A Grey-Based Risk Decision Making Approach to Prevention and Mitigation Strategy

Transcription

1 , pp A Grey-Based Risk Decision Making Approach to Prevention and Mitigation Strategy KyoungJong Park 1 1 Gwangju University, Gwangju, Republic of Korea, kjpark@gwangju.ac.kr Abstract. The sustainability of an enterprise depends on an effort that prevents a risk which does not occur and an effort that mitigates the impact of a risk which occurs. The various risks influence to enterprises. If a supply chain does not effectively control the preventable risks which give a warning to a corporation, the risks of the supply chain occur in practice. Then the resulting risks influence on the firm in some way or other. The resulting risks give less or much influence on a company, according to the response capacity and strategy. This paper, therefore, proposes a method which determines the priority of risks which encompass a preventable risk that may occur and a resulting risk that occurs. The method obtains an opinion from the risk decision makers and proposes a risk priority using a grey-based risk decision making model to decrease the impact of risk in a supply chain. Keywords: sustainability, grey-based risk decision making, preventable risk, resulting risk, risk priority, supply chain 1 Introduction The risk of a supply chain is continuously increasing because of its uncertainty and complexity. A supply chain has a risk which means disruption and destruction and it gives consistently negative effects to the chain 1]. The problem of a supply chain is explained as risk, destruction, disruption, and etc. And the activities to overcome it are explained as sustainability, resilience, and etc. 2] 3]. Zsidisin & Ritchie 4] defined a risk as uncertainty based on a well grounded (quantitative) probability and expressed it as Risk = (the probability that some event will occur) (the consequences if it does occur). Chopra & Sodhi 5] assorted a risk into system risk, forecast risk, intellectual property risk, procurement risk, receivables risk, inventory risk, and capacity risk. Tang & Musa 6] classified it as material flow risk, financial flow risk, and information flow risk and evaluated it. The studies described, so far, categorized the sort of risks and focused their studies on how a risk influces on a supply chain. But, enough research has not carried in that a risk gives a different impact among companies and the result of a risk in a supply chain is different according to the response strategy of the supply chain. This study, therefore, proposes a method which determines a processing sequence of preventable risks and resulting risks, reflecting the possibility of a risk and the intensity of a risk. Basically, this paper uses a grey-based multicriteria risk decision making method and follows a procedure which evaluates partially known risk information (grey values) and then proposes a risk response strategy. In Section 1, this paper explained risk information and research purposes and a grey-based risk decision making approach is explained in Section 2. In Section 3, this study explains an ISSN: ASTL Copyright 2015 SERSC

2 experimental process of the a grey-based risk decision making approach. Finally, the conclusions and future works are discussed in Section 4. 2 A Grey-based Risk Decision Making Model Li et al. 7] applied to a grey-based decision-making approach for a supplier selection problem. Britto et al. 8] applied to a grey-based ELECTRE approach for risk sorting of supply chains. Memon et al. 9] combined grey system theory and uncertainty theory and applied to supplier selection. A supply chain is difficult to gather accurate information about occurrence time, occurrence frequency, and intensity of a risk and the supply chain cannot accurately measure the impact of a risk which influences on a company. This study, therefore, uses grey system theory to reflect the uncertainty and inconsistency of information, to prevent a risk in advance, and to minimize the result that a risk gives to a company 10]. Grey system theory is a method which considers the condition of fuzziness and flexibility about insistent information in group decision making situations. That is, the method is one of the methods which solve complexity and uncertainty problems under discrete data and incomplete information. A grey-based approach uses a grey number which lies between known and unknown information. The grey number is expressed by the boundaries of the grey number which lies between known and unknown information in Figure 1 7]. Fig. 1. Boundaries of grey value It assumes that is a discrete set of m possible alternatives and is a set of n attributes to apply a grey-based risk decision making approach. The attributes are additively independent. And is vector of attribute weights of n attributes. The steps of grey-based risk decision making model are summarized as follows. Step 1: Gathering of decision makers opinion about attributes and alternatives Step 2: Evaluation of grey weight of each attribute x ( x ) (1) where x is the attribute weight of jth attribute with dth decision maker and can be described by grey number, x. Step 3: Evaluation of attribute rating value of each alternative (2) where x is the attribute rating value of ith alternative and jth attribute with dth decision maker. Step 4: Establishment of grey weighted average and grey decision matrix Copyright 2015 SERSC 147

3 ] (3) where x is linguistic variables based on the grey number. Step 5: Normalization of grey decision matrix ] (4) where for a maximization (benefit) attribute, x is expressed as x and { } And for a minimization (cost) attribute, x is expressed as x and { } Step 6: Establishment of the weighted and normalized grey decision matrix ] (5) where x. This step considers the different importance of each attribute. Step 7: Making of the ideal alternative This step sets the ideal alternative as referential alternative. For m possible alternatives set,, the ideal referential alternative, x, can be established by Eq. (6). ] ] (6) { ] } Step 8: Calculation of the grey possibility This step calculates the grey possibility degree and compares alternatives set,, with ideal referential alternative using Eq. (7) {x } (7) Step 9: Ranking the order of alternatives This step ranks the order of alternatives and selects the best alternative from among a set of feasible alternatives. If is smaller, the ranking order of is better. 3 Experimental Process This study proposes a model which considers a preventable risk and a resulting risk which occurs when a preventable risk is not prevented by a company at the same time. The proposed method, therefore, should use a different approach which is not same to usual grey-based approach and Figure 2 explains the difference. 148 Copyright 2015 SERSC

4 Fig. 2. A grey-based risk decision making model Because this paper considers a preventable risk and a resulting risk at the same time, the proposed method gives the weight of an attribute considering the opinion of the supply chain risk experts for a preventable risk and a resulting risk. In Figure 2, therefore, a preventable risk and a resulting risk for an attribute are evaluated through the grey value of an attribute and also a preventable risk and a resulting risk for an alternative are evaluated through the grey value of an alternative. Finally, they are integrated to the weighted and normalized grey decision matrix. Table 1 represents the linguistic attribute weights in grey numbers and Table 2 shows the attribute rating value of alternatives in grey numbers. Table 1. The linguistic attribute weights in grey numbers The seriousness of attribute Linguistic variables grey numbers Very Low (VL) Low (L) Medium (M) High (H) Very High (VH) Table 2. The attribute ratings value of alternatives in grey numbers The rating value of alternative Linguistic variables grey numbers for preventable risk and resulting risk None (N) 0 2 Rare (R) 2 4 Usual (U) 4 7 Serious (S) 7 9 Very Serious (VS) 9 10 This study fulfills the algorithm of the grey-based risk decision making model using Table 1 and 2. Finally, the priority of risks is determined by the algorithm and a company in a supply Copyright 2015 SERSC 149

5 chain can select a risk which is the worst risk. If a company prepares ahead the various strategies to avoid a preventable risk and a resulting risk and then the sustainability of a company will increase. 4 Conclusions and Future Works The accurate decision for the risk is not easily made because information for a risk which occurs in a supply chain is vague and fuzzy. Also, if the prevention strategies for a risk in a supply chain are effectively established in advance, a risk does not occur. Even if a risk occurs, the impact of a risk may be less than the prevention strategies are not established. This study, therefore, proposed a grey-based risk decision making model considering a preventable risk and a resulting risk at the same time to find a risk factor which has the most risk possibility and to avoid the impact of a risk. If a company accepts the warning of a preventable risk, which budget needs more to manufacture a product and sells it in a supply chain and then the firm establishes the procurement of budget or the strategy of budget savings, the risk is removed from the supply chain and the manufacturing and selling process works normally. But, a corporation cannot secure budget, there occurs many problems in the purchasing of raw materials and facilities, facilities maintenance, the payment of wage, and etc. Finally, the supply chain is affected by the problems and then many companies of the chain cost businesses money in lost productivity. This study classified risks into preventable risks and resulting risks and then evaluated its impact and made a priority. But its classification should conducted more detail. In future works, the criteria for classification technique needs to classify them for preventable risks and resulting risks. References 1. Nair, A., Vidal, J.M.: Supply Network Topology and Robustness Against Disruptions- An Investigation Using Multiagent Model. Int. J. Prod. Res. 49(5), (2011) 2. Derissen, S., Quaas, M.F., Baumgartner, S.: The Relationship Between Resilience and Sustainability of Ecological-Economic Systems. Ecol. Econ. 70, (2011) 3. Park, K.J., Kyung, G.: Optimization of Total Inventory Cost and Order Fill Rate in a Supply Chain Using PSO. Int. J. Adv. Manuf. Tech. 70(9-12), (2014) 4. Zsidisin, G.A., Ritchie, B.: Supply Chain Risk. Springer, New York (2008) 5. Chopra, S., Sodhi, M.S.: Managing Risk to Avoid Supply-Chain Breakdown. MIT Sloan Manage. Rev. 46(1), (2004) 6. Tang, O., Musa, S.N.: Identifying Risk Issues and Research Advancements in Supply Chain Risk Management. Int. J. Prod. Econ. 133, (2011) 7. Li, G.D., Yamaguchi, D., Nagai, M.: A Grey-Based Decision-Making Approach to the Supplier Selection Problem, Math. Comput. Model. 46, (2007) 8. Brito, A.J., Almeida, A.T., Mota, C.M.M.: A Multi Criteria Model for Risk Sorting of Natural Gas Pipelines Based on ELECTRE TRI Integrating Utility Theory. Eur. J. Oper. Res. 200(3), (2010) 9. Memon, M.S., Lee, Y.H., Mari, S.I.: Group Multi-Criteria Supplier Selection Using Combined Grey Systems Theory and Uncertainty Theory. Expert Syst. Appl. 42, (2015) 10. Deng, J.L.: The Fundamental Methods of Grey System. Science and Technology of Central China University Press, Wuhan, China, 3--8 (2005) 150 Copyright 2015 SERSC