Decision support for operational ERP systems implementation in small and medium enterprises

Today organisations, due to increased competition, globalisation and cost saving, are seeking ways to improve their operational effectiveness and sustain their competitive advantage through effective deployment of available resources and strategically implementing business processes. It is observed that incorporating new developments in information technology with core business processes results in enhanced functioning and improved services to customers. To benefit from the available IT support, organisations are adopting application software, such as ERP systems, to improve operation efficiency and productivity. ERP system is primarily implemented to integrate business processes and enhance productivity. However, ERP system comes with a high price tag, implementation complexities, and prerequisite changes in how organisation and its staff functions. Implementing ERP is a challenging task for SMEs since it consumes a major portion of limited resources and carries a high risk of causing adverse consequences. To overcome the implementation challenges and assist SMEs in ERP implementation, an integrated decision support system for ERP implementation (DSS_ERP) is developed in this research. This decision support system consists of analytical regression models, a simulation model and nonlinear programming models, and it enables SMEs to identify the resources requirements for achieving the predetermined goals prior to ERP implementation. The key contribution from this research are: i) the DSS_ERP offers an analytical models to monitor the implementation progress and cost consumed by each critical success factor (CSF) during the implementation against time; ii) it assists in determining the priorities of CSFs, based on which it facilitates decision makings on resource allocations to achieve the predetermined target; iii) and it can be applied to evaluate the impacts of changes to the resources allocations.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

University of Greenwich

Year dc:date.issued

2013

Author dc:creator

• Mahmood, Ali

Advisor dc:contributor.advisor

• Xie, Ying

• HD Industries. Land use. Labor
• TS Manufactures

Language dc:language

en