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HOLISTIC AND AGGREGATED FRAMEWORK FOR SUPPLY CHAIN RISK MANAGEMENT
Abstract
In recent years, research into risk management (RM) (Flage, et al., 2014; Aven, 2015; Qazi et al., 2017) has highlighted the need for further action to highlight the uncertainties and subjectivity of this discipline, specifically in Supply Chain Management (SCM). This research aims to propose a holistic and multi‐objective framework focusing on RM in SCM and quantifying risk in an aggregate form in processes. For this, Fuzzy Logic, event tree analysis and Balanced Scorecard will be used. The main results expected from the model are based on the optimization of mitigation strategies, often in potential conflict with each other, and also on integration into the strategic management of the organization to improve decision‐making.
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Risk management plays a vital role in effectively operating supply chains in the presence of a variety of uncertainties. Over the years, many researchers have focused on supply chain risk management (SCRM) by contributing in the areas of defining, operationalising and mitigating risks. In this paper, we review and synthesise the extant literature in SCRM in the past decade in a comprehensive manner. The purpose of this paper is threefold. First, we present and categorise SCRM research appearing between 2003 and 2013. Second, we undertake a detailed review associated with research developments in supply chain risk definitions, risk types, risk factors and risk management/mitigation strategies. Third, we analyse the SCRM literature in exploring potential gaps.
Vast amounts of oil & gas (O&G) are consumed around the world everyday that are mainly transported and distributed through pipelines. Only in Canada, the total length of O&G pipelines is approximately 100,000 km, which is the third largest in the world. Integrity of these pipelines is of primary interest to O&G companies, consultants, governmental agencies, consumers and other stakeholder due to adverse consequences and heavy financial losses in case of system failure. Fault tree analysis (FTA) and event tree analysis (ETA) are two graphical techniques used to perform risk analysis, where FTA represents causes (likelihood) and ETA represents consequences of a failure event. ‘Bow-tie’ is an approach that integrates a fault tree (on the left side) and an event tree (on the right side) to represent causes, threat (hazards) and consequences in a common platform. Traditional ‘bow-tie’ approach is not able to characterize model uncertainty that arises due to assumption of independence among different risk events. In this paper, in order to deal with vagueness of the data, the fuzzy logic is employed to derive fuzzy probabilities (likelihood) of basic events in fault tree and to estimate fuzzy probabilities (likelihood) of output event consequences. The study also explores how interdependencies among various factors might influence analysis results and introduces fuzzy utility value (FUV) to perform risk assessment for natural gas pipelines using triple bottom line (TBL) sustainability criteria, namely, social, environmental and economical consequences. The present study aims to help owners of transmission and distribution pipeline companies in risk management and decision-making to consider multi-dimensional consequences that may arise from pipeline failures. The research results can help professionals to decide whether and where to take preventive or corrective actions and help informed decision-making in the risk management process. A simple example is used to demonstrate the proposed approach.
L'utilisation de plus en plus fréquente du terme « stratégique » dans les domaines les plus divers, par exemple dans les analyses de marketing ou de contrôle de gestion, permet de poser la question suivante : la stratégie d'entreprise est-elle en train de perdre, en tant qu'objet et outil de recherche, sa spécificité et son autonomie ? Et si tout devient stratégique, ne serait-ce pas parce que plus rien ne l'est ? Les auteurs, regrettant cette évolution qui facilite la confusion des genres, entendent ici redéfinir la stratégie et évoquer ses apports spécifiques en gestion. Notamment en proposant d'identifier la stratégie d'une entreprise par les processus qui la concrétisent.
Purpose
The aim of the research is to provide a method to evaluate supply chain risks that stand in the way of the supply chain objectives.
Design/methodology/approach
An analytical hierarchy process model is proposed to identify supply chain risk factors with a view to improving the objective of customer value. The two phases of the method are the prioritization of supply chain objectives; and the selection of risk indicators. A case study is also presented.
Findings
The appreciation of the most critical supply chain risks comes from careful evaluations of the impacts and a consideration of the cause‐effect relationships. The involvement of key managers is essential. In the case study the two most divergent evaluations were from the logistics manager and the sales manager.
Research limitations/implications
Further application in various companies and industry sectors would be helpful to compare different cases and findings.
Practical implications
The model allows for flexibility in using (and the frequent monitoring of) a panel of indicators by management. The dashboard is composed of only a few indicators and helps in ensuring a synthesis among different perspectives. For these reasons it gives an useful contribution to practitioners.
Originality/value
The model seems helpful in creating awareness of supply chain risk. The involvement of managers from different areas is essential in establishing a thorough consideration of critical issues and interdependencies in determining a complete risk analysis. The method can support managers in setting up a priority hierarchy for risk treatment.
We create a model which analyses the various risks involved in a food supply chain with the help of interpretive structural modelling (ISM). The various types of risks were identified based on a review of the literature and in consultation with experts in the food industry. The types of risks are clustered into five categories and risk mitigation is discussed. The model developed is validated with the help of a case study involving a food products manufacturing firm.
Many firms implemented various initiatives such as outsourced manufacturing which could make a supply chain (SC) more vulnerable to various types of disruptions. So managing risk has become a critical component of SC management. Different types of SC vulnerability management methodologies have been proposed for managing SC risk, most offer only point-based solutions that deal with a limited set of risks. This research aims to reinforce SC risk management by proposing an integrated approach. SC risks are identified and a risk index classification structure is created. Then we develop a SC risk assessment approach based on the analytic network process (ANP) and the VIKOR methods under the fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by triangular fuzzy numbers. By using FANP, risks weights are calculated and then inserted to the FVIKOR to rank the SC members and find the most risky partner.
Purpose
Supply chain risk management (SCRM) has recently gained increasing attention in the supply chain context, both from the practitioners' perspective and as a research area. Given the relevance of the topic, the aim of the present paper is to present a focused literature review, investigating the process of knowledge creation, transfer and development from a dynamic perspective within the context of SCRM.
Design/methodology/approach
A review of the literature on SCRM was undertaken. The new proposed methodology combines the systematic literature review approach to identify the most relevant articles to be included in the study with the citation network analysis in order to unfold the dynamics of the field under study. The authors define this new methodology as systematic literature network analysis.
Findings
The paper demonstrates that there are a number of key themes in the field of SCRM. The contributions that influenced the field were identified and, by analysing the evolution over time of key concepts, a number of research directions were identified and discussed.
Research limitations/implications
The dynamic nature of current literature review allows the identification of the directions in which research is moving and thus the recognition of streams of research that appear most promising. However, the application of the research methodology, and in particular of the citation network analysis, requires the support of specific computer programs. Moreover, the underlying assumption of the citation network analysis is that, by analysing the network of citations made to and from articles, it is possible to explain the flows of knowledge used to generate new results. This is only partially true since the spread of measures based on impact assessment led many researchers to an excessive use of citations, even if their content is not always decisive for the outcome of their work.
Practical implications
The present paper outlines a research agenda that may facilitate the development of models for managing supply chain risk. Furthermore from the evidence of the performed literature review some managerial insights can be derived on how to manage supply chain risk: by considering uncertainty in the design of supply chains, by understanding the impact of risks arising from network collaboration and interactions between supply chain partners, by developing proactive mitigation capabilities to hedge the increasing level of risk.
Originality/value
The novelty of this research lies in the combination of two existing methodologies for reviewing the literature and in the adoption of a dynamic perspective in order to analyse theory development.
Purpose
The purpose of this paper is to propose a comprehensive and coherent approach for managing risks in supply chains.
Design/methodology/approach
Building on Tummala et al. 's Risk Management Process (RMP), this paper develops a structured and ready‐to‐use approach for managers to assess and manage risks in supply chains.
Findings
Supply chain risks can be managed more effectively when applying the Supply Chain Risk Management Process (SCRMP). The structured approach can be divided into the phases of risk identification, risk measurement and risk assessment; risk evaluation, and risk mitigation and contingency plans; and risk control and monitoring via data management systems. Specific techniques for conducting this process are suggested.
Originality/value
While supply chain risk management is an emerging and important topic in our dynamic and interconnected world, conceptual frameworks providing a clear meaning and normative guidance are scarce (Manuj and Mentzer, 2008). This paper presents such a framework, offering structure and decision support for managers.
Purpose – The aim of this paper is that of individualizing a framework for the management of uncertainty in supply chain finalized to reduce the firm risks. Design/methodology/approach – Since a way for reducing the damages deriving from uncertainty sources is increasing the level of flexibility inside the supply chain, and the real option theory allows the increase of the flexibility level, in order to achieve the aim of this work, we utilize the real options theory to coverage of one or more risks inside the supply chain. Findings – A useful theoretical framework has been individualized enabling the selection of possible options to protect the firm against the risk originating from every source of uncertainty. In particular, on two types of risks, using Matlab software, a test has been conducted that proves the ability of the outsource option to cover risks under examination. Practical implications – In the paper a framework providing useful information for the supply chain management is presented. Originality/value – The paper attempts to provide an original tool for the risks management deriving from production activities inside a supply chain.
This research operationalizes several supply chain risk sources and investigates their relationships with supply chain performance. The responses of 760 executives from firms operating in Germany reveal that demand side and supply side risks do have a negative impact on performance whereas regulatory, legal and bureaucratic risks, infrastructure risks, as well as catastrophic risks do not. The analysis and results augment previous research regarding the impact of supply chain risks on the operational performance of firms and shareholder value and provide a detailed analysis of supply chain risk sources as contextual variables in strategic decision-making.
In recent years the issue of supply chain risk has been pushed to the fore, initially by fears related to possible disruptions from the much publicised “millennium bug”. Y2K passed seemingly without incident, though the widespread disruptions caused by fuel protests and then Foot and Mouth Disease in the UK, and by terrorist attacks on the USA have underlined the vulnerability of modern supply chains. Despite increasing awareness among practitioners, the concepts of supply chain vulnerability and its managerial counterpart supply chain risk management are still in their infancy. This paper seeks to identify an agenda for future research and to that end the authors go on to clarify the concept of supply chain risk management and to provide a working definition. The existing literature on supply chain vulnerability and risk management is reviewed and compared with findings from exploratory interviews undertaken to discover practitioners' perceptions of supply chain risk and current supply chain risk management strategies.
Purpose
Increasingly, companies need to be vigilant with the risks that can harm the short‐term operations as well as the long‐term sustainability of their supply chain (SC). The purpose of this paper is to provide a framework to proactively manage SC risks. The framework will enable the company to select a set of risk agents to be treated and then to prioritize the proactive actions, in order to reduce the aggregate impacts of the risk events induced by those risk agents.
Design/methodology/approach
A framework called house of risk (HOR) is developed, which combines the basic ideas of two well‐known tools: the house of quality of the quality function deployment and the failure mode and effect analysis. The framework consists of two deployment stages. HOR1 is used to rank each risk agent based on their aggregate risk potentials. HOR2 is intended to prioritize the proactive actions that the company should pursue to maximize the cost‐effectiveness of the effort in dealing with the selected risk agents in HOR1. For illustrative purposes, a case study is presented.
Findings
The paper shows that the innovative model presented here is simple but useful to use.
Research limitations/implications
In the proposed framework, the correlations between risk events are ignored, something that future studies should consider including.
Practical implications
The framework is intended to be useful in practice. For the calculation processes, a simple spreadsheet application would be sufficient. However, most of the entries needed in the model are based on subjective judgment and hence cross‐functional involvement is needed.
Originality/value
The paper adds to the SC management literature, a novel practical approach of managing SC risks, in particular to select a set of proactive actions deemed cost‐effective.
Many researchers and practitioners have long recognized the significance of measuring performance. Although general guidelines for measuring business performance are widely available, no appropriate measurement frameworks have been developed for measuring agri-food supply chain performance. Particularly, food quality and risk-related indicators have not been well integrated into existing performance measurement systems. Our research, therefore, addresses this knowledge gap by first providing an in-depth review of extant performance measurement systems and frameworks. It then develops an analytical framework by extending the Supply Chain Operations Reference (SCOR) model which has been extensively implemented across non-food industries. The analytical framework is further validated by utilizing a case study of 50 farmers and 10 dairy companies, operating in the New Zealand dairy industry.
Our pilot testing and subsequent findings show that the individual metrics interlocked with the analytical framework are in-line with the key industrial practices adapted by the New Zealand dairy industry. In addition, the framework is flexible and scalable to evaluate and benchmark other agri-food supply chains – ranging from fresh products such as fruits and vegetables to processed foods such as canned fruits. The findings further show that the detailed information required for measuring the level-3 SCOR metrics is not easily available in the industry, as researchers need to access specific company records that may be confidential. Consequently, this study provides how agri-food supply chain managers can employ our new analytical framework in-conjunction with the SCOR model for a deeper understanding of the complicated performance measurement indicators applied in their agri-food production systems and relevant supply chains.
In a world of rapid and unpredictable change, the problem with strategic planning is that if you follow your plan through to the end, you will get exactly what you used to want.
What you need is a framework for planning and implementing a strategy that is agile enough to adapt to a dynamic environment but focused enough to deliver. That framework is the Dynamic Balanced Scorecard.
The original Balanced Scorecard system has proven the most popular, successful and enduring framework for strategy execution over the last 25 years. Comprising a Strategy Map and a scorecard of KPIs, targets and initiatives, the framework helped organizations distil a strategy into actionable components and measure progress towards a strategic vision, while also implementing and monitoring the actions that drove change.
However, for all its success, the Balanced Scorecard system now needs to evolve for the digital age. Until now, building the system, rolling it out enterprise-wide and adapting it to external changes has been a lengthy process. While the fundamental principles of the system are still sound and relevant, it needs to become nimbler and more responsive.
The book provides a step-by-step guide to agile strategy management: from formulation to implementation to learning and adapting. For each of the steps, the book explains how Dynamic Balanced Scorecards, fit for the digital age, are built and deployed.
Purpose
Risk management is crucial for all organizations, especially those in the global supply chain network. Failure may result in huge economic loses and damage to company reputation. Risk assessment usually involves quantitative and qualitative decisions. The purpose of this paper is to apply fuzzy logic to capture and inference qualitative decisions made in the House of Risk (HOR) assessment method.
Design/methodology/approach
In the existing HOR model, aggregate risk potential (ARP) is calculated by the risk event times the risk agent value and its occurrence. However, these values are usually obtained from interviews, which may involve subjective decisions. To overcome this shortcoming, a fuzzy-based approach is proposed to calculate ARP instead of the current deterministic approach.
Findings
Risk analyses are conducted in five major categories of risk sources: internal, global environment, supplier, customer and third-party logistics provider. Moreover, each category is further divided into different sub-categories. The results indicate that the fuzzy-based HOR successfully inferences the inputs of the risk event, risk agents and its occurrence, and can prioritize the risk agents in order to take proactive decisions.
Practical implications
The proposed fuzzy-based HOR model can be used practically by manufacturers in the global supply chain. It provides a framework for decision makers to systematically analyze the potential risks in different categories.
Originality/value
The proposed fuzzy-based HOR approach improves the traditional approach by more precise modeling of the qualitative decision-making process. It contributes to a more accurate reflection of the real situation that manufacturers are facing.
captures
interdependencies between risks, multiple (potentially conflicting) performance measures and risk mitigation strategies within a (risk) network setting. The process helps in prioritising risks and strategies specific to the decision maker's risk appetite. The process is demonstrated through a case study conducted in a global manufacturing supply chain involving semi-structured interviews and focus group sessions with experts in risk management. Theoretically grounded in the framework of Bayesian Belief Networks (BBNs) and Expected Utility Theory (EUT), the modelling approach has a number of distinctive characteristics. It utilises a top-down approach
of Fault Tree Analysis (FTA). Performance measures are identified first and subsequently connected to risks. A ‘probability-conditional expected utility’ matrix is introduced to reflect the propagation impact of interdependent risks on all performance measures identified. A ‘weighted net evaluation of risk mitigation’ method is proposed and the method of ‘swing weights’ is used to capture the trade-off between the efficacy of strategies and the associated cost keeping in view the decision maker's risk appetite. The approach adapts and integrates techniques from safety and reliability engineering (FTA), decision making under uncertainty (EUT), and multi-criteria decision analysis (swing weights). The merits and challenges associated with the implementation of interdependency based frameworks are discussed. Propositions are presented to elucidate the significance of
modelling interdependency between risks and strategies.
This paper presents a systemic decision-support approach to safety risk analysis for metro construction projects under uncertainty using a fuzzy comprehensive Bayesian network (FCBN), which combines the fuzzy comprehensive evaluation method (FCEM) and a Bayesian network (BN). The results of the safety risk assessment based on the FCBN are composed of three aspects: risk probability, risk loss, and risk controllability. In this assessment, two of the aspects—risk loss and risk controllability—are calculated in terms of intervals or fuzzy numbers. Through the application of the FCEM, the levels of risk loss and risk controllability are then estimated. The risk probability is calculated from the BN, in which the relationships among the dependent variables are expressed in the form of a directed graph. A comprehensive safety risk assessment allows engineers to assess potential safety hazards and provides a basis for dynamic early risk warning and control ahead of metro construction, which is acquired by combining the FCEM and the BN. A case study relating to safety risk analysis in the construction of the Dalian Metro in China is used to verify the feasibility of the approach as well as its application potential. A comparison of the results with the actual construction state shows its effectiveness in estimating the risk level of a metro construction project under uncertainty. The proposed approach provides a powerful tool with which planners and engineers can systematically assess and mitigate the inherent risks associated with metro construction.
In this paper, we introduce an integrated supply chain risk management process that is grounded in the theoretical framework of Bayesian Belief Networks capturing interdependency between risks and risk mitigation strategies, and integrating all stages of the risk management process. The proposed process is unique in four different ways: instead of mapping the supply network, it makes use of Failure Modes and Effects Analysis to model the risk network which is feasible for modelling global supply chains; it is driven by new dependency based risk measures that can effectively capture the network wide impact of risks for prioritisation; it utilises the concept of Shapley value from the field of cooperative game theory to determine a fair allocation of resources to the critical risks identified; and the process helps in prioritising potential risk mitigation strategies (both preventive and reactive) subject to budget and resource constraints. We demonstrate its application through a simulation study.
Executives know that a company's measurement systems strongly affect employee behaviors. But the traditional financial performance measures that worked for the industrial era are out of sync with the skills organizations are trying to master. Frustrated by these inadequacies, some managers have abandoned financial measures like return on equity and earnings per share. "Make operational improvements, and the numbers will follow,"the argument goes. But managers want a balanced presentation of measures that will allow them to view the company from several perspectives at once. In this classic article from 1992, authors Robert Kaplan and David Norton propose an innovative solution. During a yearlong research project with 12 companies at the leading edge of performance management, the authors developed a "balanced scorecard;" a new performance measurement system that gives top managers a fast but comprehensive view of their business. The balanced scorecard includes financial measures that tell the results of actions already taken. And it complements those financial measures with three sets of operational measures related to customer satisfaction, internal processes, and the organization's ability to learn and improve-the activities that drive future financial performance. The balanced scorecard helps managers look at their businesses from four essential perspectives and answer Some important questions. First, How do customers see us? Second, What must we excel at? Third, Can we continue to improve and create value? And fourth, How do we appear to shareholders? By looking at all of these parameters, managers can determine whether improvements in one area have come at the expense of another. Armed with that knowledge, the authors say, executives can glean a complete picture of where the company stands-and where it's headed.
Presently, the hospital is one of important service industries of healthcare for patients. The emergency department is the heart of all hospitals because of errors or failures that significantly affect on the safety of patients and the confidence in healthcare. Therefore, all of emergency department should pay much attention on these problems importantly. This research proposed the application of using Fuzzy FMEA, aimed to develop the form of prioritization and assessment on the suitable failures for the working process in emergency department by analysis and identification of the failures according to the methodology of Failure Mode and Effects Analysis (FMEA). Moreover, the prioritization and assessment of the problems are improved with the implementation of Fuzzy method. In conclusion, the application of using Fuzzy FMEA in the emergency department can be adopted suitably. All of members were able to assess dependently without any bias from the team members. In addition, it could reduce the drawback of occurred Traditional FMEA. From the prioritization to the risk of problems, it could also help the emergency department in making decision to choose properly the problem for improving and corrective actions. Finally, it was able to build the confidence to the process of emergency department as well.
This paper develops an operational perspective of supply chain sustainability, by considering it as a risk management process. It explores the nature of sustainability-related supply chain risks, distinguishes them from typical supply chain risks and develops an analytical process for their management. An empirical study is conducted to generate insights about how sustainability-related risks should be managed in an integrated way. A mixed method approach is adopted for data collection and analysis. Through an extensive literature review and personal interviews, 30 risks across the three main pillars of sustainability (environmental, social and economic) are identified first. A large survey across different industrial sectors and two exploratory empirical case studies in two textile manufacturing companies are subsequently conducted to assess and analyse several dimensions of sustainability-related risk. The failure mode and effect analysis (FMEA) technique is utilised to assess the relative importance of the selected risks, to identify their potential causes and effects and test potential correlations between the identified risks. Based on the findings of the study, risk treatment strategies are proposed for all the identified sustainability-related supply chain risks. The findings show that endogenous environmental risks are perceived to be the most important across different industries and the interconnectedness between several sustainability-related risks is very high. This points to the need for integrated sustainability risk management approaches to facilitate the development of effective sustainable strategies.
This research presents an integrated framework for supply chain risk assessment. The framework consists of three main components: survey, Bow-Tie analysis, and fuzzy inference system (FIS). The survey component consists of questionnaires used to identify the risk factors and their likelihoods and impacts. Potential risks are identified based on experts׳ knowledge, historical data, and supply chain structure. The identified risks are measured by aggregating the estimated values of risk parameters. Bow-Tie, which is a diagram that displays the links between potential causes, preventative and mitigative controls and consequences of a risk, is used to calculate the aggregated likelihood and impact of the risk. FIS is then used to calculate the total risk score considering the risk management parameters and risk predictability. A case study from a high-end server manufacturing environment is considered. For the two main product types produced by the company, risks are assessed and aggregated per product type. Given the individual and aggregated risk scores, decision makers can either perform top-down or bottom-up risk analysis and focus on the significant risks that could affect their business operations.
As supply chain risk management has transitioned from an emerging topic to a growing research area, there is a need to classify different types of research and examine the general trends of this research area. This helps identify fertile research streams with great potential for further examination. This paper presents a systematic review of the quantitative and analytical models (i.e. mathematical, optimization and simulation modeling efforts) for managing supply chain risks. We use bibliometric and network analysis tools to generate insights that have not been captured in the previous reviews on the topic. In particular, we complete a systemic mapping of the literature that identifies the key research clusters/topics, interrelationships, and generative research areas that have provided the field with the foundational knowledge, concepts, theories, tools, and techniques. Some of our findings include (1) quantitative analysis of supply chain risk is expanding rapidly; (2) European journals are the more popular research outlets for the dissemination of the knowledge developed by researchers in United States and Asia; and (3) sustainability risk analysis is an emerging and fast evolving research topic.
There are numerous examples of supply chain disruptions that have occurred which have had devastating impacts not only on a single firm but also on various other firms in the supply network. We utilize a Bayesian Network (BN) approach and develop a model of risk propagation in a supply network. The model takes into account the inter-dependencies among different risks, as well as the idiosyncrasies of a supply chain network structure. Specific risk measures are derived from this model and a simulation study is utilized to illustrate how these measures can be used in a supply chain setting.
Purpose
The purpose of this paper is to present a generic framework to assess and simulate outsourcing risks in the supply chain.
Design/methodology/approach
This combination approach involves a qualitative risk analysis methodology termed as the supply chain risk‐failure mode and effect analysis (SCR‐FMEA) which integrates risk identification, analysis and mitigation actions together to evaluate supply chain outsourcing risk. The qualitative risk assessment will allow risk manager to provide a visual presentation of imminent risks using the risk map. Monte Carlo simulation (MCS) on the imminent risks of delivery outsourcing using the Milk‐Run system is adopted.
Findings
With basic statistical concepts, key performance variables and the risk of delivery outsourcing are analyzed. It is found that a newly implemented delivery outsourcing arrangement on the Milk‐Run system reduces the average customer lead‐time and total cost. However, a certain extent of risk or uncertainty can still be detected due to the presence of variation.
Research limitations/implications
This paper reveals that company can manage the risk by adopting a systematic method for identifying the potential risks before outsourcing and MCS can be applied for examining the quantifiable risks such as lead time and cost.
Practical implications
The paper provides a generic guideline for practitioners to assess logistics outsourcing, especially for logistics management consultants and professionals for evaluating the risk and impact of outsourcing. It is believed that the proposed risk assessment framework can help to analyze the operational cost uncertainty and ensure the stability of the supply chain. However, the limitation of this research is that the full spectrum of outsourcing risk, especially the non‐quantifiable risk may not be analyzed by MCS.
Originality/value
This paper proposed an integrated framework which combines qualitative and quantitative method together for managing outsourcing risk. This research provides a standardized metric to quantify risk in the supply chain so as to determine the effectiveness of outsourcing.
One of the most complicated decision making problems for managers is the evaluation of supply chain (SC) performance which involves various criteria. Though vast studies have been recorded on supply chain efficiency evaluation via balanced scorecard (BSC) approach, these studies do not focus on the relationships between the four perspectives of BSC approach. The present paper is an attempt focusing on these relationships, especially the returnable ones. To do so, at first, all relationships between the four perspectives of BSC were determined and then the DEMATEL approach was employed to obtain a network structure. This network structure was then used to create a network DEA model. Since it was not possible to calculate the efficiency evaluation score by BSC, the data envelopment analysis (DEA) model was used for such an evaluation. Moreover, after reviewing different tools to evaluate the performance of supply chain, a new approach, relying on network DEA with BSC approach, was generated. Finally, this model was applied in the Iranian food industry to evaluate its supply chains efficiency and the results proved the high efficiency of the model designed. The findings could be used in various evaluation processes in different industries.
Bow-tie analysis is a fairly new concept in risk assessment that can describe the relationships among different risk control parameters, such as causes, hazards and consequences to mitigate the likelihood of occurrence of unwanted events in an industrial system. It also facilitates the performance of quantitative risk analysis for an unwanted event providing a detailed investigation starting from basic causes to final consequences. The credibility of quantitative evaluation of the bow-tie is still a major concern since uncertainty, due to limited or missing data, often restricts the performance of analysis. The utilization of expert knowledge often provides an alternative for such a situation. However, it comes at the cost of possible uncertainties related to incompleteness (partial ignorance), imprecision (subjectivity), and lack of consensus (if multiple expert judgments are used). Further, if the bow-tie analysis is not flexible enough to incorporate new knowledge or evidence, it may undermine the purpose of risk assessment.Fuzzy set and evidence theory are capable of characterizing the uncertainty associated with expert knowledge. To minimize the overall uncertainty, fusing the knowledge of multiple experts and updating prior knowledge with new evidence are equally important in addition to addressing the uncertainties in the knowledge. This paper proposes a methodology to characterize the uncertainties, aggregate knowledge and update prior knowledge or evidence, if new data become available for the bow-tie analysis. A case study comprising a bow-tie for a typical offshore process facility has also been developed to describe the utility of this methodology in an industrial environment.
Supply chain risk management (SCRM) is of growing importance, as the vulnerability of supply chains increases. The main thrust of this article is to describe how Ericsson, after a fire at a sub-supplier, with a huge impact on Ericsson, has implemented a new organization, and new processes and tools for SCRM. The approach described tries to analyze, assess and manage risk sources along the supply chain, partly by working close with suppliers but also by placing formal requirements on them. This explorative study also indicates that insurance companies might be a driving force for improved SCRM, as they now start to understand the vulnerability of modern supply chains. The article concludes with a discussion of risk related to traditional logistics concepts (time, cost, quality, agility and leanness) by arguing that supply chain risks should also be put into the trade-off analysis when evaluating new logistics solutions – not with the purpose to minimize risks, however, but to find the efficient level of risk and prevention.
Purpose
To permit the system safety and reliability analysts to evaluate the criticality or risk associated with item failure modes.
Design/methodology/approach
The factors considered in traditional failure mode and effect analysis (FMEA) for risk assessment are frequency of occurrence ( S f ), severity ( S ) and detectability ( S d ) of an item failure mode. Because of the subjective and qualitative nature of the information and to make the analysis more consistent and logical, an approach using fuzzy logic is proposed. In the proposed approach, these parameters are represented as members of a fuzzy set fuzzified by using appropriate membership functions and are evaluated in fuzzy inference engine, which makes use of well‐defined rule base and fuzzy logic operations to determine the criticality/riskiness level of the failure. The fuzzy conclusion is then defuzzified to get risk priority number. The higher the value of RPN, the greater will be the risk and lower the value of RPN, and the lesser will be the risk. The fuzzy linguistic assessment model was developed using toolbox platform of MATLAB 6.5 R.13 .
Findings
The applicability of the proposed approach is investigated with the help of an illustrative case study from the paper industry. Fuzzy risk assessment is carried out for prioritizing failure causes of the hydraulic system, a primary element of the feeding system. The results provide an alternate ranking to that obtained by the traditional method. It is concluded from the study that the fuzzy logic‐based approach not only resolves the limitations associated with traditional methodology for RPN evaluation but also permits the experts to combine probability of occurrence ( S f ), severity ( S ) and detectability ( S d ) of failure modes in a more flexible and realistic manner by using their judgement, experience and expertise.
Originality/value
The paper integrates the use of fuzzy logic and expert database with FMEA and may prove helpful to system safety and reliability analysts while conducting failure mode and effect analysis to prioritize failures for taking corrective or remedial actions.
Purpose
The purpose of this paper is to empirically examine what senior supply chain executives measure and how they perceive performance measurement from a balanced scorecard (BSC) perspective.
Design/methodology/approach
A survey designed from the four perspectives of the BSC framework is conducted on senior executives involved in the supply chain functions of client firms, and those executives from the logistics service provider industry.
Findings
Despite the need to provide a balanced approach to performance measurement, firms remain focused on traditional financial measures (gross revenue, profit before tax, and cost reduction). From a supply chain perspective, the non‐tangible measures such as customer satisfaction are most measured. Other key logistics performance indicators include on‐time delivery, and customer satisfaction.
Research limitations/implications
The findings are based on a sample size of 113. Thus, some respondent clusters are smaller than others. Hence, the results may not be representative of the individual clusters.
Practical implications
Each supply chain entity must adopt a more balanced perspective in its performance measurement and management. Companies need to recognize the importance of the drivers of strategic future performance. Managing a given supply chain's overall performance necessitates the coordination of measures across the different entities on the supply chain, often requiring all entities to adopt a common balanced perspective in their performance management to facilitate the overall performance and competitiveness of the entire supply chain.
Originality/value
This paper is the first attempt to apply the BSC framework on the logistics industry.
Global supply chains are exposed to different kinds of risks that increase along with increasing globalisation. There appears to be a cost associated with the event that certain goods are not at the required location (location of activity cell of a global supply chain), at the required time, and of the required quality and quantity. It is suggested that the costs of risk in a supply chain, which is exposed to internal and external risk are measured using net present value of activities approach. This is how the vulnerability of a supply chain is predicted. The model of risk measurement is based on the previous work of Input–Output Analysis and Laplace transforms of the supply chain model developed by Grubbström and his Linköping School (IPE), based on the ideas of MRP–DRP stochastic modelling, presented in the paper by Bogataj and Bogataj [Bogataj, M., Bogataj, L., 2004. On the compact presentation of the lead times perturbations in distribution networks. International journal of production economics 88–92, 145–155] which describes the compact presentation of the lead-time perturbations in production–distribution networks. Parametric linear programming approach is suggested to achieve numerical results.
Increasing product/service complexity, outsourcing and globalisation have led to increasingly complex, dynamic supply networks. This has impacted on risk, changing it and changing its location in supply networks. This paper provides a review of definitions and classifications of types of risk; an holistic view of risk assessment and management is taken here. Little evidence is apparent of empirical research on risk in supply networks or tools to help identify, assess and manage that risk. A tool is provided and its testing and development in four case studies in the electronics sector is described. Experiences of using the tool are discussed and conclusions drawn on its potential further development and application.
As supply chains become more complex, firms face increasing risks of supply disruptions. The process through which buyers make decisions in the face of these risks, however, has not been explored. Despite research highlighting the importance of behavioral approaches to risk, there is limited research that applies these views of risk in the supply chain literature. This paper addresses this gap by drawing on behavioral risk theory to investigate the causal relationships amongst situation, representations of risk, and decision-making within the purchasing domain. We operationalize and explore the relationship between three representations of supply disruption risk: magnitude of supply disruption, probability of supply disruption, and overall supply disruption risk. Additionally, we draw on exchange theories to identify product and market factors that impact buyers’ perceptions of the probability and magnitude of supply disruption. Finally, we look at how representations of risk affect the decision to seek alternative sources of supply. We test our model using data collected from 223 purchasing managers and buyers of direct materials. Our results show that both the probability and the magnitude of supply disruption are important to buyers’ overall perceptions of supply disruption risk. We also find that product and market situational factors impact perceptions of risk, but they are best understood through their impact on perceptions of probability and magnitude. Finally, we find that decisions are based on assessments of overall risk. These findings provide insight into the decision-making process and show that all three representations of risk are necessary for fully understanding risky decision-making with respect to supply disruptions.
This paper develops a balanced scorecard for supply chain management (SCM) that measures and evaluates day-to-day business operations from following four perspectives: finance, customer, internal business process, and learning and growth. Balanced scorecard has been developed based on extensive review of literature on SCM performance measures, supported by three case studies, each illustrating ways in which BSC was developed and applied in small and medium sized enterprises (SMEs) in India. The paper further suggests that a balanced SCM scorecard can be the foundation for a strategic SCM system provided that certain development guidelines are properly followed, appropriate metrics are evaluated, and key implementation obstacles are overcome. The balanced scorecard developed in this paper provides a useful guidance for the practical managers in evaluation and measuring of SCM in a balanced way and proposes a balanced performance measurement system to map and analyze supply chains. While suggesting balanced scorecard, different SCM performance metrics have been reviewed and distributed into four perspectives. This helps managers to evaluate SCM performance in a much-balanced way from all angles of business.
The aim of this study is to show how a timed Petri nets framework can be used to model and analyze a supply chain (SC) network which is subject to various risks. The method is illustrated by an industrial case study. We first investigate the disruption factors of the SC network by a failure mode, effects and criticality analysis (FMECA) technique. We then integrate the risk management procedures into design, planning, and performance evaluation process of supply chain networks through Petri net (PN) based simulation. The developed PN model provides an efficient environment for defining uncertainties in the system and evaluating the added value of the risk mitigation actions. The findings of the case study shows that the system performance can be improved using risk management actions and the overall system costs can be reduced by mitigation scenarios.
All purchasing organizations encounter supply risk, whether it is explicitly understood and assessed, or reactively managed. The purpose of this paper is to provide a grounded definition of supply risk. Case study data from seven purchasing organizations uncovered various definitions of supply risk. These definitions focus on the sources of supply risk, emanating from individual supplier factors and market characteristics, and the outcomes of supply risk events, which involve the inability of purchasing firms to meet customer requirements and threats to customer life and safety. Findings from this research provide practitioners and academicians a starting point for understanding supply risk and insights as to how supply risk can negatively affect business operations.
Managing risk has become a critical component of supply chain management. The implications of supply chain failures can be costly and lead to significant customer delivery delays. Though, different types of supply chain vulnerability management methodologies have been proposed for managing supply risk, most offer only point-based solutions that deal with a limited set of risks. This research aims to reinforce inbound supply chain risk management by proposing an integrated methodology to classify, manage and assess inbound supply risks. The contributions of this paper are four-fold: (1) inbound supply risk factors are identified through both an extensive academic literature review on supply risk literature review as well as a series of industry interviews; (2) from these factors, a hierarchical risk factor classification structure is created; (3) an analytical hierarchy processing (AHP) method with enhanced consistency to rank risk factor for suppliers is created; and (4) a prototype computer implementation system is developed and tested on an industry example.
Risk management of a supply chain (SC) has a great influence on the stability of dynamic cooperation among SC partners and hence very important for the performance of the SC operations as a whole. A suitable decision-making model is the cornerstone for the efficiency of SC risk management. We propose in this paper a decision-making model based on the internal triggering and interactive mechanisms in an SC risk system, which takes into account dual cycles, the operational process cycle (OPC) and the product life cycle (PLC). We explore the inter-relationship among the two cycles, SC organizational performance factors (OPF) and available risk operational practice (ROP), as well as the risk managerial elements in OPC and PLC. In particular, three types of relationship, bilateral, unilateral and inter-circulative ones, are analyzed and verified. We build this dynamic relation into SC risk managerial logic and design a corresponding decision-making path. Based on the analytic network process (ANP), a methodology is designed for an optimal selection of risk management methods and tools. A numerical example is provided as an operational guideline for how to apply it to tailor operational tactics in SC risk management. The results verify that this strategic decision model is a feasible access to the suitable risk operational tactics for practitioners.
Failure mode and effects analysis (FMEA) is a method that examines potential failures in products or processes and has been used in many quality management systems. One important issue of FMEA is the determination of the risk priorities of failure modes. In this paper we propose an FMEA which uses data envelopment analysis (DEA), a well-known performance measurement tool, to determine the risk priorities of failure modes. The proposed FMEA measures the maximum and minimum risks of each failure mode. The two risks are then geometrically averaged to measure the overall risks of failure modes. The risk priorities are determined in terms of overall risks rather than maximum or minimum risks only. Two numerical examples are provided and examined using the proposed FMEA to show its potential applications and benefits.
Nowadays supply chains are facing challenges in managing risk issues. Supply of raw materials may exhibit a random yield due to technical failure of production resources or supply disruption after a natural disaster. In case supply has a random yield, one way to reduce supply chain loss is by introducing a dynamic pricing policy, with the aim of manipulating demand in the market while inducing the customer to buy substitute products temporarily. This paper investigates newsvendor problem with random demand and random yields, in which the price decision will be postponed and determined upon recognition of random yield and prior to realising demand uncertainties. With the objective of maximising expected profits, we develop the optimal price and ordering decisions in the system, while comparing the system's performances with dynamic and fixed pricing policies. Further, we investigate the conditions of adapting dynamic pricing policy. An interesting finding shows that such a policy brings increase in benefit when demand uncertainty is small. The outcome of this research provides alternative solutions in designing a robust supply chain.
Special issue of production and operations management: Global supply chain risk management
- H Gurnani
- S Ray
- Y Wang
Gurnani, H.; Ray, S.; Wang, Y. Special issue of production and operations management: Global supply chain
risk management. Prod. Oper. Manag. 2011, 20, 786. [CrossRef]
An Integrated Balanced Scorecard and Fuzzy BOCR Decision Model for Performance Evaluation
- K Karabece
- T Gürbüz
Karabece, K.; Gürbüz, T. An Integrated Balanced Scorecard and Fuzzy BOCR Decision Model for Performance
Evaluation. Adv. Intell. Syst. Comput 2019, 843-851. [CrossRef]
Strategy Mapping for Learning Organizations: Building Agility into Your Balanced Scorecard
- P Jones
Jones, P. Strategy Mapping for Learning Organizations: Building Agility into Your Balanced Scorecard; Routledge:
Abingdon-on-Thames, UK, 2016; ISBN 978-0-566-08811-7.
Business Process Management and the Balanced Scorecard: Using Processes as Strategic Drivers
- R F Smith
Smith, R.F. Business Process Management and the Balanced Scorecard: Using Processes as Strategic Drivers;
John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2007; ISBN 978-0-470-04746-0.
The Analytic Hierarchy Process
- T L Saaty
Saaty, T.L. The Analytic Hierarchy Process; McGraw-Hill: New York, NY, USA, 1980.
Fuzzy Sets and Systems: Theory and Applications
- D Dubois
- H Prade
Dubois, D.; Prade, H. Fuzzy Sets and Systems: Theory and Applications; Academic Press: New York, NY, USA, 1980.
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