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Jorge Andrés Alvarado Valencia http://orcid.org/0000-0001-8331-2031

Daniel Silva

Abstract

Introduction: We developed a model for a make-to-order supply chain to evaluate the effects of worker unpunctuality, tolerance to delay and word-of-mouth according  to customer waiting time (dis)satisfaction in four customer lifetime value measures (CLTV): switching customers, the number of sales per customer, the average customer loyalty and the potential market reached.  Methods: We developed a hybrid (agent-based and discrete-event) simulation in a 33 * 4 experimental design. Results: All of the variables were significant in the four CLTV measures, except for tolerance to delay. The positive word-of-mouth effect was greater than the negative word-of-mouth effect. There were significant interactions between positive and negative word-of-mouth.  Conclusions:  This type of model becomes a decision support tool for businesses to evaluate their mid-to-long term performance taking into account their customers’ long-term behaviors and the relationships between potential customers in repetitive and competitive environments.

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Keywords

Hybrid simulation, Customer lifetime value, Word of mouth, Tolerance to delay, Worker unpunctuality

References
[1] B. M. Beamon, "Measuring supply chain performance," International Journal of Operations and Production Management, vol. 19, no. 3, pp. 275-292, 1999.
[2] Y. Lu, A. Musalem, M. Olivares, and A. Schilkrut, "Measuring the effect of queues on customer purchases," Management Science, vol. 59, no. 8, pp. 1743-1763, 2013.
[3] A. Pruyn and A. Smidts, "Effects of waiting on the satisfaction with the service: Beyond objective time measures1Both authors contributed equally to this article.1," International Journal of Research in Marketing, vol. 15, pp. 321-334, 1998.
[4] O. I. Tukel and A. Dixit, "Application of customer lifetime value model in make‐to‐order manufacturing," Journal of Business & Industrial Marketing, vol. 28, no. 6, pp. 468-474, 2013/07/29 2013.
[5] D. Jain and S. S. Singh, "Customer lifetime value research in marketing: A review and future directions," Journal of Interactive Marketing, vol. 16, no. 2, pp. 34-46, 2002.
[6] B. A. Nicholds, J. P. T. Mo, and L. O’Rielly, "An integrated performance driven manufacturing management strategy based on overall system effectiveness," Computers in Industry, vol. 97, pp. 146-156, 2018/05/01/ 2018.
[7] M. Yu, J. Tang, F. Kong, and C. Chang, "Fluid models for call centers with delay announcement and retrials," Knowledge-Based Systems, Article vol. 149, pp. 99-109, 2018.
[8] B. Yan and L. Liu, "Simulation of multi-echelon supply chain inventory transshipment models at different levels," Simulation, Article in Press 2017.
[9] P. Afèche, M. Araghi, and O. Baron, "Customer Acquisition, Retention, and Service Access Quality: Optimal Advertising, Capacity Level, and Capacity Allocation," Manufacturing & Service Operations Management, vol. 19, no. 4, pp. 674-691, 2017/10/01 2017.
[10] R. Niraj, M. Gupta, and C. Narasimhan, "Customer Profitability in a Supply Chain," Journal of Marketing, vol. 65, no. 3, pp. 1-16, 2001/07/01 2001.
[11] A. Negahban and L. Yilmaz, "Agent-based simulation applications in marketing research: an integrated review," (in English), Journal of Simulation, vol. 8, no. 2, pp. 129-142, May 2014 2014.
[12] A. V. Ackere and E. R. Larsen, "Long-term and short-term customer reaction : a two-stage queueing approach," System Dynamics Review, vol. 22, pp. 349-369, 2006.
[13] T. Hara and T. Arai, "Simulation of product lead time in design customization service for better customer satisfaction," CIRP Annals, vol. 60, no. 1, pp. 179-182, 2011/01/01/ 2011.
[14] Q. Meng, Z. Li, H. Liu, and J. Chen, "Agent-based simulation of competitive performance for supply chains based on combined contracts," International Journal of Production Economics, vol. 193, pp. 663-676, 2017/11/01/ 2017.
[15] J. A. Alvarado-Valencia, G. C. Tueti Silva, and J. R. Montoya-Torres, "Modeling and simulation of customer dissatisfaction in waiting lines and its effects," Simulation, Article vol. 93, no. 2, pp. 91-101, 2017.
[16] L. W. Porter and R. M. Steers, "Organizational, work, and personal factors in employee turnover and absenteeism," Psychological Bulletin, vol. 80, no. 2, pp. 151-176, 1973.
[17] G. R. Ferris, S. A. Youngblood, and V. L. Yates, "Personality, training performance, and withdrawal: A test of the person-group fit hypothesis for organizational newcomers," Journal of Vocational Behavior, vol. 27, no. 3, pp. 377-388, 12// 1985.
[18] R. D. Iverson and S. J. Deery, "Understanding the "personological" basis of employee withdrawal: the influence of affective disposition on employee tardiness, early departure, and absenteeism," (in eng), J Appl Psychol, vol. 86, no. 5, pp. 856-66, Oct 2001.
[19] M. Dishon-Berkovits and R. Berkovits, "Work-Related Tardiness: Lateness Incident Distribution and Long-Range Correlations," Fractals, vol. 05, no. 02, pp. 321-324, 1997/06/01 1997.
[20] R. V. Levine, L. J. West, and H. T. Reis, "Perceptions of time and punctuality in the United States and Brazil," (in eng), J Pers Soc Psychol, vol. 38, no. 4, pp. 541-50, Apr 1980.
[21] L. T. White, R. Valk, and A. Dialmy, "What Is the Meaning of “on Time”? The Sociocultural Nature of Punctuality," Journal of Cross-Cultural Psychology, vol. 42, no. 3, pp. 482-493, 2011.
[22] N. Hollender, C. Hofmann, M. Deneke, and B. Schmitz, "Integrating cognitive load theory and concepts of human-computer interaction," Computers in human behavior, vol. 26, no. 6, pp. 1278-1288, Nov 2010.
[23] S. H. Chan, "The roles of user motivation to perform a task and decision support system (DSS) effectiveness and efficiency in DSS use," Computers in human behavior, vol. 25, no. 1, pp. 217-228, Jan 2009.
[24] S. Y. Rieh and D. R. Danielson, "Credibility: A multidisciplinary framework," Annual Review of Information Science and Technology, vol. 41, pp. 307-364, 2007.
[25] J. Singh, "A typology on consume disatisfaction response styles," Journal of Retailing, vol. 66, no. 1, pp. 57-43, 1990.
[26] S. M. Keaveney, "Customer Switching Behavior in Service Industries: An Exploratory Study," Journal of Marketing, vol. 59, no. 2, pp. 71-82, 1995.
[27] R. East, K. Hammond, and W. Lomax, "Measuring the impact of positive and negative word of mouth on brand purchase probability," International Journal of Research in Marketing, vol. 25, no. 3, pp. 215-224, 9// 2008.
[28] R. East, M. D. Uncles, J. Romaniuk, and W. Lomax, "Measuring the impact of positive and negative word of mouth: A reappraisal," Australasian Marketing Journal (AMJ), vol. 24, no. 1, pp. 54-58, 2016/02/01/ 2016.
[29] P. O. Siebers, U. Aickelin, H. Celia, and C. W. Clegg, "Simulating customer experience and word-of-mouth in retail - A Case Study," Simulation, vol. 86, no. 1, pp. 5-30, 2010.
[30] R. Axtell, "Why Agents? On the Varied Motivations for Agent Computing in the Social Sciences," The Brookings Institution, Center on Social and Economic Dynamics
mso-fareast-font-family:"Times New Roman";mso-ansi-language:EN-US;mso-fareast-language:
ES-CO;mso-bidi-language:AR-SA">, Washington, DC2000.
[31] G. Fullerton, "The service quality–loyalty relationship in retail services: does commitment matter?," Journal of Retailing and Consumer Services, vol. 12, no. 2, pp. 99-111, 2005/03/01/ 2005.
[32] J. Bloemer, K. de Ruyter, and P. Peeters, "Investigating drivers of bank loyalty: The complex relationship between image, service quality and satisfaction," International Journal of Bank Marketing, Article vol. 16, no. 7, pp. 276-286, 1998.
[33] T. F. Jaeger, "Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models," Journal of memory and language, vol. 59, no. 4, pp. 434-446, 2008.
[34] A. Cook, G. Tanner, and A. Lawes, "The Hidden Cost of Airline Unpunctuality," Journal of Transport Economics and Policy, vol. 46, no. 2, pp. 157-173, 2012.
How to Cite
Alvarado Valencia, J., & Silva, D. (2018). Modeling and simulation of customer dissatisfaction, worker unpunctuality and tolerance to delay in make-to-order supply chains measured through customer lifetime value performance. Ingenieria Y Universidad, 22(2). https://doi.org/10.11144/Javeriana.iyu22-2.mscd
Section
Industrial and systems engineering