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Articles

Enterprise resource planning (ERP) implementation using the value engineering methodology and Six Sigma tools

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Pages 1243-1261 | Received 31 Mar 2015, Accepted 17 Jul 2016, Published online: 04 Aug 2016
 

ABSTRACT

Enterprise resource planning (ERP) is a software solution that integrates the operational processes of the business functions of an enterprise. However, implementing ERP systems is a complex process. In addition to the technical issues, companies must address problems associated with business process re-engineering, time and budget control, and organisational change. Numerous industrial studies have shown that the failure rate of ERP implementation is high, even for well-designed systems. Thus, ERP projects typically require a clear methodology to support the project execution and effectiveness. In this study, we propose a theoretical model for ERP implementation. The value engineering (VE) method forms the basis of the proposed framework, which integrates Six Sigma tools. The proposed framework encompasses five phases: knowledge generation, analysis, creation, development and execution. In the VE method, potential ERP problems related to software, hardware, consultation and organisation are analysed in a group-decision manner and in relation to value, and Six Sigma tools are applied to avoid any project defects. We validate the feasibility of the proposed model by applying it to an international manufacturing enterprise in Taiwan. The results show improvements in customer response time and operational efficiency in terms of work-in-process and turnover of materials. Based on the evidence from the case study, the theoretical framework is discussed together with the study’s limitations and suggestions for future research.

Acknowledgement

This work has been developed within the framework of a research project funded by the Ministry of Science and Technology (MOST) Taiwan, reference MOST 104-2410-H-008-066.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Ministry of Science and Technology, Taiwan: [Grant Number MOST 104-2410-H-008-066].

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