Analysis of supply chain risk mitigation integrated with fuzzy logic, house of risk and AHP (Case study at CV. Multiguna)

Analysis of supply chain risk mitigation integrated with fuzzy logic, house of risk and AHP (Case study at CV. Multiguna) Cite as: AIP Conference Proceedings 2097, 030091 (2019); https://doi.org/10.1063/1.5098266 Published Online: 23 April 2019 Ari Andriyas Puji, Agus Mansur, and Imam Djati Widodo AIP Conference Proceedings 2097, 030091 (2019); https://doi.org/10.1063/1.5098266 © 2019 Author(s). 2097, 030091 Analysis of Supply Chain Risk Mitigation Integrated with Fuzzy Logic, House Of Risk and AHP (Case Study at CV. Multiguna) Ari Andriyas Puji1,a), Agus Mansur2,b) and Imam Djati Widodo3,c) 1 Post Graduate Program, Industrial Management Department, Faculty of Industrial Technology, Islamic University of Indonesia Jalan Kaliurang KM 14,5, Umbulmartani, Ngemplak, Krawitan, Umbulmartani, Ngemplak, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55584, Indonesia 2 Industrial Engineering Department, Faculty of Industrial Technology, Islamic University of Indonesia Jalan Kaliurang KM 14,5, Umbulmartani, Ngemplak, Krawitan, Umbulmartani, Ngemplak, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55584, Indonesia a) Corresponding author: [email protected] b) [email protected] c) [email protected]. Abstract. Risks are potential to be incurred in the supply chain activity.Therefore risk management is indispensable for handling therisk. The research was carried out in the CV. Multiguna in Krikilan, Sariharjo, ngaglik, Sleman, Yogyakarta, 55581. In the process of the supply chain at CV. Multiguna had chances onset of risk.Therefore, it is necessary to identify risks and design mitigation. This research was conducted using fuzzy logic approach, house of risk and AHP. House of risk model consists of two phases. The first phase covers risk identification and risk agents. The calculation of the value of aggregate risk on ARP priority is done by using fuzzy logic approach to measure the severity and occurrence. From the interviews and discussions that resulted in supply chain activity at CV. Multiguna, there are 18 events and 16 identified agent risks and relevant risks. Pareto diagram of the results obtained 5 selected risk agents with ratio of 60:40 in order to focus on risk mitigation actions. After conducting discussions and interviews, it was obtained 10 preventive actions as the input value for house of risk phase 2. The mapping from House of risk phase 2 resulted10 mitigation options, according to the value of ETD (effectiveness to difficulty). Then, the 10 mitigation options are reprocessed using the AHP. AHP is used to determine priorities based on preference of policy’s makers with some criteria. From AHP processing, it was obtained the consistency value as 0.09 and this value was considered valid for CR <0.1. Alternative complement negotiation for supporting the infrastructure was elected as the first priority with a value of 0.160, mitigation alternative treatments was followed by the regular machine maintenance with a value of 0.143 and eight other alternatives are ordered from large to small. INTRODUCTION The supply chain can be defined as a network consisting of several companies (including suppliers, manufacturers, distributors and retailers) who collaborate and engage both directly and indirectly in meeting consumer demand, where these companies carry out material procurement functions, material transformation processes into semi-finished products and finished products, as well as the distribution of finished products to the end customer [1]. The supply chain processes encounter various risks that can affect the supply chain flow cannot run smoothly. Business managers must increase their awareness on the potential risks that could endanger both the short and long term processes. The research objective of this supply chain is to provide a working picture to manage risk proactively. The work description in this research can be useful for companies to choose some risks to be prioritized to be addressed. Risks in the supply chain can be defined as disruption of information and resource flows in supply chain networks due to uncertain terminations and variations [2]. This research design uses the integration method of several methods, namely Fuzzy Logic, House of Risk and Analytical Hierarchy Process. This approach begins by identifying the risks that enter the Phase 1 HOR stage. Then managing fuzzy data as the input value of severity and occurrence. Furthermore, phase 1 HOR output is the risk The 4th International Conference on Industrial, Mechanical, Electrical, and Chemical Engineering AIP Conf. Proc. 2097, 030091-1–030091-8; https://doi.org/10.1063/1.5098266 Published by AIP Publishing. 978-0-7354-1827-1/$30.00 030091-1 agent priority to be handled. HOR Phase 2 is intended to prioritize the mitigation actions that must be carried out by the company to maximize the cost effectiveness of selected risk agents in Phase 1. HOR. risk mitigation in accordance with his wishes. Similar research was conducted by [3] in her journal Operational Risk Analysis at the Battleship Division of PT. PAL Indonesia with the House of Risk Method, one of the objectives of this research is to identify risk events and determine risk mitigation strategies for problems that arise. Then later [4] House Of Risk (HOR) Model Application for Risk Mitigation in the Leather Raw Material Supply Chain also aimed to identify risk events and determine risk mitigation strategies for existing risks. The difference between this research and the two studies above is about giving severity and occurrence values, managing risk priorities in the final stage, object and subject of research and risk mitigation as the end result. On CV. Multiguna that produces apparels (pants, clothes, cardigan, hijab), make to stock is selected as the production process while market share is still considered. This industry is located in Krikilan, Sariharjo, Ngaglik, Sleman, Yogyakarta, 55581. With average demand fluctuations increasing every year, this industry must pay attention to innovative, effective and efficient production processes to meet the demands of consumers on time. After performing discussions and interviews with the owners that play as the experts, it was identified several risks that arise in the supply chain process. Some of these risks include: Demand fluctuations when there is a sudden additional demand from consumers, the industry orders additional raw materials from suppliers and not all suppliers are able to fulfill them. As a result, besides late supply of raw materials, inappropriate delivery constraints and other risks could impact the next production process. Workers are also in a hurry to meet production targets. This problem definitely could harm the company both in terms of time and cost. Therefore, it is necessary to conduct risk analysis and design a risk mitigation strategy, to minimize risks or disturbances that potential to arise in the supply chain. To identify and measure the potential risks that existed in the CV. Multiguna supply chain, the researcher has chosen to use the house of risk (HOR) model. This model is a framework developed by Laudine H. Geraldin and I. Nyoman Pujawan by developing the FMEA method (Failure Mode and Effect Analysis) and QFD (Quality Function Deployment) method [1,5]. Fuzzy logic in this study is used as a means to accommodate doubts in decision making by experts and then doubt will lead to incorrect data. This is supported by [6] stated that fuzzy logic has a tolerance for incorrect data. While AHP is used to prioritize preventive action by comparing various appropriate criteria from the expert.This is supported by [7] described the advantages of the AHP model if compared with other decisionmaking models lies in the AHP's ability to solve multiobjectives and multicriteria problems. Broadly speaking, the stages in this framework are divided into two phases, namely the risk identification phase and the risk treatment phase. The risk identification phase is the phase where risk events and risk agents are identified and measured. The risk-handling phase is the phase where the selected risk agent from the first phase is assessed by handling actions or mitigation actions. Based on the description above, it can be set the expected objectives of this research, as follows: 1. Presenting risks that are a priority to be given to the supply chain of CV. Multiguna. 2. Providing risk mitigation priority to the CV. Multiguna with (Analytic Hierarchy Process). RESEARCH METHODOLOGY This research uses the House of Risk (HOR) method.[8] developed a supply chain risk management model using the House of Quality (HOQ) and Failure Models and Effects Analysis (FMEA) concept methods to develop a framework for managing supply chains known as the House of Risk (HOR) approach. The HOR approach aims to identify risks and design a handling strategy to reduce the probability of emergence of risk agents by providing precautionary measures on risk agents. Risk agents or sources of risk are the causative factors that encourage risk. The second method that is used is Fuzzy Logic. Zadeh first introduced fuzzy Logic in 1965. The basis of fuzzy logic is the fuzzy set theory. In fuzzy set theory, the role of membership degree as a determinant of the existence of elements in a set is very important. Membership value or membership level or membership function is the main characteristic of reasoning with fuzzy logic [6]. The third method used is the AHP (Analytical Hierarchy Process) method. AHP (Analytical Hierarchy Process) is one of the decision-making techniques to break a complex and unstructured problem into its groups [9]. The initial stage in this research is the identification stage, where this stage is carried out by observing directly to identify problems that exist in the research location. The problems that have been identified, then being formulated to set the research objectives. Later, literature studies and field studies were carried out to support the research so that the research went well and correctly. The second stage is the collection of data, which consists of supply chain 030091-2 activities mapping and risk identification and risk agents. The mapping of supply chain activities for fabric materials was obtained by observation and company archives. Furthermore, the supply chain activities on fabric materials were mapped in the SCOR (Supply Chain Operations Reference) model to classify supply chain activities. Risks and risk agents are identified based on supply chain activities that have been classified by means of interviews, discussions and reference to related journals. The next stage is the data processing stage, including risk analysis using the Fuzzy Logic method, which determines the severity of risk events and occurrence which is then mapped to the phase 1 house of risk (HOR) model. In this model, risk events and risk agents are considered correlated, with the result of aggregate risk priority (ARP. From these results, then they are sorted by using the 80/20 principles from the Pareto diagram to produce selected risk agents. But in this research the 80/20 principles was changed to 60/40 so that risk mitigation could be focused on the risks chosen and expected to provide a handling of 31% risk of completing 69 other risks. Next is the identification of mitigation actions, which are then mapped on the phase 2 HOR model together with selected risk agents. In the second phase, it is calculated the total value of the effectiveness of mitigation actions (TEk), the degree of difficulty in carrying out mitigation actions (Dk) and the total effectiveness of the degree of difficulty in carrying out mitigation actions (ETDk) [10]. After the data is processed with the Phase 2 HOR matrix, the preventive action priority is obtained according to the HOR method. The final step is to manage the preventive action priority with AHP to maximize handling according to the wishes of the expert. RESULTS AND DISCUSSION The results of the first research were the mapping of suppy chain activities using the SCOR model. The mapping process was carried out by means of interviews and discussions. Referring to the model, the description of supply chain activities CV. Multiguna is shown in table 1. From the results of the mapping process with the SCOR model in table 1, it is then identified and measured risk events and risk agents. This measurement is done to determine the scale of severity (the level of severity) from the identification of risk events and to determine the scale of occurrence (the level of probability of occurrence) of the risk agent using Fuzzy Logic. This measurement is carried out by means of discussions and interviews with industry owners. The results of these measurements are shown in table 2 and table 3. TABLE 1. Mapping supply chain activities with the SCOR approach Plan Source Make Delivery Return 1. 2. 1. 2. 3. 1. 2. • • • • 3. 4. 1. 2. 1. 2. Production planning Calculation of raw materials requirements Purchase of raw materials Checking the raw materials received Store raw materials Bring out raw materials Production process with stages Cut raw material Make patterns using a benchmark Cut pattern Sewing pattern Product Inspection Store finished goods to the warehouse Take and package the product Product Delivery Product return Raw material return After measurement of risks agent and risks event is done, then the next step is to determine the value of severity and occurrence using fuzzy logic. The membership function is shown in the table 2. The membership function is same between risks event and risks agent. 030091-3 TABLE 2. Determination of the variables and universe of discourse Function Input Output Variable Name Universe of Discourse Production lead time disturbed Operational costs increased Market demand disturbed Planning the amount of production is not appropriate [0-5] [0-5] [0-5] [0-10] Description The impact that caused by the output The impact that caused by the output The impact that caused by the output The level of the impact that may occured TABLE 3. Results of fuzzy logic calculation of risk events Code E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 E16 E17 E18 Risk Event Planning the amount of production is not appropriate Production scheduling is not appropriate Error calculation of raw materials All raw material supplies are not met Increase in raw material prices Delays in delivery of raw materials There are defects in raw materials Raw material specifications do not match the catalog Not achieving production targets The process is not perfect pattern making / repair The cutting process is not perfect pattern / repair The process is not perfect sewing / repair Declining product quality The production process stopped Product incompatibility received by consumers Delays in delivery by the shipping division Returns products from consumers Suppliers return raw materials SCOR Preparation Preparation Preparation Procurement Procurement Procurement Procurement Procurement Manufacture Manufacture Manufacture Manufacture Manufacture Manufacture Delivery Delivery Return Return Impact level 8.76 8.76 8.76 8.1 8.76 5.19 8.76 8.76 8.76 8.76 8.76 8.76 8.94 8.94 8.76 8.76 5 5.75 TABLE 4. Results of fuzzy logic calculation risk agents Code A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 Risk Agent Forecasting errors Sudden demand from consumers Less negotiation skills Selection of alternative suppliers Production capacity and lead time Lack of supervision of work Limit on the number of workers Raw material price fluctuations Human error Product diversity Excessive working hours Damage to production machinery 030091-4 Probability Levels 8.75 5.88 8.76 8.1 5.75 5.88 5.88 7.71 8.76 8.76 8.76 8.94 TABLE 4. Continued Code A13 A14 A15 A16 Risk Agent Probability Levels 5.88 8.76 8.76 8.94 Product maintenance and care Delivery division error Limited product variations The agreement / negotiation document is not complete Phase 1 of house of risk (HOR) mapping The mapping of this model is performed by including the results of measuring the severity of the risk event (table 3) and the occurrence of the risk agent (table 4) and measuring the correlation. The purpose of this mapping is to find the ARP (aggregate risk priority) value. The ARP value is obtained from the results of multiplication between severity value, occurrence value and correlation value of risk events and risk agents. The results of the first phase HOR mapping model are then ranked using the Pareto diagram shown in Figure 1. FIGURE 1. Pareto diagram of risk agents From figure 1 using the Pareto 60/40 principle, selected risk agents that will be taken into consideration in the preparation of risk mitigation actions are shown in Table 5. TABLE 5. Selected risk agents based on Pareto diagrams Ranking ARP 1 2 3 4 Code A12 A9 A6 A1 5 A16 Risk Agent Damage to production machinery Human error Lack of supervision of work Forecasting errors The agreement / negotiation document is not complete value ARP 4492.89 3782.31 2533.46 2370.38 Severity Occurrence 8 8 6 6 7 7 5 5 2271.65 6 4 Risk agent rank is entering the second phase of HOR model for designing mitigation actions. The mitigation action in question is an action to reduce the impact of a risk agent before the risk occurs. Alternative mitigation actions are obtained from interviews, discussions and reference to related journals. The focus of designing mitigation actions is based on selected risk agents. The next step is to do a house of risk mapping phase 2. The mapping of mitigation actions is carried out with the aim to see the effect of mitigation actions on risk agents by mapping the mitigation action options with selected risk agents. The first step that must be done is to measure the value of the correlation between mitigation actions and 030091-5 selected risk agents. The second step is to measure the degree of difficulty (Dk). The purpose of this measurement is to determine the degree of difficulty of the application of mitigation actions. The results of mapping these mitigation actions are shown in table 6. The third step is to measure total effectiveness, by multiplying the correlation values between risk agents (j) with preventive actions (k). The fourth step is to measure the effectiveness to difficulty ratio, by dividing the total value of effectiveness (TEk) with the degree of difficulty performing. The process to find ETDk. TABLE 6. Phase 2 House of Risk mitigation actions of agents selected risk The results of the mapping of risk mitigation actions in the HOR phase 2 shown in Table 7. TABLE 7. Mitigation actions for selected risk agents No. 1 2 3 4 5 6 7 8 9 10 Preventive Action or Mitigation Action Providing continuous worker training Providing training and the right forecasting method Provide training and negotiation skills support Periodic engine maintenance Improving the company's standard operating procedures Set the machine usage schedule Complete facilities and infrastructure supporting negotiations Improving a disciplined and timely work culture Provide a flow of information about market developments Provide a comfortable working environment Code PA3 PA7 PA10 PA1 PA5 PA2 PA9 PA6 PA8 PA4 ETD 29933.50 27687.06 27242.82 20217.99 15538.15 13478.66 13298.78 12930.02 8371.89 7994.47 After the risk mitigation priority results are obtained, the next step is to look back at the risk map position through a probability impact matrix with severity and occurrence values from the risk agents that have been refilled after being given treatment. TABLE 8. Selected risk agents based on Pareto diagrams Ranking ARP 1 2 3 4 Code Risk Agent A12 A9 A6 A1 5 A16 Damage to production machinery Human error Lack of supervision of work Forecasting errors The agreement / negotiation document is not complete 030091-6 Value ARP 4492.89 3782.31 2533.46 2370.38 2271.65 Severity Occurrence 5 5 5 5 6 6 4 4 4 2 From the table 8, it can be seen that the severity and occurrence decrease after being treatment. This means that the handling of risks can minimize the impact and probability of these risks. The final step is to process preventive action using AHP. Starting with composing the hierarchy. In the model proposed in this research, there are at least 3 levels of hierarchy as follows: Level I: The goals or objectives of the decisions to be taken are placed at the top of the hierarchy. In this case the intended target is "Best Mitigation". Level II: At the second level, the assessment criteria are proposed which can show guidelines for the selection of the proposed alternatives. These criteria consist of easy to do, improve the quality of human resources, have an impact on market improvement, not interfere with the production process, improve the company's image, be able to handle fluctuations in demand, and respond to rapid price changes. Level III: At the third level, an alternative is proposed as follows: continuous worker training, providing appropriate training and forecasting methods, Providing training and negotiation skills support, periodic machine maintenance, reviewing the company's standard operating procedures, arranging machine usage schedules, completing facilities and supporting negotiation infrastructure, improving the quality and work culture of the company, providing a flow of information about market developments, providing a comfortable work environment. Pairwise comparison matrix is the second step at this stage there are several comparisons between criteria and alternative comparisons. From the matrix processing using Expert Choice 11 Software, it is obtained the value of consistency ratio (CR) of 0.09 or considered as acceptable because the requirement of CR is <0.1. Alternative weighting and criteria are described in step 3. The results of the priority weighting of each alternative are the final output of this research and can be seen in Figure 2 below: FIGURE 2.The final result of weighting alternative priorities From the results of the weight calculation using Expert Choice 11 software, the eigen vector results are obtained as above. The consistency value is 0.09 and the value is considered valid. From the processing of the data, it was found that the position of completing the facilities and infrastructure supporting the negotiation was chosen as the first priority as a mitigation measure with a value of 0.160. Then followed by regular maintenance of the machine with a value of 0.143 and 8 other mitigation alternatives with each value according to the picture above CONCLUSION Results of research conducted in the CV. Multiguna with the theme of supply chain risk management can be concluded as follows: 1. From the results of pareto diagram, there are 5 risk sources that are prioritized to be given to the supply chain of CV. Multiguna. These sources of risk are: damage to production machinery, human error, lack of work supervision, forecasting errors, incomplete agreement / negotiation documents. 2. There are 10 risk mitigations approach that are prioritized in CV. Multiguna (analytic hierarchy process).There are, namely: complete the facilities and infrastructure supporting the negotiation with the highest score 0.160, periodic maintenance of the machine with a score of 0.143, providing continuous worker training with a score 030091-7 of 0.108, providing a flow of information about market developments with the second highest score that is 0.105, Improving the standard operating procedure of the company with a score of 0.103, Improving a disciplined and timely work culture with a score of 0.95, providing a comfortable work environment with a score of 0.89, arranging the machine usage schedule with a score of 0.76, providing training and support negotiation skills with a score of 0.70, providing the right training and forecasting method with 0.51. REFERENCES 1. Geraldin, L. H. 2007. Risk Management and Mitigation Actions to Create Robust Supply Chains. Thesis. Sepuluh Nopember Institute of Technology, Surabaya. 2. 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