Abstract
This paper proposes a graphical method to easy decision-making in industrial plants operations. The proposed tool “Graphical Analysis for Operation Management Method” (GAOM) allows to visualize, and to analyze, production related parameters, integrating assets/systems maintenance aspects. This integration is based on the TPM model, using its quantitative management techniques for optimal decision-making in day-to-day operations. On the one hand, GAOM monitors possible production target deviations, and on the other, the tool illustrates different aspects to gain control on the production process, such as availability (A), repair time, cumulative production or overall equipment effectiveness. Through appropriate information filtering, individual analysis by class of intervention (corrective maintenance, preventive maintenance or operational intervention) and production level can be developed. GAOM integrates maintenance information (number of intervention, type of intervention, required/not required stoppage) with production information (cumulative production, cumulative defective products, and cumulative production target) during a certain timeframe (cumulative calendar time, duration of intervention). Then the tool computes basic performance indicators supporting operational decision-making. GAOM provides interesting graphical outputs using scatter diagrams integrating indicators on the same graph. GAOM is inspired in the GAMM (Graphical Analysis for Maintenance Management) method, published by the authors (LB, AC and PV) in 2012.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahuja IPS, Khamba JS (2008) Total productive maintenance: literature review and directions. Int J Qual Reliab Manag 25(7):709–756
Zhu Q, Lujia F, Mayyas A, Omar MA, Al-Hammadi Y, Al Saleh S (2015) Production energy optimization using low dynamic programming, a decision support tool for sustainable manufacturing. J Clean Prod 105(2015):178–183. doi:10.1016/j.jclepro.2014.02.066
May G, Barletta I, Stahl B, Taisch M (2015) Energy management in production: a novel method to develop key performance indicators for improving energy efficiency. Appl Energy 149(2015):46–61. doi:10.1016/j.apenergy.2015.03.065
van Veen-Dirks Paula (2005) Management control and the production environment: a review. Int J Prod Econ 93–94(2005):263–272. doi:10.1016/j.ijpe.2004.06.026
Fleischer J, Weismann U, Niggeschmidt S (2006) Calculation and optimisation model for costs and effects of availability relevant service elements. In: Proceedings of LCE, pp 675–680
Birolini A (2007) Reliability engineering, 5th edn. Springer, Berlin. ISBN: 978-3-540-49388-4
Barberá L, Crespo A, Viveros P, Stegmaier R (2012) Advanced model for maintenance management in a continuous improvement cycle: integration into the business strategy. Int J Syst Assur Eng Manag 3(January–March (1):47–63. doi:10.1007/s13198-0120092-y
Crespo Márquez A, Gupta J (2006) Contemporary maintenance management. Process, framework and supporting pillars. Omega. Int J Manag Sci 34(3):325–338. doi:10.1016/j.omega.2004.11.003
Chai J, Liu JNK, Ngai EWT (2013) Application of decision-making techniques in supplier selection: a systematic review of literature. Expert Syst Appl 40(2013):3872–3885. doi:10.1016/j.eswa.2012.12.040
Watson H, Goodhue D, Wixom B (2002) The benefits of data warehousing: why some organizations realize exceptional payoffs. Inf Manag 39(6):491–502
Park Y (2006) An empirical investigation of the effects of data warehousing on decision performance. Inf Manag 43:51–61
March S, Hevner A (2007) Integrated decision support systems: a data warehousing perspective. Decis Support Syst 43:1031–1043
Pomponio L, Le Goc M (2014) Reducing the gap be-tween experts’ knowledge and data: the TOM4D methodology. Data Knowl Eng 94:1–37
Watson H, Goodhue D, Wixom B (2002) The benefits of data warehousing why some organizations realize exceptional payoffs. Inf Manag 39:491–502
Neely A, Gregory M, Platts K (1995) Performance measurement system design: a literature review and research agenda. Int J Oper Prod Manag 15(4):80–116
Kaplan RS, Norton DP (1992) The balanced scorecard: measures that drive performance. Harvard Bus Rev, pp 71–79
Ireland F, Dale BG (2001) A study of total productive maintenance implementation. J Qual Maint Eng 7(3):183–192
Biasotto E, Dias A, Ogliari A (2012) Balanced score-card for TPM maintenance management. Santa Catarina Federal University, vol 8(2)
Barberá L, Crespo A, Viveros P, Arata A (2013) The graphical analysis for maintenance management method: a quantitative graphical analysis to support maintenance management decision making. Qual Reliab Eng Int 29(1):77–87
Crespo A (2007) The maintenance management frame-work. Springer, London
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendices
Appendices
Appendix 1
Standard format for data collection.
Regarding authors’ experiences, it is proposed a standard format to collect historical data, specifically: Intervention Data, Production Data and Time Data (Table 8).
Appendix 2
Integrated databases.
GAOM requires the development of two integrated databases, which relate the information in Tables 9 and 10. These databases are necessary for graphics construction.
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Viveros Gunckel, P., Crespo Márquez, A., Barberá Martínez, L., González, J.P. (2018). A Graphical Method to Support Operation Performance Assessment. In: Crespo Márquez, A., González-Prida Díaz, V., Gómez Fernández, J. (eds) Advanced Maintenance Modelling for Asset Management. Springer, Cham. https://doi.org/10.1007/978-3-319-58045-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-58045-6_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58044-9
Online ISBN: 978-3-319-58045-6
eBook Packages: EngineeringEngineering (R0)