INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING RESEARCH AND DEVELOPMENT (IJIERD)

INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING RESEARCH AND DEVELOPMENT (IJIERD) International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 ISSN 0976 6979 (Print) ISSN 0976 6987 (Online) Volume 4, Issue 1, January - April (2013), pp. 10-18 IAEME: www.iaeme.com/ijierd.asp Journal Impact Factor (2013): 5.1283 (Calculated by GISI) www.jifactor.com IJIERD I A E M E DETERMINATION OF IMPORTANCE OF CRITERIA: ANALYTIC HIERARCHY PROCESS (AHP) IN TECHNOLOGICAL EVOLUTION OF AUTOMOBILE STEERING Rajnish Katarne 1, Dr. Jayant Negi 2 1 (Mechanical Engineering Department, Swami Vivekanand College of Engineering/ Rajiv Gandhi Proudhyogiki Vishwavidyalaya, Bhopal, M.P., India) 2 (Mechanical Engineering Department, Swami Vivekanand College of Engineering/ Rajiv Gandhi Proudhyogiki Vishwavidyalaya, Bhopal, M.P., India) ABSTRACT The Analytic Hierarchy Process (AHP) is a popular multi-criteria decision making method. It provides ratio-scale measurements of the priorities of elements in various levels of a hierarchy. These priorities are obtained through pairwise comparisons of elements in one level with reference to each element in the immediate higher level. In this research paper an attempt has been made to present/discuss the principles and techniques of the Analytic Hierarchy Process (AHP) in the prioritization/selection of criteria of automobile steering technology. AHP is one of the mathematical models available to guide the decision theory. However, judgment making is, in its totality, a cognitive and mental process derived from the most possible adequate selection based on tangible and intangible criteria which are promptly chosen by those who make the decisions. It also demonstrates AHP in a step-by-step manner, where the resulting/emerging precedence are shown and the possible inconsistencies are identified. Keywords: Analytic Hierarchy Process, Automobile Steering, Multi-Criteria Decision- Making, Pair wise Comparisons. 1. INTRODUCTION Dynamic business environment, rapid technological change(s) and increasing customers awareness are posing major challenges in today s business. Technology-based 10

companies look for R&D investment in emerging technologies as a key solution. Successful implementation of technologies can strongly boost a company s competitiveness. However, due to funding constraints/compulsion, companies must cautiously evaluate/ estimate technologies before they invest. The integration of the activities derived from disciplines of science and engineering, the essential and related, functional administrative disciplines and managing them in order to meet the operational objectives of an enterprise could be a possible definition for technology management. These concepts of technology management need special attention in the field of automobile sector. Automobile sector industry has gone through several changes in the past. These changes have become more frequent in resent past, and are likely to increase in future. To mention a few among them are Fuel system, Steering system, Brake system, Emission control system. (Figure 1 to Figure 3) These technological changes necessitate the automobile industries to manage and adapt themselves when these changes take place. The scientific and technological research base available in the country is substantial. This needs to be fully exploited to take advantage of emerging opportunities. The country, as a whole, needs to be poised for development and fully primed towards attainment of technological excellence. Technology management provides an important tool to achieve this objective. The companies have a strong tendency to provide new products to their respective markets that give a feeling of satisfaction and confidence to their customer. Fig. 1: Technological Evolution of brakes Fig. 2: Technological Evolution of fuel injection 11

2. LITERATURE REVIEW Fig. 3: Technological Evolution of steering Thomas L. Saaty, given The Analytic Hierarchy Process (AHP) is a theory of measurement through pairwise comparisons and relies on the judgments of experts to derive priority scales. It is these scales that measure intangibles in relative terms. The comparisons are made using a scale of absolute judgments that represents how much more; one element dominates another with respect to a given attribute. [1] The Analytic Hierarchy Process A multicriteria decision making approach in which factors is arranged in a hierarchic structure [2]. Belton and Gear (1983) observed that the AHP may reverse the ranking of the alternatives when an alternative identical to one of the already existing alternatives is introduced. In order to overcome this deficiency, Belton and Gear proposed that each column of the AHP decision matrix to be divided by the maximum entry of that column. Thus, they introduced a variant of the original AHP, called the revised-ahp. Later, Saaty (1994) accepted the previous variant of the AHP and now it is called the Ideal Mode AHP. Besides the revised-ahp, other authors also introduced other variants of the original AHP. The fact that rank reversal also occurs in the AHP when near copies are considered, has also been studied by Dyer and Wendell (1985). Saaty (1983a and 1987) provided some axioms and guidelines on how close a near copy can be to an original alternative without causing a rank reversal. He suggested that the decision maker has to eliminate alternatives from consideration that score within 10 percent of another alternative.[3] AHP helps capture both subjective and objective evaluation measures, providing a useful mechanism for checking their consistency relative to considered alternatives, thus reducing bias in decision making. AHP concepts can be applied to problems of size estimating in support of cost modeling. [4] In many industrial engineering applications the final decision is based on the evaluation of a number of alternatives in terms of a number of criteria. This problem may become a very difficult one when the criteria are expressed in different units or the pertinent data are difficult to be quantified. The Analytic Hierarchy Process (AHP) is an effective approach in dealing with this kind of decision problems. [3] The Analytic Hierarchy Process (AHP) has been proposed in recent literature as an emerging solution approach to large, dynamic, and complex real world multi-criteria decision making problems, such as the strategic planning of organizational resources and the justification of new manufacturing technology. [5] Nathasit Gerdsria, & Dundar F. Kocaoglu emphasizes how the Analytical Hierarchy Process (AHP) is applied as a part of the TDE framework. The TDE is developed to transform the technology roadmapping approach to a level in which it is dynamic, flexible, and operationalizable. This new approach provides an effective way to help organizations to overcome the challenge of keeping a roadmap alive. Authors Integrate AHP into the TDE framework. Time-based format with multilayer linking is used. [6] Technology Development Envelope (TDE) tool supports in the technology roadmapping, Daim Tugrul, Gerdsri Northeast, Kockan Irmak, and Kocaoglu Dundar used for the automobile sector. TDE is a combination of Technology forecasting, Technological characterization, Technology assessment, Hierarchical modeling, and Mathematical modeling. [7] 12

3. THE ANALYTIC HIERARCHY PROCESS OVERVIEW The Analytic Hierarchy Process (AHP) is a theory of measurement through pairwise comparisons and relies on the judgments of experts/ professional to derive preference scales. It is these scales that measure intangibles in relative terms. The comparisons are made using a scale of absolute judgments that represents, how much more, one element relates to another with respect to a given attribute. The judgments may be inconsistent, and how to measure inconsistency and improve the judgments, when possible to obtain better consistency is a concern of the AHP. 3.1 Analytical Hierarchy Process Background The precepts of AHP are reflected in observations of workings of the human mind. When confronted with a complex problem, humans tend to group elements of the problem by certain properties that we believe we can compare. Since factors of a decision are usually interrelated, it is necessary to establish a measuring scheme that allows each factor to influence the goal in proportion to its importance relative to all other factors. This poses the question for each comparison factor: How strongly do the factors at the lowest level of the hierarchy influence the top factor (goal)? In most cases, the answer to this is that each has a non-uniform influence, which necessitates use of an intensity measure one that not only defines the most influential factors, but also yields relative measures of influence differentials. AHP uses simple pairwise comparison of components of a decision to produce intensity measures.[4] 3.2 Conceptual Hierarchical Model The evaluation model (N. Gerdsri 2007) is used/adopted in a hierarchical format with four levels: objective, criteria, factors, and characteristic metrics as shown in Fig 4. Fig. 4: Hierarchical model for evaluating emerging technologies.[6] It uses a multi-level hierarchical structure of objectives, criteria, sub criteria, and alternatives. The pertinent data are derived by using a set of pairwise comparisons. 13

3.3 Fundamental Scale The decision-maker expresses his/her opinion regarding the relative importance of the criteria and preferences among the alternatives by making pairwise comparisons using a ninepoint system ranging from 1 (the two choice options are equally preferred) to 9 (one choice option is extremely preferred over the other) (Table 1). The AHP scoring system is a ratio scale where the ratios between values indicate the degree of preference. The nine-point scale has been the standard rating system used for the AHP (Saaty, 2000). Its use is based upon research by psychologist George Miller, (1956) which indicated that decision makers were unable to consistently repeat their expressed gradations of preference finer than seven plus or minus two. [8] Intensity of Importance Table 1: Fundamental Scale of Absolute Numbers [9] Definition Explanation 1 Equal Importance Two activities contribute equally to the objective 2 Weak or slight 3 Moderate importance Experience and judgment slightly favor one activity over another 4 Moderate plus 5 Strong importance Experience and judgment strongly favor one activity over another 6 Strong plus 7 Very strong or demonstrated 8 Very, very strong An activity is favored very strongly over another; its dominance demonstrated in practice 9 Extreme importance The evidence favoring one activity over another is of the highest possible order of affirmation 1.1 to1.9 When activities are very close a decimal is added to 1 to show their difference as appropriate A better alternative way to assigning the small decimals is to compare two close activities with other widely contrasting ones, favoring the larger one a little over the smaller one when using the 1 9 values. Reciprocals of above Measurements from ratio scales If activity i has one of the above nonzero numbers assigned to it when compared with activity j, then j has the reciprocal value when compared with i A logical assumption When it is desired to use such numbers in physical applications. Alternatively, often one estimates the ratios of such magnitudes by using judgment 14

4. AHP MODELING OF AUTOMOBILE STEERING The AHP provides a convenient approach for solving complex Multi-criteria decisionmaking (MCDM) problems in engineering. However, as in this paper an attempt has been made to apply decision-making tool in the context of automobile steering technologies. The decision-maker needs to be very cautious that when some alternatives appear to be very close with each other. The following section describes the application of AHP model of automobile steering technologies: 4.1. AHP Modeling 4.1.1. STEP1: Technology Characterization Table 2 describes the criteria and corresponding factors affecting each criteria in achieving the objective technological competitiveness for automobile steering. Six criteria and factors associated with each criterion on the measure of effectiveness were finalized. Table 2 : Hierarchical structure Objective Criteria Factors F11: Steering cost F21: Maintenance cost F21:System Efficiency F22:System control technology F23:Vehical Width F24:Dureability To achieve technological competitiveness for Steering mechanism C3: F31:Turning Radius F32:Technology F33: Effort required C4: Market F41: Availability of vehicle F42: Sales Volume C5: Serviceability, Maintenance and Reliability 15 F51: Time period of service F52: Maintenance Time F53: condition F54: % of failure of steering C6: Flexibility F61: Upgrade ability F62: Adjustment as per driver 4.1.2. STEP 2: Hierarchical Modeling Experts provided their comparative judgments for each pair of criteria and factors. The inputs were analyzed to determine the relative priority of criteria as well as the relative importance of factors associated with each criterion this are in Table 3 to Table 7.

Table 3: Cost comparative judgments C3: C4: Market C5:Serviceability, Maintenance and Reliability C6:Flexibility Table 4: comparative judgments C3: C4: Market C5:Serviceability, Maintenance and Reliability C6:Flexibility C3: C3: C3: Table 5: comparative judgments C4: Market C5:Serviceability, Maintenance and Reliability C6:Flexibility Table 6: Market comparative judgments C4: Market C5:Serviceability, Maintenance and Reliability C4: Market C6:Flexibility Table 7: Serviceability, Maintenance and Reliability comparative judgments C5: Serviceability, Maintenance and Reliability C6:Flexibility 16

5. PAIR WISE COMPARISONS MATRIX OF STEERING FACTORS Criteria in the left vertical column are compared with the criteria in the top row and the comparisons scored with the 1 9 system (in Table 3). A comparison is assigned a reciprocal score if the item in the left vertical column is preferred less than that in the top row. Each criterion compared with itself results in a diagonal of 1s (i.e. equal preference). A similar scoring table would also be developed for each alternative criterion. [10] There are two pairwise comparison matrices, these criteria are: Cost,,, Market, Serviceability Maintenance and Reliability and last Flexibility. Experts provided their comparative judgments to determine the Weightage of the criteria with respect to the goal, which is shown here in Table 8 Table 8 : Pairwise comparison of steering main criteria matrix with respect to the Goal C1 C2 C3 C4 C5 C6 C1 1 5/3 7/2 3 7/2 3 C2 3/5 1 3/5 2 3/7 2 C3 2/7 5/3 1 3 2/9 3/2 C4 1/3 1/2 1/3 1 2/7 2 C5 2/7 7/3 9/2 7/2 1 7 C6 1/3 1/2 2/3 1/2 1/7 1 C1: Cost,, C3:, C4: Market, C5: Serviceability, Maintenance and Reliability, C6: Flexibility The relative importance of one criterion over another can be expressed by using pairwise comparisons, and calculated Eigenvector values for each criteria shown in Table 9. Table 9 : Eigenvector Value for criteria 1 to criteria 7 C1 C2 C3 C4 C5 C6 Eigenvector C1 1 1.666 3.5 3 3.5 3 0.34 C2 0.6 1 0.6 2 0.4285 2 0.11 C3 0.2857 1.6666 1 3 0.2222 1.5 0.12 C4 0.3333 0.5 0.3333 1 0.2857 2 0.08 C5 0.2857 2.3333 4.5 3.5 1 7 0.29 C6 0.3333 0.5 0.6666 0.5 0.1428 1 0.07 17

Table 10: Weightage of steering technology criteria Criteria Eigen Value/ Rank/Importance Weightage C1 Cost, 0.34 The Most Important Criterion C2 0.11 Fourth Most Important Criterion C3 0.12 Third Most Important Criterion C4 Market 0.08 Fifth Most Important Criterion C5 Serviceability, Maintenance 0.29 Second Most Important Criterion and Reliability C6 Flexibility 0.07 Sixth Most Important Criterion 6. CONCLUSIONS AND DISCUSSION Weightages of criteria with respect to each other have been determined by using pair wise comparison. It has been found that Cost has been adjusted as the most important criteria amounts all. These weightage will be used for further analysis of factors, which will finally lead to facilities in selection of appropriate technology. REFERENCES [1] Thomas L. Saaty, Decision making with the analytic hierarchy process, International Journal Services Sciences, 1(1), 2008, 83-98 [2] Thomas L. Saaty, How to make a decision: The Analytic Hierarchy Process, European Journal of Operational Research 48, 1990, 9-26 [3] Evangelos Triantaphyllou and Stuart H. Mann, Using the analytic hierarchy process for decision making in engineering applications: some challenges, International Journal of Industrial Engineering: Applications and Practice, 2(1), 1995, 35-44 [4] Bruce E. Fad, Analytical Hierarchy Process (AHP) Approach to Size Estimation, [5] Jiaqin Yang and Ping Shi,Applying Analytic Hierarchy Process in Firm's Overall Evaluation: A Case Study in China, International Journal of Business, 7(1), 2002, 29-45 [6] Nathasit Gerdsri and Dundar F. Kocaoglu, Applying the Analytic Hierarchy Process (AHP) to build a strategic framework for technology roadmapping, Mathematical and Computer Modelling, 46, 2007, 1071 1080 [7] DAIM Tugrul, GERDSRI Nathasit, KOCKAN Irmak and KOCAOGLU Dundar, Technology Development Envelope Approach for the Adoption of Future Powertrain Technologies: A Case Study on Ford Otosan Roadmapping Model, Journal of transportation Systems engineering and information technology, 11(2), 2011, 58-69 [8] N. gerdsri, An analytical approach to building a technology development envelope (TDE) for roadmapping of emerging technologies, International Journal of Innovation and Technology Management, 4(2), 2007, 121 135 [9] Thomas L. Saaty, Relative Measurement and Its Generalization in Decision Making Why Pairwise Comparisons are Central in Mathematics for the Measurement of Intangible Factors The Analytic Hierarchy/Network Process, Rev. R. Acad. Cien. Serie A. Mat., 102 (2), 2008, 251 318 [10] Theresa Mau-Crimmins, J.E. de Steiguer and Donald Dennis, AHP as a means for improving public participation: a pre post experiment with university students, Forest Policy and Economics 7, 2005, 501 514 [11] Dr. R. Dilli Babu, R.Baskaran and Dr.K.Krishnaiah, Evaluation of Bus Depots using AHP International Journal of Industrial Engineering Research and Development (IJIERD), Volume 1, Issue 1, 2010, pp. 49-63, ISSN Print: 0976 6979, ISSN Online: 0976 6987. 18