Abstract
The objective of this chapter is to propose a new approach for evaluating Research and Development (R&D) projects at different stages of their life cycle. The approach is based on the integration of the balanced scorecard, Data Envelopment Analysis (DEA), and Multiple Objective (MO) linear programming. An interactive MO-DEA model is presented to incorporate Decision Maker's (DM) preference to effectively establish a common basis for fully ranking projects. The approach is illustrated on 50 R&D projects from the literature to highlight the effectiveness of the approach to fully rank all competing projects, hence increasing the discrimination power of DEA approach.
TopIntroduction
The balanced scorecard (BSC) is a model for the analysis of strategic management performance in all types of organization. It was developed by Kaplan & Norton (1992), and it has been the subject of much research attention as a tool for strategic management. The BSC is composed of a collection of attributes, arranged in groups, and denoted as cards. The attributes are related to four BSC original perspectives (financial, customer, internal-business processes, learning and growth): A new uncertainty perspective is introduced into BSC for the selection among competing R&D projects. This importance of considering the cost-benefit and risk-opportunity quantitative analysis for the evaluations of short and long term performance and impact analyses of organizations, people and systems were highlighted in the introduction of new COBRA framework in Osman et al (2014a) and the IMEET framework where COBRA and DEA are combined in Osman et al. (2014b) to determine an overall valuation from all perspectives.
BSC combines financial and operational measures, and focuses both on the short-and long-term objectives of an organization. The cards offer a balanced evaluation of the organizational performance along financial, marketing, operational and strategic dimensions. Both DEA and BSC are used for the evaluation of research and development (R&D) projects including, Rickards (2003), Tsang et al., (1999), Rouse et al., (2002), Banker et al., (2004), and Wang & Huang (2007). Eilat et al., (2006) combined DEA and BSC models to analyze the efficiency, effectiveness and balance R&D projects. The evaluation method was executed in seven phases: resource allocation, individual project evaluation, projects variability control, generation of portfolios, applying an accumulation function to determine the inputs and outputs of the candidate portfolio, evaluating alternative portfolios, sensitivity analysis, commencing with a distribution of resources among the categories associated with the main areas of strategy (product lines, technology areas, etc.). Once the resources have been distributed, the R&D projects were considered as decision making units (DMUs) for their relative evaluation and ranking by the DEA–BSC model. The score evaluations are then obtained to select the list of candidate projects for executions. Subsequently Eilat et al., (2008) developed a study integrating DEA and BSC models for the evaluation of R&D projects in different stages of their life cycle. The measurement of the inputs and outputs was integrated with BSC cards of R&D projects. This analytical framework was then applied in a research laboratory to select and execute a large number of research projects on a yearly basis.
Key Terms in this Chapter
International-Business Perspective: This perspective focuses on the internal processes that will have a greatest impact on customers’ satisfaction and achievement of an organization’s financial objectives.
Customer Perspective: This perspective considers external customers’ point of view, of organizations, which are a crucial factor for creating financial success, revenue from buying products and services.
Uncertainty Perspective: This new uncertainty perspective includes measures on the probability of technical success and the probability of commercial success.
Balanced Scorecard: The BSC is a management tool composed of a collection of measures, arranged in groups, and denoted as cards . The measures are related to four major managerial perspectives, and are aimed at providing top managers with a comprehensive view of their business.
Interactive Multi Objective Programming: In multiobjective programming problems, the final decision is made on the basis of the value judgment of DM. Hence it is important how the value judgment of DM is elicited. In interactive multi objective Programming decision makers preference is incorporates during problem-solving.
Financial Perspective: This perspective links the company to its shareholders with a main question in mind: ‘‘How do we look at our shareholders based on financial interest in the organization?”
Data Envelopment Analysis: DEA is a method for measuring the efficiency of DMUs using linear programming techniques to ‘‘envelop” observed input–output vectors as tightly as possible.
Learning and Growth Perspective: This perspective identifies the infrastructure that the organization must build to create long-term growth and improvement.