Analysis of the causes and level of maintenance for enterprise systems in construction companies
A construction company without a similar information technology (IT) system in the past has insufficient historical data to use for investment decision-making of IT system. An estimation of maintenance costs is especially more uncertain than the initial investment costs, and the uncertainty is greater when the IT system is used over a long period, such as an enterprise system (ES). This study proposes estimation criteria for the maintenance costs of an ES for an accurate investment decision. First, the causes of maintenance are determined, and the level of maintenance analyzed. Then, the result is compared with a general trend of maintenance incidence (bathtub curve) that is widely used as reference criteria to estimate maintenance cost. The level of maintenance was high during the early stage but steadily decreased in the middle and end stage because high-cost maintenance activities were postponed with the approach of the time in which the ES was being restructured. This trend is different from the bathtub curve that increases again during the end stage. Thus, when a maintenance contract is negotiated, the maintenance level that affects maintenance cost should be considered as well as the incidence of maintenance.
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April, A. 2010. Studying supply and demand of software maintenance and evolution services, in Seventh International Conference on the Quality of Information and Communications Technology (QUATIC), 29 September – 2 October 2010, Oporto, Portugal, 352–357. https://doi.org/10.1109/QUATIC.2010.65
Buchmann, I.; Frischbier, S.; Putz, D., 2011. Towards an estimation model for software maintenance costs, in 15th European Conference on Software Maintenance and Reengineering (CSMR), 1–4 March 2011, 313–316. https://doi.org/10.1109/CSMR.2011.45
Camastra, F.; Ciaramella, A.; Giovannelli, V.; Lener, M.; Rastelli, V.; Staiano, A.; Staiano, G.; Starace, A. 2015. A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference, Expert Systems with Applications 42: 1710–1716. http://doi.org/10.1016/j.eswa.2014.09.041
Correa, S. L. L.; Mexas, M. P.; Drumond, G. M.; Meirino, M. J. 2016. Cost elements identification for maintenance and support of ERP systems in Brazilian IFES: An approach based on TCO and ITIL, IEEE Latin America Transactions 14: 2372–2381. https://doi.org/10.1109/TLA.2016.7530435
Hadidi, L.; Assaf, S.; Alkhiami, A. 2017. A systematic approach for ERP implementation in the construction industry, Journal of Civil Engineering and Management 23(5): 594–603. https://doi.org/10.3846/13923730.2016.1215348
Hsiang-Jui, K. 2004. Quantitative method to determine software maintenance life cycle, in IEEE International Conference on Software Maintenance, 232–241. https://doi.org/10.1109/ICSM.2004.1357807
IEEE 1219-1998 IEEE standard for software maintenance. IEEE, 1998.
ISO/IEC 14764 Software engineering – Software life cycle processes – Maintenance. Geneva: International Organization for Standardization, 2006.
Jiang, R. 2013. A new bathtub curve model with a finite support, Reliability Engineering & System Safety 119: 44–51. https://doi.org/10.1016/j.ress.2013.05.019
Korea IT Service Industry Association. 2006. Survey of the IT outsourcing market’s environment.
Li, J.; Stålhane, T.; Kristiansen, J. M. W.; Conradi, R. 2010. Cost drivers of software corrective maintenance: An empirical study in two companies, in IEEE International Conference on Software Maintenance (ICSM 2010), 12–18 September 2010, Timișoara, Romania, 1–8. https://doi.org/10.1109/ICSM.2010.5609538
Li, S.-H.; Yen, D. C.; Lu, W.-H.; Chen, T.-Y. 2014. The characteristics of information system maintenance: an empirical analysis, Total Quality Management & Business Excellence 25: 280–295. https://doi.org/10.1080/14783363.2013.807679
Matijevic, T.; Ognjanovic, I.; Sendelj, R. 2012. Enhancement of software projects’ Function Point Analysis based on conditional non-functional judgments, in First Mediterranean Conference on Embedded Computing (MECO 2012), 19–21 June 2012, Bar, Montenegro, 283–287.
Mendes, J.; Araújo, R.; Sousa, P.; Apóstolo, F.; Alves, L. 2011. An architecture for adaptive fuzzy control in industrial environments, Computers in Industry 62: 364–373. https://doi.org/10.1016/j.compind.2010.11.001
Ministry of Knowledge Economy. 2011. The standard of software cost estimation.
Ng, C. S.-P.; Gable, G. G. 2010. Maintaining ERP packaged software – A revelatory case study, Journal of Information Technology 25: 65–90. https://doi.org/10.1057/jit.2009.8
Nguyen, V.; Boehm, B.; Danphitsanuphan, P. 2011. A controlled experiment in assessing and estimating software maintenance tasks, Information and Software Technology 53: 682–691. https://doi.org/10.1016/j.infsof.2010.11.003
Niu, N.; Xu, L. D.; Bi, Z. 2013. Enterprise information systems architecture – analysis and evaluation, IEEE Transactions on Industrial Informatics 9: 2147–2154. https://doi.org/10.1109/TII.2013.2238948
Niu, N.; Xu, L. D.; Cheng, J. R. C.; Niu, Z. 2014. Analysis of architecturally significant requirements for enterprise systems, IEEE Systems Journal 8: 850–857. https://doi.org/10.1109/JSYST.2013.2249892
Ranaldo, N.; Zimeo, E. 2016. Capacity-driven utility model for service level agreement negotiation of cloud services, Future Generation Computer Systems 55: 186–199. https://doi.org/10.1016/j.future.2015.03.007
Ren, Y.; Xing, T.; Chen, X.; Chai, X. 2011a. Research on software maintenance cost of influence factor analysis and estimation method, in 3rd Workshop on Intelligent Systems and Applications, 15–18 June 2011, Chaves, Portugal, 1–4. https://doi.org/10.1109/ISA.2011.5873461
Ren, Y.; Xing, T.; Qiang, Q. 2011b. Function point analysis to knowledge representation of software size measurement, in 2011 International Conference on Information Science and Technology, 24–25 September 2011, 122–125. https://doi.org/10.1109/ICIST.2011.5765224
Romero, D.; Vernadat, F. 2016. Enterprise information systems state of the art: Past, present and future trends, Computers in Industry 79: 3–13. https://doi.org/10.1016/j.compind.2016.03.001
Salmeron, J. L.; Lopez, C. 2012. Forecasting risk impact on ERP maintenance with augmented fuzzy cognitive maps, IEEE Transactions on Software Engineering 38: 439–452. https://doi.org/10.1109/TSE.2011.8
Sarno, R.; Sidabutar, J.; Sarwosri, 2015. Improving the accuracy of COCOMO’s effort estimation based on neural networks and fuzzy logic model, in International Conference on Information & Communication Technology and Systems (ICTS), 197–202. https://doi.org/10.1109/ICTS.2015.7379898
Shen, Y.-C.; Chen, P.-S.; Wang, C.-H. 2016. A study of enterprise resource planning (ERP) system performance measurement using the quantitative balanced scorecard approach, Computers in Industry 75: 127–139. https://doi.org/10.1016/j.compind.2015.05.006
Soja, P.; Themistocleous, M.; Cunha, P. R. d.; Mira da Silva, M. 2015. Determinants of enterprise system adoption across the system lifecycle: Exploring the role of economic development, Information Systems Management 32: 341–363. https://doi.org/10.1080/10580530.2015.1080005
Tsunoda, M.; Monden, A.; Matsumoto, K.; Ohiwa, S.; Oshino, T. 2015. Benchmarking software maintenance based on working time, in 3rd Applied Computing and Information Technology/International Conference on Computational Science and Intelligence (ACIT-CSI), 12–16 July 2015, Okayama, Japan, 20–27.
Ul Haq, I.; Huqqani, A. A.; Schikuta, E. 2011. Hierarchical aggregation of service level agreements, Data & Knowledge Engineering 70: 435–447. https://doi.org/10.1016/j.datak.2011.01.006
Unger, T.; Mietzner, R.; Leymann, F. 2009. Customer-defined service level agreements for composite applications, Enterprise Information Systems 3: 369–391. https://doi.org/10.1080/17517570903033431
Wu, S. L.; Xu, L.; He, W. 2009. Industry-oriented enterprise resource planning, Enterprise Information Systems 3: 409–424. https://doi.org/10.1080/17517570903100511
Xu, L. D. 2011. Enterprise systems: State-of-the-art and future trends, IEEE Transactions on Industrial Informatics 7: 630–640. https://doi.org/10.1109/TII.2011.2167156
Zhang, J.; Qin, W.; Wu, L. H.; Zhai, W. B. 2014. Fuzzy neural network-based rescheduling decision mechanism for semiconductor manufacturing, Computers in Industry 65: 1115–1125. https://doi.org//10.1016/j.compind.2014.06.002