Jurnal Teknik Industri Vol.
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August 2024, pp.
ISSN : 1978-1431 print | 2527-4112 online Supply Chain Risk Mitigation Based on The Integration of House of Risk and MOORA Ari Andriyas Puji Department of Industrial Engineering.
Muhammadiyah Riau University.
Jl.
Tuanku Tambusai.
Tampan.
Pekanbaru.
Riau.
Indonesia Corresponding author: andriyasari@umri.
ARTICLE INFO
ABSTRACT
Article history Received.
November 7, 2023 Revised.
March 20, 2024 Accepted.
July 31, 2024 Available Online.
August 31, 2024 Supply chains play a critical role in the operational success of organizations, impacting both costs and product quality.
However, they are often exposed to various risks that can disrupt business processes.
This research aims to identify supply chain risks and propose mitigation strategies using the House of Risk (HOR) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) methods.
Through interviews, key issues were identified in a fiberglass company's supply chain, including raw material supply fluctuations, procurement cost variability, defective materials, inappropriate specifications, outdated technology, insufficient worker skills, and ambitious company The novelty of this study lies in the application of MOORA, which introduces a correlation matrix for risk mitigation by considering both cost minimization and benefit maximization.
The analysis identified 12 risk agents and 26 risk events, which were prioritized using HOR stage 2 with the MOORA method.
The top preventive actions were ranked, providing actionable recommendations for companies to address supply chain risks more effectively.
The findings of this research offer practical insights for companies in the fiberglass industry to enhance supply chain resilience by integrating cost and benefit considerations into their risk management strategies.
Keywords House of Risk
MOORA
Risk Mitigation Supply Chain This is an open-access article under the CCAeBY-SA license.
Introduction The supply chain is a critical component of a company's business operations, representing the flow of goods from upstream suppliers to downstream consumers.
However, it is susceptible to various challenges that can disrupt this flow.
Common issues include the Bullwhip Effect (BE), product variations, product aging, shifting customer demands, owner fragmentation, and the complexities brought about by globalization.
The Bullwhip Effect, for instance, refers to the phenomenon where order variability intensifies as it moves upstream in the supply chain .
, .
Effective supply chain management ensures smooth business operations by controlling costs and product quality .
Companies must manage their supply chain as an integrated whole to avoid inefficiencies such as shortages or excesses in supply.
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Supply Chain Risk Mitigation Based on The Integration of House of Risk and MOORA .
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in the supply chain is vital to preventing disruptions that could negatively affect business processes and lead to financial losses .
Managing supply chain risks is also critical in ensuring long-term success, especially for firms engaged in international operations .
lack of systematic risk management can significantly reduce a company's performance and overall resilience .
Supply chain risk management involves identifying, assessing, and mitigating risks that affect economic, social, and environmental factors, ensuring the sustainability of supply chain operations .
As emphasized by .
, effective risk management has a direct and significant impact on the success of a company's overall Previous research on the House of Risk (HOR) method has primarily focused on ranking preventive actions based on comparisons between risk agents and mitigation Studies conducted by .
highlight this approach but also reveal limitations in expanding the criteria used for correlation matrix comparisons.
Specifically, the criteria selected by experts are restricted within the existing framework of the HOR model, which does not allow for adding new variables or criteria .
This limitation is significant, especially in cases where a decision support system is required to assess both cost minimization and benefit maximization in risk mitigation .
, .
Thus, previous research has not fully addressed the complexity of integrating these factors into the HOR methodology, leaving gaps in the comprehensive evaluation of supply chain risks and their A limitation of previous research on supply chain risk mitigation is its reliance on Multi-Criteria Decision-Making (MCDM) systems without fully incorporating the critical factors of costs and benefits.
In reality, companies consistently consider both costs and profits in their decision-making processes.
It creates a gap in existing research, as many studies have not adequately addressed the need for a comprehensive approach that balances these factors.
The novelty of this research lies in its focus on the decision-making process at the final stage of risk mitigation, emphasizing the integration of cost minimization and profit maximization principles.
This research aims to map the risks across the supply chain and identify prioritized mitigation actions using a cost-benefit analysis framework.
The Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method was selected for this purpose, as it is well-suited for evaluating risk mitigation by considering company-specific criteria based on benefits and costs.
MOORA,
developed by Brauers and Zavadskas in 2006 .
, compares the alternatives' responses to a denominator that represents all objectives.
This ratio system provides a structured approach to decision-making in complex environments .
The primary method employed in this study is the House of Risk (HOR), which is recognized as an effective strategy for identifying and addressing hazards along the supply chain of the Fiberglass Company.
The HOR model is based on established methodologies such as Failure Mode and Effect Analysis (FMEA) and Quality Function Deployment (QFD), as introduced by Geraldin, et al.
and Pujawan and Mahendrawathi .
The HOR framework is divided into two phases: risk identification and risk treatment .
begins by mapping supply chain activities, identifying risks, and processing the matrix of risk agents and events to determine the priority of risks.
Preventive actions are then developed to address these risks, resulting in a prioritized sequence of risk mitigation actions .
Integrating the MOORA method with the House of Risk framework provides a novel decision support system for risk mitigation.
This approach identifies risks and prioritizes mitigation actions based on a cost-benefit analysis, making it a valuable tool for supply chain management.
MOORA has been successfully applied in various decisionmaking problems in real-time manufacturing environments, demonstrating its practicality .
, .
This approach aligns with the company's principle that costs should Please cite this article as: Puji.
Supply Chain Risk Mitigation Based on The Integration of House of Risk and MOORA .
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ISSN : 1978-1431 print | 2527-4112 online be minimized while maximizing profits.
The final step involves collaboration with policymakers to select appropriate mitigation strategies that suit the company's operational needs .
Methods Produces various Fiberglass Reinforced Plastic (FRP) products and offers customized designs based on customer requests.
Initial interviews revealed several significant issues within the company's supply chain processes, including production procedures that fail to meet expectations, worker fatigue due to overtime demands, and delays in raw material deliveries.
These challenges, compounded by the company's high production targets, directly impact both efficiency and effectiveness, threatening longterm sustainability .
In several cases, production delays were caused by late delivery of crucial raw materials, such as fiber and matt, which extended production timelines.
Other problems stem from production planning failures, leading to excess or inadequate inventory, which increases warehouse storage costs.
Additionally, insufficient worker training has resulted in quality control failures, causing product rejections.
Fluctuations in raw material prices have exacerbated the company's struggle to meet production Previous studies have identified similar risks in supply chain processes, including limited supply, rework, partner dependencies, raw material shortages, delayed shipments, stockouts, returns, bullwhip effect, and IT system failures .
, .
This study integrates multiple approaches to address these challenges, expanding comparison criteria to support decision-making.
The Supply Chain Operations Reference (SCOR) model is applied to map supply chain activities.
The data is processed through the House of Risk (HOR) methodology in stages 1 and 2, leading to the identification of priority preventive These measures are then reanalyzed using the MOORA method, incorporating cost and benefit criteria to ensure alignment with the company's strategic goals.
This novel approach, which has not been previously explored, offers a fresh perspective on implementing preventive actions within supply chain risk management.
The detailed procedure of this research is illustrated in Figure 1.
Figure 1.
Research Procedures 1 Identify risk-based Supply Chain Operation Reference (SCOR) The initial step in this research involves mapping the supply chain processes using the Supply Chain Operations Reference (SCOR) model.
SCOR is a framework developed by the Supply Chain Council in 1996 to standardize the management of supply chain processes and enhance customer satisfaction .
The model categorizes supply chain Please cite this article as: Puji.
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activities into six primary processes: plan, source, make, deliver, return, and enable.
Each of these processes encompasses various levels within the supply chain and includes management practices widely recognized across different industries .
ee Figure .
The mapping of supply chain activities is designed to identify each process and delineate the scope of the supply chain.
Based on discussions with experts from Fiberglass Company, the supply chain processes were mapped as shown in Table 1.
Figure 2.
Major management processes proposed by the SCOR model Table 1.
Supply Chain Operation Reference Process Plan Activity Production planning and analysis Planning the procurement of materials and tools Source Procurement of materials and tools Raw material inspection Raw material storage Preparing raw materials for production Carry out the manufacturing process.
Finishing production results Product storage in the Warehouse area Make Delivery Product Distribution Return Product returns that are not appropriate Table 1 illustrates the supply chain activities defined by SCOR based on the input from company experts.
Following the mapping process, the identification of risk events and risk agents was carried out .
ee Table .
Each process and activity has specific risks that can disrupt supply chain operations.
This detailed table illustrates the risk events and corresponding agents identified in the supply chain.
These insights form the foundation for the risk management strategies applied in this study.
Please cite this article as: Puji.
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ISSN : 1978-1431 print | 2527-4112 online Table 2.
Results of Supply Chain Risk Event and Risk Agent Identification Process Activity Code Risk Event Code Risk Agent Plan Production planning and analysis Excess product inventory Uncertainty in the number of consumer orders Consumer demand adjustments Error in raw material calculation Price fluctuations in raw materials Delay in raw material delivery Unprofessional vendor Warehouse management is not Human error Weather conditions are unfavorable.
Human error
Small matt fiber fragments Human error
Matt fiber is placed in the mold
E16
Basting the mixture onto the mold's matt fiber
Wooden frame installation on Separation between the mold and the finished product
E17
Lack of product Changes to the production schedule Product storage issue .
imited storage spac.
Raw materials are in short supply Excess raw material Changes in procurement A lack of materials impeded the manufacturing process.
The production target was not achieved.
There is a defect in the raw material that was The specifications of the raw materials sent do not Stacking of raw Mistakes in prepared Because the combination hardens faster, it cannot be manufactured.
The mixture overflows Mixture not according to Compilation requires Lapisan tidak merata E18
Asymmetrical design
A10
The frame is made of wood
E19
E20
Human error
Insufficient equipment Finishing production results Product storage in the Warehouse area
Product Distribution E21
E22
Broken Products Defective product with Reject product Products with physical defects .
Excess product inventory Human error
Human error
A11
The transportation fleet is limited Product returns that are not E24 Lack of product Changes to the production schedule Product storage issue .
imited storage spac.
A12
The product specs are incompatible Planning the procurement of materials and tools Source Procurement of materials and Raw material inspection E10
Make Raw material storage E11
Preparing raw materials for Product color, catalyst, and resin
material mixing E12
E13
E14
E15
Delivery Return
E23
E25
2 House of Risk The House of Risk (HOR) model focuses on preventive measures aimed at minimizing the occurrence of risk agents by systematically identifying risk events.
single risk agent can be responsible for multiple risk events, and the HOR model assigns probabilities to these risk agents while assessing the severity of each risk event .
In the initial stages, a thorough examination of each activity within the business process was conducted to map existing issues .
The next stage involved identifying Please cite this article as: Puji.
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specific risk events and evaluating their severity.
Experts with deep knowledge of their respective fields carried out this risk identification, and the company validated these findings .
The severity of risk events was then rated on a scale from 1 .
o impac.
to 10 .
azardous impac.
Following this, risk agents were identified for each activity, and their occurrence probability was assessed on a scale from 1 .
to 10 .
Stage 4 of HOR involves determining the Aggregate Risk Potential (ARP) based on At this point, experts assessed the correlation between risk events and their respective agents.
The ARP value was calculated as in Equation .
yaycIycEyc = 0yc Ocycn ycIyc ycIycnyc In stage 5, the most critical risk agents were identified using Pareto analysis .
In stage 7, preventive actions or mitigation strategies were determined through discussions with experts.
These actions were designed to reduce the likelihood and severity of risk agents.
The effectiveness of the preventive measures was reassessed based on the new values of risk agent severity and occurrence, which can be seen in Equation .
The effectiveness-to-difficulty ratio (ETD) of implementing each preventive action was then calculated to prioritize mitigation efforts (Equation .
ycNyayco = Ocyc yaycIycEyc yaycnyc yaycNyayco = ycNyayco yayco Expert respondents who understood the company's operations provided input by rating the severity and occurrence of each risk event, as shown in Table 3.
The severity and occurrence scores are determined through interviews and brainstorming sessions with expert respondents.
These values serve as the basis for calculating the ARP and determining which risk agents should be prioritized for 3 multi-objective optimization on the basis of ratio analysis (MOORA) The MOORA method is widely used for multi-attribute optimization in decisionmaking processes .
, .
, and was first introduced by .
In this research.
MOORA is applied to prioritize mitigation actions based on expert-determined criteria, with a focus on balancing benefits and costs.
This method provides a structured framework for evaluating multiple alternatives and identifying the most effective course of action.
The first step in the MOORA process is constructing a decision matrix that represents the performance of various alternatives with respect to different criteria.
The matrix is defined as Equation .
ycUycn1 X= [ .
ycUyco1 ycU1ycu ycUycoycu Where ycUycnyc is the performance measure of ycnycEa alternative on yc ycEa criterion, m is the number of alternatives and n is the number of criteria.
Next, the decision matrix is normalized using Equation .
Where ycycnyc represents the normalized value for each alternative on the given criterion.
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ISSN : 1978-1431 print | 2527-4112 online ycuycnyc ycycnyc = Ocyco ycu=1 ycuycnyc Table 3.
Weighting of Severity and Occurrence Code Risk Agent Uncertainty in the number of consumer orders Consumer demand adjustments Error in raw material calculation Price fluctuations in raw materials Code Risk Event Excess product inventory Delay in raw material delivery Unprofessional vendor Warehouse management is not Human error Lack of product inventory Changes to the production schedule Product storage issue .
imited storage Raw materials are in short supply Excess raw material inventory Changes in procurement costs A10
Small matt fiber fragments The frame is made of wood
E10
A11
The transportation fleet is limited E11
A12
The product specs are incompatible E12 A lack of materials impeded the manufacturing process.
The production target was not achieved There is a defect in the raw material that was delivered.
The specifications of the raw materials sent do not match Stacking of raw materials Mistakes in prepared materials Because the combination hardens faster, it cannot be manufactured.
The mixture overflows Mixture not according to measurements Compilation requires time.
Lapisan tidak merata Asymmetrical design Broken Products Defective product with holes Reject product Products with physical defects .
Delays in delivery Consumers' product returns Distribution costs increase E13
E14
E15
E16
E17
E18
E19
E20
E21
E22
E23
E24
E25
E26
Once the decision matrix is normalized, weights are assigned to each criterion based on expert preferences, and these weights are applied to the normalized matrix.
This step allows the model to reflect the relative importance of different criteria in decisionmaking.
The assessment value for each alternative is then calculated by finding the difference between the sum of beneficial criteria .
hose to be maximize.
and the sum of non-beneficial criteria .
hose to be minimize.
, as shown in Equation .
Where yci is the number of criteria to be maximized, .
cu Oe yc.
is the number of criteria to be minimized.
yaycyceycycycoyceycuyc ycOycaycoycyce = Ocyc=ycn ycuycnyc Oe Ocyc=yci 1 ycuycnyc Finally, the assessment values are ranked in descending order, with the highest value representing the most favorable alternative.
This ranking provides the priority order of mitigation actions based on a ratio analysis of benefits and costs.
Using MOORA Please cite this article as: Puji.
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ensures that the selected mitigation strategies align with operational objectives and financial constraints.
Results and Discussion 1 House of Risk Stage 1 The Aggregate Risk Potential (ARP) is calculated to prioritize the risk agents in the supply chain.
The ARP is determined by assessing the severity and occurrence of each risk event and its corresponding risk agent.
Once the ARP values are calculated, the risk agents are ranked from the highest to the lowest ARP values, as shown in Table 4.
Table 4.
House of Risk Stage 1 presents the correlation between risk events (E.
and the associated risk agents (A.
For instance, risk agent A8, related to human error, has the highest ARP value of 6966, making it the top priority for mitigation.
Other highpriority agents include A3 .
rrors in raw material calculatio.
and A6 .
nprofessional vendor.
, with ARP values of 2970 and 2898, respectively.
These results highlight the critical areas that require immediate attention to minimize disruptions in the supply Table 4.
House of Risk Stage 1 Risk Agents (A.
Risk Event (E.
E10
E11
E12
E13
E14
E15
E16
E17
E18
E19
E20
E21
E22
E23
E24
E25
E26
Occurrence of Agent Aggregate Risk Potential Priority Rank of Agent Severity of Risk Event (S.
A10
A11
A12
A Pareto analysis was conducted to refine risk agent prioritization further, as illustrated in Figure 3.
The Pareto diagram, commonly called the 80:20 rule, helps Please cite this article as: Puji.
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ISSN : 1978-1431 print | 2527-4112 online distinguish between critical and non-critical risk agents.
It suggests that 80% of the company's losses are likely caused by 20% of the most significant risks.
The company can mitigate most of the risk impact by focusing on high-priority risk agents, such as A8 .
uman erro.
This analysis allows for targeted preventive actions to be implemented, ensuring that resources are allocated effectively to address the most pressing risks in the supply chain.
120,00%
100,00%
80,00%
60,00%
40,00%
20,00%
0,00%
ARP
A10
A12
A11
% ARP
Figure 3.
Agent Risk Pareto Diagram 2 Preventive Action Following the first stage of the House of Risk analysis, specific mitigation strategies were developed, which are referred to as preventive actions.
These actions address the prioritized risk agents identified in the previous stage.
Table 5 outlines the preventive actions, their associated risk agents, and the degree of difficulty in implementing each Table 5.
Preventive Action Code Risk Agent PAi Preventive Action Upgrading Skills Implement work-hour division.
Increase the amount of employees Provide rewards for work achievements without errors Create a good procurement calculating system to reduce errors Creating effective standard operational Examine every raw material calculation Add another vendor's reserves that meet the Improve the order administration system between producers and consumers.
Human Error
PA1
PA2
PA3
PA4
Error in raw material PA5
PA6
PA7
PA8
Unprofessional vendor
Uncertainty in the number of consumer Price fluctuations in raw materials Delay in raw material PA9
PA10
PA11
Determine the selling price based on the price variations of raw materials.
Improve the inventory system so that there is no shortage of raw materials Degree of Difficulty After determining the appropriate mitigation measures and evaluating their difficulty, the next step involves analyzing the relationship between these preventive actions and the identified risk agents in House of Risk Stage 2.
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3 House of Risk Stage 2 In the second stage of the House of Risk analysis, preventive actions were evaluated based on their effectiveness in mitigating risks and the difficulty of implementing them.
Table 6 shows the results of this evaluation, where the Aggregate Risk Potential (ARP) values are mapped against various preventive actions (PA) associated with each risk agent (A.
The total effectiveness of each action is calculated and compared to the degree of difficulty in implementing the action, resulting in an Effectiveness to Difficulty Ratio (ETD).
Table 6.
House of Risk Stage 2 Risk Event (E.
Total
effectiveness of Degree of Effectiveness to difficulty ratio
PA1
PA2
PA3
PA4
Risk Agents (A.
PA5
PA6
PA7
ARP
PA8
PA9
PA10
PA11
Table 7 presents the ranking of the preventive actions based on their ETD values.
The top-ranked actions include upgrading skills (PA.
, creating effective standard operational processes (PA.
, and implementing work-hour division (PA.
These actions are prioritized for implementation due to their high impact and feasibility.
Other actions, such as improving the order administration system (PA.
and adding vendor reserves (PA.
, also rank highly but have slightly lower ETD values.
The prioritized list of preventive actions provides a clear roadmap for the company to focus on the most impactful and practical strategies for mitigating supply chain risks.
This structured approach ensures that resources are allocated effectively, addressing the most critical risks while considering the ease of implementation.
Table 7.
Preventive Action Rank
Code
PA1
PA6
PA2
PA4
PA3
PA9
PA7
PA8
PA5
PA11
PA10
Preventive Action Upgrading Skills Creating effective standard operational processes Implement work-hour division.
Provide rewards for work achievements without errors Increase the amount of employees Improve the order administration system between producers and consumers.
Examine every raw material calculation Add another vendor's reserves that meet the criteria Create a good procurement calculating system to reduce errors Improve the inventory system so that there is no shortage of raw materials Determine the selling price based on the price variations of raw materials.
ETD
Rank Please cite this article as: Puji.
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ISSN : 1978-1431 print | 2527-4112 online 4 MOORA A descriptive analysis was conducted to define the weights for each criterion, as shown in Table 8.
The criteria include ease of implementation, impact on operational procedures, enhancement of productivity, reduction of risks, and improvement of the company's work culture.
The weights were assigned based on expert opinions, with the highest priority given to actions that significantly reduce risks .
%), followed by actions that enhance productivity .
%) and are easy to implement .
%).
Table 8.
Risk Mitigation Action Criteria Criteria Actions easy to implement Actions do not disrupt operational procedures Actions have an impact on enhancing productivity Risks can be reduced by action Actions can help to improve the company's work culture Weight The assigned weights for each sub-criterion, displayed in Table 9, were based on a range value from 1 to 4, depending on the experts' assessment of how each action fits within the specified criteria.
Table 9.
Criteria Weight
Criteria
Actions easy to implement
Actions do not disrupt operational procedures Actions have an impact on enhancing productivity Risks can be reduced by action
Actions can help to improve the company's work culture Range Value 0 - 25
26 - 50
51 - 75
76 - 100
0 - 25
26 - 50
51 - 75
76 - 100
0 - 25
26 - 50
51 - 75
76 - 100
0 - 25
26 - 50
51 - 75
76 - 100
0 - 25
26 - 50
51 - 75
76 - 100
Weight Value Table 10 presents the normalized decision matrix, which evaluates the performance of each alternative .
reventive actio.
across multiple criteria.
The matrix provides a comprehensive comparison, allowing each preventive action to be measured relative to the others in terms of both benefits and costs.
The normalized matrix weights are calculated to account for these factors, as seen in Table 10.
Normalize Matrix Please cite this article as: Puji.
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Alternative Upgrading Skills Creating effective standard operational processes Implement work-hour division.
Provide rewards for work achievements without errors Increase the number of employees Improve the order administration system between producers and consumers.
Examine every raw material calculation Add another vendor's reserves that meet the criteria Create a good procurement calculating system to reduce errors Improve the inventory system so that there is no shortage of raw materials Determine the selling price based on the price variations of raw materials.
Optimum
Criteria Code
0,3746
0,1811
0,3780
0,2739
0,3612
0,1873
0,2716
0,1890
0,2739
0,2408
0,3746
0,3621
0,2835
0,2739
0,3612
0,3746
0,3621
0,3780
0,3651
0,3612
0,1873
0,2716
0,2835
0,2739
0,2408
0,3746
0,3621
0,1890
0,2739
0,2408
0,2810
0,2716
0,3780
0,3651
0,2408
0,1873
0,2716
0,2835
0,2739
0,2408
0,1873
0,3621
0,2835
0,3651
0,3612
0,2810
0,2716
0,1890
0,2739
0,2408
0,3746
0,2716
0,3780
0,2739
0,3612
Min Max Max Max Max Table 11.
These weights reflect the relative importance of each criterion about the preventive actions.
For instance, upgrading skills (PA.
and providing rewards for errorfree performance (PA.
rank highly across multiple criteria, showcasing their broad Table 10.
Normalize Matrix Alternative Upgrading Skills Creating effective standard operational processes Implement work-hour division.
Provide rewards for work achievements without errors Increase the number of employees Improve the order administration system between producers and consumers.
Examine every raw material calculation Add another vendor's reserves that meet the criteria Create a good procurement calculating system to reduce errors Improve the inventory system so that there is no shortage of raw materials Determine the selling price based on the price variations of raw materials.
Optimum
Criteria Code
0,3746
0,1811
0,3780
0,2739
0,3612
0,1873
0,2716
0,1890
0,2739
0,2408
0,3746
0,3621
0,2835
0,2739
0,3612
0,3746
0,3621
0,3780
0,3651
0,3612
0,1873
0,2716
0,2835
0,2739
0,2408
0,3746
0,3621
0,1890
0,2739
0,2408
0,2810
0,2716
0,3780
0,3651
0,2408
0,1873
0,2716
0,2835
0,2739
0,2408
0,1873
0,3621
0,2835
0,3651
0,3612
0,2810
0,2716
0,1890
0,2739
0,2408
0,3746
0,2716
0,3780
0,2739
0,3612
Min Max Max Max Max Table 11.
Weighted Normalization Matrix Alternative Criteria Code Upgrading Skills 0,0749 0,0272 0,0756 0,0959 0,0361 Creating effective standard operational processes 0,0375 0,0407 0,0378 0,0959 0,0241 Implement work-hour division.
0,0749
0,0543
0,0567
0,0959
0,0361
Please cite this article as: Puji.
Supply Chain Risk Mitigation Based on The Integration of House of Risk and MOORA .
Jurnal Teknik Industri, 25.
, 145Ae160.
https://doi.
org/10.
22219/JTIUMM.
Vol25.
No2.
Jurnal Teknik Industri Vol.
No.
August 2024, pp.
ISSN : 1978-1431 print | 2527-4112 online Alternative Criteria Code Provide rewards for work achievements without errors 0,0749 0,0543 0,0756 0,1278 0,0361 Increase the amount of employees 0,0375 0,0407 0,0567 0,0959 0,0241 Improve the order administration system between producers and consumers.
0,0749
0,0543
0,0378
0,0959
0,0241
Examine every raw material calculation 0,0562 0,0407 0,0756 0,1278 0,0241 Add another vendor's reserves that meet the criteria
0,0375
0,0407
0,0567
0,0959
0,0241
Create a good procurement calculating system to reduce errors 0,0375 0,0543 0,0567 0,1278 0,0361 Improve the inventory system so that there is no shortage of raw materials 0,0562 0,0407 0,0378 0,0959 0,0241 Determine the selling price based on the price variations of raw materials.
0,0749
0,0407
0,0756
0,0959
0,0361
Min Max Max Max Max Optimum Finally.
Table 12 shows the preference values for each alternative, computed by subtracting the minimum value .
from the maximum value .
This step allows for a final ranking of preventive actions, prioritizing the most beneficial and cost-effective According to this analysis, the top-ranked actions include creating a good procurement system (PA.
, providing rewards for work achievements (PA.
, and examining every raw material calculation (PA.
These actions stand out due to their ability to deliver substantial benefits while maintaining manageable implementation This ranking system offers a clear roadmap for selecting the most effective risk mitigation strategies, ensuring that decisions are financially sound and operationally Table 12.
Preferences Value Alternative Min Max Cost Benefit Yi = Max-Min Rank Upgrading Skills 0,0749 0,0272 0,0756 Creating effective standard operational processes 0,0375 0,0407 0,0378 Implement work-hour division.
0,0749
0,0543
0,0567
Provide rewards for work achievements without errors 0,0749 0,0543 0,0756 Increase the number of employees 0,0375 0,0407 0,0567 Improve the order administration system between producers and consumers.
0,0749
0,0543
0,0378
Examine every raw material calculation 0,0562 0,0407 0,0756 Add another vendor's reserves that meet the criteria
0,0375
0,0407
0,0567
Create a good procurement calculating system to reduce errors 0,0375 0,0543 0,0567 Improve the inventory system so that there is no shortage of raw materials 0,0562 0,0407 0,0378 Determine the selling price based on the price variations of raw materials.
0,0749
0,0407
0,0756
5 The implications of this research The findings of this research provide significant implications for companies, particularly in developing a comprehensive risk management and mitigation model that integrates benefits and costs.
This model allows companies to strategically select and implement risk mitigation actions that align with their financial and operational goals.
Unlike previous approaches that prioritized mitigation actions based on limited criteria, the model introduced in this study incorporates a more robust analysis of cost-benefit trade-offs, as demonstrated in Table 12.
This enhanced framework allows for more Please cite this article as: Puji.
Supply Chain Risk Mitigation Based on The Integration of House of Risk and MOORA .
Jurnal Teknik Industri, 25.
, 145Ae160.
https://doi.
org/10.
22219/JTIUMM.
Vol25.
No2.
ISSN : 1978-1431 print | 2527-4112 online Jurnal Teknik Industri Vol.
No.
August 2024, pp.
informed decision-making, enabling companies to address the most pressing risks in their supply chains while optimizing resource allocation.
The recommendations from this research are based on expert insights into the evolving risks within the Fiberglass supply chain.
As a result, the proposed solutions are tailored to the specific needs and characteristics of the company.
By adopting these recommendations, businesses can achieve more effective risk mitigation, ensuring smoother operations and greater resilience in supply chain disruptions.
Conclusion This research has successfully identified and mapped the risks present in the supply chain using the SCOR model, which resulted in identifying 12 risk agents and 26 risk events.
Through applying the House of Risk (HOR) methodology in Stage 1, six critical risk agents were prioritized for mitigation, leading to the development of 11 preventive In Stage 2 of HOR, these risk agents were correlated with the preventive actions, resulting in the prioritization of mitigation measures.
The highest-ranked actions included upgrading skills (PA.
, creating effective standard operational processes (PA.
, and implementing work-hour division (PA.
The Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method further refined these priorities by incorporating a costbenefit analysis.
The final mitigation action rankings emphasized creating a good procurement calculation system (PA.
and rewarding work achievements (PA.
However, one limitation of this study is the use of interval values for assessing the severity and occurrence of risks.
These intervals can lead to variations in respondent perceptions, which may affect the consistency of the results.
Future research should focus on adopting more precise, definite values for these parameters to ensure uniformity in risk For future studies, it is recommended to explore integrating other decisionmaking tools that could complement the HOR and MOORA methods.
Additionally, further research could expand the scope by examining how external factors, such as market dynamics or regulatory changes, influence the effectiveness of risk mitigation strategies in supply chains.
Data Availability This publication does not include all of the raw data.
Please contact the author through email if you want the raw data.
Declarations Author contribution: The First Author created the concept, grand theory, methodology, data retrieval, data processing, and validation.
The Second Author wrote and edited the Funding statement: This research was fully funded by Kemendikbudristek.
Conflict of interest: The authors declare no conflicts of interest.
Additional information: No additional information is available for this paper.
Acknowledgments The author would like to thank colleagues who have provided input and ideas in improving this paper.
Please cite this article as: Puji.
Supply Chain Risk Mitigation Based on The Integration of House of Risk and MOORA .
Jurnal Teknik Industri, 25.
, 145Ae160.
https://doi.
org/10.
22219/JTIUMM.
Vol25.
No2.
Jurnal Teknik Industri Vol.
No.
August 2024, pp.
ISSN : 1978-1431 print | 2527-4112 online References