Abstract
For companies that distribute services such as telecommunications, water, energy, gas, etc., quality perceived by the customers has a strong impact on the fulfillment of financial goals, positively increasing the demand and negatively increasing the risk of customer churn (loss of customers). Failures by these companies may cause customer affection in a massive way, augmenting the intention to leave the company. Therefore, maintenance performance and specifically service reliability has a strong influence on financial goals. This paper proposes a methodology to evaluate the contribution of the maintenance department in economic terms based on service unreliability by network failures. The developed methodology aims to provide an analysis of failures to facilitate decision-making about maintenance (preventive/predictive and corrective) costs versus negative impacts in end customer invoicing based on the probability of losing customers. Survival analysis of recurrent failures with the General Renewal Process distribution is used for this novel purpose with the intention to be applied as a standard procedure to calculate the expected maintenance financial impact, for a given period of time. Also, geographical areas of coverage are distinguished, enabling the comparison of different technical or management alternatives. Two case studies in a telecommunications services company are presented in order to illustrate the applicability of the methodology.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Newbery DMG (2002) Privatization, restructuring, and regulation of network utilities. MIT Press, Cambridge
Van Vliet B, Chappells H, Shove E (2005) Infrastructures of consumption: environmental innovation in the utility industries. Earthscan Publications Limited, London. ISBN 1-85383-996-5
Johnson RL, Tsiros M, Lancioni RA (1995) Measuring service quality: a system approach. J Serv Mark 9(5):6–19
Peters T (1987) Thriving on chaos: handbook for a management revolution. Alfred A. Knopf, New York
Bitner M, Booms B, Tetreault M (1990) The service encounter: diagnosing favourable and unfavourable incidents. J Mark 54:71–86
Zeithaml VA, Bitner MJ (2003) Services marketing: integrating customer focus across the firm. McGraw-Hill Higher Education, Boston
Parasuraman A, Zeithaml VA, Berry LL (1985) A conceptual model of service quality and its implications for future research. J Mark 49:41–50
Gellings GW (2009) The retail electricity service business in a competitive environment. In: Andreas Bausch, Burkhard Schwenker (eds) Handbook utility management. Springer, Berlin, pp 545–558
De Matos CA, Henrique JL, Vargas Rossi CA (2007) Service recovery paradox: a meta-analysis. J Serv Res 10(1):60–77
Maxham JG III (2001) Service recovery’s influence on consumer satisfaction, positive word-of-mouth, and purchase intentions. J Bus Res 54:11–24
Tschohl J (1996) Achieving excellence through customer service. Best Sellers Publishing, Minnesota
Gómez JF, Crespo A (2009) Framework for implementation of maintenance management in distribution network service providers. Reliab Eng Syst Saf 94(10):1639–1649
Küssel R, Liestmann V, Spiess M, Stich V (2000) “Teleservice” a customer oriented and efficient service. J Mater Process Technol 107:363–371
Murthy DNP, Atrens A, Eccleston JA (2002) Strategic maintenance management. J Qual Maint Eng 8(4):287–305
Zhu G, Gelders L, Pintelon L (2002) Object/objective-oriented maintenance management. J Qual Maint Eng 8(4):306–318
Marais KB (2013) Value maximizing maintenance policies under general repair. Reliab Eng Syst Saf 119:76–87
Dixon JR (1966) Design engineering: inventiveness, analysis, and decision making. McGraw-Hill, Inc., New York
Woodhouse J (1993) Managing industrial risk. Chapman Hill, London
Wilson RL (1986) Operations and support cost model for new product concept development. In: Proceedings of the 8th annual conference on components and industrial engineering, pp 128–131
Remy E, Corset F, Despréaux S, Doyen L, Gaudoin O (2013) An example of integrated approach to technical and economic optimization of maintenance. Reliab Eng Syst Saf 116:8–19
Saleh JH, Marais KB (2006) Reliability: how much is it worth? Beyond its estimation or prediction, the (net) present value of reliability. Reliab Eng Syst Saf 91:665–673
Janawadea V, Bertranda D, Léo P-Y, Philippea J (2015) Assessing ‘meta-services’: customer’s perceived value and behaviour. Serv Ind J 35(5):275–295
Günthera C-C, Tvetea I, Aasa K, Sandnesb G, Borganc Ø (2014) Modelling and predicting customer churn from an insurance company. Scand Actuar J 1:58–71
Wang K-Y, Hsu L-C, Chih W-H (2014) Retaining customers after service failure recoveries: a contingency model. Manag Serv Qual 24(49):318–338
Knox G, van Oest R (2014) Customer complaints and recovery effectiveness: a customer base approach. J Mark 78(5):42–57
Bolton R (1998) A dynamic model of the duration of the customer’s relationship with a continuous service provider: the role of satisfaction. Mark Sci 17(1):45–65
Marais KB, Saleh JH (2009) Beyond its cost, the value of maintenance: an analytical framework for capturing its net present value. Reliab Eng Syst Saf 94:644–657
Martorell S, Sanchez A, Serradell V (1999) Age-dependent reliability model considering effects of maintenance and working conditions. Reliab Eng Syst Saf 64(1):19–31
Wua F, Niknamb SA, Kobzac JE (2015) A cost effective degradation-based maintenance strategy under imperfect repair. Reliab Eng Syst Saf 144:234–243
Jiang R (2010) A simple approximation for the renewal function with an increasing failure rate. Reliab Eng Syst Saf 95(9):963–969
Crespo A, Sánchez A (2002) Models for maintenance optimization: a study for repairable systems and finite time periods. Reliab Eng Syst Saf 75(3):367–377
Crosby PB (1979) Quality is free. Mentor Books, New York
Dale BG, Plunkett JJ (1991) Quality costing. Gower Publishing, Hampshire
ASQC (1970) Quality costs: what and how. American Society for Quality Control, New York
BS 4778 (1987) Quality vocabulary. British Standards Institute, London
Avizienis A, Laprie JC, Randell B (2001) Fundamental concepts of dependability. LAAS-CNRS; Research report no. 1145. April
Gómez Fernández JF, Crespo Márquez A (2012) Maintenance management in network utilities: framework and practical implementation. Springer, London
Goodman J (1986) Technical assistance research program (TARP). US Office of Consumer Affairs Study on Complaint Handling in America, USA
Keaveney S (1995) Customer switching behavior in service industries: an exploratory study. J Mark 59:71–82
Gupta S, Lehman DR (2005) Managing customers as investments: the strategic value of customers in the long run. Wharton School Publishing, New Jersey
Wacker G, Tollefson G (1994) Electric power system customer interruption cost assessment. Reliab Eng Syst Saf 46(1):75–81
Yanamandram V, White L (2006) Switching barriers in business-to-business services: a qualitative study. Int J Serv Ind Manag 17(2):158–192
UNE 200001–3-11 (2003) Gestión de la confiabilidad. Parte 3–11: Guía de aplicación. Mantenimiento centrado en la fiabilidad. UNE
Dekker R (1996) Applications of maintenance optimization models: a review and analysis. Reliab Eng Syst Saf 51:229–240
Wang W (2008) Condition based maintenance modeling. In: Kobbacy KAH, Murthy DNP (eds) Complex Systems maintenance handbook. Springer, London
Greves D, Schreiber B (1993) Engineering costing techniques in ESA. Available online: http://esapub.esriu.esa.it.pointtobullet/greves1.html
Harter HL, Moore AH (1965) Point and interval estimators based on order statistics, for the scale parameter of a Weibull population with known shape parameter. Technometrics 7(3):405–422
Parmar MKB, Machin D (1996) Survival analysis: a practical approach. Wiley, Chichester
Cox DR, Oakes D (1984) Analysis of survival data. London Chapman and Hall, London
Klein J, Moeschberguer M (1997) Survival analysis techniques for censored and truncated data. Springer, New York
Andersen PK, Borgan O, Gill R, Keilding N (1993) Statistical models based on counting process. Springer, New York
Blischke WR, Murthy DNP (2000) Reliability modelling, prediction and optimization. Wiley, New York
Hosmer DW, Lemeshow S (1999) Regression modeling of time to event data. Wiley, New York
Hougaard P (2000) Analysis of multivariate survival data. Springer, New York
Lee ET (1992) Statistical methods for survival data analysis. Wiley, New York
Harrell FE (2001) Regression modeling estrategies. Springer, New York
Yañez M, Joglar F, Mohammad M (2002) Generalized renewal process for analysis of reparable systems with limited failure experience. Reliab Eng Syst Saf 77:167–180
Kijima M, Sumita N (1986) A useful generalization of renewal theory: counting process governed by non-negative Markovian increments. J Appl Probab 23:71–88
Mettas A, Zhao W (2005) Modeling and analysis of repairable systems with general repair. In: Proceedings of the reliability and maintainability symposium, pp 176–182. ISBN: 0-7803-8824-0
Cohen AC (1965) Maximum likelihood estimation in the Weibull distribution based on complete and on censored samples. Technometrics 7(4):579–588
Acknowledgements
The authors wish to thank the Institution “Fundación Iberdrola” for providing a research grant during the years 2011 and 2012, making the development of projects possible related to the implementation of advanced maintenance strategies, technologies and services.
This research is funded by the Spanish Ministry of Science and Innovation, Project EMAINSYS (DPI2011-22806) “Sistemas Inteligentes de Mantenimiento. Procesos emergentes de E-maintenance para la Sostenibilidad de los Sistemas de Producción, besides FEDER funds.”
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Gómez Fernández, J.F., Crespo Márquez, A., López-Campos, M.A. (2018). Customer-oriented Risk Assessment in Network Utilities. 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_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-58045-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58044-9
Online ISBN: 978-3-319-58045-6
eBook Packages: EngineeringEngineering (R0)