- Email: [email protected]
Decision Making in Lean Smart Maintenance: Criticality Analysis as a Support Tool
13th Workshop on Manufacturing August 12-14, 2019. Oshawa, Canada 13th IFAC IFAC Workshop on Intelligent Intelligent Manufacturing Systems Systems 13th IFAC Workshop on Intelligent Manufacturing Systems August 12-14, 2019. Oshawa, Canada August 12-14, 2019. Oshawa, Canada 13th IFAC Workshop on Intelligent Manufacturing Systems Available online at www.sciencedirect.com August 12-14, 2019. Oshawa, Canada 13th IFAC Workshop on Intelligent Manufacturing Systems August 12-14, 2019. Oshawa, Canada August 12-14, 2019. Oshawa, Canada
ScienceDirect
IFAC PapersOnLine 52-10 (2019) 364–369 Decision Making in Lean Smart Maintenance: Criticality Analysis as aa Support Decision Making in Lean Smart Maintenance: Criticality Analysis as Decision Making in Lean Smart Maintenance: Criticality Analysis as aa Support Support Decision Criticality Analysis as Support Tool Decision Making Making in in Lean Lean Smart Smart Maintenance: Maintenance: Criticality Analysis as a Support Tool Tool Decision Making in Lean Smart Maintenance: Criticality Analysis as a Support Tool Tool Theresa Passath*. Katharina Mertens** Tool Theresa Theresa Passath*. Passath*. Katharina Katharina Mertens** Mertens**
Theresa Passath*. Katharina Mertens** Theresa Passath*. Katharina Mertens** Theresa Passath*. Katharina Mertens** *Department of Economic- and Business-Management, Montanuniversitaet Leoben, Leoben, Austria (Tel: 0043/3842 402 *Department of Economicand Business-Management, Montanuniversitaet *Department of Economic- and Business-Management, Montanuniversitaet Leoben, Leoben, Leoben, Leoben, Austria Austria (Tel: (Tel: 0043/3842 0043/3842 402 402 6013; email: [email protected]) *Department of Economic- and Business-Management, Montanuniversitaet Leoben, Leoben, Austria (Tel: 0043/3842 402 6013; email: [email protected]) *Department of Economicand Business-Management, Montanuniversitaet Leoben, Leoben, Austria (Tel:Austria 0043/3842 402 6013; email: [email protected]) ** Department of Economicand Business-Management, Montanuniversitaet Leoben, Leoben, 6013; email: [email protected]) *Department of Economicand Business-Management, Montanuniversitaet Leoben, Leoben, Austria (Tel:Austria 0043/3842 402 ** of and Business-Management, Montanuniversitaet Leoben, Leoben, 6013; [email protected]) ** Department Department of EconomicEconomicand email: Business-Management, Montanuniversitaet Leoben, Leoben, Austria (e-mail: [email protected]) ** Department of Economic-6013; and Business-Management, Montanuniversitaet Leoben, Leoben, Austria email: [email protected]) (e-mail: [email protected]) ** Department of Economic-(e-mail: and Business-Management, Montanuniversitaet Leoben, Leoben, Austria [email protected]) [email protected]) ** Department of Economic-(e-mail: and Business-Management, Montanuniversitaet Leoben, Leoben, Austria (e-mail: [email protected]) (e-mail: [email protected]) Abstract: Abstract: Abstract: Abstract: The increasing complexity and level of automation of assets and asset components make it difficult to Abstract: The complexity and of of components make it Abstract: The increasing increasing complexity and level level of automation automation of assets assets and and asset asset components make it difficult difficult to to choose the optimal maintenance strategy for them. Approaches criticality and risk assessment, The increasing complexity and level of automation of assets andfor asset components make it difficultthat to choose the optimal maintenance strategy for them. Approaches for criticality and risk assessment, The increasing complexity andoflevel of automation of assets andfor asset components make it difficultthat to choose the optimal maintenance strategy for them. Approaches criticality and risk assessment, that dynamically evaluate the state the asset in its life cycle stage and in the production environment, are choose the optimal maintenance strategy for them. Approaches for criticality and risk assessment, that The increasing complexity and level of automation of assets and asset components make it difficult to dynamically evaluate the state the asset in its cycle stage and in production environment, are choose the by optimal maintenance strategy them. for and risk assessment, that dynamically evaluate thequality state of ofand thehuman assetfor indecisions. its life lifeApproaches cycle stage and criticality in the the production environment, are influenced both data In the following, both aspects will be considered dynamically evaluate thequality state ofand thehuman assetfor indecisions. its lifeApproaches cycle stage and criticality in the production environment, are choose the optimal maintenance strategy them. for and risk assessment, that influenced by both data In the following, both aspects will be considered dynamically thequality state ofand thehuman asset indecisions. its life cycle stage and in the environment, are influenced both data In the following, bothproduction aspects will be considered and related by toevaluate the approaches. influenced by both data In the following, bothproduction aspects will be considered dynamically thequality state ofand thehuman asset indecisions. its life cycle stage and in the environment, are and related to the approaches. influenced both data quality and human decisions. In the following, both aspects will be considered and related by toevaluate the approaches. and related to the approaches. influenced by both data quality and human decisions. In the following, both aspects will be considered © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Support System, Asset Management, Lean Smart Maintenance, Criticality Analysis, and related Decision to the approaches. Keywords: Support System, Lean Criticality and related Decision toOrientation, the approaches. Keywords: Decision Support System, Asset Asset Management, Management, Lean Smart Smart Maintenance, Maintenance, Criticality Analysis, Analysis, Life-Cycle Industry support Risk Management, Keywords: Decision Support System,4.0, AssetIntelligent Management, Leansystem, Smart Maintenance, CriticalityIntelligent Analysis, Life-Cycle Orientation, Industry 4.0, Intelligent support system, Risk Management, Keywords: Decision Support System,4.0, AssetIntelligent Management, Leansystem, Smart Maintenance, CriticalityIntelligent Analysis, Life-Cycle Orientation, Industry support Risk Management, Intelligent Knowledge-Based System Life-Cycle Orientation, Industry support Risk Management, Keywords: Decision Support System,4.0, AssetIntelligent Management, Leansystem, Smart Maintenance, CriticalityIntelligent Analysis, Knowledge-Based System Life-Cycle Orientation, Industry 4.0, Intelligent support system, Risk Management, Intelligent Knowledge-Based System Knowledge-Based System Industry 4.0, Intelligent support system, Risk Management, Intelligent Life-Cycle Orientation, Knowledge-Based System Knowledge-Based System asset’s availability by choosing an asset specific maintenance 1. INTRODUCTION asset’s availability by choosing asset maintenance 1. INTRODUCTION asset’s availability choosing an an asset specific specific maintenance strategy and its by continuous adoption. Therefore, it is 1. INTRODUCTION asset’s availability by choosing an asset specific maintenance 1. INTRODUCTION strategy and its continuous adoption. Therefore, it is asset’s availability by choosing an asset specific maintenance The requirements 1.for maintenance, but also for the strategy and its continuous adoption. Therefore, itlevel is necessary to not only focus on the strategic management INTRODUCTION strategy and its continuous adoption. Therefore, it is The requirements for maintenance, but also for the asset’s availability by choosing an asset specific maintenance necessary to not only focus on the strategic management level 1. INTRODUCTION The requirements for maintenance, buthave also changed for the strategy and its continuous adoption. Therefore, it is maintenance personnel themselves, necessary to not only focus on the strategic management level but also involve the normative and operative ones, as known The requirements for maintenance, buthave also changed for the necessary to notitsonly focus on the strategic management level maintenance personnel themselves, strategy and continuous adoption. Therefore, it is but also involve the normative and operative ones, as known The requirements for maintenance, but also for the maintenance personnel themselves, have changed necessary to not only focus on the strategic management level considerably in recent years, because of the observable but also involve the normative and operative ones, as known form the St.Galler Management Concept, in maintenance maintenance personnel themselves, have changed The requirements for years, maintenance, but alsoobservable for the but also involve the normative andstrategic operative ones, as known considerably in recent because of the necessary to not only focus on the management level form the St.Galler Management Concept, in maintenance maintenance personnel themselves, have changed considerably in recent years, because of the observable the normative and Concept, operative ones, as known increase in digitalization and automation as as the but formalso theinvolve St.Galler Management in maintenance strategy decision. (Bardmann, 2014, p. 413) This considerably inpersonnel recent years, because of have thewell observable maintenance themselves, changed form theinvolve St.Galler Management in maintenance increase in and automation well as the but also the normative and Concept, operative ones, as known strategy decision. (Bardmann, 2014, p. 413) This considerably intherecent years, because of as the observable increase in ofdigitalization digitalization and automation as well as the consideration form the St.Galler Management Concept, in maintenance complexity assets. To be able to assert oneself in strategy decision. (Bardmann, 2014, p. 413) This of all three management levels enables the increase in ofdigitalization and automation well as the considerably inthe recent years, because of as the observable strategy decision. (Bardmann, 2014,levels p. 413) This complexity assets. To be able to assert oneself in the consideration form the St.Galler Management Concept, in maintenance of all three management enables the increase in digitalization and automation as well as the complexity of the assets. To be able to assert oneself in strategy decision. (Bardmann, 2014, p. 413) This consideration of all three management levels enables the automated production economy also in the future, a fast strategic success factors cost, quality, time, flexibility agility complexity ofdigitalization the assets. To be automation able to assert oneself increase in production and asfuture, well as the consideration of factors all three levels the automated economy also the aain strategy success decision. (Bardmann, p. enables 413) agility This strategic success cost,management quality,2014, time, flexibility agility complexity of the assets. To be able oneself in fast the automated production economy alsotoin inassert the future, fast consideration of all three management levels enables the strategic factors cost, quality, time, flexibility reaction, adaption of the maintenance strategy, due to and safety to be positively influenced and to secure automated production economy also in the future, a fast complexity of the assets. To be able to assert oneself in the strategic success factors cost, quality, time, flexibility agility reaction, adaption of the maintenance strategy, due to consideration of all three management levels enables and safety to be positively influenced and to secure the automated production alsoinfluence in the future, a fast reaction, adaption ofineconomy theorder maintenance strategy, due to strategic success factors cost, quality, time, flexibility agility production changes to neither the and safety to be positively influenced and to secure the advantages of the influenced company. (Biedermann, 2018, reaction, adaption ofineconomy the maintenance strategy, due to competitive automated production alsoinfluence in the future, a fast and safety to befactors positively and to secure the production changes to neither the strategic success cost, quality, time, flexibility agility competitive advantages of the company. (Biedermann, 2018, reaction, adaption maintenance strategy, due to production changes intheorder order to influence neither the and safety to be positively influenced and to secure the maintenance costs ofnor the availability negatively is pp. competitive advantages of thePassath, company. (Biedermann, 2018, 23; Kinz and 2018, pp. 30) production changes in order to influence neither the reaction, adaption of the maintenance strategy, due to competitive advantages of the company. (Biedermann, 2018, maintenance costs nor the availability negatively is and safety to be positively influenced and to secure the pp. 23; Kinz and Passath, 2018, pp. 30) production changes in this order to very influence neither the maintenance costs nor theis still availability negatively is pp. competitive advantages of the company. (Biedermann, 2018, necessary. Unfortunately, uncommon and far 23; Kinz and Passath, 2018, pp. 30) Therefore, it is necessary not only to focus on the utilization maintenance costs nor theis still availability negatively is pp. production changes in order to influence neither the 23; Kinz and Passath, 2018, pp. 30) necessary. Unfortunately, this very uncommon and far competitive advantages of the company. (Biedermann, 2018, Therefore, it is necessary not only to focus on the utilization maintenance costs nor the availability negatively is necessary.from Unfortunately, this is still very uncommon and far 23;anit asset andalso 2018, 30) removed dynamic asset-specific strategy adaptation, as pp. Therefore, isKinz necessary not Passath, only to afocus on utilization of but to to take look at the the pp. life cycle necessary. Unfortunately, this still very uncommon and far maintenance costs norasset-specific theis availability negatively is phase Therefore, it asset isKinz necessary not Passath, only to afocus on the utilization removed dynamic strategy adaptation, as pp. 23;an andalso 2018, pp. 30) phase of but to to take look at the life cycle necessary. Unfortunately, this is still very uncommon and far removed from from dynamic asset-specific strategy adaptation, as Therefore, it is necessary not only to focus on the utilization phase of an asset but to also to take a look at the life cycle provided by the Lean Smart Maintenance (LSM) orientation, starting from the procurement phase until the removed from dynamic asset-specific strategy adaptation, as orientation, necessary. Unfortunately, this Smart is still very uncommon and far phase of anitstarting asset butfrom to also toprocurement take afocus lookon at the utilization life cycle provided by the Lean Maintenance (LSM) Therefore, is necessary not only to the the phase until the removed from dynamic asset-specific strategy adaptation, as provided by the Lean Smart Maintenance (LSM) phase of an asset but to also to take a look at the life cycle (Biedermann, 2016a, p. 119, 2016b, pp. 19) – concept. orientation, starting from the procurement phase until the selection phase in the sense of value added asset management provided by 2016a, the Lean Smart Maintenance (LSM) removed from dynamic asset-specific strategy adaptation, as orientation, starting from the procurement phase untilcycle the (Biedermann, p. 119, 2016b, pp. 19) –– concept. phase of an asset but to also to take a look at the life selection phase in the sense of value added asset management provided by the Lean Smart Maintenance (LSM) (Biedermann, 2016a, p. 119, 2016b, pp. 19) concept. orientation, starting from the procurement phase until the (Passath and Huber, 2019, pp. 7) To fulfill the LSM selection phase in the sense of value added asset management system in the sense of LSM. (Kinz and Passath, 2018, pp. 29; (Biedermann, 2016a, p.2019, 119,Smart 2016b, pp. fulfill 19) –the concept. provided by the Lean Maintenance (LSM) selection phase in the sense of value added asset management (Passath and Huber, pp. 7) To LSM orientation, starting from the procurement phase until the system in the sense of LSM. (Kinz and Passath, 2018, pp. (Biedermann, 2016a, 119, 2016b, pp. fulfill 19) –learning concept. (Passath and maintenance Huber, p.2019, pp. the LSM phase in the sense value added asset management requirements, needs to7) be To intelligent, – selection system inand the Huber, sense of2019, LSM.of (Kinz and Passath, 2018, pp. 29; 29; Passath p. 7) Not least, for this reason, (Passath and Huber, 2019, pp. 7) To fulfill the LSM (Biedermann, 2016a, p. 119, 2016b, pp. 19) – concept. system in the sense of LSM. (Kinz and Passath, 2018, pp. 29; requirements, maintenance needs to be intelligent, learning – selection phase in the sense of value added asset management Passath and Huber, 2019, p. 7) Not least, for this reason, (Passath and Huber, pp.to7) To fulfill the LSM requirements, be due intelligent, – system inand the Huber, sense of2019, LSM.p.standards (Kinz and least, Passath, pp. 29; SMART – inmaintenance order to2019, beneeds adapted to the learning changing 7) Not this reason, international and national such for as 2018, ISO 55000 requirements, maintenance needs be due intelligent, learning – Passath (Passath and Huber, pp.to 7) To fulfill the LSM Passath inand 7) Not for this reason, SMART – order be adapted to changing the Huber, sense of2019, LSM.p. (Kinz and least, Passath, 2018, pp. 29; international and national standards such as ISO 55000 requirements, to beefficient intelligent, learning – system SMART – in inmaintenance order to to2019, beneeds adapted due to the the changing Passath and Huber, 2019, p. 7) Not least, for this reason, international and national standards such as ISO 55000 environmental condition and also cost – LEAN – to "Asset Management" and DIN 13306, such DIN EN 1664655000 have SMART – inmaintenance order to and beneeds adapted due to the changing requirements, to be intelligent, learning – international and national standards as ISO environmental condition also cost efficient – LEAN – to Passath and Huber, 2019, p. 7) Not least, for this reason, "Asset Management" and DIN 13306, DIN EN 16646 have SMART its – position in condition orderontothe bemarket adapted dueterm. to the changing environmental and also cost efficient –(Biedermann LEAN – to international and national standards such as maintenance ISO "Asset Management" and DIN 13306, DINand EN 1664655000 have maintain in long life cycle orientation in asset environmental and also cost efficient –(Biedermann LEAN – to placed SMART its – position in condition orderon bemarket adapted dueterm. to the changing Management" and DIN 13306, DINand EN 1664655000 have maintain in international and national standards such as maintenance ISO placed life cycle orientation in asset environmental condition and also cost efficient –(Biedermann LEAN – 2) to "Asset maintain its position ontothe the market in long long term. "Asset Management" and DIN 13306, DIN EN 16646 have and Kinz, 2019, p. 13; Mertens et al., 2018, p. placed life cycle orientation in asset and maintenance management at the center of these regulations. According to maintain its position on the market in long term. (Biedermann environmental condition and also cost efficient – LEAN – to placed life cycle orientation in asset and maintenance and Kinz, 2019, p. 13; Mertens et al., 2018, p. 2) "Asset Management" and DIN 13306, DINand ENAccording 16646 have management at the center of these regulations. to maintain its position on the in long (Biedermann and Kinz, 2019, p. 13; market Mertens et term. al., 2018, p. 2) DIN placed life cycle orientation in asset maintenance Furthermore, the extreme competitive pressure forces management at the center of these regulations. According to 13306, maintenance is "a combination of all technical and Kinz, 2019, p. 13; market Mertens et term. al., 2018, p. 2) DIN maintain its position on the in long (Biedermann management at the center of "a these regulations. According to Furthermore, the extreme competitive pressure forces placed life cycle orientation in asset and maintenance 13306, maintenance is combination of all technical and Kinz, 2019, p. 13; Mertens et al., 2018, p. 2) Furthermore, the extreme competitive pressure forces management at the center of these regulations. According to companies to permanently improve their assets by using new DIN 13306, maintenance is "a combination of all technical and administrative measures, as well as management Furthermore, the p. extreme competitive pressure forces and Kinz, 2019, 13; Mertens et al., 2018, p. 2) DIN 13306, maintenance is "a combination of all technical companies to permanently improve their assets by using new management at the center of these regulations. According to and administrative measures, as well as management Furthermore, thesystems extreme competitive pressure forces companies to and permanently improve their assets by the using new DIN 13306, maintenance is "a combination of all technical technologies for an enhancement of asset’s and administrative measures, as well as management measures during the life cycle of a unit, which serves to companies to and permanently improve their assets by the using new DIN Furthermore, the extreme competitive pressure forces and administrative measures, as well as management technologies systems for an enhancement of asset’s 13306, maintenance is "a combination of all technical measures during the life cycle aa well unit, which serves to companies to and permanently improve their assets by using new and technologies systems for an enhancement administrative measures, as condition measures during the its lifefunctional cycle of of unit, as which serves to efficiency and effectiveness with a focus onof a the lifeasset’s cycle or restore somanagement that it can technologies systems for an enhancement of asset’s companies to and permanently improve their assets by using new maintain measures during the its lifefunctional cycle of a well unit, as which serves to efficiency and effectiveness with aa focus on aa the life cycle and administrative measures, as condition management maintain or restore so that it can technologies and systems for an enhancement the efficiency and effectiveness with focus onof lifeasset’s cycle measures during the life cycle of a unit, which serves to maintain or restore its functional condition so that it can orientated sustainable asset management. (Passath and fulfill theorspecified requirements" (DIN 13306, 2018-02). efficiency and and effectiveness with a focus onof a the lifeasset’s cycle technologies and systems for an enhancement maintain restore its functional condition so that it can orientated sustainable asset management. (Passath and measures during the life cycle of a unit, which serves to fulfill the specified requirements" (DIN 13306, 2018-02). efficiency effectivenessasset withmanagement. a focus on (Passath a life cycle orientated andp. sustainable and Exactly maintain or restore its functional condition so that it can fulfill the specified requirements" (DIN 13306, 2018-02). Huber, 2019, 13) thisspecified starting point, the life cycle orientation, is also orientated andp. sustainable asset management. (Passath and Exactly efficiency effectiveness with a focus on a life cycle fulfill the requirements" (DIN 13306, 2018-02). Huber, 2019, 13) maintain or restore its functional condition so that it can this starting point, the life cycle orientation, is also orientated andp. sustainable asset management. (Passath and taken Huber, 2019, 13) fulfill up the requirements" (DIN orientation, 13306, 2018-02). Exactly this starting point, the life cycle is also byspecified the criticality assessment. Huber, 2019, 13)to change orientated andp. sustainable asset management. (Passath and this starting point, the life cycle is also fulfill up the specified requirements" (DIN orientation, 13306, 2018-02). Maintenance has towards the requirements of Exactly taken by the criticality assessment. Huber, 2019, p. 13) Exactly this starting point, the life cycle orientation, is also taken up by the criticality assessment. Maintenance has to change towards the requirements of Huber, 2019, p. 13) taken up by the criticality assessment. Maintenance hasbecome to change towardsforthetherequirements of The Exactly this starting point, themakes life cycle orientation, is also Industry 4.0 to an enabler smart factory. uniform asset evaluation it possible to carry out taken up by the criticality assessment. Maintenance has to change towards the requirements of Industry to become an for the smart factory. uniform asset evaluation makes it possible to carry out Maintenance hasp.10; to change towards of The Industry 4.0 4.0 to become an enabler enabler forthe therequirements smart factory. taken up by the criticality assessment. The uniform asset evaluation makes it possible to carry out (acatech, 2017, Biedermann and Kinz, 2019, p. 13) the first evaluation of an asset starting in the procurement Industry 4.0 to become an enabler forthe therequirements smart factory. Maintenance hasp.10; to change towards of The uniform asset evaluation makes it possible to carry out (acatech, 2017, Biedermann and Kinz, 2019, p. 13) the first evaluation of an asset starting in the procurement Industry 4.0 to become an enabler for the smart factory. (acatech, 2017, p.10; Biedermann and Kinz, 2019, p. 13) The uniform asset evaluation makes it possible to carry out Because of this reason, it is getting even more important to the first evaluation of an asset starting in the procurement phase according to a standardized procedure in order not to (acatech, 2017, p.10; Biedermann 2019, p. 13) Industry of 4.0 to reason, become enablerand for Kinz, the factory. the first evaluation an asset starting in the procurement Because this it is getting even more important to uniform asset evaluation makes it possible to carry out phase according to aaof standardized procedure in order not to (acatech, 2017, p.10; Biedermann and Kinz, 2019, p. 13) Because of this reason, it an isasset getting even moresmart important to The the first evaluation of an asset starting in the procurement implement a strategic management system phase according to standardized procedure in order not to rely solely on manufacturer information or empirical Because this it isasset getting evenKinz, more 2019, important to phase (acatech, of p.10; Biedermann and p. 13) according aofstandardized procedure inorprocurement order not to implement aa reason, strategic management system to evaluation an asset starting in the rely first solely on to manufacturer information empirical Because of2017, this reason, it is getting even more important implement strategic asset management system to the phase according to a standardized procedure in order not to permanently minimize the life cycle costs and maximize the rely solely on manufacturer information or empirical knowledge. This asset evaluation can be performed implement a strategic asset management system to Because of this reason, it is getting even important rely solely on tomanufacturer information empirical permanently the life cycle costs and procedure inor order not to according aasset standardized knowledge. This evaluation can be performed implement aminimize strategic asset management system the to phase permanently minimize the life cycle costs more and maximize maximize the rely solely on manufacturer information or empirical knowledge. This asset evaluation can be performed permanently the life costs and maximize implement aminimize strategic assetcycle management system the to knowledge. asset evaluation can beor performed rely solely This on manufacturer information empirical permanently2019 minimize the life cycle costs and maximize the This asset evaluation can be performed knowledge. Copyright@ IFAC permanently minimize the life cycle costs and maximizeControl) the 364 2405-8963 © 2019, IFAC (International Federation of Automatic by Elsevier All rights reserved. can be performed knowledge. ThisLtd.asset evaluation Copyright@ 2019 IFAC 364Hosting Copyright@ 2019 IFAC 364Control. Peer review under responsibility of International Federation of Automatic Copyright@ 2019 IFAC 364 Copyright@ 2019 IFAC 364 10.1016/j.ifacol.2019.10.058 Copyright@ 2019 IFAC 364
2019 IFAC IMS August 12-14, 2019. Oshawa, Canada
Theresa Passath et al. / IFAC PapersOnLine 52-10 (2019) 364–369
differently according to the data quality, starting from a static criticality analysis up to a dynamic one by connecting the assets to an ERP system, which fully exploits new possibilities of digitalization regarding horizontal and vertical integration of data and information in the supply chain in order to achieve competitive advantages. (Biedermann, 2018, p. 23; Kinz and Passath, 2018, p. 29; Passath and Huber, 2019, pp. 8) Especially in times of increasing digitalization and automatization the criticality analysis should be a decision base for maintenance strategy adaption by reducing the human influence in this evaluation process to a minimum. This is done by either using predefined standards for the asset evaluation (static) or avoiding it by real-time data analysis and automatic asset evaluation.
365
survey performed by the ÖVIA in cooperation with the Department of Economic- and Business Management (WBW) of the Montanuniversitaet Leoben shows (Fig. 1).
Fig. 1. Results of the benchmark analysis regarding the influences on the choice of the maintenance strategy. (Modified Kinz, 2018, p. 71)
For this reason, the focus of this research is to develop on the one side a model for the minimization of human influence in the criticality assessment and subsequently in maintenance strategy optimization, and on the other hand to generate a maturity assessment based on the prevailing data quality that defines types of criteria used for the asset evaluation. The higher the prevailing degree of digitization and automation, the better the data quality must be in order to use quantitative criteria for asset evaluation and subsequently eliminate human influence. Therefore, the criticality assessment is able to tackle some issues regarding the problems of digital transformation identified by acatech, starting from missing standards and technologies to the changing qualification requirements and the problems connected with the implementation of industry 4.0 (acatech,2017, pp.17). In the end, the aim of the criticality analysis should be to implement a standard as a basis of an agile, dynamic, smart, costefficient and value orientated asset management system using a dynamical adaption of the maintenance strategy to redress the constantly changing environmental and production related conditions.
Out of the 134 companies questioned in this survey, 89% choose their maintenance strategy based on empirical values, which easily contradicts the basic idea of LSM. (Kinz, 2018, p. 71) Another problem in defining the maintenance strategy is the adjustment period. In most cases, the strategy is defined once (46%) which is followed the entire useful life of the asset, and is not dynamically updated (Fig. 2). (Kinz, 2018, p. 71) However, the maintenance strategy should be continuously adapted to the changes in the sales market and environmental conditions in order to reduce costs and enhance efficiency.
Fig. 2. Results of the benchmark analysis regarding assetrelated strategy planning. (Modified Kinz, 2018, p. 71)
To get an insight into the current situation of maintenance strategy adjustment, prevailing problems in the maintenance strategy selection based on the results of a survey of the Austrian Society of Maintenance and Asset Management (ÖVIA) are first described. Subsequently, the human influence in the strategy optimization process, as well as the problem of the currently prevailing poor or unknown data quality in industrial assets is discussed before the model for the criticality assessment is described in detail. The correct selection of criteria, the automation of the asset evaluation as well as the elimination of human influence in criteria selection will be discussed. Finally, the strategy optimization based on the results of the asset valuation is briefly discussed and an outlook is given for further developments in the area of dynamic asset evaluation and strategy optimization.
Furthermore, also historical data are mostly used to define the maintenance strategy (66%) (Kinz, 2018, p. 71) ( Fig. 1). Another problem is that maintenance strategy adoption is only done during the life-time of an asset. It would be better to already consider it in the asset’s planning and construction phase, since 80% of the costs already arise in this phase of the asset life cycle. (acatech, 2015, p. 32; AlRadhi and Heuer, 1995, p. 123; Biedermann, 2015, pp. 25.; Mandelartz, 2009, p. 316) 2.2 Role of Human Decisions in Maintenance Decisions In all these decisions concerning maintenance and its strategy adaption, the human aspect still plays an important role because of either unavailable or insufficient data. (Price and Shanks, 2008, p. 65) Based on subjective estimations, be it personal experiences, empirical values or simply gut feeling, most important decisions are made this way, which can usually take weeks, as the acatech study shows. (acatech, 2017, p. 10) In most cases, activities like maintenance, asset improvements, and inspections are determined based on these empirical values. Unfortunately, it is still uncommon to make decisions based on data, since in most companies data
2. MAINTENANCE STRATEGY ADAPTION 2.1. Definition and adoption of the maintenance strategy – current problems The problem with the definition of the maintenance strategy is that there is still no standardized procedure for defining it. In most cases, manufacturer information, empirical knowledge or historical data are used, as the benchmark365
2019 IFAC IMS 366 August 12-14, 2019. Oshawa, Canada
Theresa Passath et al. / IFAC PapersOnLine 52-10 (2019) 364–369
analysis is not yet established or real-time data is rarely available. Therefore, companies are forced to use personal experiences for the definition of the maintenance strategy and associated activities. (Kinz, 2018, pp. 72; Kühnast, 2018, pp. 77) The problem of human decisions, which are made without concrete defaults, only from the gut, is the fear of human failure. Studies show that faulty acting is the cause of all errors in 60-80%. Therefore, it is more important to establish clear standards, such as uniform asset valuation, in order to overcome the fear of making the wrong decision through a uniform method of procedure and to reduce the causes of errors (Hofinger, 2007, p. 19).
is more important to have clear guidelines for decisionmaking in order to also relieve people of the pressure to fail and the stress. 2.3. Data Quality Aspect Data quality is a further aspect in the process of decision making (Price and Shanks, 2008, pp. 65). Decisions based on historical data (Kinz, 2018, p. 71) require a certain/high level of data quality. Already in 1979, CROSBY stated, that poor data quality causes losses up to 25% of the operating profit. (Crosby, 1978, p. 15) To control data quality, dimensions were defined. (Wang and Strong, 1996, pp. 10) and it is possible to distinguish between damage potentials that can be assessed in monetary and non-monetary terms. (Trumpetter and Meinken, 2016, p. 568) Another result of the benchmark study shows that while 86% of the companies using a CMMS only 37% of companies have a very good IT structure. These facts questions the applicability of the data collected in the maintenance related fields. (Kühnast, 2018, p. 77) based on these considerations, it is appropriate to question historical data and their quality and to take data quality into account when automating decisions.
This is also reflected by the results of the benchmark analysis of the ÖVIA. The results of the question of the most commonly used maintenance strategies show that 71% of the interviewees use preventive maintenance but almost half of the companies surveyed are still pursuing the reactive maintenance strategy (Fig. 3). Predictive Maintenance is only used in a few cases (17%) (Kinz, 2018, pp. 71) which represents the insufficient data quality companies are forcing.
The aim of the criticality analysis is to reduce the human factor in important decisions through targeted data use and automatization of the asset evaluation and/or to facilitate decisions through a standardized procedure. 3. CRITICALITY ANALYSIS The criticality analysis is a tool to standardize identify critical assets and dynamically adapt their maintenance strategy based on its results. The criticality analysis (Fig. 4) consists of three steps, the criteria evaluation of the assets, the identification of either risk or cost-critical assets via assetpriority-portfolio and a risk-/cost-analysis of the previous as critical identified assets. A uniform asset structure is, therefore, a basic prerequisite for correct data evaluation and, subsequently, for carrying out the criticality assessment (Kinz, 2017, pp. 137; Passath and Huber, 2019., p. 9; Passath and Kinz, 2018, pp. 51)
Fig. 3. Most commonly used maintenance strategies (Kinz, 2018, p. 71) For effective maintenance, the knowledge of the respective technical condition during the service life is required. The reliability of the decision for the operational management and maintenance depends much on the available information about the asset’s conditions. (Sturm and Förster, 2013, pp. 13) As already mentioned, the human being still has a decisive role when it comes to decisions in the field of asset management. There is a need for decisions in many areas of maintenance, starting with the preparation of maintenance and inspection, the definition of the maintenance strategy for each asset, investment decisions, and budget planning. According to BADKE, this need for decision-making is defined as its situation, the outcome of which determines the further development of a process (Badke-Schaub, 2002, p. 138) This reflects the leading role, the responsibility that has been assigned to the decision-makers. Decisions in the area of maintenance are also linked to common characteristics (Hofinger, 2007, p. 18):
Fig. 4. Criticality analysis as the basis for the maintenance strategy adaption (Modified Passath and Huber, 2019, p. 9) 3.1. Criteria evaluation In the first step, the criteria evaluation, assets are evaluated based on qualitative and/or quantitative criteria derived from the success factors of the company. For the criteria selection it is important not only to focus on the area of maintenance and its key figures, but also to try to derive criteria from all three management levels (normative, operational and strategic), to obtain an overall picture of the company and the most important influencing factors. (Kinz, 2017, pp. 138; Passath and Huber, 2019, p. 7) Therefore, employees from
• Time Pressure • Risk and Danger • Necessity of Stress Management It becomes obvious that in these cases the human being will be given a leading role and a lot of responsibility, especially if data are not recorded or available. Due to the fact that most companies, as the benchmark study also shows, have very little additional monitoring in the direction of their assets, it 366
2019 IFAC IMS August 12-14, 2019. Oshawa, Canada
Theresa Passath et al. / IFAC PapersOnLine 52-10 (2019) 364–369
367
critical assets, which have the potential to increase the valueadded contribution, a portfolio is generated in the second step, in which the asset index, the result of the criteria evaluation is compared to the direct maintenance costs. Depending on the position, either detailed cost or riskedanalysis or even both have to be performed, in the third step of the approach to improve the asset’s effectivity. (Kinz et al., 2017, p. 193; Kinz and Bernerstätter, 2016, pp. 91; Schröder and Kleindienst, 2013, pp. 118)
different departments (Maintenance, quality management, technology, R&D, production, management) should join the criteria selection. (Passath and Kinz, 2018, pp. 49) Due to the increasing digitalization and the prevailing resource bottlenecks, the more important it is to handle such processes as automatically as possible, which is an advantage of the criticality analysis. Therefore, good data quality is indispensable. Depending on a company`s data quality different criteria (qualitative, quantitative) are used for the criticality analysis. If the data are unavailable or data quality is poor, qualitative (not-measurable) criteria are used for the evaluation of the assets. In this case, the human influence in the decision process is high because experiences, know-how and manufacturer information are the decisive factors for the criteria choice and their evaluation (Fig. 5).
Fig. 6: Asset priority portfolio (Modified Biedermann, 2016b, p. 23) As shown in Fig. 6, the asset priority portfolio is divided into four quadrants. The cost dividing line can be defined company specific. Assets above this cost line are classified as cost critical. The goal here is to achieve cost reduction through little to no risk increase. Assets to the right of the index line are classified as risky. The most critical assets are placed at the top right, as they are classified as critical in terms of both costs and risk. In contrast, the assets on the top left or bottom right have only one critical influencing factor, either cost or the risk. The aim should be to shift all critical assets to the left bottom in the uncritical quadrant by deriving the appropriate maintenance measures and adjusting the maintenance strategy. (Kinz, 2017, pp. 143; Passath and Kinz, 2018, pp. 52) The next step is to examine the assets identified as critical in detail and try to adjust the maintenance strategy with regard to risk and cost reduction, starting with those that have two critical influencing factors. If the asset evaluation is carried out automatically, step three, the detailed risk or cost analysis of already as a critical identified asset, is the first step in which human resources are needed. (Kinz et al., 2017, p. 193; Passath and Kinz, 2018, p. 53)
Fig. 5. Maturity assessment for the criticality analysis (Maturity Levels 1-5) The better the quality of data is, the easier it becomes to implement an automatically asset evaluation. Therefore, only quantitative (measurable) criteria are used to for the asset evaluation. In the best case, quantitative criteria drawn from the ERP system by real-time interfaces are used in order to dynamically adapt the maintenance strategy (Passath and Huber, 2019, p. 9) and to reduce the human influence and the related faults. Once the criteria for asset valuation have been defined, the states of expression of the individual criteria must be prescribed for each company. For a better understanding, if one criterion is the availability of the asset, a definition of high, medium and low availability in terms of moderate numbers have to be considered. A numerical value is then stored for each of these states of expression, which, after successful evaluation, delivers the asset index, the evaluation´s result. Once the assessment preparations have been completed, the assets can be rated based on the previously defined criteria. In the best case, this step is carried out automatically (Kinz, 2017, pp. 140; Kinz et al., 2017, pp. 190 ).
3.3. Risk analysis For the risk analysis a RMEA, risk mode and effects analysis is performed to analyze risk-critical assets and to identify the cause for their critical points by determining the risk priority number (RPN) of the critical assets and their risks. (König, 2008, p. 59; Kinz and Bernerstätter, 2016, pp. 73) For this purpose, all risks, current and potential ones, are assessed based on 3 dimensions: the frequency of occurrence, the extent of damage, and the probability of detection. The
3.2. Identification of critical assets The asset priority portfolio is a possibility to visualize the specific criticality of an asset. In order to identify the most 367
2019 IFAC IMS 368 August 12-14, 2019. Oshawa, Canada
Theresa Passath et al. / IFAC PapersOnLine 52-10 (2019) 364–369
•
product of these 3 factors results in the RPN. In the final step, risk and cost reduction measures are defined for the assets having the highest RPZ to reduce it, also by adjusting the maintenance strategy.(Biedermann, 2016, p. 21 f; Kinz, 2017, pp. 149; Kinz and Passath, 2018, pp. 53)
Elimination of inefficiencies through adaptation of maintenance processes • Introduction /expansion of autonomous maintenance • Changes in the allocation of external services • Introduction of new technologies • Qualification needs • Investments in new additional facilities or replacement of old facilities • Optimizations in spare parts management The cost analysis shows whether the high costs of an asset are justified (e.g. due to a large repair) or whether the asset has conspicuously high preventive and/or reactive costs, as well as which activities these have caused. In this case, costreducing measures are defined. These can be organizational ones, such as outsourcing strategies, implementation of autonomous maintenance or adjustments to the current maintenance strategy, such as extending an inspection or maintenance interval. These alternatives have to be assessed with regard to the associated risks for quality, safety, environment and availability/reliability. The achievable cost savings by reducing the direct maintenance costs are compared with the changed risk potential of the new measure. The alternative with the best cost/risk ratio is choosen as the new strategy. (Kinz, 2017, p. 154)
3.3 Cost Analysis For detailed cost analysis, the maintenance costs of the previously selected priority assets are converted into their individual cost elements (Kinz et al., 2017, p. 197): • Own material costs • External material costs • Own wage costs • External wage costs • Other costs to identify the driving cost factor and subsequently to derive corresponding measures for cost reduction. The most costintensive individual component is analyzed with regard to its structure of maintenance costs, reactive or preventive. If, for example, the own wage costs are responsible for high maintenance costs, these are examined with regard to the amount of work (hours worked) used for maintenance and inspections and for malfunctions, in order to adjust maintenance schedules if necessary. If material costs are the cost drivers, they are examined based on the proportion of material used for malfunctions and maintenance in order to investigate the cause of the high costs. As a result of the cost analysis, the biggest cost-drivers are identified, which form the basis for the strategy adjustment (Kinz et al., 2017, pp. 197).
As a result of the risk analysis, a ranking of all risks (potential and actual ones) was made. This ranking shows the organizational and technical weaknesses of critical assets. Organizational weaknesses can be eliminated by adjusting resources (human, structure, and relationships). (Kinz et al., 2017, pp. 197) Based on the results of the risk assessment, the next step is the definition of measures to minimize technical weaknesses by defining measures to decrease the 3 dimensions of the RPN. (Parajes et al., 2018, p. 464; Kinz et al., 2017, p. 157) Furthermore, the additional maintenance costs incurred for the derived measures are estimated, as well as the cost savings potential resulting from the troubleshooting. Measures that have a positive saving potential (reduction RPN) should be implemented. In the case of several measures, the ones with the highest potential is implemented or several if they complement each other. (Passath and Huber, 2019, p. 11)
4. MAINTENANCE STRATEGY MIX ADOPTION AND DERIVATION MEASURES The detailed analyses performed form the basis for adjusting the asset-specific maintenance strategy mix, the next step in the strategy optimization process. The aim is to critically scrutinize the current maintenance strategy for assets that are conspicuous in terms of cost and/or criticality aspects and to optimize it based on a cost-benefit assessment. The ideal maintenance strategy mix results from the perfect combination of the following strategies (Kinz et al., 2017, pp. 202; Passath and Huber, 2019, pp. 10):
5. CONCLUSIONS The more complex and diverse assets are, the more important it is to have a standard to dynamically adapt the maintenance strategy due to the changing environmental as well as production conditions. Therefore, the criticality analysis can on the one hand help to significantly reduce the effort and create a standard independent of the data quality and, on the other hand, reduce the human factors influencing such maintenance decisions and to create a more objective and comparable evaluation.
• Reactive (failure-oriented) maintenance • Preventive maintenance • Predictive (condition-based) maintenance • Perfective (asset improving) maintenance In the case of the strategy optimization, not only technical and cost-driving weak points are uncovered but also organizational ones and their optimization potentials. Among other things, the following adjustments in the area of maintenance resources (human, structural or relational capital) can result from the maintenance strategy planning and adaption (Kinz et al., 2017, p. 203): •
Changes in organization
the
structural
and/or
The future goal should be to generate a holistic model for an objective/data based asset evaluation as a decision-making basis for the dynamic maintenance strategy adaption. Important criteria should be chosen by using a morphological box for the classification of the companies, to compare them
process
368
2019 IFAC IMS August 12-14, 2019. Oshawa, Canada
Theresa Passath et al. / IFAC PapersOnLine 52-10 (2019) 364–369
with the maturity assessment and the prevailing data quality and to generate a model to replace quality criteria with quantitative criteria in order to eliminate subjective evaluation of the assets.
369
des Lean Smart Maintenance Ansatzes, in: Biedermann, H. (Ed.), Lean Smart Maintenance: Konzepte, Instrumente und Anwendungen für eine effiziente und intelligente Instandhaltung. TÜV Media GmbH TÜV Rheinland Group, Köln, pp. 61–100. Kinz, A., Passath, T. (2018). Ressourceneffiziente Anlagenwirtschaft, in: WINGbusiness, 1/18, p. 29–32. Kinz, A., Schröder, W., Passath, Theresa, Praher, P., Freudenthaler, B. (2017). Optimierung des anlagenspezifischen Instandhaltungsstrategiemix am Beispiel eines komplexen Fertigungssystems, in: Erfolg durch Lean Smart Maintenance: 31. Instandhaltungsforum. TÜV Media, Köln, pp. 185–216. Kinz, A. (2018). Bewertungskategorie “Instandhaltungsstrategie,” in: Benchmark-Instandhaltung, Eine Studie Zum Reifegrad von Instandhaltungsorganisationen in der DACH-Region. TÜV Media, Köln pp. 68–76. König, R. (2008). Management betrieblicher Risiken bei produzierenden Unternehmen. RWTH Aachen Kühnast, R. (2018). Bewertungskategorie “IT-Struktur,” in: Benchmark-Instandhaltung, Eine Studie Zum Reifegrad von Instandhaltungsorganisationen in der DACH-Region. TÜV Media, Köln, pp. 77–86. Mandelartz, J. (2009). VDI-Richtlinie-mit technischen Regeln Wirtschaftlichkeit erhöhen und Standards setzen, in: Betriebliche Instandhaltung. Springer Berlin Heidelberg, Berlin, pp. 311–318. Mertens, K., Kinz, A., Bernerstätter, R. (2018). Lean Smart Maintenance. Parajes, J.S., Marquez, A.C., Rosique, A.S. (2018). Criticality analysis for preventive maintenance optimazation purposes in gas network infrastructures, in: Risk and Reliab. 232, p. 464–472. Passath, T., Huber, C. (2019). Dynamische Instandhaltungsstrategieanpassung durch Anlagenkritikalitätsbewertung, in: BHM Berg- Hüttenmänn. Monatshefte 1, p. 7–12. Passath, T., Kinz, A. (2018). Einführung eines strategischen Asset Management Systems am Beispiel der Zellstoffindustrie, in: Predicitive Maintenance-Realität und Vision, 32. Instandhaltungsforum. TÜV Media, Köln, pp. 41–64. Price, R., Shanks, G. (2008). Data Quality and Decision Making, in: Burstein, F., W. Holsapple, C. (Eds.), Handbook on Decision Support Systems 1: Basic Themes, International Handbooks Information System. Springer, Heidelberg, pp. 65–82. Schröder, W., Kleindienst, B. (2013). Resource Risk Based Maintenance: Ein Vorgehensmodell und dessen beispielhafte Umsetzung in der Stahlindustrie, in: Biedermann, H. (Ed.), Ressourceneffizientes Anlagenmanagement: 27. Instandhaltungsforum. TÜV Media, Köln, pp. 105–130. Sturm, A., Förster, R. (2013). Maschinen- und Anlagendiagnostik: Für die zustandsbezogene Instandhaltung. Springer-Verlag. Trumpetter, J., Meinken, N. (2016). Monetäres Schadenpotenzial von Datenqualitätsfehlern, in: Controlling, 28, p. 567–576. Wang, R.Y., Strong, D.M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers, in: Journal of Management Information Systems 12, 5–33.
REFERENCES acatech, (2017). Industrie 4.0 Maturity Index: Die digitale Transformation von Unternehmen gestalten. Herbert Utz Verlag, München. acatech, (2015). Smart Maintenance für Smart Factories: Mit intelligenter Instandhaltung die Industrie 4.0 vorantreiben. Herbert Utz Verlag, München. Al-Radhi, M., Heuer, J. (1995). Total productive maintenance: Konzept, Umsetzung, Erfahrung. Hanser, München. Badke-Schaub, P. (2002). Kritische Situationen als Analyseeinheit komplexer Handlungen, in: Psychologie Der Arbeitssicherheit Und Gesundheit, Neue Welten - Alte Welten. Asanger, pp. 137–142. Bardmann, M. (2014). Grundlagen der allgemeinen betriebswirtschaftslehre. Gabler, p.l. Biedermann, H. (2015). Smart Maintenance - Intelligente, lernorientierte Instandhaltung, in: Biedermann, H. (Ed.), Smart Maintenance: intelligente, lernorientierte Instandhaltung: 29. Instandhaltungsforum. TÜV Media, Köln, pp. 23–33. Biedermann, H. (2016a). Lean Smart Maintenance, in: Biedermann, H. (Ed.), Industrial Engineering Und Management: Beiträge Des Techno-Ökonomie-Forums der TU Austria. Springer Gabler, pp. 119–141. Biedermann, H. (2016b). Lean Smart Maintenance: Wertschöpfende, lernorientierte und ressourceneffiziente Instandhaltung, in: Biedermann, H. (Ed.), Lean Smart Maintenance: Konzepte, Instrumente und Anwendungen für eine effiziente und intelligente Instandhaltung. TÜV Media, Köln, pp. 19–29. Biedermann, H. (2018). Predictive Maintenance – Möglichkeiten und Grenzen, in: Predictive Maintenance-Realität und Vision, 32. Instandhaltungsforum. TÜV Media, Köln, pp. 23–40. Biedermann, H., Kinz, A. (2019). Lean Smart Maintenance Value Adding, Flexible and Intelligent Asset Management, in: BHM Berg- Hüttenmänn. Monatshefte, 1, 13–18. Crosby, P.B. (1978). Quality is Free: The Art of Making Quality Certain. Mcgraw-Hill Professional, New York. DIN EN 13306 (2018): DIN EN 13306:2018-02, Instandhaltung Begriffe der Instandhaltung. https://dx.doi.org/10.31030/2641990 Hofinger, G. (2007). Fehler und Fallen beim Entscheiden in kritischen Situationen, in: Entschieden in Kritischen Situationen. Verlag für Polizeiwissenschaft. Jöbstl, O. (1999). Einsatz von Qualitätsinstrumenten und methoden: ein Anwendungsmodell für Dienstleistungen am Beispiel der Instandhaltung, Techno-ökonomische Forschung und Praxis. Dt. Univ.-Verl, Wiesbaden. Kinz, A. (2017). Ausgestaltung einer dynamischen, lern- und wertschöpfungsorientierten Instandhaltung. Montanuniversität Leoben. Kinz, A., Bernerstätter, R. (2016). Instandhaltungsoptimierung mittels Lean Smart Maintenance: Einführung 369
ScienceDirect
IFAC PapersOnLine 52-10 (2019) 364–369 Decision Making in Lean Smart Maintenance: Criticality Analysis as aa Support Decision Making in Lean Smart Maintenance: Criticality Analysis as Decision Making in Lean Smart Maintenance: Criticality Analysis as aa Support Support Decision Criticality Analysis as Support Tool Decision Making Making in in Lean Lean Smart Smart Maintenance: Maintenance: Criticality Analysis as a Support Tool Tool Decision Making in Lean Smart Maintenance: Criticality Analysis as a Support Tool Tool Theresa Passath*. Katharina Mertens** Tool Theresa Theresa Passath*. Passath*. Katharina Katharina Mertens** Mertens**
Theresa Passath*. Katharina Mertens** Theresa Passath*. Katharina Mertens** Theresa Passath*. Katharina Mertens** *Department of Economic- and Business-Management, Montanuniversitaet Leoben, Leoben, Austria (Tel: 0043/3842 402 *Department of Economicand Business-Management, Montanuniversitaet *Department of Economic- and Business-Management, Montanuniversitaet Leoben, Leoben, Leoben, Leoben, Austria Austria (Tel: (Tel: 0043/3842 0043/3842 402 402 6013; email: [email protected]) *Department of Economic- and Business-Management, Montanuniversitaet Leoben, Leoben, Austria (Tel: 0043/3842 402 6013; email: [email protected]) *Department of Economicand Business-Management, Montanuniversitaet Leoben, Leoben, Austria (Tel:Austria 0043/3842 402 6013; email: [email protected]) ** Department of Economicand Business-Management, Montanuniversitaet Leoben, Leoben, 6013; email: [email protected]) *Department of Economicand Business-Management, Montanuniversitaet Leoben, Leoben, Austria (Tel:Austria 0043/3842 402 ** of and Business-Management, Montanuniversitaet Leoben, Leoben, 6013; [email protected]) ** Department Department of EconomicEconomicand email: Business-Management, Montanuniversitaet Leoben, Leoben, Austria (e-mail: [email protected]) ** Department of Economic-6013; and Business-Management, Montanuniversitaet Leoben, Leoben, Austria email: [email protected]) (e-mail: [email protected]) ** Department of Economic-(e-mail: and Business-Management, Montanuniversitaet Leoben, Leoben, Austria [email protected]) [email protected]) ** Department of Economic-(e-mail: and Business-Management, Montanuniversitaet Leoben, Leoben, Austria (e-mail: [email protected]) (e-mail: [email protected]) Abstract: Abstract: Abstract: Abstract: The increasing complexity and level of automation of assets and asset components make it difficult to Abstract: The complexity and of of components make it Abstract: The increasing increasing complexity and level level of automation automation of assets assets and and asset asset components make it difficult difficult to to choose the optimal maintenance strategy for them. Approaches criticality and risk assessment, The increasing complexity and level of automation of assets andfor asset components make it difficultthat to choose the optimal maintenance strategy for them. Approaches for criticality and risk assessment, The increasing complexity andoflevel of automation of assets andfor asset components make it difficultthat to choose the optimal maintenance strategy for them. Approaches criticality and risk assessment, that dynamically evaluate the state the asset in its life cycle stage and in the production environment, are choose the optimal maintenance strategy for them. Approaches for criticality and risk assessment, that The increasing complexity and level of automation of assets and asset components make it difficult to dynamically evaluate the state the asset in its cycle stage and in production environment, are choose the by optimal maintenance strategy them. for and risk assessment, that dynamically evaluate thequality state of ofand thehuman assetfor indecisions. its life lifeApproaches cycle stage and criticality in the the production environment, are influenced both data In the following, both aspects will be considered dynamically evaluate thequality state ofand thehuman assetfor indecisions. its lifeApproaches cycle stage and criticality in the production environment, are choose the optimal maintenance strategy them. for and risk assessment, that influenced by both data In the following, both aspects will be considered dynamically thequality state ofand thehuman asset indecisions. its life cycle stage and in the environment, are influenced both data In the following, bothproduction aspects will be considered and related by toevaluate the approaches. influenced by both data In the following, bothproduction aspects will be considered dynamically thequality state ofand thehuman asset indecisions. its life cycle stage and in the environment, are and related to the approaches. influenced both data quality and human decisions. In the following, both aspects will be considered and related by toevaluate the approaches. and related to the approaches. influenced by both data quality and human decisions. In the following, both aspects will be considered © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Support System, Asset Management, Lean Smart Maintenance, Criticality Analysis, and related Decision to the approaches. Keywords: Support System, Lean Criticality and related Decision toOrientation, the approaches. Keywords: Decision Support System, Asset Asset Management, Management, Lean Smart Smart Maintenance, Maintenance, Criticality Analysis, Analysis, Life-Cycle Industry support Risk Management, Keywords: Decision Support System,4.0, AssetIntelligent Management, Leansystem, Smart Maintenance, CriticalityIntelligent Analysis, Life-Cycle Orientation, Industry 4.0, Intelligent support system, Risk Management, Keywords: Decision Support System,4.0, AssetIntelligent Management, Leansystem, Smart Maintenance, CriticalityIntelligent Analysis, Life-Cycle Orientation, Industry support Risk Management, Intelligent Knowledge-Based System Life-Cycle Orientation, Industry support Risk Management, Keywords: Decision Support System,4.0, AssetIntelligent Management, Leansystem, Smart Maintenance, CriticalityIntelligent Analysis, Knowledge-Based System Life-Cycle Orientation, Industry 4.0, Intelligent support system, Risk Management, Intelligent Knowledge-Based System Knowledge-Based System Industry 4.0, Intelligent support system, Risk Management, Intelligent Life-Cycle Orientation, Knowledge-Based System Knowledge-Based System asset’s availability by choosing an asset specific maintenance 1. INTRODUCTION asset’s availability by choosing asset maintenance 1. INTRODUCTION asset’s availability choosing an an asset specific specific maintenance strategy and its by continuous adoption. Therefore, it is 1. INTRODUCTION asset’s availability by choosing an asset specific maintenance 1. INTRODUCTION strategy and its continuous adoption. Therefore, it is asset’s availability by choosing an asset specific maintenance The requirements 1.for maintenance, but also for the strategy and its continuous adoption. Therefore, itlevel is necessary to not only focus on the strategic management INTRODUCTION strategy and its continuous adoption. Therefore, it is The requirements for maintenance, but also for the asset’s availability by choosing an asset specific maintenance necessary to not only focus on the strategic management level 1. INTRODUCTION The requirements for maintenance, buthave also changed for the strategy and its continuous adoption. Therefore, it is maintenance personnel themselves, necessary to not only focus on the strategic management level but also involve the normative and operative ones, as known The requirements for maintenance, buthave also changed for the necessary to notitsonly focus on the strategic management level maintenance personnel themselves, strategy and continuous adoption. Therefore, it is but also involve the normative and operative ones, as known The requirements for maintenance, but also for the maintenance personnel themselves, have changed necessary to not only focus on the strategic management level considerably in recent years, because of the observable but also involve the normative and operative ones, as known form the St.Galler Management Concept, in maintenance maintenance personnel themselves, have changed The requirements for years, maintenance, but alsoobservable for the but also involve the normative andstrategic operative ones, as known considerably in recent because of the necessary to not only focus on the management level form the St.Galler Management Concept, in maintenance maintenance personnel themselves, have changed considerably in recent years, because of the observable the normative and Concept, operative ones, as known increase in digitalization and automation as as the but formalso theinvolve St.Galler Management in maintenance strategy decision. (Bardmann, 2014, p. 413) This considerably inpersonnel recent years, because of have thewell observable maintenance themselves, changed form theinvolve St.Galler Management in maintenance increase in and automation well as the but also the normative and Concept, operative ones, as known strategy decision. (Bardmann, 2014, p. 413) This considerably intherecent years, because of as the observable increase in ofdigitalization digitalization and automation as well as the consideration form the St.Galler Management Concept, in maintenance complexity assets. To be able to assert oneself in strategy decision. (Bardmann, 2014, p. 413) This of all three management levels enables the increase in ofdigitalization and automation well as the considerably inthe recent years, because of as the observable strategy decision. (Bardmann, 2014,levels p. 413) This complexity assets. To be able to assert oneself in the consideration form the St.Galler Management Concept, in maintenance of all three management enables the increase in digitalization and automation as well as the complexity of the assets. To be able to assert oneself in strategy decision. (Bardmann, 2014, p. 413) This consideration of all three management levels enables the automated production economy also in the future, a fast strategic success factors cost, quality, time, flexibility agility complexity ofdigitalization the assets. To be automation able to assert oneself increase in production and asfuture, well as the consideration of factors all three levels the automated economy also the aain strategy success decision. (Bardmann, p. enables 413) agility This strategic success cost,management quality,2014, time, flexibility agility complexity of the assets. To be able oneself in fast the automated production economy alsotoin inassert the future, fast consideration of all three management levels enables the strategic factors cost, quality, time, flexibility reaction, adaption of the maintenance strategy, due to and safety to be positively influenced and to secure automated production economy also in the future, a fast complexity of the assets. To be able to assert oneself in the strategic success factors cost, quality, time, flexibility agility reaction, adaption of the maintenance strategy, due to consideration of all three management levels enables and safety to be positively influenced and to secure the automated production alsoinfluence in the future, a fast reaction, adaption ofineconomy theorder maintenance strategy, due to strategic success factors cost, quality, time, flexibility agility production changes to neither the and safety to be positively influenced and to secure the advantages of the influenced company. (Biedermann, 2018, reaction, adaption ofineconomy the maintenance strategy, due to competitive automated production alsoinfluence in the future, a fast and safety to befactors positively and to secure the production changes to neither the strategic success cost, quality, time, flexibility agility competitive advantages of the company. (Biedermann, 2018, reaction, adaption maintenance strategy, due to production changes intheorder order to influence neither the and safety to be positively influenced and to secure the maintenance costs ofnor the availability negatively is pp. competitive advantages of thePassath, company. (Biedermann, 2018, 23; Kinz and 2018, pp. 30) production changes in order to influence neither the reaction, adaption of the maintenance strategy, due to competitive advantages of the company. (Biedermann, 2018, maintenance costs nor the availability negatively is and safety to be positively influenced and to secure the pp. 23; Kinz and Passath, 2018, pp. 30) production changes in this order to very influence neither the maintenance costs nor theis still availability negatively is pp. competitive advantages of the company. (Biedermann, 2018, necessary. Unfortunately, uncommon and far 23; Kinz and Passath, 2018, pp. 30) Therefore, it is necessary not only to focus on the utilization maintenance costs nor theis still availability negatively is pp. production changes in order to influence neither the 23; Kinz and Passath, 2018, pp. 30) necessary. Unfortunately, this very uncommon and far competitive advantages of the company. (Biedermann, 2018, Therefore, it is necessary not only to focus on the utilization maintenance costs nor the availability negatively is necessary.from Unfortunately, this is still very uncommon and far 23;anit asset andalso 2018, 30) removed dynamic asset-specific strategy adaptation, as pp. Therefore, isKinz necessary not Passath, only to afocus on utilization of but to to take look at the the pp. life cycle necessary. Unfortunately, this still very uncommon and far maintenance costs norasset-specific theis availability negatively is phase Therefore, it asset isKinz necessary not Passath, only to afocus on the utilization removed dynamic strategy adaptation, as pp. 23;an andalso 2018, pp. 30) phase of but to to take look at the life cycle necessary. Unfortunately, this is still very uncommon and far removed from from dynamic asset-specific strategy adaptation, as Therefore, it is necessary not only to focus on the utilization phase of an asset but to also to take a look at the life cycle provided by the Lean Smart Maintenance (LSM) orientation, starting from the procurement phase until the removed from dynamic asset-specific strategy adaptation, as orientation, necessary. Unfortunately, this Smart is still very uncommon and far phase of anitstarting asset butfrom to also toprocurement take afocus lookon at the utilization life cycle provided by the Lean Maintenance (LSM) Therefore, is necessary not only to the the phase until the removed from dynamic asset-specific strategy adaptation, as provided by the Lean Smart Maintenance (LSM) phase of an asset but to also to take a look at the life cycle (Biedermann, 2016a, p. 119, 2016b, pp. 19) – concept. orientation, starting from the procurement phase until the selection phase in the sense of value added asset management provided by 2016a, the Lean Smart Maintenance (LSM) removed from dynamic asset-specific strategy adaptation, as orientation, starting from the procurement phase untilcycle the (Biedermann, p. 119, 2016b, pp. 19) –– concept. phase of an asset but to also to take a look at the life selection phase in the sense of value added asset management provided by the Lean Smart Maintenance (LSM) (Biedermann, 2016a, p. 119, 2016b, pp. 19) concept. orientation, starting from the procurement phase until the (Passath and Huber, 2019, pp. 7) To fulfill the LSM selection phase in the sense of value added asset management system in the sense of LSM. (Kinz and Passath, 2018, pp. 29; (Biedermann, 2016a, p.2019, 119,Smart 2016b, pp. fulfill 19) –the concept. provided by the Lean Maintenance (LSM) selection phase in the sense of value added asset management (Passath and Huber, pp. 7) To LSM orientation, starting from the procurement phase until the system in the sense of LSM. (Kinz and Passath, 2018, pp. (Biedermann, 2016a, 119, 2016b, pp. fulfill 19) –learning concept. (Passath and maintenance Huber, p.2019, pp. the LSM phase in the sense value added asset management requirements, needs to7) be To intelligent, – selection system inand the Huber, sense of2019, LSM.of (Kinz and Passath, 2018, pp. 29; 29; Passath p. 7) Not least, for this reason, (Passath and Huber, 2019, pp. 7) To fulfill the LSM (Biedermann, 2016a, p. 119, 2016b, pp. 19) – concept. system in the sense of LSM. (Kinz and Passath, 2018, pp. 29; requirements, maintenance needs to be intelligent, learning – selection phase in the sense of value added asset management Passath and Huber, 2019, p. 7) Not least, for this reason, (Passath and Huber, pp.to7) To fulfill the LSM requirements, be due intelligent, – system inand the Huber, sense of2019, LSM.p.standards (Kinz and least, Passath, pp. 29; SMART – inmaintenance order to2019, beneeds adapted to the learning changing 7) Not this reason, international and national such for as 2018, ISO 55000 requirements, maintenance needs be due intelligent, learning – Passath (Passath and Huber, pp.to 7) To fulfill the LSM Passath inand 7) Not for this reason, SMART – order be adapted to changing the Huber, sense of2019, LSM.p. (Kinz and least, Passath, 2018, pp. 29; international and national standards such as ISO 55000 requirements, to beefficient intelligent, learning – system SMART – in inmaintenance order to to2019, beneeds adapted due to the the changing Passath and Huber, 2019, p. 7) Not least, for this reason, international and national standards such as ISO 55000 environmental condition and also cost – LEAN – to "Asset Management" and DIN 13306, such DIN EN 1664655000 have SMART – inmaintenance order to and beneeds adapted due to the changing requirements, to be intelligent, learning – international and national standards as ISO environmental condition also cost efficient – LEAN – to Passath and Huber, 2019, p. 7) Not least, for this reason, "Asset Management" and DIN 13306, DIN EN 16646 have SMART its – position in condition orderontothe bemarket adapted dueterm. to the changing environmental and also cost efficient –(Biedermann LEAN – to international and national standards such as maintenance ISO "Asset Management" and DIN 13306, DINand EN 1664655000 have maintain in long life cycle orientation in asset environmental and also cost efficient –(Biedermann LEAN – to placed SMART its – position in condition orderon bemarket adapted dueterm. to the changing Management" and DIN 13306, DINand EN 1664655000 have maintain in international and national standards such as maintenance ISO placed life cycle orientation in asset environmental condition and also cost efficient –(Biedermann LEAN – 2) to "Asset maintain its position ontothe the market in long long term. "Asset Management" and DIN 13306, DIN EN 16646 have and Kinz, 2019, p. 13; Mertens et al., 2018, p. placed life cycle orientation in asset and maintenance management at the center of these regulations. According to maintain its position on the market in long term. (Biedermann environmental condition and also cost efficient – LEAN – to placed life cycle orientation in asset and maintenance and Kinz, 2019, p. 13; Mertens et al., 2018, p. 2) "Asset Management" and DIN 13306, DINand ENAccording 16646 have management at the center of these regulations. to maintain its position on the in long (Biedermann and Kinz, 2019, p. 13; market Mertens et term. al., 2018, p. 2) DIN placed life cycle orientation in asset maintenance Furthermore, the extreme competitive pressure forces management at the center of these regulations. According to 13306, maintenance is "a combination of all technical and Kinz, 2019, p. 13; market Mertens et term. al., 2018, p. 2) DIN maintain its position on the in long (Biedermann management at the center of "a these regulations. According to Furthermore, the extreme competitive pressure forces placed life cycle orientation in asset and maintenance 13306, maintenance is combination of all technical and Kinz, 2019, p. 13; Mertens et al., 2018, p. 2) Furthermore, the extreme competitive pressure forces management at the center of these regulations. According to companies to permanently improve their assets by using new DIN 13306, maintenance is "a combination of all technical and administrative measures, as well as management Furthermore, the p. extreme competitive pressure forces and Kinz, 2019, 13; Mertens et al., 2018, p. 2) DIN 13306, maintenance is "a combination of all technical companies to permanently improve their assets by using new management at the center of these regulations. According to and administrative measures, as well as management Furthermore, thesystems extreme competitive pressure forces companies to and permanently improve their assets by the using new DIN 13306, maintenance is "a combination of all technical technologies for an enhancement of asset’s and administrative measures, as well as management measures during the life cycle of a unit, which serves to companies to and permanently improve their assets by the using new DIN Furthermore, the extreme competitive pressure forces and administrative measures, as well as management technologies systems for an enhancement of asset’s 13306, maintenance is "a combination of all technical measures during the life cycle aa well unit, which serves to companies to and permanently improve their assets by using new and technologies systems for an enhancement administrative measures, as condition measures during the its lifefunctional cycle of of unit, as which serves to efficiency and effectiveness with a focus onof a the lifeasset’s cycle or restore somanagement that it can technologies systems for an enhancement of asset’s companies to and permanently improve their assets by using new maintain measures during the its lifefunctional cycle of a well unit, as which serves to efficiency and effectiveness with aa focus on aa the life cycle and administrative measures, as condition management maintain or restore so that it can technologies and systems for an enhancement the efficiency and effectiveness with focus onof lifeasset’s cycle measures during the life cycle of a unit, which serves to maintain or restore its functional condition so that it can orientated sustainable asset management. (Passath and fulfill theorspecified requirements" (DIN 13306, 2018-02). efficiency and and effectiveness with a focus onof a the lifeasset’s cycle technologies and systems for an enhancement maintain restore its functional condition so that it can orientated sustainable asset management. (Passath and measures during the life cycle of a unit, which serves to fulfill the specified requirements" (DIN 13306, 2018-02). efficiency effectivenessasset withmanagement. a focus on (Passath a life cycle orientated andp. sustainable and Exactly maintain or restore its functional condition so that it can fulfill the specified requirements" (DIN 13306, 2018-02). Huber, 2019, 13) thisspecified starting point, the life cycle orientation, is also orientated andp. sustainable asset management. (Passath and Exactly efficiency effectiveness with a focus on a life cycle fulfill the requirements" (DIN 13306, 2018-02). Huber, 2019, 13) maintain or restore its functional condition so that it can this starting point, the life cycle orientation, is also orientated andp. sustainable asset management. (Passath and taken Huber, 2019, 13) fulfill up the requirements" (DIN orientation, 13306, 2018-02). Exactly this starting point, the life cycle is also byspecified the criticality assessment. Huber, 2019, 13)to change orientated andp. sustainable asset management. (Passath and this starting point, the life cycle is also fulfill up the specified requirements" (DIN orientation, 13306, 2018-02). Maintenance has towards the requirements of Exactly taken by the criticality assessment. Huber, 2019, p. 13) Exactly this starting point, the life cycle orientation, is also taken up by the criticality assessment. Maintenance has to change towards the requirements of Huber, 2019, p. 13) taken up by the criticality assessment. Maintenance hasbecome to change towardsforthetherequirements of The Exactly this starting point, themakes life cycle orientation, is also Industry 4.0 to an enabler smart factory. uniform asset evaluation it possible to carry out taken up by the criticality assessment. Maintenance has to change towards the requirements of Industry to become an for the smart factory. uniform asset evaluation makes it possible to carry out Maintenance hasp.10; to change towards of The Industry 4.0 4.0 to become an enabler enabler forthe therequirements smart factory. taken up by the criticality assessment. The uniform asset evaluation makes it possible to carry out (acatech, 2017, Biedermann and Kinz, 2019, p. 13) the first evaluation of an asset starting in the procurement Industry 4.0 to become an enabler forthe therequirements smart factory. Maintenance hasp.10; to change towards of The uniform asset evaluation makes it possible to carry out (acatech, 2017, Biedermann and Kinz, 2019, p. 13) the first evaluation of an asset starting in the procurement Industry 4.0 to become an enabler for the smart factory. (acatech, 2017, p.10; Biedermann and Kinz, 2019, p. 13) The uniform asset evaluation makes it possible to carry out Because of this reason, it is getting even more important to the first evaluation of an asset starting in the procurement phase according to a standardized procedure in order not to (acatech, 2017, p.10; Biedermann 2019, p. 13) Industry of 4.0 to reason, become enablerand for Kinz, the factory. the first evaluation an asset starting in the procurement Because this it is getting even more important to uniform asset evaluation makes it possible to carry out phase according to aaof standardized procedure in order not to (acatech, 2017, p.10; Biedermann and Kinz, 2019, p. 13) Because of this reason, it an isasset getting even moresmart important to The the first evaluation of an asset starting in the procurement implement a strategic management system phase according to standardized procedure in order not to rely solely on manufacturer information or empirical Because this it isasset getting evenKinz, more 2019, important to phase (acatech, of p.10; Biedermann and p. 13) according aofstandardized procedure inorprocurement order not to implement aa reason, strategic management system to evaluation an asset starting in the rely first solely on to manufacturer information empirical Because of2017, this reason, it is getting even more important implement strategic asset management system to the phase according to a standardized procedure in order not to permanently minimize the life cycle costs and maximize the rely solely on manufacturer information or empirical knowledge. This asset evaluation can be performed implement a strategic asset management system to Because of this reason, it is getting even important rely solely on tomanufacturer information empirical permanently the life cycle costs and procedure inor order not to according aasset standardized knowledge. This evaluation can be performed implement aminimize strategic asset management system the to phase permanently minimize the life cycle costs more and maximize maximize the rely solely on manufacturer information or empirical knowledge. This asset evaluation can be performed permanently the life costs and maximize implement aminimize strategic assetcycle management system the to knowledge. asset evaluation can beor performed rely solely This on manufacturer information empirical permanently2019 minimize the life cycle costs and maximize the This asset evaluation can be performed knowledge. Copyright@ IFAC permanently minimize the life cycle costs and maximizeControl) the 364 2405-8963 © 2019, IFAC (International Federation of Automatic by Elsevier All rights reserved. can be performed knowledge. ThisLtd.asset evaluation Copyright@ 2019 IFAC 364Hosting Copyright@ 2019 IFAC 364Control. Peer review under responsibility of International Federation of Automatic Copyright@ 2019 IFAC 364 Copyright@ 2019 IFAC 364 10.1016/j.ifacol.2019.10.058 Copyright@ 2019 IFAC 364
2019 IFAC IMS August 12-14, 2019. Oshawa, Canada
Theresa Passath et al. / IFAC PapersOnLine 52-10 (2019) 364–369
differently according to the data quality, starting from a static criticality analysis up to a dynamic one by connecting the assets to an ERP system, which fully exploits new possibilities of digitalization regarding horizontal and vertical integration of data and information in the supply chain in order to achieve competitive advantages. (Biedermann, 2018, p. 23; Kinz and Passath, 2018, p. 29; Passath and Huber, 2019, pp. 8) Especially in times of increasing digitalization and automatization the criticality analysis should be a decision base for maintenance strategy adaption by reducing the human influence in this evaluation process to a minimum. This is done by either using predefined standards for the asset evaluation (static) or avoiding it by real-time data analysis and automatic asset evaluation.
365
survey performed by the ÖVIA in cooperation with the Department of Economic- and Business Management (WBW) of the Montanuniversitaet Leoben shows (Fig. 1).
Fig. 1. Results of the benchmark analysis regarding the influences on the choice of the maintenance strategy. (Modified Kinz, 2018, p. 71)
For this reason, the focus of this research is to develop on the one side a model for the minimization of human influence in the criticality assessment and subsequently in maintenance strategy optimization, and on the other hand to generate a maturity assessment based on the prevailing data quality that defines types of criteria used for the asset evaluation. The higher the prevailing degree of digitization and automation, the better the data quality must be in order to use quantitative criteria for asset evaluation and subsequently eliminate human influence. Therefore, the criticality assessment is able to tackle some issues regarding the problems of digital transformation identified by acatech, starting from missing standards and technologies to the changing qualification requirements and the problems connected with the implementation of industry 4.0 (acatech,2017, pp.17). In the end, the aim of the criticality analysis should be to implement a standard as a basis of an agile, dynamic, smart, costefficient and value orientated asset management system using a dynamical adaption of the maintenance strategy to redress the constantly changing environmental and production related conditions.
Out of the 134 companies questioned in this survey, 89% choose their maintenance strategy based on empirical values, which easily contradicts the basic idea of LSM. (Kinz, 2018, p. 71) Another problem in defining the maintenance strategy is the adjustment period. In most cases, the strategy is defined once (46%) which is followed the entire useful life of the asset, and is not dynamically updated (Fig. 2). (Kinz, 2018, p. 71) However, the maintenance strategy should be continuously adapted to the changes in the sales market and environmental conditions in order to reduce costs and enhance efficiency.
Fig. 2. Results of the benchmark analysis regarding assetrelated strategy planning. (Modified Kinz, 2018, p. 71)
To get an insight into the current situation of maintenance strategy adjustment, prevailing problems in the maintenance strategy selection based on the results of a survey of the Austrian Society of Maintenance and Asset Management (ÖVIA) are first described. Subsequently, the human influence in the strategy optimization process, as well as the problem of the currently prevailing poor or unknown data quality in industrial assets is discussed before the model for the criticality assessment is described in detail. The correct selection of criteria, the automation of the asset evaluation as well as the elimination of human influence in criteria selection will be discussed. Finally, the strategy optimization based on the results of the asset valuation is briefly discussed and an outlook is given for further developments in the area of dynamic asset evaluation and strategy optimization.
Furthermore, also historical data are mostly used to define the maintenance strategy (66%) (Kinz, 2018, p. 71) ( Fig. 1). Another problem is that maintenance strategy adoption is only done during the life-time of an asset. It would be better to already consider it in the asset’s planning and construction phase, since 80% of the costs already arise in this phase of the asset life cycle. (acatech, 2015, p. 32; AlRadhi and Heuer, 1995, p. 123; Biedermann, 2015, pp. 25.; Mandelartz, 2009, p. 316) 2.2 Role of Human Decisions in Maintenance Decisions In all these decisions concerning maintenance and its strategy adaption, the human aspect still plays an important role because of either unavailable or insufficient data. (Price and Shanks, 2008, p. 65) Based on subjective estimations, be it personal experiences, empirical values or simply gut feeling, most important decisions are made this way, which can usually take weeks, as the acatech study shows. (acatech, 2017, p. 10) In most cases, activities like maintenance, asset improvements, and inspections are determined based on these empirical values. Unfortunately, it is still uncommon to make decisions based on data, since in most companies data
2. MAINTENANCE STRATEGY ADAPTION 2.1. Definition and adoption of the maintenance strategy – current problems The problem with the definition of the maintenance strategy is that there is still no standardized procedure for defining it. In most cases, manufacturer information, empirical knowledge or historical data are used, as the benchmark365
2019 IFAC IMS 366 August 12-14, 2019. Oshawa, Canada
Theresa Passath et al. / IFAC PapersOnLine 52-10 (2019) 364–369
analysis is not yet established or real-time data is rarely available. Therefore, companies are forced to use personal experiences for the definition of the maintenance strategy and associated activities. (Kinz, 2018, pp. 72; Kühnast, 2018, pp. 77) The problem of human decisions, which are made without concrete defaults, only from the gut, is the fear of human failure. Studies show that faulty acting is the cause of all errors in 60-80%. Therefore, it is more important to establish clear standards, such as uniform asset valuation, in order to overcome the fear of making the wrong decision through a uniform method of procedure and to reduce the causes of errors (Hofinger, 2007, p. 19).
is more important to have clear guidelines for decisionmaking in order to also relieve people of the pressure to fail and the stress. 2.3. Data Quality Aspect Data quality is a further aspect in the process of decision making (Price and Shanks, 2008, pp. 65). Decisions based on historical data (Kinz, 2018, p. 71) require a certain/high level of data quality. Already in 1979, CROSBY stated, that poor data quality causes losses up to 25% of the operating profit. (Crosby, 1978, p. 15) To control data quality, dimensions were defined. (Wang and Strong, 1996, pp. 10) and it is possible to distinguish between damage potentials that can be assessed in monetary and non-monetary terms. (Trumpetter and Meinken, 2016, p. 568) Another result of the benchmark study shows that while 86% of the companies using a CMMS only 37% of companies have a very good IT structure. These facts questions the applicability of the data collected in the maintenance related fields. (Kühnast, 2018, p. 77) based on these considerations, it is appropriate to question historical data and their quality and to take data quality into account when automating decisions.
This is also reflected by the results of the benchmark analysis of the ÖVIA. The results of the question of the most commonly used maintenance strategies show that 71% of the interviewees use preventive maintenance but almost half of the companies surveyed are still pursuing the reactive maintenance strategy (Fig. 3). Predictive Maintenance is only used in a few cases (17%) (Kinz, 2018, pp. 71) which represents the insufficient data quality companies are forcing.
The aim of the criticality analysis is to reduce the human factor in important decisions through targeted data use and automatization of the asset evaluation and/or to facilitate decisions through a standardized procedure. 3. CRITICALITY ANALYSIS The criticality analysis is a tool to standardize identify critical assets and dynamically adapt their maintenance strategy based on its results. The criticality analysis (Fig. 4) consists of three steps, the criteria evaluation of the assets, the identification of either risk or cost-critical assets via assetpriority-portfolio and a risk-/cost-analysis of the previous as critical identified assets. A uniform asset structure is, therefore, a basic prerequisite for correct data evaluation and, subsequently, for carrying out the criticality assessment (Kinz, 2017, pp. 137; Passath and Huber, 2019., p. 9; Passath and Kinz, 2018, pp. 51)
Fig. 3. Most commonly used maintenance strategies (Kinz, 2018, p. 71) For effective maintenance, the knowledge of the respective technical condition during the service life is required. The reliability of the decision for the operational management and maintenance depends much on the available information about the asset’s conditions. (Sturm and Förster, 2013, pp. 13) As already mentioned, the human being still has a decisive role when it comes to decisions in the field of asset management. There is a need for decisions in many areas of maintenance, starting with the preparation of maintenance and inspection, the definition of the maintenance strategy for each asset, investment decisions, and budget planning. According to BADKE, this need for decision-making is defined as its situation, the outcome of which determines the further development of a process (Badke-Schaub, 2002, p. 138) This reflects the leading role, the responsibility that has been assigned to the decision-makers. Decisions in the area of maintenance are also linked to common characteristics (Hofinger, 2007, p. 18):
Fig. 4. Criticality analysis as the basis for the maintenance strategy adaption (Modified Passath and Huber, 2019, p. 9) 3.1. Criteria evaluation In the first step, the criteria evaluation, assets are evaluated based on qualitative and/or quantitative criteria derived from the success factors of the company. For the criteria selection it is important not only to focus on the area of maintenance and its key figures, but also to try to derive criteria from all three management levels (normative, operational and strategic), to obtain an overall picture of the company and the most important influencing factors. (Kinz, 2017, pp. 138; Passath and Huber, 2019, p. 7) Therefore, employees from
• Time Pressure • Risk and Danger • Necessity of Stress Management It becomes obvious that in these cases the human being will be given a leading role and a lot of responsibility, especially if data are not recorded or available. Due to the fact that most companies, as the benchmark study also shows, have very little additional monitoring in the direction of their assets, it 366
2019 IFAC IMS August 12-14, 2019. Oshawa, Canada
Theresa Passath et al. / IFAC PapersOnLine 52-10 (2019) 364–369
367
critical assets, which have the potential to increase the valueadded contribution, a portfolio is generated in the second step, in which the asset index, the result of the criteria evaluation is compared to the direct maintenance costs. Depending on the position, either detailed cost or riskedanalysis or even both have to be performed, in the third step of the approach to improve the asset’s effectivity. (Kinz et al., 2017, p. 193; Kinz and Bernerstätter, 2016, pp. 91; Schröder and Kleindienst, 2013, pp. 118)
different departments (Maintenance, quality management, technology, R&D, production, management) should join the criteria selection. (Passath and Kinz, 2018, pp. 49) Due to the increasing digitalization and the prevailing resource bottlenecks, the more important it is to handle such processes as automatically as possible, which is an advantage of the criticality analysis. Therefore, good data quality is indispensable. Depending on a company`s data quality different criteria (qualitative, quantitative) are used for the criticality analysis. If the data are unavailable or data quality is poor, qualitative (not-measurable) criteria are used for the evaluation of the assets. In this case, the human influence in the decision process is high because experiences, know-how and manufacturer information are the decisive factors for the criteria choice and their evaluation (Fig. 5).
Fig. 6: Asset priority portfolio (Modified Biedermann, 2016b, p. 23) As shown in Fig. 6, the asset priority portfolio is divided into four quadrants. The cost dividing line can be defined company specific. Assets above this cost line are classified as cost critical. The goal here is to achieve cost reduction through little to no risk increase. Assets to the right of the index line are classified as risky. The most critical assets are placed at the top right, as they are classified as critical in terms of both costs and risk. In contrast, the assets on the top left or bottom right have only one critical influencing factor, either cost or the risk. The aim should be to shift all critical assets to the left bottom in the uncritical quadrant by deriving the appropriate maintenance measures and adjusting the maintenance strategy. (Kinz, 2017, pp. 143; Passath and Kinz, 2018, pp. 52) The next step is to examine the assets identified as critical in detail and try to adjust the maintenance strategy with regard to risk and cost reduction, starting with those that have two critical influencing factors. If the asset evaluation is carried out automatically, step three, the detailed risk or cost analysis of already as a critical identified asset, is the first step in which human resources are needed. (Kinz et al., 2017, p. 193; Passath and Kinz, 2018, p. 53)
Fig. 5. Maturity assessment for the criticality analysis (Maturity Levels 1-5) The better the quality of data is, the easier it becomes to implement an automatically asset evaluation. Therefore, only quantitative (measurable) criteria are used to for the asset evaluation. In the best case, quantitative criteria drawn from the ERP system by real-time interfaces are used in order to dynamically adapt the maintenance strategy (Passath and Huber, 2019, p. 9) and to reduce the human influence and the related faults. Once the criteria for asset valuation have been defined, the states of expression of the individual criteria must be prescribed for each company. For a better understanding, if one criterion is the availability of the asset, a definition of high, medium and low availability in terms of moderate numbers have to be considered. A numerical value is then stored for each of these states of expression, which, after successful evaluation, delivers the asset index, the evaluation´s result. Once the assessment preparations have been completed, the assets can be rated based on the previously defined criteria. In the best case, this step is carried out automatically (Kinz, 2017, pp. 140; Kinz et al., 2017, pp. 190 ).
3.3. Risk analysis For the risk analysis a RMEA, risk mode and effects analysis is performed to analyze risk-critical assets and to identify the cause for their critical points by determining the risk priority number (RPN) of the critical assets and their risks. (König, 2008, p. 59; Kinz and Bernerstätter, 2016, pp. 73) For this purpose, all risks, current and potential ones, are assessed based on 3 dimensions: the frequency of occurrence, the extent of damage, and the probability of detection. The
3.2. Identification of critical assets The asset priority portfolio is a possibility to visualize the specific criticality of an asset. In order to identify the most 367
2019 IFAC IMS 368 August 12-14, 2019. Oshawa, Canada
Theresa Passath et al. / IFAC PapersOnLine 52-10 (2019) 364–369
•
product of these 3 factors results in the RPN. In the final step, risk and cost reduction measures are defined for the assets having the highest RPZ to reduce it, also by adjusting the maintenance strategy.(Biedermann, 2016, p. 21 f; Kinz, 2017, pp. 149; Kinz and Passath, 2018, pp. 53)
Elimination of inefficiencies through adaptation of maintenance processes • Introduction /expansion of autonomous maintenance • Changes in the allocation of external services • Introduction of new technologies • Qualification needs • Investments in new additional facilities or replacement of old facilities • Optimizations in spare parts management The cost analysis shows whether the high costs of an asset are justified (e.g. due to a large repair) or whether the asset has conspicuously high preventive and/or reactive costs, as well as which activities these have caused. In this case, costreducing measures are defined. These can be organizational ones, such as outsourcing strategies, implementation of autonomous maintenance or adjustments to the current maintenance strategy, such as extending an inspection or maintenance interval. These alternatives have to be assessed with regard to the associated risks for quality, safety, environment and availability/reliability. The achievable cost savings by reducing the direct maintenance costs are compared with the changed risk potential of the new measure. The alternative with the best cost/risk ratio is choosen as the new strategy. (Kinz, 2017, p. 154)
3.3 Cost Analysis For detailed cost analysis, the maintenance costs of the previously selected priority assets are converted into their individual cost elements (Kinz et al., 2017, p. 197): • Own material costs • External material costs • Own wage costs • External wage costs • Other costs to identify the driving cost factor and subsequently to derive corresponding measures for cost reduction. The most costintensive individual component is analyzed with regard to its structure of maintenance costs, reactive or preventive. If, for example, the own wage costs are responsible for high maintenance costs, these are examined with regard to the amount of work (hours worked) used for maintenance and inspections and for malfunctions, in order to adjust maintenance schedules if necessary. If material costs are the cost drivers, they are examined based on the proportion of material used for malfunctions and maintenance in order to investigate the cause of the high costs. As a result of the cost analysis, the biggest cost-drivers are identified, which form the basis for the strategy adjustment (Kinz et al., 2017, pp. 197).
As a result of the risk analysis, a ranking of all risks (potential and actual ones) was made. This ranking shows the organizational and technical weaknesses of critical assets. Organizational weaknesses can be eliminated by adjusting resources (human, structure, and relationships). (Kinz et al., 2017, pp. 197) Based on the results of the risk assessment, the next step is the definition of measures to minimize technical weaknesses by defining measures to decrease the 3 dimensions of the RPN. (Parajes et al., 2018, p. 464; Kinz et al., 2017, p. 157) Furthermore, the additional maintenance costs incurred for the derived measures are estimated, as well as the cost savings potential resulting from the troubleshooting. Measures that have a positive saving potential (reduction RPN) should be implemented. In the case of several measures, the ones with the highest potential is implemented or several if they complement each other. (Passath and Huber, 2019, p. 11)
4. MAINTENANCE STRATEGY MIX ADOPTION AND DERIVATION MEASURES The detailed analyses performed form the basis for adjusting the asset-specific maintenance strategy mix, the next step in the strategy optimization process. The aim is to critically scrutinize the current maintenance strategy for assets that are conspicuous in terms of cost and/or criticality aspects and to optimize it based on a cost-benefit assessment. The ideal maintenance strategy mix results from the perfect combination of the following strategies (Kinz et al., 2017, pp. 202; Passath and Huber, 2019, pp. 10):
5. CONCLUSIONS The more complex and diverse assets are, the more important it is to have a standard to dynamically adapt the maintenance strategy due to the changing environmental as well as production conditions. Therefore, the criticality analysis can on the one hand help to significantly reduce the effort and create a standard independent of the data quality and, on the other hand, reduce the human factors influencing such maintenance decisions and to create a more objective and comparable evaluation.
• Reactive (failure-oriented) maintenance • Preventive maintenance • Predictive (condition-based) maintenance • Perfective (asset improving) maintenance In the case of the strategy optimization, not only technical and cost-driving weak points are uncovered but also organizational ones and their optimization potentials. Among other things, the following adjustments in the area of maintenance resources (human, structural or relational capital) can result from the maintenance strategy planning and adaption (Kinz et al., 2017, p. 203): •
Changes in organization
the
structural
and/or
The future goal should be to generate a holistic model for an objective/data based asset evaluation as a decision-making basis for the dynamic maintenance strategy adaption. Important criteria should be chosen by using a morphological box for the classification of the companies, to compare them
process
368
2019 IFAC IMS August 12-14, 2019. Oshawa, Canada
Theresa Passath et al. / IFAC PapersOnLine 52-10 (2019) 364–369
with the maturity assessment and the prevailing data quality and to generate a model to replace quality criteria with quantitative criteria in order to eliminate subjective evaluation of the assets.
369
des Lean Smart Maintenance Ansatzes, in: Biedermann, H. (Ed.), Lean Smart Maintenance: Konzepte, Instrumente und Anwendungen für eine effiziente und intelligente Instandhaltung. TÜV Media GmbH TÜV Rheinland Group, Köln, pp. 61–100. Kinz, A., Passath, T. (2018). Ressourceneffiziente Anlagenwirtschaft, in: WINGbusiness, 1/18, p. 29–32. Kinz, A., Schröder, W., Passath, Theresa, Praher, P., Freudenthaler, B. (2017). Optimierung des anlagenspezifischen Instandhaltungsstrategiemix am Beispiel eines komplexen Fertigungssystems, in: Erfolg durch Lean Smart Maintenance: 31. Instandhaltungsforum. TÜV Media, Köln, pp. 185–216. Kinz, A. (2018). Bewertungskategorie “Instandhaltungsstrategie,” in: Benchmark-Instandhaltung, Eine Studie Zum Reifegrad von Instandhaltungsorganisationen in der DACH-Region. TÜV Media, Köln pp. 68–76. König, R. (2008). Management betrieblicher Risiken bei produzierenden Unternehmen. RWTH Aachen Kühnast, R. (2018). Bewertungskategorie “IT-Struktur,” in: Benchmark-Instandhaltung, Eine Studie Zum Reifegrad von Instandhaltungsorganisationen in der DACH-Region. TÜV Media, Köln, pp. 77–86. Mandelartz, J. (2009). VDI-Richtlinie-mit technischen Regeln Wirtschaftlichkeit erhöhen und Standards setzen, in: Betriebliche Instandhaltung. Springer Berlin Heidelberg, Berlin, pp. 311–318. Mertens, K., Kinz, A., Bernerstätter, R. (2018). Lean Smart Maintenance. Parajes, J.S., Marquez, A.C., Rosique, A.S. (2018). Criticality analysis for preventive maintenance optimazation purposes in gas network infrastructures, in: Risk and Reliab. 232, p. 464–472. Passath, T., Huber, C. (2019). Dynamische Instandhaltungsstrategieanpassung durch Anlagenkritikalitätsbewertung, in: BHM Berg- Hüttenmänn. Monatshefte 1, p. 7–12. Passath, T., Kinz, A. (2018). Einführung eines strategischen Asset Management Systems am Beispiel der Zellstoffindustrie, in: Predicitive Maintenance-Realität und Vision, 32. Instandhaltungsforum. TÜV Media, Köln, pp. 41–64. Price, R., Shanks, G. (2008). Data Quality and Decision Making, in: Burstein, F., W. Holsapple, C. (Eds.), Handbook on Decision Support Systems 1: Basic Themes, International Handbooks Information System. Springer, Heidelberg, pp. 65–82. Schröder, W., Kleindienst, B. (2013). Resource Risk Based Maintenance: Ein Vorgehensmodell und dessen beispielhafte Umsetzung in der Stahlindustrie, in: Biedermann, H. (Ed.), Ressourceneffizientes Anlagenmanagement: 27. Instandhaltungsforum. TÜV Media, Köln, pp. 105–130. Sturm, A., Förster, R. (2013). Maschinen- und Anlagendiagnostik: Für die zustandsbezogene Instandhaltung. Springer-Verlag. Trumpetter, J., Meinken, N. (2016). Monetäres Schadenpotenzial von Datenqualitätsfehlern, in: Controlling, 28, p. 567–576. Wang, R.Y., Strong, D.M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers, in: Journal of Management Information Systems 12, 5–33.
REFERENCES acatech, (2017). Industrie 4.0 Maturity Index: Die digitale Transformation von Unternehmen gestalten. Herbert Utz Verlag, München. acatech, (2015). Smart Maintenance für Smart Factories: Mit intelligenter Instandhaltung die Industrie 4.0 vorantreiben. Herbert Utz Verlag, München. Al-Radhi, M., Heuer, J. (1995). Total productive maintenance: Konzept, Umsetzung, Erfahrung. Hanser, München. Badke-Schaub, P. (2002). Kritische Situationen als Analyseeinheit komplexer Handlungen, in: Psychologie Der Arbeitssicherheit Und Gesundheit, Neue Welten - Alte Welten. Asanger, pp. 137–142. Bardmann, M. (2014). Grundlagen der allgemeinen betriebswirtschaftslehre. Gabler, p.l. Biedermann, H. (2015). Smart Maintenance - Intelligente, lernorientierte Instandhaltung, in: Biedermann, H. (Ed.), Smart Maintenance: intelligente, lernorientierte Instandhaltung: 29. Instandhaltungsforum. TÜV Media, Köln, pp. 23–33. Biedermann, H. (2016a). Lean Smart Maintenance, in: Biedermann, H. (Ed.), Industrial Engineering Und Management: Beiträge Des Techno-Ökonomie-Forums der TU Austria. Springer Gabler, pp. 119–141. Biedermann, H. (2016b). Lean Smart Maintenance: Wertschöpfende, lernorientierte und ressourceneffiziente Instandhaltung, in: Biedermann, H. (Ed.), Lean Smart Maintenance: Konzepte, Instrumente und Anwendungen für eine effiziente und intelligente Instandhaltung. TÜV Media, Köln, pp. 19–29. Biedermann, H. (2018). Predictive Maintenance – Möglichkeiten und Grenzen, in: Predictive Maintenance-Realität und Vision, 32. Instandhaltungsforum. TÜV Media, Köln, pp. 23–40. Biedermann, H., Kinz, A. (2019). Lean Smart Maintenance Value Adding, Flexible and Intelligent Asset Management, in: BHM Berg- Hüttenmänn. Monatshefte, 1, 13–18. Crosby, P.B. (1978). Quality is Free: The Art of Making Quality Certain. Mcgraw-Hill Professional, New York. DIN EN 13306 (2018): DIN EN 13306:2018-02, Instandhaltung Begriffe der Instandhaltung. https://dx.doi.org/10.31030/2641990 Hofinger, G. (2007). Fehler und Fallen beim Entscheiden in kritischen Situationen, in: Entschieden in Kritischen Situationen. Verlag für Polizeiwissenschaft. Jöbstl, O. (1999). Einsatz von Qualitätsinstrumenten und methoden: ein Anwendungsmodell für Dienstleistungen am Beispiel der Instandhaltung, Techno-ökonomische Forschung und Praxis. Dt. Univ.-Verl, Wiesbaden. Kinz, A. (2017). Ausgestaltung einer dynamischen, lern- und wertschöpfungsorientierten Instandhaltung. Montanuniversität Leoben. Kinz, A., Bernerstätter, R. (2016). Instandhaltungsoptimierung mittels Lean Smart Maintenance: Einführung 369