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An analysis of enterprise resource planning systems and key determinants using the Delphi method and an analytic hierarchy process

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posted on 2023-10-31, 02:42 authored by Yueh-Hsia Huang, Lan SunLan Sun, Tyng-Bin Ger
Enterprise resource planning (ERP) systems are software module packages which can be customized up to a certain limit to suit the specific needs of each organization. Many ERP projects have not been effective at achieving all of the intended results due to high cost and high failure risks in ERP implementation. This study integrates the prior theories and knowledge gained from several textile industry practitioners for ERP projects. A two-stage method involving the Delphi and analytic hierarchy process decision support methodology was conducted. Based on the case of the textile industry in Taiwan, the findings illustrate the top 10 key factors: a clear project plan by defined ERP capacity requirement; a limited scope and focused flowchart; goal congruence between ERP project implementation and corporate strategy; top management support and commitment; the extent of standard operating procedures and institutional processes; a user-friendly interface; systems integrated with aggressive schedules and timelines; provision of technical assistance for rapid, effective transfer of best practice interventions; good interdepartmental communication and coordination on a focused issue solution; and enablement of business process reengineering and solid management for project team building. The findings of this research will be beneficial to those apparel companies that adopted the ERP.

History

Volume

3

Issue

2

Start Page

166

End Page

183

Number of Pages

18

eISSN

2769-2140

ISSN

2769-2140

Publisher

American Institute of Mathematical Sciences (AIMS)

Additional Rights

CC BY 4.0 DEED

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2023-05-17

External Author Affiliations

Minghsin University of Science and Technology, Chinese Culture University, Taiwan

Era Eligible

  • Yes

Journal

Data Science in Finance and Economics

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