A real option theoretic fuzzy evaluation model for enterprise resource planning investment
Introduction
Enterprise resource planning (ERP) systems are integrated systems proposed for seamless information transfer between business functions, promising to deliver mesmerizing business benefits that include standardizing processes across multiple business units, consistent information base across the entire organization and reducing cost (Lozinsky, 1998, Blackwell et al., 2006, Papiernik, 2001). Successful ERP implementation can achieve operational improvements, including reduction of time to market, reduction in cycle time, product development time, improvement in operation, reduction of inventory cost (Stein, 1999) and higher customer satisfaction level (Al-Mashari, 2002). Reaction time to competitive pressures and market opportunities could also be improved by technology (Badawy, 2009). However, these success examples are only minority. Statistical data from the past studies found out that 70% of ERP implementation projects fail to achieve the expected goals set prior to the implementation (Buckhout et al., 1999). Examples of failure are abundant. FoxMeyer Drug went bankrupt in 1996 and filed a US$500 million lawsuit against SAP, blaming for its woes (Key, 1998). Unisource Worldwide, Inc wrote off US$168 million as it abandoned the nationwide implementation of ERP software (Stein, 1998). Dell abandoned the SAP implementation after months of delays and cost overruns, claiming that SAP was too monolithic to be altered for changing business needs. The list continues, but yet does not deter the trend that ERP systems are changing from a competitive advantage to a basic integrated system for enterprises.
To implement a system, decision makers need to consider various aspects such as the corporate requirements, the role of social and intellectual capital in achieving competitive advantages by system (Lengnick-Hall et al., 2004), system performance and infrastructure (Hicks et al., 2010), capability of the vendors (Badawy, 2003), adequacy of training and consultancy. Most of the decision makers need to identify the critical successful factor such as clear understanding of strategic goals, commitment by top management, excellent project management, organizational change management together with the pitfall such as poor planning and management, change in business goals during project and lack of business management support (Umble et al., 2003, Sun et al., 2005, Ngai et al., 2008). The decision makers need to evaluate the risk undertaken by the company and calculate the Return On Investment (ROI). Failure as an implementation that does not achieve the ROI identified in the project approval phase finds that failure rates are in the range of 60–90% (Ptak and Schragenheim, 2000).
In order to minimize the risk, organizations need to select the software carefully and there are various approaches related to ERP selection and assessment. Wang suggested using PERT-embedded genetic algorithm to find out the optimal solution for time–cost–quality (Wang et al., 2008). Mulebeke and Zheng (2006) suggested to use analytical network process for selecting software by considering multi-criteria and multi-attribute factors such as product and technology platform. Other artificial intelligence techniques such as fuzzy logic (Cebeci, 2009, Bueno and Salmeron, 2008, Ordoobadi and Mulvaney, 2001) and neural network (Yazgan et al., 2009, Chang et al., 2008) have been adopted to select the suitable ERP system that meets the corporate strategy and enhances the operation efficiency. Some enterprises may lower the risk by exploring knowledge sharing (Newell et al., 2003, O’Leary, 2002) and studying the user acceptance with technology acceptance model (Bueno and Salmeron, 2008). Predicate/transition net (Hsu and Hsu, 2008) and PDES/STEP (Ming et al., 1998) have been used for ERP modeling with the ability of abstraction and refinement and the configuration support.
There are various research related to ERP implementation, evaluation and technology aspect (Tseng et al., 1999, Ordoobadi and Mulvaney, 2001, Lengnick-Hall et al., 2004). However, there is less research related to guiding the decision makers to understand the value of the alterative options in different economic environment. Wier et al. (2007) analyze interaction between firm size and financial status for ERP adopters with respect to return on asset (ROA), ROI, and return on sales (ROS) and do the comparison about the performance with non adopters. The research proposition that “The creation of ERP-related operating options will lead to enhanced flexibility in the further deployment of ERPS and/or add-on applications and enhance the value of the ERPS investment.” (Nicolaou, 2008), is supported based on the argument that Real Option Theory provides a theoretic model and systematic approach to handle ERP investment (Fichman, 2004). It is necessary to minimize the uncertainties to investment-specific skills and strike the balance between risks and benefits (Benaroch, 2002). Real Option Theory is used to analyze and evaluate various options to make budget decisions. In dynamic business environment, decision makers need to realize the benefit and cost of investment by balancing the risk and opportunity. Real Option Theory has been adopted for analysis of IT investment (Kim and Sanders, 2002, Yuan, 2009) such as selection among IT infrastructure (Hilhorst et al., 2008), hospital information systems (Özogul et al., 2009) and ERP systems (Wu et al., 2008).
The implementation cost of ERP system is high for SME and numerous implementation failures have been reported. It is importance to examine on the subject of critical successful factors and risk management for ERP implementation (Umble et al., 2003, Sun et al., 2005, Ngai et al., 2008). Most of the researches concern about the investment cost at the initial stage of implementation. However, there has only limited relevant research conducted on evaluating the risk and uncertainties at various stage of ERP implementation cycle. As information for economic investment evaluation such as interest rate and cash flow is vague and uncertain, option theory with Fuzzy payoff method is suggested in this paper as alternative means of decision-making and valuation. This paper is the first attempt to adopt fuzzy Real Option Theory in ERP implementation according to author's knowledge.
Section snippets
ERP system installation layout
Business processes will be matched with the solutions within the business blueprint in the package. As ERP system is modular in structure, it is advisable to have phase by phase installation instead of one time complete installation. Company may classify the modules into levels of essentiality to their business; starting with the primary modules of their core business processes in the first phase, then extending to the secondary and tertiary in the subsequent phases. For example, a logistics
Case study
One SME, the alias name as CC Ltd, is a trading company focusing on imports and exports and provides integrated logistics services. Established in 1990, CC Ltd currently operates globally, including Singapore, Malaysia, Indonesia, Philippines, United States, Thailand. The company business module includes warehousing, consign shipment, freight, custom declaration and procurement. The company maintains two warehouses, one in Guangzhou (which is normally the exporting end where the containers are
Conclusions
This paper reports a Real Option Theory with Fuzzy payoff method for evaluating ERP system investment. The method introduces active management in dealing with uncertainties in order to minimize the risk of failure and maximize the benefits of an ERP system to the enterprise. Within the implementation lifecycle, ex-ante cost analysis and business environment have been particularly looked into, respectively suggesting the application of Real Option Theory (Fuzzy payoff method). Throughout the
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