DECISION SUPPORT SYSTEMS

Decision Support Systems

Abstract

This paper investigated development/implementation strategies that will promote success on decision support systems (DSS) projects designed to enable supply chain management (SCM) within manufacturing companies. Decision support systems designed to enable SCM are difficult to implement, and the implementation success rate can be improved. The appropriate amount of time and financial resources should be allocated to DSS projects, which is not always the case. Many software companies sell software solutions promising short implementations and quick paybacks. Haettenschwiler (2009) warned that care must be taken when planning time and budgets for operations research (OR) type IT projects.

Decision Support Systems

Introduction

This paper investigated development/implementation strategies that will promote success on decision support systems (DSS) projects designed to enable supply chain management (SCM) within manufacturing companies. Decision support systems designed to enable SCM are difficult to implement, and the implementation success rate can be improved. An actual rate of project failure would be difficult to acquire because manufacturing companies are not eager to share this information. Although the failure rate is not well documented, there is research that supports this claim of difficulty. Power (2002) reported that 30% to 75% of information systems designed to enable business processes do not meet original expectations, and some even end in catastrophe. Efraim, Jay and Ting (2008) referred to the term “implementation paradox” when they discussed the adoption of decision support systems. They proposed that the breadth of these systems actually hindered adoption. According to Stanhope (2002), information systems that include artificial intelligence (AI) technologies fail at an extremely high rate and management support systems (MSS) are difficult to implement because they tend to change the way organizations operate.

This study adopted the definition of a decision support system and an operational information system from a paper written by Haettenschwiler (2009). A decision support system is any computer-based system that helps business users utilize data and models to solve structured and semi-structured business problems. Operational business systems are systems utilized to keep track of routine transactions and elementary activities of the organization; they are described as ERP in this paper.

Discussion

Project development teams often utilize the same approach to implement decision support systems as other types of large-scale business systems, such as enterprise resource planning systems (ERP). An important difference between DSS and ERP systems is that decision support systems generally include complicated algorithms or mathematical models that allow the system to generate operational business plans (e.g., demand forecasts, inventory plans, capacity plans, and production schedules). These algorithms often take advantage of solvers, which are not typically found embedded within ERP systems. Mixed integer linear programs, genetic algorithms, time-series and regressive techniques, and simulation are all algorithms commonly used within decision support systems. These quantitative methods contribute to the complexity of these information systems designed for decision support. Project development teams need to consider the complexity of the proposed information system when selecting strategies that the team will follow.

Enterprise resource planning (ERP) systems traditionally collect, distribute, and manipulate large amounts of ...