Maritime University of Szczecin
Akademia Morska w Szczecinie
2011, 27(99) z. 1 pp. 34–39 2011, 27(99) z. 1 s. 34–39
Universal maintenance performance indicator for technical
objects operated on floating units
Uniwersalny wskaźnik efektywności utrzymania obiektów
technicznych eksploatowanych na jednostkach pływających
Arkadiusz Burnos
BalticBerg Consulting
00-613 Warszawa, ul. Chałubińskiego 8, e-mail: aburnos@balticberg.com
Key words: KPI, efficiency ratio, operating efficiency, operation of vessels, universal indicator,
multi-variate analysis, multidimensional comparative analysis
Abstract
The operation of vessels is more and more accurately monitored and collected information allows for rationalization of decisions related to maintenance of technical assets. The article defines a universal maintenance performance indicator (UMPI) using methods of numerical taxonomy. It was reviewed the purposes of use of performance indicators (KPI – Key Performance Indicators) and suggested to apply a universal indicator, connecting different groups of information and allowing ranking of technical objects according to multicriterial evaluation of maintenance performance. There is an example of application of universal indicator, which was calculated for a hundred of technical objects recognized by the vessel’s shipowner as critical in the system of operation. The results allowed to rank objects from the best to the worst maintained according to multicriterial evaluation. It was defined the direction for further research on a universal indicator of maintenance performance.
Słowa kluczowe: KPI, wskaźnik efektywności, efektywność eksploatacji, eksploatacja jednostek
pływają-cych, wskaźnik uniwersalny, analiza wieloczynnikowa, wielowymiarowa analiza porównawcza
Abstrakt
Eksploatacja jednostek pływających jest coraz dokładniej monitorowana, a gromadzone informacje umożli-wiają racjonalizację decyzji związanych z utrzymaniem wykorzystywanych obiektów technicznych. W artykule zdefiniowano uniwersalny wskaźnik efektywności utrzymania z wykorzystaniem metod taksono-mii numerycznej. Autor dokonał przeglądu zastosowania wskaźników efektywności (KPI – Key Performance Indicators) oraz zaproponował wykorzystanie wskaźnika uniwersalnego, łączącego różne grupy informacji i pozwalającego na uszeregowanie obiektów technicznych według wielokryterialnej oceny efektywności utrzymania. Przedstawiono przykład zastosowania wskaźnika uniwersalnego, obliczono go dla stu obiektów technicznych uznanych przez armatora jednostek pływających za kluczowe w systemie eksploatacji. Wyniki umożliwiły uszeregowanie obiektów od najlepiej do najgorzej utrzymywanych według wielokryterialnej oce-ny. Autor określił kierunek dalszych badań dotyczących uniwersalnego wskaźnika efektywności utrzymania.
Introduction
Vessels (floating units) are complex systems of operation, which are now becoming more and more monitored, and the application of modern tech-nologies in operational areas has significantly changed the possibility of utilization of information coming from their maintenance. Collected data are
becoming a valuable resource of information, their identification and analysis allow both rationaliza-tion of steering of operarationaliza-tion process and increasing performance of monitored operating systems [1].
Maintenance of technical objects refers to mul-tidirectional activity designed to preserve the func-tional qualification of machines, devices and instal-lations. Maintenance consists of operations both
directly related to a technical object and supporting activities, usually associated with the operated ob-ject, indirectly through activities of management, information, logistics, etc. [1, 2, 3, 4, 5, 6, 7].
From the point of view of a maintenance engi-neers, it is important to have adequate information on maintained facilities which will enable making right decisions related to maintaining performance at the desired level. Performance can be understood in different ways depending on the field and condi-tions under which it is determined. In operating systems it is the property of meeting requirements in various contexts: reliability, economic, quality, performance, energy, etc. [8]. The essence of per-formance evaluation is to determine probability of retention of nominal performance by the system during its operation [9]. The efficiency of operating systems is defined in terms of categories of results achieved or expected [8] by combining useful groups of information from selected areas of using and maintaining technical objects [3, 4].
On the basis of identifiable and interpretable in-formation from the operating system, it is possible to make justified decisions assuming the choice of specific methods of maintenance of the systems. This links, among others, with the choice and im-plementation of maintenance strategy (“Run-To-Failure” strategy, Planned Maintenance and Condi-tion Based Maintenance), taking corrective acCondi-tions in the right time, taking actions of modification and / or modernization. Actions may be taken separate-ly or in the integrated way and their objective is an improvement of performance of systems according to selected criteria. However, selection and adapta-tion of activities, is only possible provided they have adequate knowledge about operated systems, which have many attributes. Information concern-ing the usage may include data from the followconcern-ing areas:
• technical – such as information on reliability, efficiency, quality, efficiency, etc.;
• organizational – such as structure of employ-ment, applied strategy of exploitation in indi-vidual subsystems, structure of equipment, etc.; • economic – for example maintenance costs per
year concerning own employees and subcontrac-tors, costs of spare parts, costs of maintenance materials (abstergents, tools), etc.
Operational decisions need to be properly moti-vated. Therefore, it is necessary to take into account information from more than one area mentioned above. This generates the need for multidirectional data analysis. For this purpose maintenance per-formance indicators were developed (KPI – Key
Performance Indicators), which describe selected groups of information in order to present substantial and unambiguous performance of measured ope-rated systems. The purpose of maintenance perfor-mance indicators, also known as key perforperfor-mance indicators are [4]:
• to obtain current and historical measure values for properties of maintenance and relations be-tween them, in order to compare obtained values with design values and with values obtained by observations of other exploitation systems or other technical objects;
• diagnostics of maintenance activities;
• implementation of a process of continuous im-provement by finding and eliminating substan-tial deviations from assumed design values; • to monitor changes and progress in the operated
system;
• motivating and assessing technical and mana-gerial personnel of achieved results.
Maintenance performance indicators are struc-tured within existing standards into three categories [10]:
• technical indicators, • organizational indicators, • economic indicators.
Within the standards [11] indicators considered by the Technical Committee CEN / TC 319 “Main-tenance” for the most important have been placed and it has been suggested to assign them to decision levels. This does not mean that companies and in-stitutions which operate vessels are imposed with restrictions. KPIs are created, selected, adapted and implemented on the basis of individual information needs of shipowners and operators.
Due to wide use of performance indicators in exploitation systems of energy systems of vessels the demand for universal information occurs. They would enable initial setting of technical objects for more detailed analysis, according to specific crite-ria. By using database software to control the main-tenance of technical assets called for short CMMS (Computerised Maintenance Management System) it became possible to collect information about many technical objects simultaneously. Many shi-powners have in their resources information letting calculate and present selected key performance indicators (technical, organizational and economic) for a thousand or more technical assets installed on vessels [3]. In such situation it requires analysis of very large amount of data to justify reasons of tak-ing actions aimed at increastak-ing performance of systems. A key problem in this situation is the
choice of objects to change in operation (improve-ment). However, this choice should be made taking into account the multifeature nature of individual objects and more generally – the whole system.
Universal maintenance performance indicator (UMPI) for technical objects
The author in his operational practice nor in well-known scientific publications did not meet with using universal performance indicator for ex-ploitation purposes. It was proposed to use a method which would allow for selection of opera-tion sub-systems, which derives from a multi-dimensional comparative analysis aimed at ranking performance of individual objects according to efficiency of their maintenance. This way both technical, organizational and economical informa-tion was taken into account. The method uses tools of numerical taxonomy concerning hierarchy of multifeature objects [7, 12, 13], which had not been used before in matters of technical maintenance and operation performance. The received hierarchy allows to identify objects which, according to mul-ticriteria evaluation, are maintained not efficiently. The consequence of designation of such objects may be more accurate analysis, or directly rationa-lization activities undertaken in selected subsys-tems.
It is proposed to make the hierarchy by valuing the performance of keeping individual technical objects with a UMPI – Universal Maintenance Per-formance Indicator ei. The variables xij taken into account in its calculation can be adopted individual-ly to each exploitation system.
The procedure for determining the universal indicator of maintenance performance ei 1. Defining the observation matrix X:
x
i n j m
X ij 1,..., ; 1, ..., (1) where:
X – matrix of observations made on the
va-riables describing individual technical ob-jects,
n – number of technical objects, m – number of variables.
a) Selection of objects for observation
Objects can be selected using different methods. It seems to be reasonable to say that they should be objects which generate the most risk in the exploi-tation system and their number should be deter-mined by a group of experts analyzing collected data. This method has no restrictions and may well
be all operated objects included in the acquisition of operating information. It is recommended that the number of objects is not less than nmin = 50.
This is connected with the possibility of deter-mining a reliable model (or antimodel).
b) Selection of variables for observation
The selection of variables for observation should be made in accordance with primary exploitation objectives. If the primary objective is to maintain complex systems in the highest reliability, you should take into account variables responsible for providing technical information about reliability, and only as complementary data to take into ac-count variables of cost of lifecycle and organi-zation. Such a situation can be attributed to objects of great strategic importance, such as warships or units extracting and processing crude oil at sea. If, however, the primary objective is to maximize reduction of operational costs of vessels, you should select variables corresponding with provid-ing information such as: energy efficiency, main-tenance costs, cost of lost power (due to possible failures), and as supplementary data to select data associated with reliability. Based on known practic-es of collecting and analyzing of information about exploitation using different CMMS systems it is recommended that the number of variables analyzed in the exploitation system for purposes of determination of ei contains within the range of
m = 5 ÷ 15
2. Transformation of variables
In order to calculate ei it is necessary to trans-form all variables into data of similar impact on the universal indicator. This means that you should choose a convenient direction of stimulation of variables. In this variant, stimulation analysis was chosen, which means that variables being destimu-lants are converted to stimudestimu-lants.
a) Stimulation of destimulants D ij S ij x x (2) where: S ij
x – value of j-th stimulant for i-th technical
object, D
ij
x – value of j-th destimulant for i-th
tech-nical object. b) Stimulation of nominants
N
ij N j N ij N j s ij x x x x x ; max ; min (3)where: N j
x
– nominal (desired) value of j-th variable,
N ij
x
– value of j-th nominant for an i-th
tech-nical object. 3. Range unifying
In order to bring variables with different values to comparable zij values the 0–1 standardization procedures were used (standardization to the aver-age value of 0 and standard deviation of 1).
i n j m
S x x z j j ij ij 1,..., ; 1,..., (4) where: jx – mean for j-th variable
n x x n i ij j
1 (5)Sj – standard deviation of j-th variable
1 1 2
n x x S n i j ij j (6)4. The assignment of system of weights to each variable
In practice, it may turn out to be necessary to assign weights (significance) to each variable (groups of information). It can be done arbitrarily – based on experience and knowledge of recipients of information – or through the system of weights according to specific criteria. It is recommended to consider applying the system preferring features with the highest variation. In such case, the indi-vidual weights wj will have the following value:
j m
V V w m j j j j 1,..., 1
(7) where:Vj – coefficient of variation of j-th diagnostic variable before the standardization:
j j j x S V (8)
5. Determining the pattern and calculation of dis-tance from the pattern
Euclidean distance was used and a system of weights based on coefficients of variation of va-riables used in constructing ej
z z
i n
w d m j j ij j i 1,..., 1 2 0
(9) where: wj – weight (significance),z0j = max{zij} – model object. 6. Normalization of ej
In order to facilitate of analysis the universal in-dicator of maintenance ei was normalized, to accept values from 0–1. This action is an optional activity that affects only the clarity of results.
i n
d d e i i 1 1,..., 0 (10) where:ei – universal maintenance performance indi-cator for the i-th object,
d0 – standard providing adoption by ej values in the range between 0–1
d S a d d0 (11) d i S q d a max (12) where: d – mean of variable d,
Sd – Standard deviation of variable di.
The procedure for derivation of the rate, even for a large number of variables is relatively not complicated. Easily set operations can be imple-mented in the CMMS systems and spreadsheets.
The use of universal maintenance performance indicator
Universal maintenance performance indicator is the basis for ranking including technical facilities according to efficiency of their maintenance. In practice, this type of action should be taken pri-marily in relation to the so-called critical objects, which are determined by the criticality analysis (e.g. FMECA) derived from analysis of state of emergency and their effects (FMEA). Critical assets on vessels usually represent from 10 to 30 percent of all technical assets and are considered the most important subsystems of operation (according to specific criteria).
Ranking of technical objects allows for justified focus of operation activities into specific areas of operation system. Applying the principle of Pareto Lorenz in finding the least efficient key objects in the system of exploitation, which generates 80% of risk (or, for example the cost), using ei it can in relatively simple and quick way calculate 20% of the least effectively held objects (according to a multicriteria evaluation). Example of calculation of 20 least efficient of 100 analyzed maintained technical objects are presented in table 1.
As a result of simulation (based on real operatio-nal data from seagoing vessels) of values of perfor-mance indicators for 100 key technical objects a universal maintenance performance indicator for each object was derived. The objects which should be further examined for values of individual per-formance indicators and potential reasons for low performance of their maintenance are marked with bold and italics. As a result of deep analysis, there should be optimization activities undertaken in machinery maintenance system, which may be
or-ganizational, technical, economic or other. An ex-ample of the object found worst maintained in the analyzed case together with values of individual indicators is in table 2.
Data presented in table 2 are batch information, therefore, are appropriate for a specific indicator (in this case, stimulant or destimulant). Description of the indicators are presented in table 3.
A comprehensive analysis of information should be referred to good operating and management practices in order to find differences in maintenance Table 1. Rank of objects according to values of universal performance maintenance indicator ei
Tabela 1. Uszeregowanie obiektów według wartości uniwersalnego wskaźnika efektywności ei
Object ei Position in ranking Object ei Position in ranking
Object 58 0.629 1. Object 19 0.1349 83. Object 46 0.598 2. Object 98 0.1341 84. Object 11 0.509 3. Object 44 0.133 85. Object 40 0.503 4. Object 75 0.1304 86. Object 41 0.410 5. Object 10 0.1302 87. Object 24 0.373 6. Object 92 0.129 88. Object 18 0.345 7. Object 66 0.127 89. Object 25 0.334 8. Object 90 0.125 90. Object 48 0.323 9. Object 56 0.121 91. Object 82 0.321 10. Object 99 0.119 92. .. .. .. Object 3 0.1185 93. Object 100 0.147 75. Object 94 0.1182 94. Object 89 0.146 76. Object 80 0.104 95. Object 42 0.146 77. Object 74 0.093 96. Object 95 0.141 78. Object 63 0.090 97. Object 12 0.139 79. Object 35 0.047 98. Object 13 0.138 80. Object 97 0.024 99. Object 62 0.136 81. Object 65 0 100. Object 86 0.135 82.
Table 2. Example of object marked for deeper analysis and optimization activities
Tabela 2. Przykład obiektu oznaczonego do dokładnej analizy oraz działań optymalizacyjnych
Type of device Ident. No. Description x1 x2 x3 x4 x5 x6 x7 x8 Pos. in ranking Auxiliary engine (0,2 to 1 MW) X80 Object 80 10824.41 9.71 0.88 0.84 0.20 18.08 0.25 0.67 95 Table 3. Performance indicators used in multicriteria evaluation and derivation of the value of ei
Tabela 3. Wskaźniki efektywności wykorzystane w wielokryterialnej ocenie i wyznaczaniu wartości ei Description
of variables Unit performance Impact on Marked according to PN-EN 15431
x1 MTBF (Mean time between failure) [h] Stimulant T17
x2 MTTR (Mean time of technical repair) [h] Destimulant T21
x3 Operational readiness [–] Stimulant T2
x4 Actual energy efficiency / Expected energy efficiency [–] Stimulant
x5 Total cost of maintenance / Replacement value [–] Destimulant E1 x6 (Total cost of maintenance+Cost of lost readiness) / Hour of object’s operation [USD/h] Destimulant E5 (modified) x7 Time of emergency operation/Total time of service downtime [–] Destimulant O11 x8 Time of executed preventive and inspective services / Time of scheduled inspective and preventive services [–] Stimulant O25
of X80 asset and other objects with better values of performance indicators relevant to the shipowner.
Conclusions
Reliable multidimensional or multifactorial analysis (and multi-criterial) requires simultaneous analysis of multiple data. The demand for tools associated with rational rank of technical objects according to multicriteria evaluation of efficiency of operation will, according to the author, grow. The use of universal indicator may turn out to be a significant help in analyzing the performance of maintenance of technical objects and may allow for accurate targeting of optimization actions.
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Recenzent: dr hab. inż. Andrzej Adamkiewicz, prof. AM Akademia Morska w Szczecinie
