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The need for a holistic view on dependable production systems
ScienceDirect ProcediaScienceDirect Manufacturing 00 (2018) 000–000
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Procedia Manufacturing 00 (2018) 000–000
Procedia Manufacturing 25 (2018) 17–22 Procedia Manufacturing 00 (2017) 000–000 www.elsevier.com/locate/procedia 8th Swedish Production Symposium, SPS 2018, 16-18 May 2018, Stockholm, Sweden
8th Swedish Production Symposium, SPS 2018, 16-18 May 2018, Stockholm, Sweden
The need for a holistic view on dependable production systems The need for a holistic view on dependable production systems Antti Salonen*
Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June Mälardalen University, Box 325, Eskilstuna, 631 05, Sweden 2017, Vigo (Pontevedra), Spain Antti Salonen* Mälardalen University, Box 325, Eskilstuna, 631 05, Sweden
Costing models for capacity optimization in Industry 4.0: Trade-off between used capacity and operational efficiency Abstract Abstract When discussing dependability of mechanized, itema,* production, the bfocus tends to be bon maintenance. By studying A. Santanaa, P.discrete Afonso , A. Zanin , R. Wernke experiences and breakdown data from eight automotive manufacturing sites in Sweden, a new view on the problem arises. It When discussing dependability discrete item production, the focus tends to be on indicates maintenance. By studying a seems that on average, 40% ofofthemechanized, breakdowns are related to poor maintenance practices. This that maintenance University of Minho, 4800-058 Guimarães, Portugal b automotiveRather, experiences and breakdown data cause from eight manufacturing sites inBrazil Sweden, a new view on thee.g. problem It management is not the dominant of breakdowns. companies should focus on human factors, skills, arises. routines, Unochapecó, 89809-000 Chapecó, SC, seems that on average, 40% of the breakdowns are related to poor maintenance practices. This indicates that maintenance and workload, among operators, as well as maintenance staff. Also, the Early Equipment Management process requires more management is not dominant cause of breakdowns. Rather, system. companies should focus on human factors, e.g. skills, routines, attention in order to the increase the dependability in the production and workload, among operators, as well as maintenance staff. Also, the Early Equipment Management process requires more attention in order to increase the dependability in the production system. Abstract © 2018 The Authors. Published by Elsevier B.V. © 2018 The Authors. Published by Elsevier B.V. Under the under concept of "Industry production will beProduction pushed to be increasingly interconnected, Peer-review responsibility of the the4.0", scientific committeeprocesses of the the8th 8thSwedish Swedish Production Symposium. Peer-review under responsibility of scientific committee of Symposium. © 2018 The Authors. Published by Elsevier B.V. necessarily, much more efficient. In this context, capacity optimization information based on a real time basis and, Peer-review under responsibility ofof theRoot scientific committee of the 8th Swedishalso Production Symposium. profitability and value. Keywords: Dependability; Human aim factors; Causemaximization, Failure Analysis goes beyond the traditional capacity contributing for organization’s
Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of Keywords: Dependability; Human factors; Root Cause Failure Analysis maximization. The study of capacity optimization and costing models is an important research topic that deserves 1. Background contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been 1. Background By transforming discrete production systems traditional towards flow-oriented developed and it was used to analyze idle capacity andfrom to design strategiesprocess towardsorientation, the maximization of organization’s production lines and applying Just-In-Time production, the production systems have become more vulnerable to value. trade-off capacity maximization vs operational efficiency is highlighted and it istowards shown flow-oriented that capacity By The transforming discrete production systems from traditional process orientation, disturbances, and in many ways resembling the continuous process industry [1]. A survey, presented by [2], optimization might hide operational inefficiency. production lines applying Just-In-Time thewith production systems have become more vulnerable to indicates 14%and of the manufacturing costsproduction, are associated unplanned downtime. This highlights the financial © 2017 Thethat Authors. Published by Elsevier B.V. disturbances, and in many ways resembling the continuous process industry [1]. A survey, presented by [2], importanceunder of dependable systems. However, the focus onEngineering research Society within International dependableConference production Peer-review responsibilityproduction of the scientific committee of the Manufacturing indicates that is 14% the manufacturing costs are associatedand with unplanned highlights the systems, still on of maintenance of production equipment, more recently,downtime. how novelThis technologies, e.g.financial IoT and 2017. importance of dependable production systems. However, the focus on research within dependable production systems, Cost stillModels; is on maintenance production equipment, and more recently, how novel technologies, e.g. IoT and Keywords: ABC; TDABC; of Capacity Management; Idle Capacity; Operational Efficiency * Corresponding author. Tel.: +46 16 153606 1.E-mail Introduction address: [email protected] * Corresponding author. Tel.: +46 16 153606 The cost of idle capacity is a fundamental for companies and their management of extreme importance E-mail address: [email protected] 2351-9789 © 2018 The Authors. Published by Elsevier information B.V. in modern under production systems. general, it is defined as unused capacity or production potential and can be measured Peer-review responsibility of theIn scientific committee of the 8th Swedish Production Symposium. 2351-9789 2018 The Authors. Published by Elsevier B.V.hours of manufacturing, etc. The management of the idle capacity in several©ways: tons of production, available Peer-review underTel.: responsibility the761; scientific committee the 8th Swedish Production Symposium. * Paulo Afonso. +351 253of 510 fax: +351 253 604of741
E-mail address: [email protected]
2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 8th Swedish Production Symposium. 10.1016/j.promfg.2018.06.052
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Antti Salonen / Procedia Manufacturing 25 (2018) 17–22 Author name / Procedia Manufacturing 00 (2018) 000–000
big data, may contribute to the dependability of production equipment. What is less studied, is the reason to poor dependability in manufacturing industry. 1.1. Aim The aim of this paper is to discuss the importance of broadening the view of dependable production systems, from an equipment maintenance focused perspective, to a more holistic production system’s perspective, including some seemingly neglected aspects of dependability in production systems. 2. Literature review When discussing dependable production systems, it is not uncommon to focus on the technical aspects of the present system, i.e. the equipment availability. Further, it is commonly assumed that low dependability mainly is caused by poor maintenance. However, when applying a system’s perspective on the production equipment, as proposed by [3], it becomes obvious that this narrow view on maintenance issues is rather limited. 2.1. A holistic view of dependability In order to study the dependability of production systems, a holistic view is necessary. One example of such a holistic view is Terotechnology: “A combination of management, financial, engineering and other practices applied to physical assets in pursuit of economical life cycle costs. Its practice is concerned with the specification and design for reliability and maintainability of plant, machinery, equipment, buildings and structures, with their installation, commissioning, maintenance, modification and replacement, and with the feedback of information on design, performance and costs” [4, p.132] Another form of holistic views is presented by Rollenhagen [5] with the concept of MTO (Man, Technology, and Organization) in order to include human and organizational aspects into safety issues. 2.2. Human factors Human factors and/or errors are well studied in various contexts, especially in safety critical systems, such as aviation or nuclear industry. According to [6], 70-80% of all aviation accidents have had human errors as a contributing factor. Human factors in manufacturing industry are less studied, although some interesting papers were presented around 1995, e.g. [7, 8]. The importance of human factors and multi skilled operators is discussed by Macduffie [7]. He highlights that the operators of flexible manufacturing systems have to be able, not only to operate the equipment, but also to perform, e.g. quality inspections, maintenance and statistical process control. Research on human factors in maintenance have been found, e.g. [9, 10, 11], but this research is mainly focusing on the performance of maintenance, rather than human errors in operations, as root causes of equipment breakdowns. Still, human errors in maintenance performance have become increasingly important with the introduction of autonomous maintenance. The idea behind the concept is that the operators of production equipment should start taking more responsibility for the maintenance of their equipment and thereby achieve a larger sense of ownership and involvement. The essential parts in autonomous maintenance are for the operators to inspect and maintain critical parts of their equipment. In order to do so, the operators need not only training but time and motivation. Nakajima [12] lists three common reasons for operators failing to inspect their equipment properly, namely when operators lack motivation, time, and/or skills to inspect. Also, it is important to highlight that the operators still need to be encouraged to take interest and responsibility in mending their equipment. Otherwise, as Nakajima [13] points out, the equipment will still inevitably deteriorate and cause breakdowns and/or quality problems. 2.3. Design factors When studying the technical aspects of the production system, it is vital to consider how to design and/or purchase dependable production equipment already in the design phase of the production system. The term Early
Antti Salonen / Procedia Manufacturing 25 (2018) 17–22 Author name / Procedia Manufacturing 00 (2018) 000–000
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Equipment Management, EEM, is mainly associated with Total Productive Maintenance, TPM, which was rather well studied during the 1990s. However, more recently, there seems to be few studies presented on how to design and/or purchase dependable production equipment. One framework for dependability assurance of production processes is presented by Lendvay [14]. It is essentially a combination of traditional reliability engineering practices and principles of EEM, as presented in TPM. With the recent technological advancements in information technology, terms as Internet of Things, IoT, and Cyber Physical Systems, CPS, have emerged, with promises of increased dependability in new production equipment. For example [15], present a CPS architecture for Industry 4.0 based manufacturing systems. This architecture is based on advanced sensors, big data analytics, decision support systems etc. 2.4. Root causes of poor dependability In order to improve the dependability of manufacturing equipment, it is vital to analyze the causes of poor dependability. Authors like [16], and [17], have highlighted the importance of Root Cause Failure Analysis, RCFA. These practices have commonly been used in safety critical branches and where the financial loss due to deficiencies is substantial. In traditional manufacturing industry however, RCFA is less used, even though it is known, e.g. through Lean-related practices such as 5 Why and Ishikawa diagrams. The presence of RCFA, doesn’t necessarily mean that the true root causes are identified. In [18], the following causes for failing in RCFA were identified: Lack of resources; Lack of competence; Improper selection of tool; Poor understanding of the RCFA process; Lack of system’s perspective; and Lack of relevant data. 2.5. Summary This literature review shows that the academia has recognized the importance of applying a system’s perspective on production systems, including the dependability, e.g. [4]. Further, both human factors and design factors have been identified as causes of poor dependability. Recent research proposes the introduction of new technologies, e.g. IoT and CPS, as a means to improve the dependability. Except for these technological leaps, there are established methods for the identification of the root causes of failures in the production equipment. 3. Methodology Even though this paper is rather argumentative, it still includes empiric material to support the discussion. The empirics were collected through two different studies. The data was collected through interviews, focus groups, and archival records from a total of eight different manufacturing sites in Swedish automotive manufacturing. The sites produce components for commercial as well as non-commercial vehicles. The production equipment includes machining centers, foundries, hardening processes and paint shops. The first study was based on eight interviews and five focus groups with maintenance engineers at six different production sites in Swedish automotive manufacturing. The interviews and focus groups focused on maintenance planning and scheduling, but also included the question: “How much breakdowns could be avoided if all your preventive maintenance was optimal in both content and interval?” The respondents’ answers were based on their experience-based conception, rather than hard data. The second, study is still ongoing and focusing on Root Cause Failure Analysis, RCFA, in manufacturing industry. This paper contains data collected through interviews with maintenance engineers, as well as archival records from two manufacturing companies in the automotive industry. 4. Empirical data All respondents but two in study 1, assessed that an optimal preventive maintenance performance could reduce breakdowns by 40-60% (the two exceptions assessed 10%, and 80%) with an average of 40%. All respondents stated that the remaining part was due to either human errors or poor equipment design.
Antti Salonen / Procedia Manufacturing 25 (2018) 17–22 Author name / Procedia Manufacturing 00 (2018) 000–000
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In study 2, one of the larger automotive manufacturers in Sweden has contributed with data from three different production sites. Even though the company work with RCFA, the concept is fairly new, and among the maintenance engineering managers there is a consensus that the recorded data is somewhat misleading due to the staff’s lack of experience in performing RCFA. In the recorded data sets, a rather large proportion of downtime was labelled Root Cause not found, which, in the maintenance engineering’s opinion, probably was due to inexperience rather than complex root causes. Therefore, the recorded failure data has been re-assessed by the experienced maintenance engineers and then validated through interviews with the experienced maintenance staff. Based on these assessments from the three different production sites the distribution between different root causes is shown in Figure 1: 100% 90%
Un-known
80%
External influence
70%
Software failure
60%
Poor material/design
50%
End of lifetime
40%
Poor installation (PM)
30%
Poor PM
20%
Poor cleaning
10% 0%
Overload
A
B
C
Poor handling
Fig. 1. Root Cause Categories from three production sites in company A.
The various root cause categories are defined as follows: • • • • • • • • • •
Poor handling: Breakdowns, due to operator’s lack of knowledge/experience. Overload: Equipment operated beyond its designed limitations of use. Poor cleaning: Breakdowns due to lack of cleaning of, e.g. chips, cutting fluids, etc. Poor PM: Breakdowns due to lack of preventive maintenance. May be caused by poor planning/scheduling, delayed preventive maintenance, or not allowed to perform maintenance due to production priority. Poor installation: Poorly performed preventive maintenance. End of lifetime: Breakdowns that are not financially just to prevent (irregular faults without P-M interval. Poor material/design: Breakdowns due to components of poor material and/or poor equipment design. Software failure: Breakdowns due to software failures e.g. in PLCs. External influence: Breakdowns due to external factors, e.g. power cuts, or truck collisions. Root cause unknown: Breakdowns, which root cause haven’t been able to identify.
On an aggregated level, the share of Operations related failures consists of Poor handling; Overload; and Poor cleaning, with a total average of 57% of the failures. The share, relating to maintenance is Poor PM; Poor installation (PM); and End of lifetime, with a total average of 25%. Root causes, relating to the equipment design are Poor material/design; and Software failure, with an average of 14%.
Antti Salonen / Procedia Manufacturing 25 (2018) 17–22 Author name / Procedia Manufacturing 00 (2018) 000–000
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Another set of root cause data was provided by another manufacturer within automotive industry, see Fig 2. Also, in this case, the data had to be reassessed, especially since 48% of the breakdowns were categorized as “Root cause not found”. By discarding that category, 150 failures remained and were categorized according to Fig. 2.
2%
6%
4%
Design weaknesses Poor external maintenance
8%
Poor internal maintenance
15%
Poor AM
65%
External influence Poor handling
Fig. 2. Root Cause Categories at one production department in company B.
Most striking in this figure is the huge proportion of failures (65%), related to poor equipment design. Of the remaining categories, Poor external, and Poor internal maintenance are obviously related to the professional maintenance and having a share of 23% of the breakdowns. Even though Poor AM is maintenance related, it is the maintenance, performed by the operators, and thus, viewed as an operations related category, together with Poor handling, standing for 8% of the breakdowns. 5. Discussion According to [2], unplanned downtime makes up 14% of the total manufacturing costs in industry. This means that there is a large financial potential in increasing the dependability in the production systems. With the broad introduction of flow oriented, JIT production systems, the awareness of the importance of maintenance has increased in manufacturing industry. In academia, as well as in industry, the potential of maintenance development has been recognized, especially within technological solutions. This has, for example led to the introduction of condition based maintenance in discrete manufacturing industry. However, these technical solutions can’t solve all shortcomings in the production maintenance. By the introduction of flow-oriented production systems, the maintenance organizations have had difficulties in coping with the increased amount of disturbances and the decreased tolerance for production downtime. As a consequence, many manufacturing sites have introduced the concept of autonomous maintenance as a means to achieve higher dependability in their production systems. However, the combination of flow-oriented cell production, manned by a decreasing number of operators, and an increased number of tasks and responsibilities delegated to shop floor, puts a hard strain on the operators. Further, in many cases the introduction of autonomous maintenance has been too fast and relatively unstructured. This is exactly what Nakajima has pointed out as a reason for failing in autonomous maintenance [12]. The empirics indicate that, 20-45% of the breakdowns relate to human errors, either by operators, or by maintenance staff. If these errors are due to stress, lack of knowledge, or other factors, is not surveyed in the studies that this paper is based upon. Still, based on the cost figure indicated by [2], these human errors may stand for 6% of the manufacturing cost.
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Antti Salonen / Procedia Manufacturing 25 (2018) 17–22 Author name / Procedia Manufacturing 00 (2018) 000–000
The root cause distribution in Figure 2 shows that some production equipment is poorly designed from a dependability perspective, at least in its present operating context. This doesn’t necessarily mean that the procurement process is weak. Some of these design related breakdowns are likely to appear due to changes in the product mix, where some new products are outside the machine specifications. The surveyed companies, all have EEM processes established, but it is unclear to what extent experiences of earlier problems are fed back into the EEM process. When studying the new possibilities with the rapid technological advancements, e.g. CPS, IoT, and big data analytics, it is apparent that theses technologies will offer great possibilities in improvement of predicitve and prescriptive maintenance, thus increasing the dependability of production systems. Non the less, since production systems of today have 14-65% of their breakdowns, due to poor equipment design, the new technologies will only help identifying the design weaknessess, rather than preventing them. Another possibility with these technologies is to achieve better means of operator control of the full production process. In order to improve the dependability of discrete item production systems, it is important that the manufacturing industry starts viewing the dependability of production systems from a holistic point of view. A good starting point for this, is to map the factors that limit the dependability, e.g. through root cause analysis, not only of failures and down-time, but of all deviations from the expected systems performance. As stated in the methodology description, parts of this paper are based on an ongoing study. This study aims at exploring how manufacturing industry is working with RCFA and which categories of root causes are the dominant ones. Another topic that would be intersting to explore further is the possible root cause of letting fewer operators perform an increasing amount of maintenance tasks on an increasing amount of advanced manufacturing equipment. References [1] Salonen, A, Strategic Maintenance Development in Manufacturing Industry, Doctoral dissertation, Mälardalen University Press, 2011. [2] Salonen, A. & Tabikh, M, Downtime Costing—Attitudes in Swedish Manufacturing Industry, In Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015), Lecture Notes in Mechanical Engineering, (2016), 539-544 [3] Hubka, V. & Eder, W.E. “Theory of technical systems and engineering design synthesis”, in Amaresh Chkrabarti (ed), Engineering Design Synthesis, London: Springer-Verlag, (2001), 49-66 [4] Kelly, A., Eng, C., Mech, E. and Eastburn, K. “Terotechnology A modern approach to plant engineering”, Physical Science, Measurement and Instrumentation, Management and Education - Reviews, IEE Proceedings, Vol. 129, No. 2, (1982), pp. 131-136.. [5] Rollenhagen, C., Sambanden människa, teknik och organisation – en introduktion, (in Swedish) Studentlitteratur., Lund, Sweden 1997. [6] Shappell SA, & Wiegmann DA: The human factors analysis and classification system (HFACS). Washington, DC, U.S. Department of Transportation, Office of Aviation Medicine, 2000 [7] Macduffie, J. Human Resource Bundles and Manufacturing Performance: Organizational Logic and Flexible Production Systems in the World Auto Industry. Industrial and Labor Relations Review, 48(2), (1995), 197-221. [8] Youndt, M., Snell, S., Dean, J., & Lepak, D, Human Resource Management, Manufacturing Strategy, and Firm Performance. The Academy of Management Journal, 39(4), (1996), 836-866. [9] Hameed, A., Khan, F., and Ahmed, S, A risk based shutdown inspection and maintenance interval estimation considering human error, Process Safety and Environmental Protection, No. 100, (2016), 9-21. [10] Galar, D., Stenström, C., Parida, A., Kumar, R. and Berges, L. Human factor in maintenance performance measurement, 2011 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 6-9 December, (2011), 1569-1576. [11] Hackworth, C.; Holcomb, K.; Dennis, M.; Goldman, S.; Bates, C.; Schroeder, D.; Johnson, W. An International Survey of Maintenance Human Factors Programs (Report No. 07/25). Oklahoma City, Oklahoma: FAA CAMI, (2007). [12] Nakajima, S., TPM development program, Implementing Total Productive Maintenance, Productivity press, Portland, Oregon, 1989. [13] Nakajima, S., Introduction to TPM Total Productive Maintenance, Productivity press, Portland, Oregon, 1988. [14]Lendvay, M. Dependability Assurance of Industrial Production Processes, Proceedings: Science in Engineering, Economics and Education, Budapest, 2004. [15] Lee, J., B., Bagheri, H. A., Kao. "A cyber-physical systems architecture for industry 4.0-based manufacturing systems." Manufacturing Letters, 2015, 3: 18-23. [16] Crespo Márquez, A., Moreu de León, P., Gómez Fernández, J.F., Parra Márquez, C., López Campos, M., The Maintenance Management Framework: A practical view to maintenance, Journal of Quality in Maintenance Engineering, Vol. 15, Issue 10, (2009), 167-178. [17] Ransom, D.L., A practical guideline for a succesful root cause failure analysis, Proceedings of the thirty-sixth turbomachinery symposium, (2007), 149-155. [18] Hussin, H., Ahmed, U., and Muhammad, M., Critical Success Factors of Root Cause Failure Analysis, Indian Journal of Science and Technology, Vol. 9 (48), (2016), 1-10.
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Procedia Manufacturing 00 (2018) 000–000
Procedia Manufacturing 25 (2018) 17–22 Procedia Manufacturing 00 (2017) 000–000 www.elsevier.com/locate/procedia 8th Swedish Production Symposium, SPS 2018, 16-18 May 2018, Stockholm, Sweden
8th Swedish Production Symposium, SPS 2018, 16-18 May 2018, Stockholm, Sweden
The need for a holistic view on dependable production systems The need for a holistic view on dependable production systems Antti Salonen*
Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June Mälardalen University, Box 325, Eskilstuna, 631 05, Sweden 2017, Vigo (Pontevedra), Spain Antti Salonen* Mälardalen University, Box 325, Eskilstuna, 631 05, Sweden
Costing models for capacity optimization in Industry 4.0: Trade-off between used capacity and operational efficiency Abstract Abstract When discussing dependability of mechanized, itema,* production, the bfocus tends to be bon maintenance. By studying A. Santanaa, P.discrete Afonso , A. Zanin , R. Wernke experiences and breakdown data from eight automotive manufacturing sites in Sweden, a new view on the problem arises. It When discussing dependability discrete item production, the focus tends to be on indicates maintenance. By studying a seems that on average, 40% ofofthemechanized, breakdowns are related to poor maintenance practices. This that maintenance University of Minho, 4800-058 Guimarães, Portugal b automotiveRather, experiences and breakdown data cause from eight manufacturing sites inBrazil Sweden, a new view on thee.g. problem It management is not the dominant of breakdowns. companies should focus on human factors, skills, arises. routines, Unochapecó, 89809-000 Chapecó, SC, seems that on average, 40% of the breakdowns are related to poor maintenance practices. This indicates that maintenance and workload, among operators, as well as maintenance staff. Also, the Early Equipment Management process requires more management is not dominant cause of breakdowns. Rather, system. companies should focus on human factors, e.g. skills, routines, attention in order to the increase the dependability in the production and workload, among operators, as well as maintenance staff. Also, the Early Equipment Management process requires more attention in order to increase the dependability in the production system. Abstract © 2018 The Authors. Published by Elsevier B.V. © 2018 The Authors. Published by Elsevier B.V. Under the under concept of "Industry production will beProduction pushed to be increasingly interconnected, Peer-review responsibility of the the4.0", scientific committeeprocesses of the the8th 8thSwedish Swedish Production Symposium. Peer-review under responsibility of scientific committee of Symposium. © 2018 The Authors. Published by Elsevier B.V. necessarily, much more efficient. In this context, capacity optimization information based on a real time basis and, Peer-review under responsibility ofof theRoot scientific committee of the 8th Swedishalso Production Symposium. profitability and value. Keywords: Dependability; Human aim factors; Causemaximization, Failure Analysis goes beyond the traditional capacity contributing for organization’s
Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of Keywords: Dependability; Human factors; Root Cause Failure Analysis maximization. The study of capacity optimization and costing models is an important research topic that deserves 1. Background contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been 1. Background By transforming discrete production systems traditional towards flow-oriented developed and it was used to analyze idle capacity andfrom to design strategiesprocess towardsorientation, the maximization of organization’s production lines and applying Just-In-Time production, the production systems have become more vulnerable to value. trade-off capacity maximization vs operational efficiency is highlighted and it istowards shown flow-oriented that capacity By The transforming discrete production systems from traditional process orientation, disturbances, and in many ways resembling the continuous process industry [1]. A survey, presented by [2], optimization might hide operational inefficiency. production lines applying Just-In-Time thewith production systems have become more vulnerable to indicates 14%and of the manufacturing costsproduction, are associated unplanned downtime. This highlights the financial © 2017 Thethat Authors. Published by Elsevier B.V. disturbances, and in many ways resembling the continuous process industry [1]. A survey, presented by [2], importanceunder of dependable systems. However, the focus onEngineering research Society within International dependableConference production Peer-review responsibilityproduction of the scientific committee of the Manufacturing indicates that is 14% the manufacturing costs are associatedand with unplanned highlights the systems, still on of maintenance of production equipment, more recently,downtime. how novelThis technologies, e.g.financial IoT and 2017. importance of dependable production systems. However, the focus on research within dependable production systems, Cost stillModels; is on maintenance production equipment, and more recently, how novel technologies, e.g. IoT and Keywords: ABC; TDABC; of Capacity Management; Idle Capacity; Operational Efficiency * Corresponding author. Tel.: +46 16 153606 1.E-mail Introduction address: [email protected] * Corresponding author. Tel.: +46 16 153606 The cost of idle capacity is a fundamental for companies and their management of extreme importance E-mail address: [email protected] 2351-9789 © 2018 The Authors. Published by Elsevier information B.V. in modern under production systems. general, it is defined as unused capacity or production potential and can be measured Peer-review responsibility of theIn scientific committee of the 8th Swedish Production Symposium. 2351-9789 2018 The Authors. Published by Elsevier B.V.hours of manufacturing, etc. The management of the idle capacity in several©ways: tons of production, available Peer-review underTel.: responsibility the761; scientific committee the 8th Swedish Production Symposium. * Paulo Afonso. +351 253of 510 fax: +351 253 604of741
E-mail address: [email protected]
2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 8th Swedish Production Symposium. 10.1016/j.promfg.2018.06.052
18 2
Antti Salonen / Procedia Manufacturing 25 (2018) 17–22 Author name / Procedia Manufacturing 00 (2018) 000–000
big data, may contribute to the dependability of production equipment. What is less studied, is the reason to poor dependability in manufacturing industry. 1.1. Aim The aim of this paper is to discuss the importance of broadening the view of dependable production systems, from an equipment maintenance focused perspective, to a more holistic production system’s perspective, including some seemingly neglected aspects of dependability in production systems. 2. Literature review When discussing dependable production systems, it is not uncommon to focus on the technical aspects of the present system, i.e. the equipment availability. Further, it is commonly assumed that low dependability mainly is caused by poor maintenance. However, when applying a system’s perspective on the production equipment, as proposed by [3], it becomes obvious that this narrow view on maintenance issues is rather limited. 2.1. A holistic view of dependability In order to study the dependability of production systems, a holistic view is necessary. One example of such a holistic view is Terotechnology: “A combination of management, financial, engineering and other practices applied to physical assets in pursuit of economical life cycle costs. Its practice is concerned with the specification and design for reliability and maintainability of plant, machinery, equipment, buildings and structures, with their installation, commissioning, maintenance, modification and replacement, and with the feedback of information on design, performance and costs” [4, p.132] Another form of holistic views is presented by Rollenhagen [5] with the concept of MTO (Man, Technology, and Organization) in order to include human and organizational aspects into safety issues. 2.2. Human factors Human factors and/or errors are well studied in various contexts, especially in safety critical systems, such as aviation or nuclear industry. According to [6], 70-80% of all aviation accidents have had human errors as a contributing factor. Human factors in manufacturing industry are less studied, although some interesting papers were presented around 1995, e.g. [7, 8]. The importance of human factors and multi skilled operators is discussed by Macduffie [7]. He highlights that the operators of flexible manufacturing systems have to be able, not only to operate the equipment, but also to perform, e.g. quality inspections, maintenance and statistical process control. Research on human factors in maintenance have been found, e.g. [9, 10, 11], but this research is mainly focusing on the performance of maintenance, rather than human errors in operations, as root causes of equipment breakdowns. Still, human errors in maintenance performance have become increasingly important with the introduction of autonomous maintenance. The idea behind the concept is that the operators of production equipment should start taking more responsibility for the maintenance of their equipment and thereby achieve a larger sense of ownership and involvement. The essential parts in autonomous maintenance are for the operators to inspect and maintain critical parts of their equipment. In order to do so, the operators need not only training but time and motivation. Nakajima [12] lists three common reasons for operators failing to inspect their equipment properly, namely when operators lack motivation, time, and/or skills to inspect. Also, it is important to highlight that the operators still need to be encouraged to take interest and responsibility in mending their equipment. Otherwise, as Nakajima [13] points out, the equipment will still inevitably deteriorate and cause breakdowns and/or quality problems. 2.3. Design factors When studying the technical aspects of the production system, it is vital to consider how to design and/or purchase dependable production equipment already in the design phase of the production system. The term Early
Antti Salonen / Procedia Manufacturing 25 (2018) 17–22 Author name / Procedia Manufacturing 00 (2018) 000–000
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Equipment Management, EEM, is mainly associated with Total Productive Maintenance, TPM, which was rather well studied during the 1990s. However, more recently, there seems to be few studies presented on how to design and/or purchase dependable production equipment. One framework for dependability assurance of production processes is presented by Lendvay [14]. It is essentially a combination of traditional reliability engineering practices and principles of EEM, as presented in TPM. With the recent technological advancements in information technology, terms as Internet of Things, IoT, and Cyber Physical Systems, CPS, have emerged, with promises of increased dependability in new production equipment. For example [15], present a CPS architecture for Industry 4.0 based manufacturing systems. This architecture is based on advanced sensors, big data analytics, decision support systems etc. 2.4. Root causes of poor dependability In order to improve the dependability of manufacturing equipment, it is vital to analyze the causes of poor dependability. Authors like [16], and [17], have highlighted the importance of Root Cause Failure Analysis, RCFA. These practices have commonly been used in safety critical branches and where the financial loss due to deficiencies is substantial. In traditional manufacturing industry however, RCFA is less used, even though it is known, e.g. through Lean-related practices such as 5 Why and Ishikawa diagrams. The presence of RCFA, doesn’t necessarily mean that the true root causes are identified. In [18], the following causes for failing in RCFA were identified: Lack of resources; Lack of competence; Improper selection of tool; Poor understanding of the RCFA process; Lack of system’s perspective; and Lack of relevant data. 2.5. Summary This literature review shows that the academia has recognized the importance of applying a system’s perspective on production systems, including the dependability, e.g. [4]. Further, both human factors and design factors have been identified as causes of poor dependability. Recent research proposes the introduction of new technologies, e.g. IoT and CPS, as a means to improve the dependability. Except for these technological leaps, there are established methods for the identification of the root causes of failures in the production equipment. 3. Methodology Even though this paper is rather argumentative, it still includes empiric material to support the discussion. The empirics were collected through two different studies. The data was collected through interviews, focus groups, and archival records from a total of eight different manufacturing sites in Swedish automotive manufacturing. The sites produce components for commercial as well as non-commercial vehicles. The production equipment includes machining centers, foundries, hardening processes and paint shops. The first study was based on eight interviews and five focus groups with maintenance engineers at six different production sites in Swedish automotive manufacturing. The interviews and focus groups focused on maintenance planning and scheduling, but also included the question: “How much breakdowns could be avoided if all your preventive maintenance was optimal in both content and interval?” The respondents’ answers were based on their experience-based conception, rather than hard data. The second, study is still ongoing and focusing on Root Cause Failure Analysis, RCFA, in manufacturing industry. This paper contains data collected through interviews with maintenance engineers, as well as archival records from two manufacturing companies in the automotive industry. 4. Empirical data All respondents but two in study 1, assessed that an optimal preventive maintenance performance could reduce breakdowns by 40-60% (the two exceptions assessed 10%, and 80%) with an average of 40%. All respondents stated that the remaining part was due to either human errors or poor equipment design.
Antti Salonen / Procedia Manufacturing 25 (2018) 17–22 Author name / Procedia Manufacturing 00 (2018) 000–000
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In study 2, one of the larger automotive manufacturers in Sweden has contributed with data from three different production sites. Even though the company work with RCFA, the concept is fairly new, and among the maintenance engineering managers there is a consensus that the recorded data is somewhat misleading due to the staff’s lack of experience in performing RCFA. In the recorded data sets, a rather large proportion of downtime was labelled Root Cause not found, which, in the maintenance engineering’s opinion, probably was due to inexperience rather than complex root causes. Therefore, the recorded failure data has been re-assessed by the experienced maintenance engineers and then validated through interviews with the experienced maintenance staff. Based on these assessments from the three different production sites the distribution between different root causes is shown in Figure 1: 100% 90%
Un-known
80%
External influence
70%
Software failure
60%
Poor material/design
50%
End of lifetime
40%
Poor installation (PM)
30%
Poor PM
20%
Poor cleaning
10% 0%
Overload
A
B
C
Poor handling
Fig. 1. Root Cause Categories from three production sites in company A.
The various root cause categories are defined as follows: • • • • • • • • • •
Poor handling: Breakdowns, due to operator’s lack of knowledge/experience. Overload: Equipment operated beyond its designed limitations of use. Poor cleaning: Breakdowns due to lack of cleaning of, e.g. chips, cutting fluids, etc. Poor PM: Breakdowns due to lack of preventive maintenance. May be caused by poor planning/scheduling, delayed preventive maintenance, or not allowed to perform maintenance due to production priority. Poor installation: Poorly performed preventive maintenance. End of lifetime: Breakdowns that are not financially just to prevent (irregular faults without P-M interval. Poor material/design: Breakdowns due to components of poor material and/or poor equipment design. Software failure: Breakdowns due to software failures e.g. in PLCs. External influence: Breakdowns due to external factors, e.g. power cuts, or truck collisions. Root cause unknown: Breakdowns, which root cause haven’t been able to identify.
On an aggregated level, the share of Operations related failures consists of Poor handling; Overload; and Poor cleaning, with a total average of 57% of the failures. The share, relating to maintenance is Poor PM; Poor installation (PM); and End of lifetime, with a total average of 25%. Root causes, relating to the equipment design are Poor material/design; and Software failure, with an average of 14%.
Antti Salonen / Procedia Manufacturing 25 (2018) 17–22 Author name / Procedia Manufacturing 00 (2018) 000–000
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Another set of root cause data was provided by another manufacturer within automotive industry, see Fig 2. Also, in this case, the data had to be reassessed, especially since 48% of the breakdowns were categorized as “Root cause not found”. By discarding that category, 150 failures remained and were categorized according to Fig. 2.
2%
6%
4%
Design weaknesses Poor external maintenance
8%
Poor internal maintenance
15%
Poor AM
65%
External influence Poor handling
Fig. 2. Root Cause Categories at one production department in company B.
Most striking in this figure is the huge proportion of failures (65%), related to poor equipment design. Of the remaining categories, Poor external, and Poor internal maintenance are obviously related to the professional maintenance and having a share of 23% of the breakdowns. Even though Poor AM is maintenance related, it is the maintenance, performed by the operators, and thus, viewed as an operations related category, together with Poor handling, standing for 8% of the breakdowns. 5. Discussion According to [2], unplanned downtime makes up 14% of the total manufacturing costs in industry. This means that there is a large financial potential in increasing the dependability in the production systems. With the broad introduction of flow oriented, JIT production systems, the awareness of the importance of maintenance has increased in manufacturing industry. In academia, as well as in industry, the potential of maintenance development has been recognized, especially within technological solutions. This has, for example led to the introduction of condition based maintenance in discrete manufacturing industry. However, these technical solutions can’t solve all shortcomings in the production maintenance. By the introduction of flow-oriented production systems, the maintenance organizations have had difficulties in coping with the increased amount of disturbances and the decreased tolerance for production downtime. As a consequence, many manufacturing sites have introduced the concept of autonomous maintenance as a means to achieve higher dependability in their production systems. However, the combination of flow-oriented cell production, manned by a decreasing number of operators, and an increased number of tasks and responsibilities delegated to shop floor, puts a hard strain on the operators. Further, in many cases the introduction of autonomous maintenance has been too fast and relatively unstructured. This is exactly what Nakajima has pointed out as a reason for failing in autonomous maintenance [12]. The empirics indicate that, 20-45% of the breakdowns relate to human errors, either by operators, or by maintenance staff. If these errors are due to stress, lack of knowledge, or other factors, is not surveyed in the studies that this paper is based upon. Still, based on the cost figure indicated by [2], these human errors may stand for 6% of the manufacturing cost.
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Antti Salonen / Procedia Manufacturing 25 (2018) 17–22 Author name / Procedia Manufacturing 00 (2018) 000–000
The root cause distribution in Figure 2 shows that some production equipment is poorly designed from a dependability perspective, at least in its present operating context. This doesn’t necessarily mean that the procurement process is weak. Some of these design related breakdowns are likely to appear due to changes in the product mix, where some new products are outside the machine specifications. The surveyed companies, all have EEM processes established, but it is unclear to what extent experiences of earlier problems are fed back into the EEM process. When studying the new possibilities with the rapid technological advancements, e.g. CPS, IoT, and big data analytics, it is apparent that theses technologies will offer great possibilities in improvement of predicitve and prescriptive maintenance, thus increasing the dependability of production systems. Non the less, since production systems of today have 14-65% of their breakdowns, due to poor equipment design, the new technologies will only help identifying the design weaknessess, rather than preventing them. Another possibility with these technologies is to achieve better means of operator control of the full production process. In order to improve the dependability of discrete item production systems, it is important that the manufacturing industry starts viewing the dependability of production systems from a holistic point of view. A good starting point for this, is to map the factors that limit the dependability, e.g. through root cause analysis, not only of failures and down-time, but of all deviations from the expected systems performance. As stated in the methodology description, parts of this paper are based on an ongoing study. This study aims at exploring how manufacturing industry is working with RCFA and which categories of root causes are the dominant ones. Another topic that would be intersting to explore further is the possible root cause of letting fewer operators perform an increasing amount of maintenance tasks on an increasing amount of advanced manufacturing equipment. References [1] Salonen, A, Strategic Maintenance Development in Manufacturing Industry, Doctoral dissertation, Mälardalen University Press, 2011. [2] Salonen, A. & Tabikh, M, Downtime Costing—Attitudes in Swedish Manufacturing Industry, In Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015), Lecture Notes in Mechanical Engineering, (2016), 539-544 [3] Hubka, V. & Eder, W.E. “Theory of technical systems and engineering design synthesis”, in Amaresh Chkrabarti (ed), Engineering Design Synthesis, London: Springer-Verlag, (2001), 49-66 [4] Kelly, A., Eng, C., Mech, E. and Eastburn, K. “Terotechnology A modern approach to plant engineering”, Physical Science, Measurement and Instrumentation, Management and Education - Reviews, IEE Proceedings, Vol. 129, No. 2, (1982), pp. 131-136.. [5] Rollenhagen, C., Sambanden människa, teknik och organisation – en introduktion, (in Swedish) Studentlitteratur., Lund, Sweden 1997. [6] Shappell SA, & Wiegmann DA: The human factors analysis and classification system (HFACS). Washington, DC, U.S. Department of Transportation, Office of Aviation Medicine, 2000 [7] Macduffie, J. Human Resource Bundles and Manufacturing Performance: Organizational Logic and Flexible Production Systems in the World Auto Industry. Industrial and Labor Relations Review, 48(2), (1995), 197-221. [8] Youndt, M., Snell, S., Dean, J., & Lepak, D, Human Resource Management, Manufacturing Strategy, and Firm Performance. The Academy of Management Journal, 39(4), (1996), 836-866. [9] Hameed, A., Khan, F., and Ahmed, S, A risk based shutdown inspection and maintenance interval estimation considering human error, Process Safety and Environmental Protection, No. 100, (2016), 9-21. [10] Galar, D., Stenström, C., Parida, A., Kumar, R. and Berges, L. Human factor in maintenance performance measurement, 2011 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 6-9 December, (2011), 1569-1576. [11] Hackworth, C.; Holcomb, K.; Dennis, M.; Goldman, S.; Bates, C.; Schroeder, D.; Johnson, W. An International Survey of Maintenance Human Factors Programs (Report No. 07/25). Oklahoma City, Oklahoma: FAA CAMI, (2007). [12] Nakajima, S., TPM development program, Implementing Total Productive Maintenance, Productivity press, Portland, Oregon, 1989. [13] Nakajima, S., Introduction to TPM Total Productive Maintenance, Productivity press, Portland, Oregon, 1988. [14]Lendvay, M. Dependability Assurance of Industrial Production Processes, Proceedings: Science in Engineering, Economics and Education, Budapest, 2004. [15] Lee, J., B., Bagheri, H. A., Kao. "A cyber-physical systems architecture for industry 4.0-based manufacturing systems." Manufacturing Letters, 2015, 3: 18-23. [16] Crespo Márquez, A., Moreu de León, P., Gómez Fernández, J.F., Parra Márquez, C., López Campos, M., The Maintenance Management Framework: A practical view to maintenance, Journal of Quality in Maintenance Engineering, Vol. 15, Issue 10, (2009), 167-178. [17] Ransom, D.L., A practical guideline for a succesful root cause failure analysis, Proceedings of the thirty-sixth turbomachinery symposium, (2007), 149-155. [18] Hussin, H., Ahmed, U., and Muhammad, M., Critical Success Factors of Root Cause Failure Analysis, Indian Journal of Science and Technology, Vol. 9 (48), (2016), 1-10.