دانلود مقاله ISI انگلیسی شماره 1172
عنوان فارسی مقاله

حمایت از تعیین توالی تصمیم گیری ماژول در فرایند اجرای برنامه ریزی منابع سازمانی (ERP)، استفاده از روش ANP

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
1172 2009 12 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
Supporting the module sequencing decision in the ERP implementation process—An application of the ANP method
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Production Economics, Volume 119, Issue 2, June 2009, Pages 259–270

کلمات کلیدی
برنامه ریزی منابع سازمانی () - پیاده سازی - اجرای ماژول -
پیش نمایش مقاله
پیش نمایش مقاله حمایت از تعیین توالی تصمیم گیری ماژول در فرایند اجرای برنامه ریزی منابع سازمانی (ERP)، استفاده از روش ANP

چکیده انگلیسی

The paper addresses the alignment between business processes and information technology in enterprise resource planning (ERP) implementation. More specifically, we concentrate on one of the key decisions at the tactical alignment level: the decision on the implementation sequence of the ERP modules. Since the module sequencing problem involves a myriad of organizational and technical issues, connected to each other in networked manner, the analytic network process (ANP) methodology is applied. As a result of the study, we present first a general level conceptual framework to sequence ERP module implementations and expand the model to a more detailed level in a case study. The priorities for the implementation sequence of the ERP modules are determined in the case study.

مقدمه انگلیسی

Contemporary organizations operate in a global environment that is characterized by constant change and different cultural settings. To avoid the fragmentation of the operational data in different business units, companies implement enterprise wide information systems, such as ERP (enterprise resource planning) systems that can increase control, improve coordination and communication, and create the picture about the corporate functions on the aggregate level. Typically, ERP-systems support financials, human resources, operations and logistics, and sales and marketing functions (Davenport, 1998). The objective of ERP systems is to conduct the business processes more efficiently and effectively, in an integrated manner. They are a way to control functions of the organization and to make all the units perform in a more uniform way. Dramatic operational improvements are possible, through integration and redesigning of processes. It is assumed that the standard software package more or less fits all organizations, and all units inside one organization, which creates risks for ERP investments. In theory, business processes are modified to fit the systems, since customizing the system is considered too expensive and too risky. But in practice both business processes and ERP systems are suspects to be changed during the implementation process. The technical nature of ERP software is already quite well described and known (see, for example, Klaus et al., 2000), but because of the ERP implementation related context-sensitive social diversity is huge, the perceived results and outcomes of ERP implementation vary a lot (Ross and Vitale, 2002 and Scott and Vessey, 2000). There are a lot of problems reported in the implementation of these systems, and even the objectives of the system investments may evolve during the process (Glover et al., 1999, Nandhakumar et al., 2003 and Themistocleous et al., 2001). Technology and the organization are in a continuous interaction with each other during technology adoption and use, according to Orlikowski (1992). The alignment between business and IT is a dynamic process with bi-directional interaction.

نتیجه گیری انگلیسی

We believe that the process presented in the paper represents a relevant model for improving business and IT alignment for ERP implementation. The ANP methodology itself seems to be applicable for this kind of decision-making, although its application is somewhat laborious and also requires theoretical knowledge on the method. However, the ANP approach enforces the analytical comparison between the alternatives as well as improves the decision visibility, for example, for external auditors and boards investing huge amounts of money in the ERP software. The analysis conducted in the present paper brings insights to the ERP module sequencing problem. We developed an ANP model for deciding on the implementation sequence of ERP modules, using the alignment of business and IT and the IT investment perspective as the theoretical foundation for the model. We described the decision-making process for ERP module sequencing and the application of the ANP method in detail to enhance the practical usability of the method. The method can be considered rather easy to use and very informative in terms of providing clear results. Our model is technology oriented focusing on software module sequencing, meanwhile social construction elements are embedded into business requirements, risks and solution constraints. However, the method has its limitations as well, such as the model becoming easily rather complicated, which may limit its use in some situations. The benefits from utilizing this ANP model for ERP module sequencing do not only come from the explicit priorities between the alternative modules. Major value can be derived from the knowledge transfer during the ANP data collection process, because answering to pairwise comparisons requires an enhanced dialog between the ERP vendor, the IS department and the business units. ERP implementation process is more knowledge, resource, requirements and change management challenge than technical IT system deployment. Thus, issues regarding IT and business alignment, as well as IT governance might surface during the data collection process. Key business requirements may be biased, current understanding about solution constraints may be limited, and priorities may be unintentionally sub-optimized because of bounded rationality and past experiences from non-integrated operations and systems with limited data models.

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