# COMPARISON OF AHP AND ANP METHODS FOR RESILIENCE MEASUREMENT IN SUPPLY CHAINS. Pavel WICHER, Radim LENORT

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2 2. CHARACTERISTICS OF AHP AND ANP The AHP and ANP methods are multistage decomposition methods used to solve decision-making problems involving more than one criterion of optimality. The basic idea is to create a decision-making structure and the subsequent evaluation of importance of the individual links among the interconnected elements. These evaluations are represented by weights, which can be determined on the basis of Saaty method of pair comparison or by normalizing direct measurements. The basic differences between the two methods are represented by the options and rules applied during the creation of a decision-making structure. The AHP method [20], [21] is a specific case of ANP method, in which the structure is formed as a hierarchy. The hierarchy is always linear and may contain any number of levels and elements. The arrangement of levels in a hierarchy goes from the general (higher level) to the specific (lower level). There are certain links and relations between the elements in successive levels. The intensity of these relations is expressed numerically. The procedure used for obtaining the final results contained in the practical part can be found in article [16]. The ANP method [22] is a general procedure, in which the structure is created as a network. The network may consist of any links among all the elements. The ANP does not limit human understanding and experience to force decision-making into a highly technical model that is unnatural and contrived. It is in essence a formalization of how people usually think, and it helps the decision-maker keep track of the process as the complexity of the problem and the diversity of its factors increase [23]. The procedure used for obtaining the final results contained in the practical part can be found in article [17]. 3. SUPER DECISIONS SOFTWARE The SuperDecisions software implements the Analytic Network Process for decision making with dependence and feedback developed by Dr. Thomas Saaty. The program was written by the ANP Team, working for the Creative Decisions Foundation. The software allows you to create any network characterizing the examined problem. You can also set graphical illustrations of the individual elements, allowing for easier orientation in the studied problem. When determining the importance of the mutual links, you can use different types of methods of entering the values, including: graphical, verbal, matrix, questionnaire and direct. This decision-making process automatically involves the calculation of inconsistency and, in case of a negative outcome, the software marks the problematic elements and recommends changes. The partial and final results of the decision-making problem are characterized by the following outcomes: Unweighted Super Matrix Limit Matrix Weighted Super Matrix Priorities Cluster Matrix Synthesize The software also makes it possible to perform a complex sensitivity analysis which, however, will not be applied in the case study. 4. SIMPLIFIED CASE STUDY The concept of the measurement system of industrial supply chain resilience is based on the decomposition of resilience, as the ability of a supply chain to return to its original state in case of its serious disturbance, to such a level of resilience sub-abilities, which will allow defining the tangible and measurable indicators for evaluation of resilience of the entire supply chain. The basis is to determine the key resilience sub-abilities, which will enable effective protection of the supply chain against anticipated vulnerability factors [16], [17]. The selected key resilience sub-abilities are [14]:

3 Adaptability (AD) ability to modify operations in response to challenges or opportunities: o Elements selected for (AD) measurement: Lead time (LT), Frozen planning period (FP). Financial Strength (FS) capacity to absorb fluctuations in cash flow: o Elements selected for (FS) measurement: Liquidity (LR), Profitability (PR). Collaboration (CO) ability to work effectively with other entities for mutual benefit. A hierarchical structure of AHP method created in SuperDecisions software is illustrated in Figure 1. The calculation procedure is described in [16]. Fig. 1 Hierarchy structure of metallurgical supply chain resilience Network structure for ANP method created in SuperDecisions software is shown in Figure 2. (calculation procedure is described in [17]. Fig. 2 Network structure of metallurgical supply chain resilience

4 The difference in comparison with AHP is in the addition of links between the selected elements. The links have been determined on the basis of an expert assessment in the following manner: Lead time (LT) is influenced by Collaboration (CO), Liquidity (LR) and Profitability (PR). Frozen planning period (FP) is influenced by Collaboration (CO), Liquidity (LR) and Profitability (PR). Collaboration (CO) is influenced by Profitability (PR). Liquidity (LR) is influenced by Collaboration (CO) and Lead time (LT). Profitability (PR) is influenced by Collaboration (CO), Lead time (LT), Frozen planning period (FP). 5. RESULTS AND THEIR DISCUSSION The final results used to determine the weights of the individual elements in the structure of resilience measurement can be shown in two versions, when SuperDecisions software is used: 1. by means of Limit Matrix, 2. by means of a table of Priorities. When applying the AHP method, the most important sub-ability was Collaboration (CO), which gained the weight of almost 66%. The second place was taken by Financial Strength (FS) with a gain of 26%, and the least important factor was Adaptability (AD) with 8%. The resulting values of the specific indicators are shown in Figure 3 (the last column marked Limiting). Fig. 3 The final weights of AHP application When applying the ANP method, the most important selected sub-ability was Financial Strength (FS), which gained the weight of almost 44%. The second place was taken by Collaboration (CO) with a gain of 41% and the least important was Adaptability (AD) with less than 15%. The resulting values of specific indicators are shown in Figure 4 (the last column marked as Limiting). The final weights of the individual indicators during the application of the AHP and ANP methods are significantly different. The main difference is the high increase in the importance of Profitability indicator, whose priority was higher by 37% when the ANP method was applied. The reason is that it takes into consideration the real interdependence among the individual indicators when measuring the supply chain resilience by means of the given method. The expert assessment has shown that all indicators are influenced by the Financial Strength, and always much more by Profitability than Liquidity. The reason is the

5 necessary condition to have sufficient investment capital for building the remaining abilities increasing the supply chain resilience. Fig. 4 The final weights of ANP application 6. CONCLUSION Both methods of multi-criteria decision-making have confirmed their applicability in evaluating and measuring the resistance of a supply chain: The advantage of AHP method is its simplicity, which makes its applicability in managerial practice in supply chain management easier. The advantage of ANP method is its complexity, which allows you to capture the complex structures of real interconnections during the assessment of the supply chain resilience. The decision on the choice of method always depends on the supply chain management, which must take into account the reasons, circumstances and source options of the performed evaluation and measurement. Further research must focus on creating a complex taxonomy of the abilities increasing the resilience of the supply chain and the possibilities of the final evaluation of the determined results. ACKNOWLEDGEMENTS The work was supported by Internal Grand Agency of ŠKODA AUTO, a.s. No. IGA/2012/3. REFERENCES [1] CHRISTOPHER M., PECK H. Building the resilient supply chain. International Journal of Logistics Management, Vol. 15, No. 2, 2004, pp [2] Cranfield University. Supply Chain Vulnerability: Executive Report. School of Business. Cranfield: Cranfield University, [3] FIKSEL J. Sustainability and Resilience: Towards a System Approach. Sustainability: Science, Practice & Policy. 2006, Vol. 2, No. 2, pp [4] SHEFFI Y., RICE J. A Supply Chain View of the Resilient Enterprise. MIT Sloan, Vol. 47, No. 1, 2005, pp [5] CARVALHO H., MACHADO V. C. Designing principles to create resilient Supply Chains. In Industrial Engineering Research Conference. Caracas: Catholic University Andres Bello, G. Bayraksan, W. Lin, Y. Son, and R. Wysk, eds [6] FALASCA M. et al., A Decision Support Framework to Assess Supply Chain Resilience. In 5th International ISCRAM Conference. Washington DC: F. Fiedrich and B. Van de Walle, eds

6 [7] World Economic Forum. Building Resilience in Supply Chains: Report. Switzerland: World Economic Forum, [8] MILLER K. A. Framework for integrated risk management in international business. Journal of International Business Studies, Vol. 23, No. 2, 1991, pp [9] PETTIT T. J., FIKSEL J. Ensuring Supply Chain Resilience: Development of a Conceptual Framework. Journal of Business Logistics, Vol. 31, No. 1, 2010, pp [10] VLČKOVÁ V., PATÁK M. Role of Demand Planning in Business Process Management. In 6th International Scientific Conference Bussiness and Management Vilnius: Business and Management - Spausdinta, Vol. 2, 2010, pp [11] BRANSKA L. Management of Supporting Processses in Quick Response System. In 6th International Scientific Conference Business and Management Vilnius: Business and Management - Spausdinta, Vol , pp [12] BUKOWSKI L. A., FELIKS J. Multi-Dimensional Concept of Supply Chain Resilience. In CLC 2012: Carpathian Logistics Congress. Ostrava: TANGER, 2012, pp [13] SHUAI Y. Research on Measuring Method of Supply Chain Resilience based on Biological Cell Elasticity Theory. In IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, 2011, pp [14] PETTIT T. J. Supply Chain Resilience: Development of a Conceptual Framework, an Assessment Tool and an Implementation Process. Dessertation. The Ohio State University, [15] LENORT R., WICHER P. Assessing the supply chain resilience. In TLM 2012: XVI konferencja logistyki stosowanej: Total Logistic Management. [CD-ROM] Zakopane, 2012, 10 p. [16] LENORT R., WICHER P. Concept of a System for Resilience Measurement in Industrial Supply Chain. In METAL 2013: 22nd International Conference on Metallurgy and Materials. [CD-ROM] Ostrava: TANGER, 2013, p [17] WICHER P., LENORT R. The Possibility of Applying ANP Method for Resilience Measurement in Supply Chains. In CLC 2013: Carpathian Logistics Congress. [CD-ROM] Krakow, 2013, 7 p. [18] MAROUŠEK R. Possible Application of Quality House in Industrial Supply Chain Resilience Assessment. In METAL 2013: 22nd International Conference on Metallurgy and Materials. [CD-ROM] Ostrava: TANGER, 2013, p [19] MAROUŠEK R., NOVOTNÝ P. Automotive, Aerospace and Logistics 3PL Industrial Supply Chain Resilience Assessment. In CLC 2013: Carpathian Logistics Congress. Ostrava: TANGER, 2013, 7 p. [20] SAATY T. L., VARGAS L. G. Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. New York: Springer, [21] SAATY T. L. Decision making with the analytic hierarchy process. International Journal of Services Sciences, Vol. 1, No. 1, 2008, pp [22] SAATY T. L., VARGAS L. G. Decision Making with the Analytic Network Process: Economic, Political, Social and Technological Applications with Benefits, Opportunities, Costs and Risks. New York: Springer, [23] Super Decisions: an Introduction [online]. CREATIVE DECISIONS FOUNDATION. Last update [cit ]. Available from:

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