen 2006). In Figure 3, the measure for production efficiency is availability. The deviation of availability from the diagonal has been presented on the vertical axis and the deviation of maintenance costs on the horizontal axis. In the case of the upper left-hand corner, a good strategy may be to concentrate on maintenance costs keeping availability at the present level. The lower right-hand corner represents a situation where a good option could be to focus on the availability of the production equipment. Using this method there is always the danger to make wrong judgements. Therefore, it would be nice to know the total costs of availability performance as discussed earlier in this paper. Total costs are, however, very seldom calculated. Maintenance costs divided by production volume (when it is available) give some idea of the total availability performance costs of plants, but the information is then partly implicit.
Processing of food and liquid Dependent variables: (1) Availability of production equipment (2) Maintenance costs / replacement value Both the factors are better than predicted
12 8 4 0
.
Observed availability - predicted availability
of these kinds of factors are the opearting rate of the production machinery, production volume, the amount of the production equipment, the integration level of the production process etc. The action variables of the maintenance function are tools of production or maintenance managers, with the aid of which objectives are achieved. Preventive and improvement maintenance, outsourcing, operator maintenance etc. are good examples of these tools.
Both the factors are worse than predicted
-4 -8 -12 3
2
1
0
-1
-2
-3
Observed costs - predicted costs
Figure 3. Positioning of availability and maintenance costs (Komonen 2006) A short cut to examine best practices is to compare cases of the upper right-hand corner (successful plants) with lower right-hand corner (unsuccessful plants) and try to find out which factors make the difference. A problem is, however, that with this research strategy we loose a large proportion of cases and the number of cases becomes too low. Therefore, we tried to find out separately which makes the difference in the case of availability and in the case of maintenance cost efficiency. We did this comparison for several industrial sectors. One example is demonstrated below. In one hand, we demonstrate how the successful plants ranked by availability differ from the less successful and in the other hand we present how the good plants ranked by cost efficiency differ from the unsuccessful ones. Although the results were as expected we considered them interesting. The factors, which seem to produce high availability, erode the cost efficiency of maintenance or do not improve it. And the factors which are connected to high cost efficiency lower availability of equipment. Although, the results are natural and understandable, it was interesting that a small sample of plants brought this phenomenon forth. Table 4. Differences in modes of operations concerning successful and unsuccessful plants ranked by availability Factor
N
Mean of N Mean of less successful successful plants plants Preventive maintenance 10 37,4 7 31,6 Improvements 9 15,7 7 9 Planned maintenance 10 69,1 7 47,3 Immediate correct.actions 10 30,9 7 52,7 Contracting 6 37 5 35,2 Spare-part store/plant RPV 8 0,7 6 0,6 Labour costs / plant RPV 10 3,5 7 3,4 Materials / plant RPV 10 1,4 7 1,3 Table 5. Differences in modes of operations concerning successful and unsuccessful plants: rank by cost efficiency
Factor
N
Preventive maintenance Improvements Planned maintenance Immediate correct. actions Contracting Spare-part store/plant RPV Labour costs / plant RPV Materials / plant RPV Condition monitoring
11 9 11 11 6 8 18 18 6
Mean of successful plants 36,4 9,2 56 44 43,1 0,8 2,8 1,3 15,3
N
11 10 10 11 10 11 16 16 5
Mean of less successful plants 35,5 14,4 64,4 35,4 50,5 0,6 4,1 2 5,3
However, the samples used are still small because of data erosion. Some questions, variables of the collected data are so fresh that the number of replies concerning those indicators is low and therefore it is difficult to draw reliable conclusions. The example of the results can be seen in Tables 4 and 5. Based on the above statistics we can state that preventive and improvement maintenance and in general planned maintenance improve availability of plants, but instead they do not necessarily improve cost efficiency of plants in question. Condition monitoring is utilized more in cost efficient plants than in less efficient plants. Other variables such as spare-part stocks did not explain differences in performance. 6. CONCLUSIONS According to the management of many industrial enterprises 'benchmarking' is a powerful tool for the development of the plant operations. However, among many problems concerning benchmarking a challenge is to make companies or plants comparable. This challenge is sometimes used as an excuse to explain away the worse ranking in comparison. In this paper, we describe some features of the system for benchmarking, which aims to make various production units comparable with the aid of statistical methods based on many years' theoretical and empirical research. The positioning of the production systems is one of instruments introduced in this paper, and the other tools of the system lean on the results of the positioning tool. The benchmarking systems is based on the research carried out in Finnish industry during the last 12 years (e.g. Komonen 1998, 2002, 2002b). Altogether, the data used has consisted of more than 550 industrial cases in Finland. The benchmarking system consists of five sections or tools (Komonen 2006) plus branch-wise statistics. 1. 2.
3.
'Data input section' is an electronic form to insert the data of the plant in the database. In the 'positioning section' there is an option to evaluate the success of the plant's maintenance function by industrial sector with the aid of several indicators such as OEE, maintenance costs etc. In the 'locating differences section' it is possible to compare the plant's mode of operations with the successful plants and with the less successful plants.
4.
5.
In the 'best practices and planning section' the user of the benchmarking system may find more hints of, which kind of modes of operations lead to successful results. Branch-wise statistics
The system is automatic and runs all the automatic calculations getting commands from the user-code and password entered in the system. All the needed calculations are carried out when changes occur or the user commands to execute any task. Simulations are also possible options, for example, for planning purposes. The determination of the variables, which should be taken into account when the system executes automatic multivariate analyses for the positioning, is an expert's job. The same applies to the determination of the impact (direction and strength) of the modes of operations on the selected objectives. The users of the system are able to see their own data only and statistical key figures concerning the industrial branch in question. REFERENCES Bhat, V. 2000. "The determinants of maintenance expenditures in chemical companies", Journal of Quality in Maintenance Engineering, Vol.6 No. 2, 2000, pp106112 Komonen, K. 1998. "The Structure and Effectiveness of Industrial Maintenance", The Summary of PhD. Dissertation, Acta Polytechnica Scandinavia. Ma 93. Espoo, The Finnish Academy of Technology, 1998 Komonen, K. 2002. "A cost model of industrial maintenance for profitability Analysis and benchmarking", International Journal of Production Economics, 79, 2002. Komonen, K. (2002b) Views on performance indicators and benchmarking in industrial maintenance. Maintenance 5, pp. 52-56 Komonen K. 2006. Availability Guarantees and Physical Assets Management: Empirical Evidence of the Impact of Some Underlying Factors. RAMS 2006. Conference Proceedings. Newport Beach. CA, USA Komonen, K., Kortelainen, H. & Räikkonen, M. 2006. Asset management framework to improve longer term return on investments in the capital intensive industries. WCEAM 2006, Australia. Komonen K. 2008. A Strategic Asset Management Model: A framework of a plant level model for strategic choices and actions. Euromaintenance 2008. Conference Proceedings. Brussels. Belgium Komonen, K. 2009. Investement, capacity and maintenance. Conference proceedings. Promaint. Helsinki, in Finnish. Swanson, L. 1997. "An empirical study of the relationship between production technology and maintenance management", International Journal of Production Economics, 53 pp. 191-207, 1997
Abstract: Business requirements and characteristics of production systems determine the effective modes of operations in industrial maintenance. In order to identify the best practices the first task is to identify the best performance in various business environments using relevant key performance indicators. The next step is to identify the modes of operations which create the best performance. There are several research strategies to find out the best practices. However, there are a lot of challenges in data collection and therefore a research process requires a long term roadmap in order to boost knowledge on behavior behind peak performance. This paper presents some of the results of a long research process, which form a solid basis for determining the best practices in the area of industrial maintenance. Key words: Maintenance management, maintenance performance, benchmarking, best practices
1. INTRODUCTION Integrated view on the development of companies’ engineering assets has become more important and even vital in the very dynamic business environment which we meet today. Productivity of capital could be increased, which results in more sustainable production. Demands for higher turnover of capital, better return on assets and improved sustainability of asset solutions in the fast changing business environment have led very sophisticated decision making situations where a lack of effective and proper decision making tools is evident. However, various industrial sectors differ from each other as far as the requirements on assets are concerned. Therefore, industry specific requirements should be clearly kept in mind when making decisions concerning engineering assets. One of the most effective tools to achieve better productivity is benchmarking. Since, it is very difficult to find or use cases from the similar business and production environments, more sophisticated methods are needed. One of the main tasks is to find methods to reveal the influence of the business, production and engineering characteristics on the performance of the maintenance function. One of the visions of the research is to find methods which release benchmarking from the prison of the industrywise differences and give opportunity to apply branch-free methods. In this paper we shall present some result of the research programme which enables the present ongoing research project to search for the best practices in the area of industrial maintenance. The objective of the earlier research projects has been to develop methods to evaluate the performance in various environments and to construct a framework to find out the best practices. The objective of the ongoing project is to identify various maintenance environments in order to form a maintenance typology and then to find typical modes of operations in each category of
the typology. The role of the former research results is to determine the best modes of operations to achieve peak performance among the various options. The latter project is to determine various options. In this paper we concentrate on the earlier projects and the final results can be reported in six months from now. 2. THE EFFECTS OF BUSINESS ENVIRONMENT ON MAINTENANCE The structure of the production system and other technological factors has a great influence on the aspired performance level. Later in this section, some empirical evidence of this influence will be presented. Increases in capacity, operating rate or OEE require e.g. investments, improvements, development of maintenance processes, and therefore cost money. High performance figures are not necessarily optimal, because the costs of these actions may be too high. The asset strategy, strategic choices, and optimal performance objectives give a basis for investments, improvement decisions and development processes (Komonen 2008). The four performance factors presented above are not independent, but must be levelled as a whole. The basis for the maintenance oriented examination is presented in the following text. The description of the maintenance system starts with the business requirements of the plant in question. These requirements influence on the production system. The plant asset strategy, customer satisfaction and safety considerations also guide maintenance operations. The requirements for the maintenance function and maintenance objectives originate from the abovementioned business requirements. We should model the effects of these requirements (Komonen et al. 2006).
replacement value" ratio decreases as the amount of machinery grows. The size of the plant offers scale economies. As the utilisation rate (operating rate) of the production system grows the pressure to retain high availability increases. "Maintenance costs / production equipment replacement value" ratio increases as the degree of integration of the production system grows if the amount of production equipment does not change.
The system of the maintenance function consists of five separate sub-processes (Komonen et al. 2006): maintenance planning resource management and development management of maintenance processes execution follow-up and continuous improvement. The above mentioned requirements on the maintenance function influence on the way the maintenance function is organised, modelled and developed.
These kinds of studies are not very common. However, there exist some interesting papers concerning the business and technological determinants of maintenance expenditures e.g. Bhat 2000 and Swanson 1997. Comparing the focus of our study with Bath's and Swanson's studies the following differences can be identified (Komonen 2006): Our focus was at the plant level instead of the company level as in Bath's approach. Our focus was on the technological explanatory factors (as Swanson's) rather than direct financial variables (Bhat). Swanson and Bhat focused on maintenance costs, but we include both the OEE (or availability) and maintenance. We tried to explain economic performance rather than modes of operations (Swanson). In this study our aim was to explain the effects of business factors on the performance of the plant with the aid of technological factors (indirect evidence).
From the maintenance strategy and policy point view the following conclusions or predictions could be drawn. These conclusions are illustrated in Figure 1. In Figure 1, there is 2x2 table to depict various business environments with two dimensions: (1) profitability and (2) operating rate. Profitability stands for contribution margin (%) and operating rate refers to shift work rate or typical operating rate of the production system: e.g. in the paper industry equipment tend to run days and nights for the whole week, instead in the food processing industry equipment are often in use only 8 hours a day for five days. If operating rate is low, down-time does not necessary mean production losses. If profitability is high, it often means that short down time is not crucial from the business success point of view. If profitability rate is low and normal operating rate is high it means that high availability is very important. Instead if profitability level is high and operating level is low a lot of unplanned maintenance could be expected. Expected performance Low
Profitability (%)
We should understand how business pressures in various circumstances determine e.g. required availability or OEE level. In order to estimate business pressures one should have information of many economic variables such as ROA, added value, contribution margin, profit level and some indication concerning competitive environment and market trends. In practice, it is very difficult to collect this kind of data from a large number of companies and from several industrial sectors at the plant level because it is considered as confidential. In this study it was easier to get data which depict e.g. the technical characteristics or the amount of assets and then apply indirect reasoning.
High
Expected performance ?
Expected performance Expected performance
• high OEE and availability • a lot of improvements • medium level OEE and availability • a lot of planned maintenance • a lot of preventive maintenance • a lot of corrective maintenance • a lot of condition based • a lot of unplanned maintenance maintenance Low
According to prior mathematical modelling based on queuing theory and on some other underlying theoretical assumptions presented by Komonen (1998, 2002 and 2006 ) for example the following conclusions could be drawn: The higher the degree of integration of the production system the higher the production losses per unit of time due to unavailability. That is, stronger pressures for higher availability. (Degree of integration measures mechanisation rate, continuity of processes, capital intensity etc. and therefore also the probability of production losses due to down time in any part of the production system). Scale economics exist in the maintenance of production equipment. "Maintenance cost / production equipment
• high OEE and availability • a lot of planned maintenance • a lot of preventive maintenance • a lot of condition based maintenance
High
Operating rate of production equipment
Figure 1. The influence of various business environments on maintenance policies (Komonen 2009) The above assumptions are supported by the data from the Finnish industry. The impact of the utilisation rate on the performance of the plants e.g. OEE (overall equipment effectiveness), availability performance, planned and scheduled maintenance or preventive maintenance has been demonstrated in Table 1. In the table shift work rate approximates the utilisation or operating rate of production equipment. Numbers 1.5, 2.5 and 3.5 refer to situations where a part of the production system is utilised at a higher rate than
Shift work rate (1-5)
1-1.5
2-2.5
3-3.5
4-5
OEE % Availability % Planned and scheduled maintenance % Preventive maintenance %
67,5 85,9 50,6
72,7 86,4 58,6
68,4 88,5 67,7
80,7 91,6 74,8
27,5
34,3
35,1
43,3
The structure of the production system may also have an impact on availability requirements. If a machine is the bottleneck of the production system, it is obvious that availability objectives are higher than in the case of a machine, which is not in the main production line and which has a low operating rate (Komonen 2002). One indicator to model any production system is the degree of integration (plant replacement value divided by the number of front line production operatives in day shift), which is a multipurpose meter to measure mechanisation rate, continuity of processes and scale economies. Generally speaking, we can state that e.g. in the chemical industry and in the pulp and paper industry the integration level is high and in the manufacture of metal products it is lower. The impact of degree of integration on the above mentioned performance indicators has been demonstrated in Table 2: the higher the degree of integration of the production systems the higher availability performance they show. The same applies to the planning rate of the maintenance activities (Komonen 2006). Table 2. Some key performance indicators of production system as a function of integration level in Finland in 2000-2003 (N =261; N= 85-89 in each category) Integration level OEE % Availability % Proportion of planned and scheduled maintenance % Proportion of preventive maintenance %
<0,65 69,5 88,3 55,0
0,65-4,5 72,7 87,3 68,6
>4,5 80,3 93,4 72,2
31,8
36,3
41,3
There exists other empirical evidence of the impact of the business environment and technological determinants e.g. on OEE (overall equipment effectiveness), availability or maintenance costs (e.g. Komonen 1998, 2002, 2006). The structure of the production system has an impact on availability requirements as stated above. If a machine is the bottleneck of the production system, it is obvious that availability objectives are higher than in the case of a
Table 3. The variables having an impact on OEE in the processing of food and liquid (Komonen 2006). Processing of food and liquid (N=12) Dependent variable: OEE Adjusted R²= ,721 F(3,8)=10,498 t(8) Intercept 6,608 Degree of integration 4,265 Production equipment -2,183 replacement value Plant turnover / plant -1,328 replacement value
p<,0037 p-level 0,0002 0,0027 0,061 0,221
The relationship between relative maintenance costs and some exogenous factors follows the same pattern as the relationship between OEE and integration level, scale factor and capital intensity. The statistical significance of various independent variables is also expressed in Table 3. It can be seen that the statistical significance of integration level is very high and scale factor (RPV) almost significant. The result is interesting because the number of cases is quite low (12). Processing of food and liquid De pendent variable: OEE
. .
Table 1. Some key performance indicators of production systems as a function of shift work rate in Finland in 2000-2003 (N =166)
machine, which is not in the main production line and which has a low operating rate (Komonen 2002). The impact of the degree of integration on the above mentioned performance indicators is demonstrated in Table 3 and is illustrated in Figure 2. In Figure 2 predicted and observed values equal on the diagonal. Cases above the diagonal represent better performance than expected.
Observed OEE .
the rest. We can see that the higher the utilisation rate, the higher is OEE, availability or proportion of scheduled maintenance activities. The same tendency was supported by the sample from the year 2000 (Komonen 2002).
Pre dicted OEE .
95% confidence
Figure 2. The predicting power of integration level on OEE in the processing of food (Komonen 2006) 3. THE IMPACT OF EXOGENOUS FACTORS The upper management of the companies should be interested in the structural causes (exogenous factors) for the maintenance performance and cost level in order to develop
the operations of the company. The maintenance management should be interested in the impact of the exogenous factors in order to draw correct conclusion concerning maintenance function when using benchmarking. In the studies mentioned before, multiple regression analyses were used to estimate the causalities between the exogenous factors and the key performance indicators (objectives). Logarithmic forms of the variables were often needed in the analyses, because the model to be estimated was non-linear. Logarithmic transformations have also other statistical and mathematical advantages (e.g. estimated parameters measure the elasticity of the variables). The explanatory power of the models (adjusted R2) was high (mainly between 40% and 90 %) and estimated parameters were statistically highly significant (e.g Komonen 1998, 2002, 2006). As mentioned above different kinds of production units or plants can be made comparable with the aid of exogenous factors. Thus, for example, objectives such as 'overall equipment effectiveness' (OEE) or availability are dependent on the external factors, which cannot be influenced in the shorter time span. Examples of the exogenous factors can be a priori the following variables (the signs + and – indicate the direction of the impact): integration level of production system (+) operating rate and /or shift work rate (+) technical capital intensity (plant turnover / plant replacement value) (+) redundancy (+) severity of the production environment (+) In the case of maintenance costs in relation to equipment replacement value exogenous factors may be the following: Integration level of production system (which has, in practice, often parallel impacts to maintenance costs as replacement value) (-) scale (replacement value of production equipment ) (-) production volume/production equipment replacement value (+) shift work rate, operating rate (+) Industry –dummy variables ((-, +) The ratio 'maintenance costs / plant replacement value' tells us, how much it costs to maintain a certain type and size of equipment or facility. When using this ratio we should make a difference between the total costs of maintenance and the maintenance costs of the machinery. In certain branches the proportion of real estate of the total fixed capital may vary a lot, and therefore this indicator may give misleading results, at least, as far as the maintenance costs of the machinery are concerned. Altogether, the problem with the above ratio is the determination of the replacement value. Very often the fire insurance value is a good estimate of replacement value. The advantage of this ratio is that it makes possible to compare various kinds of plants within or between industrial branches. The disadvantage of this ratio is that it does not take into account the availability of production equipment (Komonen et al.2006). .
There are some other key performance indicators such as the ratio 'maintenance costs/production volume' or 'maintenance costs in relation to production capacity which behave very much in the similar way. 4. INTEGRATED ASSET MANAGEMENT APPROACH ‘Asset management’ emphasizes integrated approach in decision making. It integrates asset development (capacity), operating of asset and upkeep of assets. An important part of asset development is determination of capacity needs and capacity creation which means investment planning and investments. Operating of assets means production and especially the part of production that influences assets and their prevailing production capability. The last dimension, upkeep stands for the maintenance function. The very obvious consequence from the approach presented above is that the company management should follow the development of a combined performance indicator, where the components are unavailability costs, replacement and maintenance investments and maintenance costs. If the total costs are increasing, the management of the plant in question should be worried. In the opposite case the development of the modes of operations has turned to be positive. 5. SOME VIEWS ON PERFORMANCE INDICATORS One of the important tools for the asset management is the system of the performance indicators. It is required at all the levels of asset management. Often the systems of the key indicators are lists, which are grouped according to subject matters, but which are not in any way linked to each other. Surely, there are classifications which aim to organise the performance indicators into various classes such as economic, organisational and technical. The aim of this chapter is to introduce the system of maintenance key indicators, which supports the building of a benchmarking system. This system helps a user to grasp the purpose and significance of various key figures. The simplified system of the performance indicators can be classified in a hierarchical manner. The following structure is the modified version of those ones presented earlier by Komonen.(e.g. 2002b): 1. 2. 3. 4. 5. 6. 7. 8.
business-oriented objectives first level objectives of production second level objectives of production objectives for maintenance exogenous factors for production and maintenance (external factors) intermediate internal objectives for maintenance (followup variables) action variables of maintenance function (means for improvement) internal descriptive (explanatory) variables
Exogenous independent factors are indicators, which help management to evaluate the state of technology or business environment and at the same time are beyond the scope of production or maintenance managers to determine, but which have a considerable impact on the other objectives. Examples
The success of the maintenance function can be evaluated (simplifying) with the aid three factors: performance of production system due to maintenance efforts, costeffectiveness of the maintenance function and the quality of maintenance processes. The performance of production system can be measured e.g. with the aid of overall equipment effectiveness (OEE), availability or reliability of the machinery. Typical measures for the cost-effectiveness of the maintenance function are 'maintenance costs in relation to plant replacement value' or 'maintenance costs in relation to production capacity'. The quality of the maintenance processes can be evaluated with such the indicators as planning rate, customer satisfaction, employee satisfaction, accident rate or hazard rate etc. If we assume that the above mentioned exogenous variables approximate well enough the requirements of business environment and technology, the results presented above offer also an opportunity to judge whether a plant in question has been successful or less successful (see Figure 2). If a plant is located on the diagonal, the performance of the plant is as good as predicted or expected. The deviation from the diagonal can be assumed to result from differences in modes of operations. This result can be used for planning purposes or benchmarking purposes (as the Internet tool in the home pages of Finnish Maintenance Society, in English and Finnish). When focusing on the planning option, it is possible to determine improvements needs for e.g. OEE or maintenance cost efficiency. These options are demonstrated in Figure 3 (Komonen 2006). In Figure 3, the measure for production efficiency is availability. The deviation of availability from the diagonal has been presented on the vertical axis and the deviation of maintenance costs on the horizontal axis. In the case of the upper left-hand corner, a good strategy may be to concentrate on maintenance costs keeping availability at the present level. The lower right-hand corner represents a situation where a good option could be to focus on the availability of the production equipment. Using this method there is always the danger to make wrong judgements. Therefore, it would be nice to know the total costs of availability performance as discussed earlier in this paper. Total costs are, however, very seldom calculated. Maintenance costs divided by production volume (when it is available) give some idea of the total availability performance costs of plants, but the information is then partly implicit.
Processing of food and liquid Dependent variables: (1) Availability of production equipment (2) Maintenance costs / replacement value Both the factors are better than predicted
12 8 4 0
.
Observed availability - predicted availability
of these kinds of factors are the opearting rate of the production machinery, production volume, the amount of the production equipment, the integration level of the production process etc. The action variables of the maintenance function are tools of production or maintenance managers, with the aid of which objectives are achieved. Preventive and improvement maintenance, outsourcing, operator maintenance etc. are good examples of these tools.
Both the factors are worse than predicted
-4 -8 -12 3
2
1
0
-1
-2
-3
Observed costs - predicted costs
Figure 3. Positioning of availability and maintenance costs (Komonen 2006) A short cut to examine best practices is to compare cases of the upper right-hand corner (successful plants) with lower right-hand corner (unsuccessful plants) and try to find out which factors make the difference. A problem is, however, that with this research strategy we loose a large proportion of cases and the number of cases becomes too low. Therefore, we tried to find out separately which makes the difference in the case of availability and in the case of maintenance cost efficiency. We did this comparison for several industrial sectors. One example is demonstrated below. In one hand, we demonstrate how the successful plants ranked by availability differ from the less successful and in the other hand we present how the good plants ranked by cost efficiency differ from the unsuccessful ones. Although the results were as expected we considered them interesting. The factors, which seem to produce high availability, erode the cost efficiency of maintenance or do not improve it. And the factors which are connected to high cost efficiency lower availability of equipment. Although, the results are natural and understandable, it was interesting that a small sample of plants brought this phenomenon forth. Table 4. Differences in modes of operations concerning successful and unsuccessful plants ranked by availability Factor
N
Mean of N Mean of less successful successful plants plants Preventive maintenance 10 37,4 7 31,6 Improvements 9 15,7 7 9 Planned maintenance 10 69,1 7 47,3 Immediate correct.actions 10 30,9 7 52,7 Contracting 6 37 5 35,2 Spare-part store/plant RPV 8 0,7 6 0,6 Labour costs / plant RPV 10 3,5 7 3,4 Materials / plant RPV 10 1,4 7 1,3 Table 5. Differences in modes of operations concerning successful and unsuccessful plants: rank by cost efficiency
Factor
N
Preventive maintenance Improvements Planned maintenance Immediate correct. actions Contracting Spare-part store/plant RPV Labour costs / plant RPV Materials / plant RPV Condition monitoring
11 9 11 11 6 8 18 18 6
Mean of successful plants 36,4 9,2 56 44 43,1 0,8 2,8 1,3 15,3
N
11 10 10 11 10 11 16 16 5
Mean of less successful plants 35,5 14,4 64,4 35,4 50,5 0,6 4,1 2 5,3
However, the samples used are still small because of data erosion. Some questions, variables of the collected data are so fresh that the number of replies concerning those indicators is low and therefore it is difficult to draw reliable conclusions. The example of the results can be seen in Tables 4 and 5. Based on the above statistics we can state that preventive and improvement maintenance and in general planned maintenance improve availability of plants, but instead they do not necessarily improve cost efficiency of plants in question. Condition monitoring is utilized more in cost efficient plants than in less efficient plants. Other variables such as spare-part stocks did not explain differences in performance. 6. CONCLUSIONS According to the management of many industrial enterprises 'benchmarking' is a powerful tool for the development of the plant operations. However, among many problems concerning benchmarking a challenge is to make companies or plants comparable. This challenge is sometimes used as an excuse to explain away the worse ranking in comparison. In this paper, we describe some features of the system for benchmarking, which aims to make various production units comparable with the aid of statistical methods based on many years' theoretical and empirical research. The positioning of the production systems is one of instruments introduced in this paper, and the other tools of the system lean on the results of the positioning tool. The benchmarking systems is based on the research carried out in Finnish industry during the last 12 years (e.g. Komonen 1998, 2002, 2002b). Altogether, the data used has consisted of more than 550 industrial cases in Finland. The benchmarking system consists of five sections or tools (Komonen 2006) plus branch-wise statistics. 1. 2.
3.
'Data input section' is an electronic form to insert the data of the plant in the database. In the 'positioning section' there is an option to evaluate the success of the plant's maintenance function by industrial sector with the aid of several indicators such as OEE, maintenance costs etc. In the 'locating differences section' it is possible to compare the plant's mode of operations with the successful plants and with the less successful plants.
4.
5.
In the 'best practices and planning section' the user of the benchmarking system may find more hints of, which kind of modes of operations lead to successful results. Branch-wise statistics
The system is automatic and runs all the automatic calculations getting commands from the user-code and password entered in the system. All the needed calculations are carried out when changes occur or the user commands to execute any task. Simulations are also possible options, for example, for planning purposes. The determination of the variables, which should be taken into account when the system executes automatic multivariate analyses for the positioning, is an expert's job. The same applies to the determination of the impact (direction and strength) of the modes of operations on the selected objectives. The users of the system are able to see their own data only and statistical key figures concerning the industrial branch in question. REFERENCES Bhat, V. 2000. "The determinants of maintenance expenditures in chemical companies", Journal of Quality in Maintenance Engineering, Vol.6 No. 2, 2000, pp106112 Komonen, K. 1998. "The Structure and Effectiveness of Industrial Maintenance", The Summary of PhD. Dissertation, Acta Polytechnica Scandinavia. Ma 93. Espoo, The Finnish Academy of Technology, 1998 Komonen, K. 2002. "A cost model of industrial maintenance for profitability Analysis and benchmarking", International Journal of Production Economics, 79, 2002. Komonen, K. (2002b) Views on performance indicators and benchmarking in industrial maintenance. Maintenance 5, pp. 52-56 Komonen K. 2006. Availability Guarantees and Physical Assets Management: Empirical Evidence of the Impact of Some Underlying Factors. RAMS 2006. Conference Proceedings. Newport Beach. CA, USA Komonen, K., Kortelainen, H. & Räikkonen, M. 2006. Asset management framework to improve longer term return on investments in the capital intensive industries. WCEAM 2006, Australia. Komonen K. 2008. A Strategic Asset Management Model: A framework of a plant level model for strategic choices and actions. Euromaintenance 2008. Conference Proceedings. Brussels. Belgium Komonen, K. 2009. Investement, capacity and maintenance. Conference proceedings. Promaint. Helsinki, in Finnish. Swanson, L. 1997. "An empirical study of the relationship between production technology and maintenance management", International Journal of Production Economics, 53 pp. 191-207, 1997