In today’s competitive business environment, companies need to continuously invest in both consecutive and simultaneous projects to guarantee healthy and profitable growth. Companies are being forced to improve their effectiveness and efficiency looking for effectively comparing the 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 are often confronted with having more projects to choose from than the resources to carry them out. To select from an array of projects those better adapted to the organization’s objectives and determining the priority in which these projects will be worked on is a challenging managerial task that motivates project managers and their teams and creates an improvement environment.
One must evaluate the benefits, drawbacks, and consequences of each possible choice and these comparisons can be quantitative and/or qualitative as well as tangible and/or intangible depending on the specifics of each project.
Data Envelopment Analysis is used to:
1. Identify the best alternative;
2. Rank the alternatives; or
3. Establish a shortlist of the better alternatives for detailed review.
Data Envelopment Analysis (DEA) is a mathematical programming technique that provides the correct method for project evaluation and selection.
It’s said that the difficult task is in Selecting and Ranking projects with typically more than one dimension for measuring project impacts and more than one decision-maker. As part of the selection process, the evaluation 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, intangible benefits, relevance to the organization’s mission, probability of technical and commercial 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.
The First step in specifying the DEA model is to identify the input and output variables of interest necessary to capture important differences between projects. There exist a wide variety of measures that describe the outcomes of a project and the input characteristics and factors which impact project outcomes.
The recommended maximum number of input and output variables is equal to one-half the number of DMUs in any given category or analysis.