Development of an empirical formula for machine classification: Prioritization of maintenance tasks

Stadnicka, Dorota; Antosz, Katarzyna; Ratnayake, R. M. Chandima

doi:10.1016/j.ssci.2013.10.020

Cited by 25 publications

(27 citation statements)

References 21 publications

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“…By using a multiple case study approach the phenomenon of machine criticality assessment can be studied in its natural setting and meaningful theory can be generated (Voss, Tsikriktsis, and Frohlich 2002). Additionally, the multiple case study methodology was inspired by similar research which developed an empirical formula for machine classification for prioritization of maintenance tasks (Stadnicka, Antosz, and Chandima Ratnayake 2014). The empirical formula was developed by studying three machine classifications from separate industrial cases and a generic formula was derived.…”

Section: Methodsmentioning

confidence: 99%

“…In this section, a generic approach for a data-driven criticality assessment tool is proposed by synthesizing the literature presented in Chapter 2. The structure of this framework is proposed based on the inspiration from the empirical formula proposed in Stadnicka, Antosz, and Chandima Ratnayake (2014). It essentially consists of three continuous steps.…”

Section: Proposed Generic Approach Machine Criticality Assessmentmentioning

confidence: 99%

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Data-driven machine criticality assessment – maintenance decision support for increased productivity

Gopalakrishnan

Subramaniyan

Skoogh

2020

Production Planning & Control

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Data-driven decision support for maintenance management is necessary for modern digitalized production systems. The data-driven approach enables analyzing the dynamic production system in realtime. Common problems within maintenance management are that maintenance decisions are experience-driven, narrow-focussed and static. Specifically, machine criticality assessment is a tool that is used in manufacturing companies to plan and prioritize maintenance activities. The maintenance problems are well exemplified by this tool in industrial practice. The tool is not trustworthy, seldom updated and focuses on individual machines. Therefore, this paper aims at the development and validation of a framework for a data-driven machine criticality assessment tool. The tool supports prioritization and planning of maintenance decisions with a clear goal of increasing productivity. Four empirical cases were studied by employing a multiple case study methodology. The framework provides guidelines for maintenance decision-making by combining the Manufacturing Execution System (MES) and Computerized Maintenance Management System (CMMS) data with a systems perspective. The results show that by employing data-driven decision support within the maintenance organization, it can truly enable modern digitalized production systems to achieve higher levels of productivity.

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Section: Methodsmentioning

confidence: 99%

Section: Proposed Generic Approach Machine Criticality Assessmentmentioning

confidence: 99%

Data-driven machine criticality assessment – maintenance decision support for increased productivity

Gopalakrishnan

Subramaniyan

Skoogh

2020

Production Planning & Control

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“…The model provided by Pelaez and Bowles (1994) combines qualitative and quantitative factors to determine "riskiness." Failure data are a specific requirement in many criticality assessment methods (Stadnicka et al, 2014;Bengtsson, 2011), as these are main data that directly correspond to maintenance. In other situations, purely qualitative data such as group discussion (cross-functional) with the relevant participants are used for assessment (Bengtsson, 2011;Márquez et al, 2015).…”

Section: Machine Criticality Assessmentmentioning

confidence: 99%

Machine criticality assessment for productivity improvement

Gopalakrishnan

Skoogh

Salonen

et al. 2019

IJPPM

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Purpose The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity. Design/methodology/approach An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety. Findings The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified. These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization. Originality/value Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities.

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“…To evaluate the system effectiveness different evaluation criteria may be used. In the articles [15,16,19,23,27,29,34,36,37,42,43] the authors propose the criteria of a system evaluation. Different indicators are used for each of the particular criterion.…”

Section: Measures Of the Machine Operation Effectiveness Evaluationmentioning

confidence: 99%

“…It is calculated according to the formula (1). [15,16,19,23,27,29,34,36,37,42,43] No. Criterion Characteristics Exemplary indicators…”

Section: Measures Of the Machine Operation Effectiveness Evaluationmentioning

confidence: 99%

Evaluation measures of machine operation effectiveness in large enterprises: study results

Antosz¹,

Stadnicka²

2015

Eksploatacja i Niezawodnosc - Maintenance and Reliability

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Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl IntroductionOne of the main components of the properly organized process of the machine and equipment supervision in any enterprise is the choice and the use of a proper management strategy. The literature widely describes kinds of management strategies and the actions within them [5,9,10,14,17,40]. The implementation of the particular methods of machine and equipment supervision in an enterprise requires, however, periodic evaluation of the action effectiveness as well as of the state of the owned technical infrastructure. The degree of reliability of the information obtained is a basic condition for receiving positive final reports, and it facilitates taking proper decisions concerning preventive actions. There are many ways of obtaining information on the operation of particular machines and technological equipment. However, establishing what will be measured is the most important.Thus, the choice of the appropriate evaluation measures is crucial. These measuring metrics help to evaluate the key actions realized within machines maintenance and they indicate the efficiency of the actions taken in relation to the goals of an organization [6,10].The references define different measures of the machine supervision evaluation [22], among them e.g. OEE [8,18] or MTTR [7,11,21]. The references have also been reviewed for the measures used for the machine effectiveness evaluation in an industrial sector [24,33]. The correlation of the selected indicators with other evaluation methods was assessed as well [2,24].Moreover, the analysis of the references showed that there were also studies conducted concerning maintenance activities based on the MTTF values obtained [22]. The study, that was carried out, also regarded the evaluation of the OEE measure values obtained [2,18], the possibilities of its improvement [31,39] as well as its computer based (automated) calculation [30]. Different models of optimization of the machine maintenance were also presented [32].According to the authors, the analysed references lack a comprehensive comparative analysis of the machine evaluation measures practically applied that would take into account e.g. enterprise's size, industry, capital type, or production type. Additionally, in the articles analyzed, it is difficult to find any information on the problems of the enterprises regarding the application of the machine evaluation measures. This article indicates the basic measures of machine operation, and the ways of their calculating. Next, it was checked if these measures were being actually applied. Katarzyna AnTosz Measures of the machine operation effectiveness evaluationEffectiveness is one of the characteristics determining the properties in a set of items or systems. It is commonly understood as a property of an item (or system) which conditions the achievement level of the goals of an item or a system in the specified conditions of use a...

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Development of an empirical formula for machine classification: Prioritization of maintenance tasks

Cited by 25 publications

References 21 publications

Data-driven machine criticality assessment – maintenance decision support for increased productivity

Data-driven machine criticality assessment – maintenance decision support for increased productivity

Machine criticality assessment for productivity improvement

Evaluation measures of machine operation effectiveness in large enterprises: study results

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