Analytical Hierarchy Process Essay

Abstract The analytic hierarchy process (AHP) provides a structure on decision-making processes where there are a limited numbers of choices but each has a number of attributes. This paper explores the use of AHP for deciding on car purchase. In the context of shopping, it is important to include elements that provide attributes that make consumer decision making easier, comfortable and therefore, lead to a car purchase. As the car market becomes more competitive, there is a greater demand for innovation that provides better customer service and strategic competition in the business management. This paper presents a new methodological extension of the AHP by focusing on two issues. One combines pair wise comparison with a spreadsheet method using a 5-point rating scale. The other applies the group weight to a reciprocal consistency ratio. Three newly formed car models of midsize are used to show how the method allows choice to be prioritized and analyzed statistically. INTRODUCTION The Analytic Hierarchy Process (AHP) is a structured technique for helping people deal with complex decisions. Rather than prescribing a â€Å"correct† decision, the AHP helps people to determine one. Based on mathematics and human psychology, it was developed by Thomas L. Saaty in the 1970s and has been extensively studied and refined since then. The AHP provides a comprehensive and rational framework for structuring a problem, for representing and quantifying its elements, for relating those elements to overall goals, and for evaluating alternative solutions. It is used throughout the world in a wide variety of decision situations, in fields such as government, business, industry, healthcare, and education. Users of the AHP first decompose their decision problem into a hierarchy of more easily comprehended sub-problems, each of which can be analyzed independently. The elements of the hierarchy can relate to any aspect of the decision problem. Once the hierarchy is built, the decision makers systematically evaluate its various elements, comparing them to one another in pairs. In making the comparisons, the decision makers can use concrete data about the elements, or they can use their judgments about the elements’ relative meaning and importance. It is the essence of the AHP that human judgments, and not just the underlying information, can be used in performing the evaluations. The AHP converts these evaluations to numerical values that can be processed and compared over the entire range of the problem. A numerical weight or priority is derived for each element of the hierarchy, allowing diverse and often incomm-ensurable elements to be compared to one another in a rational and consistent way. This capability distinguishes the AHP from other decision making techniques. In the final step of the process, numerical priorities are derived for each of the decision alternatives. Since these numbers represent the alternatives’ relative ability to achieve the decision goal, they allow a straightforward consideration of the various courses of action. EXPLANATION For instance let’s consider cars (an example) which touch the lives of hundreds of millions of people nearly everywhere on this planet on a daily basis. Other than a house, a car is perhaps the largest purchase that we make. With the average cost of a car well over US$ 15,000, choosing just the right one becomes a major decision. Buying a new car is regarded as a decision-making problem and a reflection of customer preference. Someone shops for a new car, he or she want to take a look at finances and options. The possible budget is then a constraint in the decision on which car to buy. Most people shopping for a new car rank safety high among their purchase considerations. Other important attributes include: fuel economy; comfort and convenience features; insurance information; specification and warranties and resale value. Constant changes in customer demands lead manufactures to produce new and improved designs. Automation of manufacturing technologies allows this. Recently the production life cycle has become shorter. For example, General Motors in the USA is leading the industry in developing ground-breaking technologies to improve the driving experience and to meet the changing needs and life styles of modern drivers. They are making efforts to lower the cost of the technology to a level that will make advanced cars an attractive purchase. As the automobile market becomes more competitive, the industry has no choice but to adopt innovation that brings better customer service. Many customers seek advice from car experts or friends when purchasing a car. In many cases, there are times when the price and special features do not match the budget. An appropriate decision-making method for selecting the best car is useful to both customers and producers. An analytic method not only reduces the dealer’s burden, but also may increase sales The analytic hierarchy process (AHP) is an intuitively easy method for formulating and analyzing decisions .It was developed to solve a specific class of problems that involves prioritization of potential alternate solutions. This is achieved by evaluation of a set of criteria elements and sub-criteria elements through a series of pair wise comparisons. The AHP model depicted in this paper uses the following decision criteria: exterior, convenience, performance, safety, economic aspect, dealer, and warranty as well as 39 sub-criteria. For the implementation of the AHP, we considered the three midsize passenger car models as alternatives 2. EVALUATION CRITERIA The source for deriving the evaluation criteria candidate was: 1. A telephone interview with dealers who are part of companies that make the models. The manufacturing company with the highest market share considered graceful body styles and smart design of facilities related to safety to be most important. On the other hand, warranty on the car and the dealer’s strategies for marketing are regarded as important customer criteria; 2. The use of personal experiences recorded on an online bulletin board was corrected using the Internet; The AHP model shown consists of three levels. Exterior involves components and factors seen from the outside such as color, length and width, tyres, trunk, wheels, doors and headlamp styles. It includes the following sub-criteria: model, style, length, quality of interior decoration, number of available color types, and instrument cluster. Convenience is related to the design of the equipments for easy operation. It includes: inside width, ease of loading or unloading packages, convenience of operating instruments, modern fittings (such as electronic systems and a burglar alarm), forward visibility and quality of the audio system. Performance is related to the functioning of the car. It includes maximum torque, maximum speed, fuel tank capacity, braking ability, cornering ability, inside noises and traveling comforts. Safety is enhanced by a body designed to protect the drivers and passengers against collisions. The most important safety features are those that reduce the risk of death or serious injury. It includes: airbags, antilock braking system ABS, impact protection systems, trunk safety, seat belts, safety of the body and number of alarm facilities. Airbags provide total chest and face protection. The ABS allows drivers full steering control and shorter stopping distance in adverse situations. The economic aspect refers to the price and cost of a new car, or maintaining the car within budgets, etc. It includes: purchasing prices, fuel consumption per month, insurance costs and installment conditions, resale prices of used cars and optional equipment costs. The dealer criterion refers to personal characteristics and attitudes that lead the customer to make the purchasing decision. This criterion includes: visits or calls needed to persuade the customer to buy, the dealer’s attitude, the dealer’s expertise and belief in the dealer’s promises. The warranty criterion include: the number of service stations, ease of acquiring spare parts, customer satisfaction after services, and the average repair time for minor troubles. We mailed questionnaires to each of two groups. The first group was given a questionnaire that contained a pair wise comparison sheet. The members consisted of 13 managers who were serving in the sales department and who had experience exceeding 10 years (see Appendix A for this questionnaire). Respondents were domain experts who easily recognized their own sales products and have valuable knowledge about the customer requirements and preferences. Twenty-two potential customers with experience over 7 years were in the second group (see Appendix B). They answered about their satisfaction with their current car. 3. Implementation and ranking A procedure of prioritizing each car model is shown in Table 1 shows the C.R. for each individual, where the circle represents meaningful C.R. Using Expert Choice, we obtained the synthesized priorities of the main criteria and sub-criteria. The reason that the group’s weight is 1/C.R. is to assign higher weights for higher consistent persons. As a result, safety gains are the highest priority in the main criteria. The body safety is especially important. The synthesized priorities and ranks resulted in Table 2 (Case-II). The priorities of the sub-criteria are not proportional to those of the main criteria. This means the decision-makers have different opinions on the importance of the main criteria. By synthesizing the drivers’ rating values with the priorities, we obtain the priorities of the car models and the ranks with respect to the goal and synthesized priorities for each main criterion when the C.R. is bounded by the limit (see Table 3). In Case-I and Case-II, the computational methods used are reasonable when the groups’ consistency is more important than the individual ones. Because the conventional AHP has no choice but to increase the Delphi rounds in order to increase the groups’ consistency, much effort is required to reduce the C.R. If the Delphi rounds are not sufficiently processed, it is unreliable through the inclusion of inconsistent matrices [12]. Using the Spearman rank correlation test [1], accepting H0 means that the ranks are either uncorrelated or negatively correlated. That is, two decision makers exhibit an insignificant level of agreement in ranking for each criterion. Rejecting H0 means that the ranks are positively correlated. As shown in Table 4, we conclude that there are significant effects between groups since the rate of the H0 acceptance is 83% (65/78 Ãâ€"100). This shows that Case-I is the more appropriate method. It rejects greater agreement between groups. 4. SENSITIVITY ANALYSIS Sensitivity analysis allowed us to verify the results of the decision. A sensitivity analysis can be formed to see how sensitive the alternatives are to change with the importance of the criteria. The Expert Choice implementation of AHP provides four graphical sensitivity analysis modes: dynamic, gradient, performance and two-dimensional analysis [4]. Here performance sensitivity analysis is employed. It depicts how well each alternative performs on each criterion by increasing or decreasing the importance of the criteria. In addition to this, each sub-criterion performs on each main criterion by increasing or decreasing the importance of the main criteria. It should be noted that if a criterion is not sensitive, it would be better to eliminate it from the AHP model. In the case of increasing importance of a criterion to the maximum value of 1.0, we assigned the alternative that gained the highest rank to score 5 and the lowest rank to score 1. The value of Model 1 is 25, Model 2 is 21 and Model 3 is 15. In summary, we can conclude Model 1 is the best among the alternatives, although the highest priorities were different in Case-I and Case-II. 5. CONCLUSION This paper presents a decision-making method for selecting the best passenger car models through combining the AHP and a spreadsheet model. The C.R. is used as the decision-maker’s weights. As an implementation of the AHP, three car models were prioritized. Through the sensitivity analysis, the fact that Model 1 ranked the highest is consistent with the result of the highest market share. References E.H. Forman, T.L. Saaty, M.A. Selly, R. Waldron, Expert Choice, Decision Support Software, McLean, VA, 1983 T.L. Saaty, K. Kearns, Analytical Planning: The Organization of Systems, Pergamon Press, Oxford, 1985. Y Wind and T L Saaty, ‘Marketing Applications of Analytic Hierarchy Process,’ Management Science, Vol. 26, No. 7, July 1980