In today’s competitive business environment,companies need to continuously invest in both consecutive and simultaneousprojects to guarantee healthy and profitable growth. Companies are being forcedto improve their effectiveness and efficiency looking for effectively comparingthe performance of various projects at a given time period.As Projects compete for resources and typically,there are always less resources available than demand, these organizations areoften confronted with having more projects to choose from than the resources tocarry them out. To select from an array of projects those better adapted to theorganization’s objectives and determining the priority in which these projectswill be worked on is a challenging managerial task that motivates projectmanagers and their teams and creates an improvement environment.One must evaluate the benefits, drawbacks, andconsequences of each possible choice and these comparisons can be quantitativeand/or qualitative as well as tangible and/or intangible depending on thespecifics of each project.Data EnvelopmentAnalysis is used to:1.
Identify the best alternative;2. Rank the alternatives; or3. Establish a shortlist of the betteralternatives for detailed review.
Data Envelopment Analysis (DEA) is a mathematical programmingtechnique that provides the correct method for project evaluation and selection.It’s said that the difficult task is in Selecting andRanking projects with typically more than one dimension for measuring projectimpacts and more than one decision-maker. As part of the selection process, theevaluation involves multiple and often conflicting goals and criteria,including maximizing net present value, achieving regulatory compliance,enhancing (or reducing) environmental impacts, minimizing risk and cost,minimizing total completion time, not exceeding a given budget, intangiblebenefits, relevance to the organization’s mission, probability of technical andcommercial success, availability of resources, etc. Moreover, the list of proposed projects invariably exceeds budgetary allocation. Thus, the decision problem becomes one of ranking projects in order of preference and selecting the best ones. The Three broad objectives that usually dominate this decision process:1.
Effectiveness. The alignment of the mix of projects in the portfolio with the strategic goals of the organization.2. Efficiency. The value of the portfolio in terms of long-term profitability, return-on- investment, likelihood of success, or other relevant performance measures.3. Balance. The diversification of the projects in the portfolio in terms of various trades-offs such as high risk versus sure bets, internal versus outsourced work, even distribution across industries, etc.
DEA using the comparative efficiency concept, it is a one non-parameter statistical method for evaluating the same types of multi input and output decision making units (DMU) through efficiency or inefficiency. Example: The study we are going to use involves 15 Major and Minor Cement Firms, the number of input and output variables that we have included in the DEA model is six. Therefore, we have four input variables and two output variables are identified for inclusion in the model for a total of six variables. CCR-Model was introduced by Charnes, Cooper and Rhodes (1978). CCR model is based on the assumption of Constant Returns to Scale (CRS); an efficiency margin is constructed to estimate the operational efficiency for DMU. Banker, Charmes and Cooper (1984) then developed the BCC model, who proposed a Variable Returns to Scale (VRS) model that extends the definition and applications of efficiency under the CCR model. Efficiency measurement begins with Farrell (1957) who drew upon the work of Debreu (1951) and Koopmans (1951) to define a simple measure of firm efficiency which could account for multiple inputs. He proposed that the efficiency of a firm consists of two components: Technical Efficiency à The ability of a firm to obtain maximal output from a given set of inputs.
Allocative Efficiency à The ability of a firm to utilize the inputs in an optimal proportion given their respective prices. These two measures are then combined to provide a measure of Total Economic Efficiency.