Decision-making is human activity whereby value judgments regarding the attractiveness of preferences play a major role. There are various ways through which an organization can supplement and improve decision analysis; however, the incorporation of human decisions with technology through the design and utilization multi criteria decision analysis (MCDA) contributes towards the success of an organization to a great extent. This essay provides a detailed analysis and evaluation of the multi criteria decision analysis as applied in logistics and supply chain decision analysis in contemporary business and organizations.
The first section of this essay introduces the various types of MCDA and provides details about how analytical hierarchy process (AHP) works, being one of the most popular MCDA techniques. This is followed by a detailed analysis of various decision problems in logistics and supply chain which involve the use of MCDA techniques: such include supply chain performance management, supplier selection, production-driven characteristics, logistics and supply chain capital and infrastructural investments.
In section that follows, the advantages, disadvantages and limitations of MCDA are clearly detailed out, in special reference to the logistics and supply chain department of an organization. Finally, the essay provides a concluding paragraph which is based on the author’s opinion regarding the effectiveness and success of multi criteria decision analysis in supply chain and logistics management as well as recommendations on what ought to be done to make the application more beneficial to decision makers and the organization at large.
Introduction MCDA refers to a multiple decision criteria that aimed at assisting decision makers who are often faced with many and inconsistent problem evaluations. The goal of MCDA is typically to emphasize on the conflicting and inconsistent areas and derive a technique of harmonization (Xiang, 2006).
The MCDA methods commonly used in the contemporary decision field include analytical hierarchy process(AHP), weighted sum model(WSM), analytical network process(ANP), inner product vectors(IPV), Weighted product model(WPM), multi attribute value theory(MAVT) , multi attribute utility theory(MAUT), SMART (simple multiple attribute rating technique), Equal Swaps, MACBETH(Measuring Attractiveness by a Categorical Based Evaluation Technique) (Xiang, 2006). Analytical Hierarchy Process (AHP) This is one of the most famous decision criteria methods.
It is a controlled technique used to deal with multifaceted decisions. AHP applies a quantitative comparison technique based on pair-wise evaluations of the decision criteria. It assists the decision maker in generating one among the best alternatives to their problem in accordance to the decision maker’s comprehension of the problem. The method applies the skills of psychology and mathematics to provide rational, comprehensive structure for framing a decision problem, presenting, computing and quantifying the elements, linking elements to goals and assessing other alternative solutions.
It is used in education, business, healthcare and other industries as a decision support tool (Jamil and Linkov, 2004). Users of this method begin by decomposing the decision problem into a hierarchy of straightforwardly understandable sub-problems, which can be separately analyzed: the chosen decision problem elements may relate to any aspect of, the decision problem, whether tangible or not. After building the hierarchy, decision makers then evaluate the elements systematically by making well thought comparisons in pairs.
Here, AHP necessitates that human judgments should be used while making the comparisons over and above the underlying information to make evaluations between the elements (Jamil and Linkov, 2004). AHP then changes the evaluations into numerical values which can be compared and processed in the whole decision problem. Numerical weights are generated for all the hierarchical elements. This allows for diversity and enables the decision maker to compare consistently and rationally between incommensurable elements.
It is this ability that distinguishes AHP from all other techniques if MCDA (Jamil and Linkov, 2004). Finally, numerical values are calculated for all the alternatives. The numerical value of each alternative represents the ability of such an alternative to accomplish the goal of the decision. This makes it possible to directly consider the ability of all available alternatives using their numerical value so that a rational decision maker chooses the alternative with the highest value since it indicates that such an alternative will best suit a solution to the decision problem (Jamil and Linkov, 2004).