A dynamic ERP critical failure factors modelling with FCM throughout project lifecycle phases

Ravansan, AZ; Mansouri, T

Abstract

Implementation of enterprise resource planning systems (ERPs) is a complex and costly task which usually results in serious failures. Numerous factors affect these projects implementation due to their size, complexity and high chance of failure. Therefore, identifying these factors in ERP projects is a critical issue. The majority of previous publications have been conducted in identifying ERP critical success factors (CSFs) rather than critical failure factors (CFFs). In order to help practitioners, this article studies the CFFs in ERP implementation projects. The implications of interdependency among failure factors are also usually overlooked by project managers due to perceived complexity in modelling and analysing influential factors. With this in mind, we have proposed a fuzzy cognitive map (FCM)-based dynamic model of ERP failure factors through project lifecycle phases. The main advantage of FCM lies in them being capable of modelling complex phenomena based on the experts’ perceptions. This tool models uncertainty and related events, imitating human reasoning. Moreover, FCMs enable the developing of forecasting exercises through simulations. Practitioners would thus assess the joint influence of ERP implementation failure factors on project outcomes. The results make known to practitioners which problems will arise if the failure factors are not treated, and how these will impact on the outcomes of projects. Therefore, the proposed approach would help them to manage ERP implementation projects in a more effective and proactive way.

Citation

Ravansan, A., & Mansouri, T. (2016). A dynamic ERP critical failure factors modelling with FCM throughout project lifecycle phases. Production Planning and Control, 27(2), 65-82. https://doi.org/10.1080/09537287.2015.1064551

• Journal Article Type: Article
• Acceptance Date: May 24, 2015
• Online Publication Date: Jul 14, 2015
• Publication Date: Jan 1, 2016
• Deposit Date: Jun 9, 2021
• Journal: Production Planning & Control
• Print ISSN: 0953-7287
• Electronic ISSN: 1366-5871
• Publisher: Taylor and Francis
• Volume: 27
• Issue: 2
• Pages: 65-82
• DOI: https://doi.org/10.1080/09537287.2015.1064551
• Publisher URL: https://doi.org/10.1080/09537287.2015.1064551
• Related Public URLs: https://www.tandfonline.com/toc/tppc20/current