Assessing Production Line Risk Using Bayesian Belief Networks and System Dynamics Sudhir Punyamurthula University of Kentucky, [email protected]

Assessing Production Line Risk Using Bayesian Belief Networks and System Dynamics Sudhir Punyamurthula University of Kentucky, Psudhir1912@Gmail.Com

University of Kentucky UKnowledge

Mechanical Engineering Faculty Publications Mechanical Engineering

8-3-2018 Assessing Production Line Risk Using Bayesian Belief Networks and System Dynamics Sudhir Punyamurthula University of Kentucky, [email protected]

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Repository Citation Punyamurthula, Sudhir and Badurdeen, Fazleena, "Assessing Production Line Risk Using Bayesian Belief Networks and System Dynamics" (2018). Mechanical Engineering Faculty Publications. 57. https://uknowledge.uky.edu/me_facpub/57

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Digital Object Identifier (DOI) https://doi.org/10.1016/j.promfg.2018.07.010

This article is available at UKnowledge: https://uknowledge.uky.edu/me_facpub/57 Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect ScienceDirect AvailableProcedia online Manufacturing at www.sciencedirect.com 00 (2018) 000–000 Available online at www.sciencedirect.com 2 Author name / Procedia Manufacturing 00 (2018) 000–000 Procedia Manufacturing 00 (2018) 000–000 www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia ScienceDirectScienceDirect such circumstances, failure to respond quickly to raw paper and future work is described in the conclusions material shortages, downtimes, deteriorating section. Procedia Manufacturing 26 (2018) 76–86 Procedia Manufacturing 00 (2017) 000–000 equipment conditions or other operational risks could www.elsevier.com/locate/procedia prove to be an expensive affair. This makes a 2. Literature review 46th SME North American Manufacturing Research Conference, NAMRC 46, Texas, USA company-wide risk assessment of critical value. Such 46th SME North American Manufacturing Research Conference, NAMRC 46, Texas, USA a practice gives a holistic view of the risks affecting a Since production line risk assessment is a less Assessing Production Line Risk using Bayesian Belief Networks company and provides opportunities to mitigate them. explored field, published literature in supply chain risk Assessing Production Line Risk using Bayesian Belief Networks In addition, ISO 31000 encourages companies to adopt assessment was also reviewed. Quantitative risk and System Dynamics risk-based decision-making and requires them to assessment was the prime focus of the research. Bustad Manufacturing Engineering Societyand InternSystemational Dynamics Conference 2017, MESIC 2017, 28-30 June develop a ‘Risk Profile’ [1]. & Bayer [3] presented a risk management process at 2017, Vigo (Pontevedra), Spain The scope of a company-wide risk assessment Coca Cola Enterprises through the Hazard and Sudhir Punyamurthulaa*, Fazleena Badurdeenb Sudhir Punyamurthulaa*, Fazleena Badurdeenb includes both internal and external operations. Operability (HAZOP) method. They identified risks a However, external/supplier risk assessment has drawn impacting the industry and these risks were assessed Costing models for capacityUniversity of Kentucky, optimiza Lexington,tion KY, 40503, in USA. Industry 4.0: Trade-off bInstitute for SustainableaUniversity Manufacturing, of Kentucky, Lexington,University KY,of Kentucky, 40503, USA. Lexington, KY, USA overwhelming attention compared to using risk-appetite matrix. This approach is good for betweenbInstitute usedfor Sustainable capacity Manufacturing, an Universityd operational of Kentucky, Lexington, efficiency KY, USA internal/production line risk assessment. William et. al creating awareness and could work as a quick [2] identified this trend when only 3 research studies overview of the risks impacting the production line. * Corresponding author. Tel.: +1-858-200-6803; fax:a +1-859-257-1071a,*. b b regarding manufacturing risk assessment were found However, the HAZOP method is mostly qualitative * CorrespondingE-mail address: author. psudhir1912@ Tel.: +1-A.uky.edu858 -Santana200 -6803; fax:, +1P.-859 Afonso-257-1071. , A. Zanin , R. Wernke E-mail address: [email protected] compared to several in the field of supply risk and cannot account for the uncertainty due to the a University of Minho, 4800-058 Guimarães, Portugal assessment. Even though the scope of risk assessment complexity in the system. bUnochapecó, 89809-000 Chapecó, SC, Brazil Abstract is much narrower with internal operations, it is still of Alternatively, the Fault Tree approach of assessing Abstract significant importance as it would give a company an the reliability of the production line was demonstrated Increased complexity in product design, strict regulations and a changing market make risk assessment critical for successful edge over its competitors, within the industry, by in [4, 5]. This approach gives an insight into the events Increased complexity in product design, strict regulations and a changing market make risk assessment critical for successful Abstractoperations. Failure in responding quickly to raw material shortages, downtimes, deteriorating equipment conditions or other ensuring financial strength, quality of goods and resulting in a failure event. However, it is deterministic operationaloperations. Failureissues canin respondingprove to be quickly an expensive to raw affair.material A shortagecompanys-, widedowntimes, risk assessment deteriorating includes equipment both external condition ands orinternal other services and customer satisfaction. Therefore, and does not capture the interdependencies between operationaloperations. However,issues can external/supplier prove to be an riskexpensive assessment affair. has A been company of major-wide interest. risk assessment Even though includes the scope both of riskexternal assessment and internal at the objectives of the research presented in this paper are the risk events, as it depends on logical operators. 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(BBN). andThe The costingimpact combination ofmodels these of risksis BBN an is important observedand SD assistsusin researchg ain simulation developing topic that model adeserves versatile of the production line risk, which can capture the dynamic uncertainty in likelihood. Unlike fault trees, BBN contributionsmethodology,production line whichfrom using canboth System capture the practicalDynamics the dynamic and (SD) causaltheoretical approach. mechanisms perspectives.The combination in a complex This of papersystem, BBN presentsand the uncertaintiesSD assistsand discusses in amongst developing a rismathematicalk aevents versatile and nature of risk events and their relationships with each make use of Node Probability Tables (NPT), which themethodology, long-term impactwhich canof operational capture the risks dynamic on the causal production mechanisms line. in a complex system, the uncertainties amongst risk events and model for capacity management based on different costing models (ABC and TDABC). A generic model has been other. capture the complex inter-dependent relationships the long-term impact of operational risks on the production line. 2. Assess the impact on the production line, between events in an efficient manner. BBN models developed© 2018 The and Authors. it was Published used to analyze by Elsevier idle B.V. capacity and to design strategies towards the maximization of organization’s © 2018 The Authors. Published by Elsevier B.V. value.© 2018 The The Authors.trade-off Published capacity by maximiza Elsevier B.V.tion vs operational efficiency is highlighted and it is shown that capacity upon exposure to risk events, over a period. have been used as a risk assessment tool in various PeerPeer-review-review under responsibility ofof thethe scientific scientific committee of theNAMRI/SME 46th SME .North American Manufacturing Research Conference. optimizationPeer-review under might responsibility hide operational of the scientific inefficiency. committee of NAMRI/SME. fields. Fault diagnosis in a hydropower plant using ©Keywords: 2017 The Risk Authors. Assessment; Published Bayesian by BeliefElsevier Networks; B.V. System Dynamics. The remainder of the paper is organized as follows. BBN was discussed by Chaur & Sou [6], supply chain Peer-reviewKeywords: Risk under Assessment; responsibility Bayesian of Beliefthe scie Networks;ntific committee System Dynamics. of the Manufacturing Engineering Society International Conference Section 2 presents a brief summary of the literature risk analysis using BBN was demonstrated by 2017. review and identifies gaps in research. Section 3 Badurdeen et. al [7] and additional case studies were 1. Introduction affecting the manufacturing sector. Moreover, provides details on the Production Line Risk presented in Amundson et. al [8]. Badurdeen et. al [7] Keywords:1. Introduction Cost Models; ABC; TDABC; Capacity Management; Idle Capacity;affectingcompanies Operational Efficiencytheare willingmanufacturing to take somesector .extra Moreover, risk to Assessment (PLRA) methodology developed by outlined a well-structured method for Supply Chain Increased complexity in products to be survivecompanies and are succeed willing in toan takeincreasingly some extra competitive risk to combining Bayesian Belief Networks (BBN) and Risk Assessment (SCRA) by linking the risk drivers to manufactured,Increased strictcomplexity regulations in andproducts a continuously to be marketsurvive. Often,and succeed production in an capacities increasingly and competitivecapabilities System Dynamics (SD). The application of the performance measures. This model captures the 1. Introduction changingmanufactured, market strict have regulations led to an and increase a continuously in risks marketare quoted. Often, aggressively production in ordercapacities to get and the capabilitiesjob. Under methodology to a production line case study is also uncertainty within the system in an effective way. changing market have led to an increase in risks are quoted aggressively in order to get the job. Under presented in this section. Results and discussion are However, risk events are not static in nature. Risk The cost of idle capacity is a fundamental information for companies and their management of extreme importance presented in Section 4 where the effectiveness of the events evolve with time and the BBN models, when in© 2018modern The Authors.production Published systems. by Elsevier In general, B.V. it is defined as unused capacity or production potential and can be measured methodology in assessing the behavior of the applied to a static data set, fail to capture this dynamic inPeer© 2018several-review The underAuthors.ways: responsibility tonsPublished of production,by of Elsevierthe scientific B.V. available committee hours of NAMRI/SME of manufacturing,. etc. The management of the idle capacity production line system is examined. A summary of the behavior. Thus, BBN models alone may not be able to Peer* Paulo-review Afonso. under Tel.:responsibility +351 253 of 510 the 761; scientific fax: +351 committee 253 604 of 741 NAMRI/SME . E-mail address: [email protected] capture the impact of these risk events over a period. Dynamic causal relations can be modelled well 2351-9789 © 2017 The Authors. Published by Elsevier B.V. using simulation tools such as System Dynamics (SD). Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. SD is a powerful tool comprising of stocks and flows. 2351-9789 © 2018 The Authors. Published by Elsevier B.V. Stocks represent levels, which can be used to represent Peer-review under responsibility of the scientific committee of the 46th SME North American Manufacturing Research Conference. 10.1016/j.promfg.2018.07.010

10.1016/j.promfg.2018.07.010 2351-9789 Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect ScienceDirect

Procedia Manufacturing 00 (2018) 000–000 Sudhir Punyamurthula et al. / Procedia Manufacturing 26 (2018) 76–86 77 2 Author name / Procedia Manufacturing 00 (2018) 000–000 Procedia Manufacturing 00 (2018) 000–000 www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia such circumstances, failure to respond quickly to raw paper and future work is described in the conclusions material shortages, downtimes, deteriorating section. equipment conditions or other operational risks could prove to be an expensive affair. This makes a 2. Literature review 46th SME North American Manufacturing Research Conference, NAMRC 46, Texas, USA company-wide risk assessment of critical value. Such 46th SME North American Manufacturing Research Conference, NAMRC 46, Texas, USA a practice gives a holistic view of the risks affecting a Since production line risk assessment is a less Assessing Production Line Risk using Bayesian Belief Networks company and provides opportunities to mitigate them. explored field, published literature in supply chain risk Assessing Production Line Risk using Bayesian Belief Networks In addition, ISO 31000 encourages companies to adopt assessment was also reviewed. Quantitative risk and System Dynamics risk-based decision-making and requires them to assessment was the prime focus of the research. Bustad and System Dynamics develop a ‘Risk Profile’ [1]. & Bayer [3] presented a risk management process at The scope of a company-wide risk assessment Coca Cola Enterprises through the Hazard and Sudhir Punyamurthulaa*, Fazleena Badurdeenb Sudhir Punyamurthulaa*, Fazleena Badurdeenb includes both internal and external operations. Operability (HAZOP) method. They identified risks aUniversity of Kentucky, Lexington, KY, 40503, USA. However, external/supplier risk assessment has drawn impacting the industry and these risks were assessed bInstitute for SustainableaUniversity Manufacturing, of Kentucky, Lexington,University KY,of Kentucky, 40503, USA. Lexington, KY, USA overwhelming attention compared to using risk-appetite matrix. This approach is good for bInstitute for Sustainable Manufacturing, University of Kentucky, Lexington, KY, USA internal/production line risk assessment. William et. al creating awareness and could work as a quick [2] identified this trend when only 3 research studies overview of the risks impacting the production line. * Corresponding author. Tel.: +1-858-200-6803; fax: +1-859-257-1071. regarding manufacturing risk assessment were found However, the HAZOP method is mostly qualitative * CorrespondingE-mail address: author. psudhir1912@ Tel.: +1-uky.edu858-200 -6803; fax: +1-859-257-1071. E-mail address: [email protected] compared to several in the field of supply risk and cannot account for the uncertainty due to the assessment. Even though the scope of risk assessment complexity in the system. Abstract is much narrower with internal operations, it is still of Alternatively, the Fault Tree approach of assessing Abstract significant importance as it would give a company an the reliability of the production line was demonstrated Increased complexity in product design, strict regulations and a changing market make risk assessment critical for successful edge over its competitors, within the industry, by in [4, 5]. This approach gives an insight into the events Increasedoperations. complexity Failure in inresponding product design, quickly strict to raw regulat materialions andshortage a changings, downtimes, market deterioratingmake risk assessment equipment critical condition for successfuls or other ensuring financial strength, quality of goods and resulting in a failure event. However, it is deterministic operationaloperations. Failureissues canin respondingprove to be quickly an expensive to raw affair.material A shortagecompanys-, widedowntimes, risk assessment deteriorating includes equipment both external condition ands orinternal other services and customer satisfaction. Therefore, and does not capture the interdependencies between operationaloperations. However,issues can external/supplier prove to be an riskexpensive assessment affair. has A been company of major-wide interest. risk assessment Even though includes the scope both of riskexternal assessment and internal at the objectives of the research presented in this paper are the risk events, as it depends on logical operators. operations.production lineHowever, level isexternal/supplier not as broad as riskit is aatssessment the supply has chain been level, of major assessing interest. risk Even would though help the recognize scope of vulnerable risk assessment areas ofat the production line,line levelwhich is would not as inbroad turn ashelp it isreduce at the damage supply causedchain level, when assessing risk events risk occur. would In helpthis research,recognize a vulnerablemethod for area productions of the to: Bayesian Belief Networks (BBN) is a good tool to lineproduction risk assessment line, which is proposedwould in byturn considering help reduce operational damage caused risks affectingwhen risk the events line. occur. Operational In this risksresearch, and their a method causal for rela productiontionships calculate the likelihood of the risk events as it captures areline representedrisk assessment using is Bayesianproposed Beliefby considering Networks operational (BBN). The risks impact affecting of these the line. risks Operational is observed risks usin andg a theirsimulation causal modelrelationships of the 1. Develop a methodology to evaluate both the interdependencies between risk events and productionare represented line usingusing BayesianSystem DynamicsBelief Networks (SD) approach. (BBN). The The impact combination of these of risks BBN is observedand SD assistsusing ain simulation developing model a versatile of the production line risk, which can capture the dynamic uncertainty in likelihood. Unlike fault trees, BBN methodology,production line which using can System capture Dynamics the dynamic (SD) causal approach. mechanisms The combination in a complex of system, BBN and the uncertaintiesSD assists in amongst developing risk aevents versatile and nature of risk events and their relationships with each make use of Node Probability Tables (NPT), which methodology, which can capture the dynamic causal mechanisms in a complex system, the uncertainties amongst risk events and the long-term impact of operational risks on the production line. other. capture the complex inter-dependent relationships the long-term impact of operational risks on the production line. 2. Assess the impact on the production line, between events in an efficient manner. BBN models © 2018 The Authors. Published by Elsevier B.V. Peer© 201-review8 The underAuthors. responsibility Published by of Elsevierthe scientific B.V. committee of NAMRI/SME. upon exposure to risk events, over a period. have been used as a risk assessment tool in various Peer-review under responsibility of the scientific committee of NAMRI/SME. fields. Fault diagnosis in a hydropower plant using Keywords: Risk Assessment; Bayesian Belief Networks; System Dynamics. The remainder of the paper is organized as follows. BBN was discussed by Chaur & Sou [6], supply chain Keywords: Risk Assessment; Bayesian Belief Networks; System Dynamics. Section 2 presents a brief summary of the literature risk analysis using BBN was demonstrated by review and identifies gaps in research. Section 3 Badurdeen et. al [7] and additional case studies were 1. Introduction affecting the manufacturing sector. Moreover, provides details on the Production Line Risk presented in Amundson et. al [8]. Badurdeen et. al [7] 1. Introduction affectingcompanies theare willingmanufacturing to take somesector .extra Moreover, risk to Assessment (PLRA) methodology developed by outlined a well-structured method for Supply Chain Increased complexity in products to be survivecompanies and are succeed willing in toan takeincreasingly some extra competitive risk to combining Bayesian Belief Networks (BBN) and Risk Assessment (SCRA) by linking the risk drivers to manufactured,Increased strictcomplexity regulations in andproducts a continuously to be survivemarket. Often,and succeed production in an capacities increasingly and competitivecapabilities System Dynamics (SD). The application of the performance measures. This model captures the changingmanufactured, market strict have regulations led to an and increase a continuously in risks marketare quoted. Often, aggressively production in ordercapacities to get and the capabilitiesjob. Under methodology to a production line case study is also uncertainty within the system in an effective way. changing market have led to an increase in risks are quoted aggressively in order to get the job. Under presented in this section. Results and discussion are However, risk events are not static in nature. Risk presented in Section 4 where the effectiveness of the events evolve with time and the BBN models, when © 2018 The Authors. Published by Elsevier B.V. Peer© 2018-review The underAuthors. responsibility Published by of Elsevierthe scientific B.V. committee of NAMRI/SME. methodology in assessing the behavior of the applied to a static data set, fail to capture this dynamic Peer-review under responsibility of the scientific committee of NAMRI/SME. production line system is examined. A summary of the behavior. Thus, BBN models alone may not be able to capture the impact of these risk events over a period. Dynamic causal relations can be modelled well using simulation tools such as System Dynamics (SD). SD is a powerful tool comprising of stocks and flows. Stocks represent levels, which can be used to represent 78 Sudhir Punyamurthula et al. / Procedia Manufacturing 26 (2018) 76–86 Author name / Procedia Manufacturing 00 (2018) 000–000 3 4 Author name / Procedia Manufacturing 00 (2018) 000–000 inventories, cash reserves, etc. Flows determine the ‘Operating risks’ sub-cluster consists of risks relevant at each station are represented as stocks. Procurement quantity of stock that is moving from one location to at the production line level. These risks, listed in the rate and production rates at each station are flows that another. Simulation of a model of a system risk taxonomy, may or may not impact a specific control the quantity of stocks. The BBN model demonstrates the change in stocks and flows over a production line but they serve as a guide during the risk calculates the likelihood of child risk events based on period. The SD approach has been applied in the field identification phase. their causal relationships with parent nodes and the of risk assessment. Risk analysis using SD on a new Alternatively, conventional techniques like prior probabilities entered. Based on this likelihood of product development process was demonstrated by brainstorming, questionnaires, incident investigation, child risk event, severity of risk event is calculated. Dehghanbaghi & Mehrjerdi [9] to study the impact of auditing and inspection and HAZOP (Hazard and Likelihood is the probability of occurrence of risk and risk events on performance metrics like sales, Operability Studies) could be used for risk severity is the severity of this risk in terms of loss in Fig 1: Node Probability Table -BBN production, government support and raw materials. identification. performance or resources. We assume proportionality Similarly, a risk management process in NASA’s The influence of these risk events is assessed by Fig. 2 shows the representation of BBN risk model between likelihood and severity of risk events. This shuttle launching system was studied by Dulac et. al studying how they affect variation of Key Performance in SD. Parent risk event 1 (RE1) and risk event 2 (RE2) relationship is captured through the use of a lookup [10] to capture the dynamic nature of risk and its Indicator (KPI). KPIs describe the overall performance are connected to child risk event 3 (RE3) using arcs. function or table function. and can be developed with impact on the shuttle launch. SD models could capture of the production line succinctly. Analysing KPI Likelihood of each risk event and their conditional expert opinion. The relationship can be entered in the the impact of risk events on the system; however, SD graphs helps understand the behaviour of the system probabilities are represented as a variable. form of table function by associating a severity (in models have difficulty in representing relationships and allow management to take further action. terms of production loss) with a likelihood range. For between risk events due to their subjective nature. example, when P(RE3) is between 0 to 0.1, severity of Therefore, combining SD and BBN can prove to be an 3.2 Risk Analysis risk event 3 is 200 parts. Similarly, when P(RE3) is effective way to capture both probabilistic exposure to between 0.2-0.3 then severity of risk event 3 is 250 risk events and transient impact over time. Mohaghegh Most techniques for risk analysis fail to capture the parts. BBN risk model is connected to the SD [11] demonstrated the combination of SD and BBN for dynamic and interdependent nature of risk events and production line model through a production line Socio-Technical Risk analysis. The model is capable their impact on the production line. In this research, a variable. The production line variable is impacted by of capturing dynamic nature of variables within the combination of BBN and SD is used to develop a more both the likelihood and severity of risk event. Equation system through SD and BBN captures inter- versatile tool for risk analysis. (3) shows the calculation of risk event impact using the relationships and uncertainty in risk events. Pearl [12] defines BBNs as directed acyclic graph, “PULSETRAIN”, an in-built Vensim function that While production line risk assessment has been which consist of nodes/variables and arcs connecting relates the impact frequency (1/P(RE)) and severity of dependent nodes. These relationships amongst nodes Fig. 2: Representing BBN in System Dynamics. addressed before, most of the methods used provide risk event: are defined through conditional probabilities. only a limited perspective, often using qualitative and For the child nodes, conditional probabilities are BBNs are fundamentally based on the Bayes’ deterministic information. Integrating capabilities calculated using the chain rule application of Bayes’ Risk__ event impact  theorem that can be stated as follows: offered by different tools can provide a more versatile theorem. For example, the probability of risk event 3 PULSETRAIN( impact _ start _, time approach to evaluate risks at the production line level. (P(RE3)) can be computed as below: P( C | Pt )* P ( Pt ) impact_ duration ,_ impact frequency , P( Pt |) C  final_ time )* Severity _ of _ risk _ event . 3. Methodology PC() P( RE 3) ( P ( RE 3) | RE 1, RE 2) * P ( RE 1) * P ( RE 2)) (3) (1) (P ( RE 3) |~ RE 1,~ RE 2)*(1 P ( RE 1))*(1 P ( RE 2))) ISO 31000:2015 [1] defines Risk Assessment as a (P ( RE 3) |~ RE 1, RE 2)*(1 P ( RE 1))* P ( RE 2)) 3-step process: where, P( Pt |) C is the conditional probability of PULSE is a Vensim function that returns 1 starting occurrence of parent node ()Pt given that child node (P ( RE 3) | RE 1,~ RE 2)* P ( RE 1)*(1 P ( RE 2))) at time start and lasting for interval width. Equation 4 (1) Risk Identification ()C occurs. Alternatively, P(| C Pt )is the probability (2) describes the math behind PULSE function. (2) Risk Analysis of given occurs. (3) Risk Evaluation For risk assessment using BBN, each risk event is SD facilitates modelling of a production line considered𝐶𝐶 𝑃𝑃𝑃𝑃as a node and the complex relationships through stocks and flows. Stocks are accumulations of If_ then _ else (( start _ time int erval _ width ) The methodology followed for each of these steps between these risk events is captured through system variables, similar to inventories. These time start_ time ,1, 0) for this research is described in the sections below. conditional probabilities. A node probability table stocks/inventories are controlled through flows, (4) (NPT) is associated with each node/risk event as similar to production rates. Rehab [13] demonstrates 3.1 Risk Identification shown in Fig. 1. This table defines relationship an effective method to the construction and analysis of A train of repeated pulses is known as PULSETRAIN between the child node and its parent nodes using a Lean manufacturing system using SD. This method Identifying risks is one of the most crucial steps for function. conditional probabilities. could be used in construction of production line model. risk assessment. Badurdeen et. al [7] presented a Fig.3 depicts a production line model consisting of comprehensive supply chain risk taxonomy. In their three workstations through which raw material gets In order to capture the dynamic nature of risk work, the ‘Organizational risks’ cluster includes processed. Raw materials and work in process (WIP) several risks impacting the organization and the events, a response variable is triggered to alter the Sudhir Punyamurthula et al. / Procedia Manufacturing 26 (2018) 76–86 79 Author name / Procedia Manufacturing 00 (2018) 000–000 3 4 Author name / Procedia Manufacturing 00 (2018) 000–000 inventories, cash reserves, etc. Flows determine the ‘Operating risks’ sub-cluster consists of risks relevant at each station are represented as stocks. Procurement quantity of stock that is moving from one location to at the production line level. These risks, listed in the rate and production rates at each station are flows that another. Simulation of a model of a system risk taxonomy, may or may not impact a specific control the quantity of stocks. The BBN model demonstrates the change in stocks and flows over a production line but they serve as a guide during the risk calculates the likelihood of child risk events based on period. The SD approach has been applied in the field identification phase. their causal relationships with parent nodes and the of risk assessment. Risk analysis using SD on a new Alternatively, conventional techniques like prior probabilities entered. Based on this likelihood of product development process was demonstrated by brainstorming, questionnaires, incident investigation, child risk event, severity of risk event is calculated. Dehghanbaghi & Mehrjerdi [9] to study the impact of auditing and inspection and HAZOP (Hazard and Likelihood is the probability of occurrence of risk and risk events on performance metrics like sales, Operability Studies) could be used for risk severity is the severity of this risk in terms of loss in Fig 1: Node Probability Table -BBN production, government support and raw materials. identification. performance or resources. We assume proportionality Similarly, a risk management process in NASA’s The influence of these risk events is assessed by Fig. 2 shows the representation of BBN risk model between likelihood and severity of risk events. This shuttle launching system was studied by Dulac et. al studying how they affect variation of Key Performance in SD. Parent risk event 1 (RE1) and risk event 2 (RE2) relationship is captured through the use of a lookup [10] to capture the dynamic nature of risk and its Indicator (KPI). KPIs describe the overall performance are connected to child risk event 3 (RE3) using arcs. function or table function. and can be developed with impact on the shuttle launch. SD models could capture of the production line succinctly. Analysing KPI Likelihood of each risk event and their conditional expert opinion. The relationship can be entered in the the impact of risk events on the system; however, SD graphs helps understand the behaviour of the system probabilities are represented as a variable. form of table function by associating a severity (in models have difficulty in representing relationships and allow management to take further action. terms of production loss) with a likelihood range. For between risk events due to their subjective nature. example, when P(RE3) is between 0 to 0.1, severity of Therefore, combining SD and BBN can prove to be an 3.2 Risk Analysis risk event 3 is 200 parts. Similarly, when P(RE3) is effective way to capture both probabilistic exposure to between 0.2-0.3 then severity of risk event 3 is 250 risk events and transient impact over time. Mohaghegh Most techniques for risk analysis fail to capture the parts. BBN risk model is connected to the SD [11] demonstrated the combination of SD and BBN for dynamic and interdependent nature of risk events and production line model through a production line Socio-Technical Risk analysis. The model is capable their impact on the production line. In this research, a variable. The production line variable is impacted by of capturing dynamic nature of variables within the combination of BBN and SD is used to develop a more both the likelihood and severity of risk event. Equation system through SD and BBN captures inter- versatile tool for risk analysis. (3) shows the calculation of risk event impact using the relationships and uncertainty in risk events. Pearl [12] defines BBNs as directed acyclic graph, “PULSETRAIN”, an in-built Vensim function that While production line risk assessment has been which consist of nodes/variables and arcs connecting relates the impact frequency (1/P(RE)) and severity of dependent nodes. These relationships amongst nodes Fig. 2: Representing BBN in System Dynamics. addressed before, most of the methods used provide risk event: are defined through conditional probabilities. only a limited perspective, often using qualitative and For the child nodes, conditional probabilities are BBNs are fundamentally based on the Bayes’ deterministic information. Integrating capabilities calculated using the chain rule application of Bayes’ Risk__ event impact  theorem that can be stated as follows: offered by different tools can provide a more versatile theorem. For example, the probability of risk event 3 PULSETRAIN( impact _ start _, time approach to evaluate risks at the production line level. (P(RE3)) can be computed as below: P( C | Pt )* P ( Pt ) impact_ duration ,_ impact frequency , P( Pt |) C  final_ time )* Severity _ of _ risk _ event . 3. Methodology PC() P( RE 3) ( P ( RE 3) | RE 1, RE 2) * P ( RE 1) * P ( RE 2)) (3) (1) (P ( RE 3) |~ RE 1,~ RE 2)*(1 P ( RE 1))*(1 P ( RE 2))) ISO 31000:2015 [1] defines Risk Assessment as a (P ( RE 3) |~ RE 1, RE 2)*(1 P ( RE 1))* P ( RE 2)) 3-step process: where, P( Pt |) C is the conditional probability of PULSE is a Vensim function that returns 1 starting occurrence of parent node ()Pt given that child node (P ( RE 3) | RE 1,~ RE 2)* P ( RE 1)*(1 P ( RE 2))) at time start and lasting for interval width. Equation 4 (1) Risk Identification ()C occurs. Alternatively, P(| C Pt )is the probability (2) describes the math behind PULSE function. (2) Risk Analysis of given occurs. (3) Risk Evaluation For risk assessment using BBN, each risk event is SD facilitates modelling of a production line considered𝐶𝐶 𝑃𝑃𝑃𝑃as a node and the complex relationships through stocks and flows. Stocks are accumulations of If_ then _ else (( start _ time int erval _ width ) The methodology followed for each of these steps between these risk events is captured through system variables, similar to inventories. These time start_ time ,1, 0) for this research is described in the sections below. conditional probabilities. A node probability table stocks/inventories are controlled through flows, (4) (NPT) is associated with each node/risk event as similar to production rates. Rehab [13] demonstrates 3.1 Risk Identification shown in Fig. 1. This table defines relationship an effective method to the construction and analysis of A train of repeated pulses is known as PULSETRAIN between the child node and its parent nodes using a Lean manufacturing system using SD. This method Identifying risks is one of the most crucial steps for function. conditional probabilities. could be used in construction of production line model. risk assessment. Badurdeen et. al [7] presented a Fig.3 depicts a production line model consisting of comprehensive supply chain risk taxonomy. In their three workstations through which raw material gets In order to capture the dynamic nature of risk work, the ‘Organizational risks’ cluster includes processed. Raw materials and work in process (WIP) several risks impacting the organization and the events, a response variable is triggered to alter the 80 Sudhir Punyamurthula et al. / Procedia Manufacturing 26 (2018) 76–86 Author name / Procedia Manufacturing 00 (2018) 000–000 5 6 Author name / Procedia Manufacturing 00 (2018) 000–000 nature of risk events through the production line 4. Case Study material shortages (RMS), caused by poor supplier 4.2 Case Study - Risk Assessment model. Usually, KPIs are the system variables that relationship (PSR), and delivery problems (DP) are the trigger a response variable. In the example, RE3 The automotive industry is one of the most major risk events leading to procurement time delay Vensim, an SD software, was used to develop the impacts the production line variable (related to competitive and risky industries in the manufacturing (PTD) risk event. BBN and production line models. production rate at station 1). The impact of risk events sector. Here, we use a case study from the automotive Impact of risk events were considered at the Causal Loop Diagram (CLD) is an effective method on the production line is monitored at the station 3 industry to demonstrate the application of the proposed fineblanking station and grinding station. Fineblanking for defining system variables and boundaries. CLD through a KPI. When the KPI value method. station was strategically targeted as it is the first stage represents the cause & effect relationship between increases/decreases beyond a certain limit, it is setup A growing supplier of precision metal components of the production line and has the highest value system variables, which acts as a useful tool while to initiate a risk management (RM) process. The RM and assemblies using fineblanking technology was addition. Grinding station was selected as it is the developing the Stock & Flow model. Fig.4 shows a reduces the likelihood of the RE3. Equation 5 shows considered for risk assessment. The company operates bottleneck station and affects the overall throughput of CLD of the case study. Cause-effect relationship how P(RE2) is impacted by RM. A residual risk is at several locations across the globe including USA, the production line. between risk events from BBN model and production associated with the risk event, which can be Canada, Mexico and China. line variables from production line SD model are determined by the use of data and/or expert judgement. One of the divisions in USA specializes in represented. P(RM) drives P(RE2) such that when P(RM) is 0, producing several kinds of engine plates and P(RE2) remains unchanged and when P(RM) is 1, transmission parts, which are supplied to major Poor supplier P(RE2) is equal to residual risk. automobile manufacturers. The company name and relationship other information is withheld due to confidentiality + OEE factors Delivery Raw material related risks reasons. One of the major and strategically important Problems shortage P( RE 2) P ( RE 2)*(1  P ( RM )) New Product + customer’s products, Engine Plates, were selected for Procurement + Development + + WIP (Residual _ risk * P ( RM )) time delay Inspection+Packing this application. Data regarding the process routing and + Manufacturing WIP Grinding delays - production capacities were acquired using internal - - + + Tapping & Beltsanding and (5) Fineblanking Drilling + - Grinding Brush+Washing Inspection & company database. Production Rate + Production Rate + Countersink + + Production rate Production rate Production rate Packing rate + + - + + - The process routing for producing engine plates, is WIP - + - + This change in value of P(RE2) is reflected on WIP Beltsanding & Fineblanking WIP Drilling WIP Tapping & Shipment rate Brush+Washing as follows: Initial Countersink + P(RE3) and thus, establishing a feedback loop. - 1. Fineblanking operation at 1600-ton press. production rate + + Demand Finished Goods (Production capacity: 3000 - 3750 parts/day) Demand Delay in - fulfilment rate Inventory (FGI) P(risk delivery + management) + + Revenue 2. Drilling station. (Production capacity: 2520 - orders - - ++ P(RE1) P(RE2) 2700 parts/day) Backlog lost sales Fig. 4: Causal loop diagram- Case study P(RE3) Severity of Risk 3. Tapping and Countersink station. Event 3 KPI (Production capacity: 2380 - 2550 part/day) Production Line variable 4. Grinding operation. (Production capacity: Based on CLD, BBN and production line SD Raw WIP at WIP at WIP at Station 1 Station 2 1680 - 1800 parts/day) models are developed. Materials Procurement Production Production Station 3 Production rate rate at station rate at Station rate at Station 5. Belt-sand and Brush Operation. (Production BBN model consists of risks identified in step1. The 1 2 3 capacity: 7000 - 7500 parts/day) likelihood of independent risk events and causal Fig. 3: Interaction between BBN and SD model. 6. Inspection and Packing operations. relationships between risk events is represented in Fig. (Production capacity: 1850 - 2025 parts/day) 5. Conditional probabilities of each risk event are 3.3 Risk Evaluation 7. Shipping (Capacity: 5500 parts/day) connected using an expression based on equation (2).

The impact of risk events on the production line KPIs is examined for risk evaluation. KPIs give a holistic idea about the behaviour of the system and aids 4.1 Case Study- Risk Identification management in decision-making. A Risk Network map of risks impacting the In addition, SD provides a platform to analyze the Fig.5: BBN risk model – Case study. production line was developed with the help of system under several scenarios. Evaluating the system under several scenarios, realistic and far-fetched, can industry personnel. General operational risks identified Fig.6 displays the prior probabilities fed into the BBN help gain further insight into the behavior of the system by Badurdeen et. al [7] were referred during this phase. risk model. Data required to construct these tables and enable companies to prepare for radical or extreme Manufacturing disruptions or delays are the primary were obtained by utilizing resources within the situations. risks impacting production line. New product testing company (managers). (NPT), procurement time delays (PTD) and OEE factors related risks (OEE) are the major risk events leading to the manufacturing delay (MD) risk. Raw Sudhir Punyamurthula et al. / Procedia Manufacturing 26 (2018) 76–86 81 Author name / Procedia Manufacturing 00 (2018) 000–000 5 6 Author name / Procedia Manufacturing 00 (2018) 000–000 nature of risk events through the production line 4. Case Study material shortages (RMS), caused by poor supplier 4.2 Case Study - Risk Assessment model. Usually, KPIs are the system variables that relationship (PSR), and delivery problems (DP) are the trigger a response variable. In the example, RE3 The automotive industry is one of the most major risk events leading to procurement time delay Vensim, an SD software, was used to develop the impacts the production line variable (related to competitive and risky industries in the manufacturing (PTD) risk event. BBN and production line models. production rate at station 1). The impact of risk events sector. Here, we use a case study from the automotive Impact of risk events were considered at the Causal Loop Diagram (CLD) is an effective method on the production line is monitored at the station 3 industry to demonstrate the application of the proposed fineblanking station and grinding station. Fineblanking for defining system variables and boundaries. CLD through a KPI. When the KPI value method. station was strategically targeted as it is the first stage represents the cause & effect relationship between increases/decreases beyond a certain limit, it is setup A growing supplier of precision metal components of the production line and has the highest value system variables, which acts as a useful tool while to initiate a risk management (RM) process. The RM and assemblies using fineblanking technology was addition. Grinding station was selected as it is the developing the Stock & Flow model. Fig.4 shows a reduces the likelihood of the RE3. Equation 5 shows considered for risk assessment. The company operates bottleneck station and affects the overall throughput of CLD of the case study. Cause-effect relationship how P(RE2) is impacted by RM. A residual risk is at several locations across the globe including USA, the production line. between risk events from BBN model and production associated with the risk event, which can be Canada, Mexico and China. line variables from production line SD model are determined by the use of data and/or expert judgement. One of the divisions in USA specializes in represented. P(RM) drives P(RE2) such that when P(RM) is 0, producing several kinds of engine plates and P(RE2) remains unchanged and when P(RM) is 1, transmission parts, which are supplied to major Poor supplier P(RE2) is equal to residual risk. automobile manufacturers. The company name and relationship other information is withheld due to confidentiality + OEE factors Delivery Raw material related risks reasons. One of the major and strategically important Problems shortage P( RE 2) P ( RE 2)*(1  P ( RM )) New Product + customer’s products, Engine Plates, were selected for Procurement + Development + + WIP (Residual _ risk * P ( RM )) time delay Inspection+Packing this application. Data regarding the process routing and + Manufacturing WIP Grinding delays - production capacities were acquired using internal - - + + Tapping & Beltsanding and (5) Fineblanking Drilling + - Grinding Brush+Washing Inspection & company database. Production Rate + Production Rate + Countersink + + Production rate Production rate Production rate Packing rate + + - + + - The process routing for producing engine plates, is WIP - + - + This change in value of P(RE2) is reflected on WIP Beltsanding & Fineblanking WIP Drilling WIP Tapping & Shipment rate Brush+Washing as follows: Initial Countersink + P(RE3) and thus, establishing a feedback loop. - 1. Fineblanking operation at 1600-ton press. production rate + + Demand Finished Goods (Production capacity: 3000 - 3750 parts/day) Demand Delay in - fulfilment rate Inventory (FGI) P(risk delivery + management) + + Revenue 2. Drilling station. (Production capacity: 2520 - orders - - ++ P(RE1) P(RE2) 2700 parts/day) Backlog lost sales Fig. 4: Causal loop diagram- Case study P(RE3) Severity of Risk 3. Tapping and Countersink station. Event 3 KPI (Production capacity: 2380 - 2550 part/day) Production Line variable 4. Grinding operation. (Production capacity: Based on CLD, BBN and production line SD Raw WIP at WIP at WIP at Station 1 Station 2 1680 - 1800 parts/day) models are developed. Materials Procurement Production Production Station 3 Production rate rate at station rate at Station rate at Station 5. Belt-sand and Brush Operation. (Production BBN model consists of risks identified in step1. The 1 2 3 capacity: 7000 - 7500 parts/day) likelihood of independent risk events and causal Fig. 3: Interaction between BBN and SD model. 6. Inspection and Packing operations. relationships between risk events is represented in Fig. (Production capacity: 1850 - 2025 parts/day) 5. Conditional probabilities of each risk event are 3.3 Risk Evaluation 7. Shipping (Capacity: 5500 parts/day) connected using an expression based on equation (2).

operators. In addition, data from previous time studies 9 High Normal High Normal performed by the sales & accounting departments, for 10 High Normal High Delayed business planning purposes, were included. Actual 11 High Delayed High Delayed production rate at each station depends on the 12 Normal Delayed High Delayed minimum of WIP quantity at the station and capable 13 Normal Delayed High Normal production rate (production capacity). Work-In- 14 High Normal Normal Delayed 15 High Delayed Normal Delayed Progress (WIP) at each station is computed based on 16 High Delayed High Normal the difference between entry and exit production rates at that station. Fig.8: Interaction between BBN and SD model – Case Study Table 2: Variables used in simulation Inspection and packing station involves quality Normal High check. Defective products are reworked and The dynamic nature of risk events is captured OEE risk mean: 0.5 mean: 0.75 introduced back to the production line. Defects through risk management variable as shown in Fig. 9. Fineblanking Response percentage was obtained through the quality reports at Risk management likelihood depends on the delay in Station 1 week 1 month the inspection station. delivery. A proportional relationship is defined time OEE risk mean: 0.42 mean: 0.75 Demand follows a Normal distribution obtained between risk management and “Delay in delivery” Grinding from demand forecasts calculated by the sales Response performance indicator. This risk management variable Station 1week 1 month Fig.6: Prior probabilities-BBN risk model department. Demand fulfilment rate is equal to the mitigates or reduces the likelihood of OEE factors time shipment rate. Order backlog is based on the difference related risks using equation 4. The model is then between demand fulfilment rate and demand. Delay in simulated for 400 days and the behaviour of the system This is followed by developing the SD production delivery is equal to order backlog divided by demand is monitored. 5. Results line model as shown in Fig. 7. Each workstation has a fulfilment rate. Revenue is the difference between production capacity, which is the maximum output at revenue made from sales and lost sales. Delivery performance has become important to the workstation without considering risk events and This production line model could also be considered Original Equipment Manufacturers (OEMs) and their WIP constraints. Production Capacities follow a as the baseline model. Baseline model produces results suppliers as customers are inclined towards Normal distribution, varying between the limits similar to the business plan for the year and what the manufacturers/service providers having reduced lead mentioned in the process routing, obtained through industry personnel expect to see without any risks. times. Hence, “Delay in delivery” performance metric comprehensive time studies performed on several was chosen to compare between scenarios and analyze system’s behaviour.

Fig. 9: Feedback loop from SD to BBN model – Case Study 5.1 Scenarios analysis – delay in delivery KPI

4.3 Case Study – Risk Evaluation The baseline model shows no delay in delivery, thus leading to maximum revenue. The cumulative revenue The company was interested in understanding the generated is $4.132 million over a period of 400 days. impact of variations in the OEE risk and response time Scenarios analysis revealed some interesting aspects on the Fineblanking and grinding stations. Table 1 about the system’s behavior which otherwise would presents 16 what-if scenarios that were modelled. have been neglected. Fig. 10 displays the performance Table 2 shows the data used for the ‘Normal’ and of the line under a few scenarios (selected based on the ‘High’ scenarios for each station. trend observed) through “Delay in delivery” performance metric. Some of the interesting Table 1: Case Study Scenarios observations are: Fineblanking station Grinding station S.No. Response Response a) Scenario 1, in Fig. 10(a), has a higher delay OEE risk OEE risk time time in delivery when compared to scenario 2, in Fig. 10(b),

Fig.7: Production line SD model – Case study 1 Normal Normal Normal Normal despite having a lower risk impacting the line. With a 2 High Normal Normal Normal higher risk at the fineblanking station in scenario 2, the The baseline production model was then connected grinding stations. Severity of risk events were 3 High Delayed Normal Normal delay in delivery is quite high initially. This high delay to the BBN model for risk assessment as shown in Fig. estimated with the help of industry personnel. Since 4 Normal Delayed Normal Normal results in a higher risk response likelihood and this in 8. Manufacturing delay risk likelihoods (fineblanking the scope of risk assessment is at the production line 5 Normal Normal High Normal turn results in an increased risk mitigation. Due to the 6 Normal Normal High Delayed and grinding), calculated from BBN model, forms the level, proportionality is estimated between severity of increased risk mitigation, “Delay in delivery”, in basis for impact frequency at the fineblanking and risk events and risk likelihood. 7 Normal Normal Normal Delayed 8 Normal Delayed Normal Delayed Sudhir Punyamurthula et al. / Procedia Manufacturing 26 (2018) 76–86 83 Author name / Procedia Manufacturing 00 (2018) 000–000 7 8 Author name / Procedia Manufacturing 00 (2018) 000–000 operators. In addition, data from previous time studies 9 High Normal High Normal performed by the sales & accounting departments, for 10 High Normal High Delayed business planning purposes, were included. Actual 11 High Delayed High Delayed production rate at each station depends on the 12 Normal Delayed High Delayed minimum of WIP quantity at the station and capable 13 Normal Delayed High Normal production rate (production capacity). Work-In- 14 High Normal Normal Delayed 15 High Delayed Normal Delayed Progress (WIP) at each station is computed based on 16 High Delayed High Normal the difference between entry and exit production rates at that station. Fig.8: Interaction between BBN and SD model – Case Study Table 2: Variables used in simulation Inspection and packing station involves quality Normal High check. Defective products are reworked and The dynamic nature of risk events is captured OEE risk mean: 0.5 mean: 0.75 introduced back to the production line. Defects through risk management variable as shown in Fig. 9. Fineblanking Response percentage was obtained through the quality reports at Risk management likelihood depends on the delay in Station 1 week 1 month the inspection station. delivery. A proportional relationship is defined time OEE risk mean: 0.42 mean: 0.75 Demand follows a Normal distribution obtained between risk management and “Delay in delivery” Grinding from demand forecasts calculated by the sales Response performance indicator. This risk management variable Station 1week 1 month Fig.6: Prior probabilities-BBN risk model department. Demand fulfilment rate is equal to the mitigates or reduces the likelihood of OEE factors time shipment rate. Order backlog is based on the difference related risks using equation 4. The model is then between demand fulfilment rate and demand. Delay in simulated for 400 days and the behaviour of the system This is followed by developing the SD production delivery is equal to order backlog divided by demand is monitored. 5. Results line model as shown in Fig. 7. Each workstation has a fulfilment rate. Revenue is the difference between production capacity, which is the maximum output at revenue made from sales and lost sales. Delivery performance has become important to the workstation without considering risk events and This production line model could also be considered Original Equipment Manufacturers (OEMs) and their WIP constraints. Production Capacities follow a as the baseline model. Baseline model produces results suppliers as customers are inclined towards Normal distribution, varying between the limits similar to the business plan for the year and what the manufacturers/service providers having reduced lead mentioned in the process routing, obtained through industry personnel expect to see without any risks. times. Hence, “Delay in delivery” performance metric comprehensive time studies performed on several was chosen to compare between scenarios and analyze system’s behaviour.

Fig. 9: Feedback loop from SD to BBN model – Case Study 5.1 Scenarios analysis – delay in delivery KPI

(a) Impact of added capacity on scenario 1 (b) Impact of added capacity on scenario 5

(a) Delay in delivery – Scenario 1 (b) Delay in delivery – Scenario 2

(c) Impact of added capacity on scenario 11 (c) Impact of added capacity on scenario 16 Fig. 11: Impact of added capacity on scenarios

(c) Delay in delivery – Scenario 5 (d) Delay in delivery – Scenario 9 Fig. 10: Delay in delivery – scenarios analysis 6. Conclusions These results not only confirm the importance of risk assessment at the production line level but also act The proposed PLRA methodology provides a Almost all delay in delivery was due to the low results, and simulated for 400 days. Overtime of 7.5 as a great reference for production planning and risk versatile technique to assess the impact of risks difference between demand and demand fulfilment hours/day, 3.75 hours/shift, when the delay in delivery management units. The likelihood of risk exposure is affecting production line performance. The BBN rate. A delay in delivery was bound to occur during a exceeds 1.5 days. An overtime cost was associated well captured through BBN and the impact of risks on model captures relationships between risk events and risk event. Most of the production delays occurring with the revenue equation. production line KPIs like delay in delivery, demand calculates their likelihoods. The dynamic nature of this during a risk event were carried until the end of Extra capacity added was deterministic in nature in fulfilment rate and revenue through SD. This BBN model is captured by combining it with SD simulation period. However, industrialists have several order to simplify the case. Fig. 11 displays the results combined approach of SD-BBN bridges the research production line model. The impact of risk events on the action plans to recover production losses. Overtime is of this experiment. gaps identified with the current techniques of risk production line is examined through various KPIs. the most common way of resolving this issue. assessment. Comparing the production line model (affected by a) Scenario 1: As seen in Fig. 11(a), Future work would include an extended risk risks) to the baseline model shows a “Delay in 5.2 Effect of overtime on scenarios performance of production line improves and delay in taxonomy at the production line level and adding back delivery” of 1.5-2 days resulting in a loss in revenue of delivery stays under one day for the majority of propagation capability. Back propagation is one of the almost $900,000. Further, analyzing several scenarios Extra capacity (through overtime) was added to a simulation period. Cumulative revenue shows an key features of BBN, which help calculate the enables understanding numerous key aspects of the few scenarios, which were selected based on initial increase of $650,000 with added capacity. likelihood of parent nodes based on the likelihood of system’s behavior. child node, thus identifying the root cause. Sudhir Punyamurthula et al. / Procedia Manufacturing 26 (2018) 76–86 85 Author name / Procedia Manufacturing 00 (2018) 000–000 9 10 Author name / Procedia Manufacturing 00 (2018) 000–000 scenario 2, in the latter part of simulation is much c) Scenario 5 shows a higher delay in delivery b) Scenario 5: As seen in Fig. 11(b), extra response. Cumulative revenue increases from -$1.4 lower when compared to that of scenario 1 than in case of scenario 9 despite having a high risk at capacity is added during the initial phase of the million to $3.719 million. b) Contrary to observation (a), scenario 1 and just the grinding station. In scenario 9, a higher risk at simulation period when a high risk was affecting the d) Scenario 16: As seen in Fig. 11(d), extra scenario 5 show a different trend. Scenario 5 shows a both fineblanking and grinding stations results in a line. Cumulative revenue increases form $3.73 million capacity is utilized several times initially. Risk higher delay in delivery despite having a higher risk higher “Delay in delivery” initially. This results in an to $3.75 million when extra capacity (overtime) is response and extra capacity reduce the delay in response likelihood. The reason for this is that the increase in risk response likelihood and thus leads to utilized. delivery to a huge extent. There is no delay in delivery grinding station is the bottleneck process and any risk mitigation. Hence, a lower delay in delivery is c) Scenario 11: As seen in Fig. 11 (c), scenario after 130th day. Cumulative revenue increases from manufacturing delay at the grinding station is tough to seen towards the latter part of simulation in scenario 9. 11 also shows a significant improvement in $1.735 million to $3.936 million. compensate for. performance with added capacity and aggressive risk

(a) Impact of added capacity on scenario 1 (b) Impact of added capacity on scenario 5

(a) Delay in delivery – Scenario 1 (b) Delay in delivery – Scenario 2

(c) Impact of added capacity on scenario 11 (c) Impact of added capacity on scenario 16 Fig. 11: Impact of added capacity on scenarios

Acknowledgements [7] Badurdeen, F., Shuaib, M., Wijekoon, K., Brown, A., Faulkner, W., Amundson, J., Jawahir, I. S., Goldsby, T., Iyengar, D., and Boden, B., “Quantitative Modeling and Analysis of Supply The authors would like to thank colleagues from Chain Risks using Bayesian Theory,” Journal of Institute for Sustainable Manufacturing, University of Manufacturing Technology Management Vol. 25, No.5 (2014): Kentucky and industry personnel from the case study pp. 631-654. company who assisted during this research. [8] Amundson, J., Brown, A., Shuaib, M., Badurdeen, F., Jawahir, I.S., Goldsby, T., & Iyengar, D., “Bayesian Methodology for Supply Chain Risk Analysis: Concept and Case Studies”, IIE References Annual Conference. Proceedings, pp. 3944-3953, Puerto Rico, USA, May 18-22, 2013. [1] ISO 31000 Risk Management: A Practical Guide for SMEs, [9] Dehghanbaghi, M., Mehrjerdi,Y., “A Dynamic Risk Analysis (2015), Accessed on New Product Development Process,” International Journal at https://www.iso.org/files/live/sites/isoorg/files/archive/pdf/e of Industrial Engineering and Production Research Vol.24, n/iso_31000_for_smes.pdf on November 12, 2017 No.1 (2013): pp. 17-35. [2] William, H., Tian, Z., Yildiz, H., Talluri, S., “Supply chain risk [10] Dulac, N., Leveson, N., Zipkin, D., Friedenthal, S., management: a literature review”, International Journal of Gershenfeld, J., Carroll, J., et al., “ Using system dynamics for Production Research Vol.53, No. 16 (2015): pp. 5031-5069. safety and risk management in complex engineering systems,” [3] Bustad,G.,Bayer,E., “Introducing Risk Management Process to Winter Simulation Conference: Proceedings of the 37th a manufacturing industry,” Masters’ thesis, Royal Institue of conference on Winter simulation, pp. 1311–1320, Orlando, Technology, Stolkholm, Sweden, 2013. Florida, Dec. 4–7, 2005. [4] Zhang, C., Sun, J., Lin, S., “Reliability analysis and [11] Mohaghegh, Z., “Combining System Dynamics and Bayesian improvement for Li/MnO2 cell production line based on fault Belief Networks for Socio-Technical Risk Analysis,” IEEE tree analysis,” Proceedings of the 2011 Industrial Engineering International Conference on Intelligence and Security and Engineering Management (IE&EM) on IEEE, Vol. 2, pp. Informatics (ISI), pp. 196-201, Vancouver, Canada, May23-26, 1132-1135. Changchun, China, September 3-5, 2011. 2010. [5] Ariavie, G., Sadjere, G., 2012, “Development of Fault Tree [12] Pearl, J., "Bayesian Networks: A Model of Self-Activated Diagram for the Production Line of a Soft Drink Bottling Memory for Evidential Reasoning," in Proceedings of the 7th Company in Benin City, Nigeria” Proceedings of the World Conference of the Cognitive Science Society, pp. 329-334 Congress on Engineering, Vol. 3, pp. 1498-1505. London, U.K, University of California, Irvine, CA, USA, August 15-17, 1985. July 4-6, 2012. [13] Rehab M. A., Ahmed, M.D., “Dynamic Lean Assessment for [6] Chaur, J., Sou, L., “Bayesian network based hydro-power fault Takt Time Implementation”, Variety Management in diagnosis system development by fault tree transformation,” Manufacturing. Proceedings of the 47th CIRP Conference on Journal of Marine Science and Technology Vol. 21, No. 4 Manufacturing Systems, Vol. 17, pp. 577-581, Windsor, (2013): pp. 367-379. Canada, April 28-30, 2014.