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Article
Publication date: 15 March 2013

L. Manning and J.M. Soon

The purpose of this paper is to review the methods for assessing food safety risk within a food safety plan.

Abstract

Purpose

The purpose of this paper is to review the methods for assessing food safety risk within a food safety plan.

Design/methodology/approach

The research involved analysis of both qualitative and quantitative methods of risk assessment.

Findings

Risk assessment is a key element of the HACCP approach to food safety. It requires food business operators and those on HACCP teams to determine both the acceptable level of contamination and the risk for the food business, and ultimately the consumer. The choice of food safety risk assessment model is crucial to an organisation. The mechanisms to determine what is acceptable can be a combination of scientific based and value based criteria and utilise qualitative or semi‐quantitative approaches. Whilst fuzzy logic has a place in making risk assessment more quantitative; specific software tools are required to enable quantitative risk assessment especially where what is acceptable at one point could, subject to other factors later in the supply chain, change to an unacceptable level of risk to the consumer. Quantitative mechanisms are required to make these decisions at organisational, or indeed at policy level, fully transparent.

Originality/value

This research is of academic value and of value to policy makers and practitioners in the food supply chain.

Details

British Food Journal, vol. 115 no. 3
Type: Research Article
ISSN: 0007-070X

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Article
Publication date: 24 April 2013

Øyvind Berle, Inge Norstad and Bjorn E. Asbjørnslett

This paper aims to address how to systematically address vulnerability in a maritime transportation system using a formal vulnerability assessment approach, create…

Abstract

Purpose

This paper aims to address how to systematically address vulnerability in a maritime transportation system using a formal vulnerability assessment approach, create quantitative measures of disruption risk and test the effect of mitigating measures. These quantitative data are prerequisites for cost efficiency calculations, and may be obtained without requiring excessive resources.

Design/methodology/approach

Supply chain simulation using heuristics‐based planning tools offers an approach to quantify the impact of disruption scenarios and mitigating measures. This is used to enrich a risk‐based approach to maritime supply chain vulnerability assessment. Monte Carlo simulation is used to simulate a stochastic nature of disruptions.

Findings

The exemplary assessment of a maritime liquefied natural gas (LNG) transportation system illustrates the potential for providing quantitative data about the cost of disruptions and the effects of mitigating measures, which are foundations for more precise cost efficiency estimates.

Research limitations/implications

This simulation was done on a simplified version of a real transportation system. For resource reasons, several simplifications were made, both with regards to modeling the transportation system and with the implementation of the formal vulnerability assessment framework. Nevertheless, the authors believe the paper serves to illustrate the approach and potential outcome.

Practical implications

Practitioners are provided with an approach to get more precise quantitative data on disruption costs and cost/efficiency of mitigating measures, providing background data for decisions on investing in reduction of supply chain vulnerability.

Originality/value

The combination of risk assessment methods and inventory routing simulation of maritime supply chain problems is a novelty. Quantifying vulnerability, effects of disruptions and effects of mitigating measures in maritime transportation systems contributes to a little‐researched area.

Details

Supply Chain Management: An International Journal, vol. 18 no. 3
Type: Research Article
ISSN: 1359-8546

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Article
Publication date: 2 September 2014

W.M.P.U. Wijeratne, B.A.K.S. Perera and L. De Silva

The purpose of this paper is to identify the risks and methods for their assessment in the case of maintenance activities in Sri Lanka. The main objectives were to…

Abstract

Purpose

The purpose of this paper is to identify the risks and methods for their assessment in the case of maintenance activities in Sri Lanka. The main objectives were to identify the occupational risks in maintenance work and the risk assessment methods in place and their drawbacks in the Sri Lankan context.

Design/methodology/approach

The identification and assessment of risks were undertaken through a study of three fast-moving consumer products manufacturing organisations. The relevant data were collected through personal interviews and site visits.

Findings

Most typical risks associated with maintenance are cuts, slips and falls, with severe or fatal injuries as the result of negligence of SOP and failure to use the PPE. Checklists, brainstorming and decomposition techniques were identified as the preferred methods in maintenance for risk identification while a risk rating matrix is used for risk analysis. Lack of awareness and indifference towards risk assessment; make effective risk assessment very difficult. These drawbacks can be minimised by education, systematic training and enforcing rules, regulations and procedures for controlling risks.

Originality/value

Studies on maintenance worldwide have identified several maintenance-specific risks such as working at heights, the pressure of time, etc. However, there is a dearth of published research on risks and risk assessment methods in maintenance in Sri Lankan context. The findings highlighted the safety risks and risk assessment tools entailed in the maintenance operations of manufacturing organisations. The findings will be useful for those in maintenance operations in managing risks effectively through designing work environments that are risk-free.

Details

Built Environment Project and Asset Management, vol. 4 no. 4
Type: Research Article
ISSN: 2044-124X

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Article
Publication date: 10 July 2017

Adrian Munteanu

This study aims to argue that in the case of quantitative security risk assessment, individuals do not estimate probabilities as a likelihood measure of event occurrence.

Abstract

Purpose

This study aims to argue that in the case of quantitative security risk assessment, individuals do not estimate probabilities as a likelihood measure of event occurrence.

Design/methodology/approach

The study uses the most commonly used quantitative assessment approach, the annualized loss expectancy (ALE), to support the three research hypotheses.

Findings

The estimated probabilities used in quantitative models are subjective.

Research limitations/implications

The ALE model used in security risk assessment, although it is presented in the literature as quantitative, is, in fact, qualitative being influenced by bias.

Practical implications

The study provides a factual basis showing that quantitative assessment is neither realistic nor practical to the real world.

Originality/value

A model that cannot be tested experimentally is not a scientific model. In fact, the probability used in ISRM is an empirical probability or estimator of a probability because it estimates probabilities from experience and observation.

Details

Information & Computer Security, vol. 25 no. 3
Type: Research Article
ISSN: 2056-4961

Keywords

Content available
Article
Publication date: 13 May 2021

Devin DePalmer, Steven Schuldt and Justin Delorit

Limited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align…

Abstract

Purpose

Limited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues.

Design/methodology/approach

A Mamdani fuzzy logic inference system is coupled with a traditional, categorical risk assessment framework to understand a facilities’ consequence of failure and its effect on an organization’s strategic objectives. Model performance is evaluated using the US Air Force’s facility portfolio, which has been previously assessed, treating facility replicability and interruptability as minimization objectives. The fuzzy logic inference system is built to account for these objectives, but as proof of ease-of-adaptation, facility dependency is added as an additional risk assessment criterion.

Findings

Results of the fuzzy logic-based approach show a high degree of consistency with the traditional approach, though the value of the information provided by the framework developed here is considerably higher, as it creates a continuous set of facility prioritizations that are unbiased. The fuzzy logic framework is likely suitable for implementation by diverse, spatially distributed organizations in which decision-makers seek to balance risk assessment complexity with an output value.

Originality/value

This paper fills the identified need for portfolio management strategies that focus on prioritizing projects by risk to organizational operations or objectives.

Details

Journal of Facilities Management , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1472-5967

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Article
Publication date: 29 July 2013

Abhijeet Ghadge, Samir Dani, Michael Chester and Roy Kalawsky

With increasing exposure to disruptions, it is vital for supply chains to manage risks proactively. Prediction of potential failure points and overall impact of these risks

Abstract

Purpose

With increasing exposure to disruptions, it is vital for supply chains to manage risks proactively. Prediction of potential failure points and overall impact of these risks is challenging. In this paper, systems thinking concepts are applied for modelling supply chain risks. The purpose of this paper is to develop a holistic, systematic and quantitative risk assessment process for measuring the overall risk behaviour.

Design/methodology/approach

A framework for supply chain risk management (SCRM) is developed and tested using an industrial case study. A systematically developed research design is employed to capture the dynamic behaviour of risks. Additionally, a system‐based supply chain risk model is conceptualized for risk modelling. Sensitivity modelling results are combined for validating the supply chain risk model.

Findings

The systems approach for modelling supply chain risks predicts the failure points along with their overall risk impact in the supply chain network. System‐based risk modelling provides a holistic picture of risk behavioural performance, which is difficult to realise through other research methodologies commonly preferred in SCRM research.

Practical implications

The developed framework for SCRM is tested in an industry setting for its viability. The framework for SCRM along with the supply chain risk model is expected to benefit practitioners in understanding the intricacies of supply chain risks. The system model for risk assessment is a working tool which could provide a perspective of future disruptive events.

Originality/value

A holistic, systematic and quantitative risk modelling mechanism for capturing overall behaviour of risks is a valuable contribution of this research. The paper presents a new perspective towards using systems thinking for modelling supply chain risks.

Details

Supply Chain Management: An International Journal, vol. 18 no. 5
Type: Research Article
ISSN: 1359-8546

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Article
Publication date: 4 November 2014

Athakorn Kengpol, Sopida Tuammee and Markku Tuominen

The purpose of this paper is to develop a framework for route selection in multimodal transportation which can reduce cost, lead time, risk and CO2 emission in multimodal…

Abstract

Purpose

The purpose of this paper is to develop a framework for route selection in multimodal transportation which can reduce cost, lead time, risk and CO2 emission in multimodal transportation systems.

Design/methodology/approach

This research proposes the development of a framework for route selection in multimodal transportation that includes a six-phase framework to select an optimal multimodal transportation route. The first phase is to collect the data of each route and select the origin and destination. The second phase is to calculate time and cost of each route by using a multimodal transport cost-model. In the third phase, the CO2 emissions are calculated based upon the 2006 guidelines of Intergovernmental Panel on Climate Change. The fourth phase proposes an integrated quantitative risk assessment, analytic hierarchy process (AHP) and data envelopment analysis methodology to evaluate the multimodal transportation risk. The fifth phase is to prioritize criteria by using the AHP which can be used in the objective function. The final phase is to calculate the optimal route by using the zero-one goal programming.

Findings

The aims of the model are to minimize transportation costs, transportation time, risk and CO2 emission.

Practical implications

The approach has been tested on a realistic multimodal transportation service, originating from Bangkok in Thailand to a destination at Da Nang port in Vietnam. The results have shown that the approach can provide guidance in choosing the lowest cost route in accordance with other criteria, and to minimize the CO2 emission effectively.

Originality/value

The contribution of this research lies in the development of a new decision support approach that is flexible and applicable to logistics service providers, in selecting multimodal transportation route under the multi-criteria in term of cost, time, risk and importantly the environmental impact.

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Article
Publication date: 20 April 2012

C.K.M. Lee, Yu Ching Yeung and Zhen Hong

The purpose of this paper is to present a generic framework to assess and simulate outsourcing risks in the supply chain.

Abstract

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.

Details

Industrial Management & Data Systems, vol. 112 no. 4
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 29 March 2011

Mostafa Jafari, Jalal Rezaeenour, Mohammad Mahdavi Mazdeh and Atefe Hooshmandi

This paper seeks to develop a model for risk management of knowledge loss in a project‐based organization in Iran.

Abstract

Purpose

This paper seeks to develop a model for risk management of knowledge loss in a project‐based organization in Iran.

Design/methodology/approach

This study uses a multi‐stage research approach. In the first stage, existing practices are examined to develop a model for risk management of knowledge loss. In the second stage, the model is evaluated by testing it in a case study. The methods integrated as the foundations of the Integrated KM and RM model are: the PMBOK risk management (RM) approach, the Fraunhofer IPK knowledge management (KM) model, and the TVA knowledge risk assessment framework.

Findings

The analytical approach includes a six‐step integrated model that manages the risk of critical knowledge in the case study. The results show that, after a year of implementing the model, the job positions facing knowledge loss were reduced by 88 percent.

Research limitations/implications

The integrated KM and RM model can be used to assist the planning, establishment and evaluation of knowledge loss in projects. This helps to ensure that key issues regarding knowledge loss are covered during the planning and implementation phases of project management.

Originality/value

This study provides an integrated perspective of KM in project‐based organizations. It offers valuable guidelines that can help decision makers consider key issues during a risk assessment of knowledge factors in project management. Outputs of this model can prepare an extensive assessment report about the risk of knowledge loss in a project‐based organization with suggestions for preservation plans to mitigate its effects.

Details

Management Decision, vol. 49 no. 3
Type: Research Article
ISSN: 0025-1747

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Article
Publication date: 11 July 2019

Chao Ren, Xiaoxing Liu and Zongqing Zhang

The purpose of this paper is to develop a risk evaluation method for the industrial network under high uncertain environment.

Abstract

Purpose

The purpose of this paper is to develop a risk evaluation method for the industrial network under high uncertain environment.

Design/methodology/approach

This paper introduces an extended safety and critical effect analysis (SCEA) method, which takes the weight of each industry in a network into risk assessment. Furthermore, expert experience and fuzzy logic are introduced for the evaluation of other parameters.

Findings

The proposed approach not only develops weight as the fifth parameter in quantitative risk assessment but also applies the interval type-2 fuzzy sets to depict the uncertainty in the risk evaluation process. The risk rating of each parameter excluding weight is determined by using the interval type-2 fuzzy numbers. The risk magnitude of each industry in the network is quantified by the extended SCEA method.

Research limitations/implications

There is less study in quantitative risk assessment in the industrial network. Additionally, fuzzy logic and expert experience are expressed in the presented approach. Moreover, different parameters can be determined by different weights in network risk assessment in the future study.

Originality/value

The extended SCEA method presents a new way to measure risk magnitude for industrial networks. The industrial network is developed in risk quantification by assessing weights of nodes as a parameter into the extended SCEA. The interval type-2 fuzzy number is introduced to model the uncertainty of risk assessment and to express the risk evaluation information from experts.

Details

Kybernetes, vol. 49 no. 3
Type: Research Article
ISSN: 0368-492X

Keywords

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