Prioritize Risk Factors Research Articles

Risk factors and population attributable fractions for displaced abomasum in Iranian dairy cattle: a retrospective analysis of field data.

Displaced abomasum (DA) usually occurs in the early postpartum period and can negatively affect production and welfare of dairy cows. The objective of this research was to identify predictors for the occurrence of DA in Iranian Holsteins. Therefore, we analyzed data collected from six commercial dairy farms in Iran (calving between January 2011 and October 2020). We used generalized linear mixed-effects model to identify predictors based on estimates of odds ratios (ORs). Population attributable fractions (PAFs) were then calculated, as these are expected to more accurately prioritize risk factors on population level by considering the association between the risk factor and the its prevalence in the population. Over the 6 herds, the average percentage of DA was 2.5% (range 1.1-3.4% across herds). The results showed that calving during winter increased the odds of DA 1.4 times. Cows with high milk fat-to-protein ratio before the occurrence of disease, abnormal calving (including dystocia, stillbirth, and twinning) and long pregnancy length (≥ 280 d) showed increased odds of DA with ORs of 1.52, 1.31 and 1.30, respectively. Milk yield was also positively associated with DA in next lactation. Among the modifiable predictors of DA, the highest PAF was for the ratio of milk fat-to-protein (11.0%); other PAFs were retained placenta (4.1%), metritis (3.1%), abnormal calving (2.7%), and ketosis (2.7%). The results of the present study can provide managers with the a helpful strategy to reduce the occurrence of DA.

Assessment of Risk Factors in Condominium Buildings Construction in Metro Manila using Analytic Hierarchy Process (AHP)

The rapid urbanization of Metro Manila led to a surge in condominium construction projects, which, despite offering efficient housing solutions, faced significant challenges due to dense urban environment, susceptibility to natural disasters & the health and safety issues related to accidents, losses, injuries, damages, and/or delays arising during the execution of a project which may adversely affect its success. This study explored the efficacy of the Analytic Hierarchy Process (AHP) in assessing and prioritizing risks specific to Metro Manila's condominium construction projects, addressing a critical gap in current risk assessment methodologies. The study engaged mixed methods, integrating Meta-Analysis, Experts Consultation, and AHP, to identify and prioritize Risk Factors (RFs) in condominium construction projects. A systematic review through meta-analysis revealed nine critical RFs, including Technical/Design, Financial/Economic, Environmental, Regulatory/Legal, Socio-Political, Logistics/Supply Chain, Management/Supervision, Quality, and Health and Safety Risks. Inputs from experts from a diverse group of qualified professionals had been pooled to validate these findings. Through AHP analysis, risks specific to Metro Manila's condominium construction projects were revealed, with Health and Safety Risk (19.52%), Quality Risk (15.97%) and Management/Supervision Risk (15.77%) as the major concerns. An internal consistency of 0.71% attested to the reliability testing of the results. Given the complexity of condominium construction projects, this study responds to an important demand: a more systemic understanding of risk factors. Moreover, this study is relevant to further enhance and improve sustainability in condominium projects as well as performance of the construction industry.

From unstructured accident reports to a hybrid decision support system for occupational risk management: The consensus converging approach

Introduction: Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts’ subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology. Method: To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights. Conclusions and Practical Applications: The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors.

Predictors of micronutrient deficiency among children aged 6-23 months in Ethiopia: a machine learning approach.

Micronutrient (MN) deficiencies are a major public health problem in developing countries including Ethiopia, leading to childhood morbidity and mortality. Effective implementation of programs aimed at reducing MN deficiencies requires an understanding of the important drivers of suboptimal MN intake. Therefore, this study aimed to identify important predictors of MN deficiency among children aged 6-23 months in Ethiopia using machine learning algorithms. This study employed data from the 2019 Ethiopia Mini Demographic and Health Survey (2019 EMDHS) and included a sample of 1,455 children aged 6-23 months for analysis. Machine Learning (ML) methods including, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Neural Network (NN), and Naïve Bayes (NB) were used to prioritize risk factors for MN deficiency prediction. Performance metrics including accuracy, sensitivity, specificity, and Area Under the Receiver Operating Characteristic (AUROC) curves were used to evaluate model prediction performance. The prediction performance of the RF model was the best performing ML model in predicting child MN deficiency, with an AUROC of 80.01% and accuracy of 72.41% in the test data. The RF algorithm identified the eastern region of Ethiopia, poorest wealth index, no maternal education, lack of media exposure, home delivery, and younger child age as the top prioritized risk factors in their order of importance for MN deficiency prediction. The RF algorithm outperformed other ML algorithms in predicting child MN deficiency in Ethiopia. Based on the findings of this study, improving women's education, increasing exposure to mass media, introducing MN-rich foods in early childhood, enhancing access to health services, and targeted intervention in the eastern region are strongly recommended to significantly reduce child MN deficiency.

Risk Assessment for Complex Systems Based on Fuzzy Cognitive Maps: A Case of the Biopharmaceutical Industry

Analyzing the supply chain (SC) of biopharmaceutical drugs can be challenging due to their complexity, the existence of a wide variety of risks, and the dynamics of the system. This paper presents a framework for evaluating the SC risks of Iranian biopharmaceutical companies based on cause‐and‐effect relationships and fuzzy cognitive maps (FCMs). We first interviewed several biopharmaceutical supply experts to learn about potential SC risks, causal relationships among FCM concepts, FCM structure, and FCM activation cycle. The most critical and relevant risks and significant elements of the SCs, such as cost, time, and quality, were identified as relevant FCM concepts. Then, we used failure mode and effects analysis (FMEA) and the FCM of the SC risks to assess the impacts of the biopharmaceutical SC risks on each other and on the crucial elements of the SCs. The Hebbian learning algorithms were then applied to train the FCM models. We tested different scenarios to evaluate the impacts of FCM concepts on the SC risks. The proposed approach can prioritize risk factors and, more importantly, predict and analyze the effect of each risk factor/risk group on other risks or the outcome of a given risk. The proposed FCM features and the knowledge gained from evaluating them can provide practical and helpful information to pharmaceutical companies to deal with their supply risks more efficiently.

Ranking and Prioritizing Risk Factors for Gastric Cancer

Background: Gastric cancer is one of the gastrointestinal tract cancers with the highest mortality rate; hence, accurate knowledge of the main causes of this disease is of paramount importance to plan, monitor, and evaluate national and regional programs to control this cancer. The present study was to rank and prioritize gastric cancer risk factors. Methods: In this study, gastric cancer risk factors were first extracted in a systematic review, and they were then ranked and prioritized using a focused group discussion. MICMAC software was used to rank the factors. Results: According to the findings, the following 13 factors had the highest effect on the incidence of gastric cancer: socioeconomic status, age, consumption of pickles and sour vegetables, salt consumption, meat consumption (red, smoked, and processed and salty), consumption of fried foods, consumption of fats and oils, consumption of fish (Salty, smoked, and processed), consumption of bread and leftovers and moldy foods, irregular eating habits, excessive daily calorie intake, smoking (cigarettes, opium, and hookah), and alcohol consumption. Conclusion: Prioritizing risk factors would help policymakers identify and present executive strategies and detect action priorities to manage gastric cancer risk factors. According to the findings of the present study, national planning to support vulnerable socio-economic groups, the development of screening programs, and the early detection of diseases in the early stages at an early age, and diet adjustment to increase the consumption of fresh fruits and vegetables and reduce salt, high-fat and fried foods, salted foods, and processed meats are recommended.

Acquired risk factors and incident atrial fibrillation according to age and genetic predisposition.

Atrial fibrillation (AF) is the most common sustained arrhythmia in adults. Investigations of risk factor profiles for AF according to age and genetic risk groups are essential to promote individualized strategies for the prevention and control of AF. A total of 409 661 participants (mean age, 56 years; 46% men) free of AF at baseline and with complete information about risk factors were included from the UK Biobank cohort. The hazard ratios and population-attributable risk (PAR) percentages of incident AF associated with 23 risk factors were examined, including 3 social factors, 7 health behaviours, 6 cardiometabolic factors, 6 clinical comorbidities, and the genetic risk score (GRS), across 3 age groups (40-49, 50-59, and 60-69 years) and 3 genetic risk groups (low, moderate, and high GRS). After a follow-up of 5 027 587 person-years, 23 847 participants developed AF. Most cardiometabolic factors and clinical comorbidities showed a significant interaction with age, whereby the associations were generally strengthened in younger groups (Pinteraction < .002). However, only low LDL cholesterol, renal dysfunction, and cardiovascular disease showed a significant interaction with genetic risk, and the associations with these factors were stronger in lower genetic risk groups (Pinteraction < .002). Cardiometabolic factors consistently accounted for the largest number of incident AF cases across all age groups (PAR: 36.2%-38.9%) and genetic risk groups (34.0%-41.9%), with hypertension and overweight/obesity being the two leading modifiable factors. Health behaviours (PAR: 11.5% vs. 8.7%) and genetic risk factors (19.1% vs. 14.3%) contributed to more AF cases in the 40-49 years group than in the 60-69 years group, while the contribution of clinical comorbidities remained relatively stable across different age groups. The AF risk attributable to overall cardiometabolic factors (PAR: 41.9% in the low genetic risk group and 34.0% in the high genetic risk group) and clinical comorbidities (24.7% and 15.9%) decreased with increasing genetic risk. The impact of social factors on AF was relatively low across the groups by age and genetic risk. This study provided comprehensive information about age- and genetic predisposition-related risk factor profiles for AF in a cohort of UK adults. Prioritizing risk factors according to age and genetic risk stratifications may help to achieve precise and efficient prevention of AF.

Prioritizing risk factors of hazardous material road transportation accidents using the fuzzy AHP method.

Annually, large amounts of hazardous materials (hazmat) are transported through the roads and this movement causes various accidents. Identifying the causes of these accidents is a critical issue for all public governments. This study aimed to identify the effective risk factors for hazmat road transport accidents and determine their relative weight using the fuzzy analytical hierarchy process (AHP) method. This qualitative study was conducted in 2021 in Iran and included four steps, i.e., the identification (using literature review and semi-structured interview), determination (according to the expert panel opinion), classification, and prioritization of effective factors in hazmat road transportation accidents. To prioritize and determine the relative weight of the effective factors, the fuzzy AHP technique was used. In total, 159 risk factors were identified, which were classified into six factors (including road, transportation management, vehicle, cargo, driver, and weather conditions) and 24 sub-factors. The main factor (greatest relative weight) with the highest priority was the driver (0.181). The road (0.167), cargo (0.166), vehicle (0.169), transportation management (0.161), and weather conditions (0.159) were the next priorities, in that order. The results demonstrated that the driver is the most important factor in causing accidents when transporting hazmat by road. The findings of this study might have the potential to decrease the frequency and consequence of accidents caused by the road transport of hazmat.

A Phenome-wide Association and Mendelian Randomization Study for Alzheimer’s Disease: A Prospective Cohort Study of 502,493 Participants From the UK Biobank

BackgroundConsiderable uncertainty remains regarding associations of multiple risk factors with Alzheimer’s disease (AD). We aimed to systematically screen and validate a wide range of potential risk factors for AD. MethodsAmong 502,493 participants from the UK Biobank, baseline data were extracted for 4171 factors spanning 10 different categories. Phenome-wide association analyses and time-to-event analyses were conducted to identify factors associated with both polygenic risk scores for AD and AD diagnosis at follow-up. We performed two-sample Mendelian randomization analysis to further assess their potential causal relationships with AD and imaging association analysis to discover underlying mechanisms. ResultsWe identified 39 factors significantly associated with both AD polygenic risk scores and risk of incident AD, where higher levels of education, body size, basal metabolic rate, fat-free mass, computer use, and cognitive functions were associated with a decreased risk of developing AD, and selective food intake and more outdoor exposures were associated with an increased risk of developing AD. The identified factors were also associated with AD-related brain structures, including the hippocampus, entorhinal cortex, and inferior/middle temporal cortex, and 21 of these factors were further supported by Mendelian randomization evidence. ConclusionsTo our knowledge, this is the first study to comprehensively and rigorously assess the effects of wide-ranging risk factors on AD. Strong evidence was found for fat-free body mass, basal metabolic rate, computer use, selective food intake, and outdoor exposures as new risk factors for AD. Integration of genetic, clinical, and neuroimaging information may help prioritize risk factors and prevention targets for AD.

Prioritizing risk factors and identifying target areas to address with interventions to improve sustainable employment of persons with a brain injury or a spinal cord injury - A multi-stakeholder consensus process.

Achieving sustainable long-term employment is the goal of work integration for persons with acquired brain injury (ABI) or spinal cord injury (SCI). However, decreasing employment rates over time for persons with ABI and SCI indicate that remaining employed in the long-term is a challenge. To identify the most important risk factors that pose a barrier to sustainable employment of persons with ABI or SCI from a multi-stakeholder perspective, and to propose corresponding interventions that address them. Multi-stakeholder consensus conference and follow-up survey. From 31 risk factors to sustainable employment of persons with ABI or SCI identified in previous studies, nine were defined as most important to address with interventions. These risk factors either impacted the person, the work environment or service provision. Potential interventions to address these factors were proposed in mixed condition groups, of which ten were voted on as priority interventions. The follow-up survey revealed strong agreement on the intervention proposals, strong to moderate agreement on impact, but moderate to low feasibility, as most of the interventions were measures at the meso- (service) and macro- (legislation and state regulation) level. Holding micro-level stakeholder conferences is a valuable method for identifying the most important risk factors to sustainable employment and for developing measures to address them. To implement measures that involve decisions at the meso- or macro-level, representatives from these levels of the healthcare and social system have to be involved.

An integrated fuzzy-FMEA risk assessment approach for reinforced concrete structures in oil and gas industry

The oil and gas industry is one of the harshest environments on reinforced concrete structures. Enhancing the reliability of these industries has been identified as a critical goal to meet anticipated production targets and maintain competitiveness. The purpose of this paper is to rank and prioritize risk factors on reinforced concrete structural systems in the oil and gas industry to reduce failures and improve system reliability. The risk factors influencing reinforced concrete systems are identified based on the experts interviewed who specialized in risk analysis. In this paper, a risk assessment approach based on a hybrid fuzzy failure mode and effect analysis is developed in order to rank the factors and improve the process of reinforced concrete maintenance prioritization. The developed approach is also compared with the other two methods; namely, conventional failure mode and effect analysis (FMEA) and grey rational analysis (GRA) integrated with FMEA. The three developed approaches are designed to acquire the highest risk priority number (RPN) values; conventional RPN, GRA-FMEA RPN, and Fuzzy-FMEA RPN. These values will be utilized as the focus of improvements to reduce the possibility of some kind of failure occurring a second time and improve the deteriorated reinforced concrete structure to minimize the likelihood of failures. The results revealed that high-risk systems include the compression train, steam turbine, and combustion gas turbine generator, while the majority require maintenance of the supporting concrete foundation as soon as second-degree deterioration occurs. Furthermore, the results indicated that the Fuzzy FMEA approach was appropriate for assessing deteriorated reinforced concrete structures.. This work represents a step forward in the development of a tool that can be used to assess the risk of degraded concrete structures and improve their integrity through proper monitoring and maintenance.

Application of the best-worst method approach for prioritizing risk factors of musculoskeletal disorders among mobile device users: A case study

Musculoskeletal disorders (MSDs) are the most common work-related health issues among mobile device (MD) users due to long hour of working during homestay. Various researches have been carried out earlier for identifying the MSD risk factors, though the risk factors' priority and weightage play an important role for risk management. In the current study, the priority and weightage for MSD risk factor (individual factor, personal factor, psychosocial factor, and subfactors in each factor) were identified using the best-worst method (BWM) for MD users through a case study. A framework was proposed for evaluating the risk hierarchy. The rating data provided by decision-makers was analysed using BWM for optimistic risk factor outcomes. A sensitivity analysis was also performed on the prioritized risk factors/subfactors for the consistency check. Research results revealed that personal factor was the most prominent risk factor of MSDs, followed by psychosocial factor and individual factor. Further, the total time spent and gender were identified as most and least dominating risk subfactor of MSDs respectively. The evaluated priority and weightage of MSD risk factors were further validated using sensitivity analysis. The obtained outcomes may be helpful in reducing the MSDs risk factors among the MD users by ergonomic improvement.

Linkages analysis risk factors of the return process in logistics fast moving consumer goods

This study analyzed the linkage of risk factors in the return process of fast-moving consumer good (FMCG) logistics systems. The risk of returning products due to expired, near expiration, order errors and bad stock (damaged) haunts sustainable supply chains in the industry. In four business processes, warehousing, transport/distribution, product­ion/supply and order processing identified twenty-two risk factors that cause the return process. The decision-making and trial evaluation laboratory (DEMATEL) method helps decision-makers simplify causal relationships between twenty-two complex risk factors. Through the depiction of the matrix and the network relationship map, twelve risk factors entered the dispatcher group, namely risk factors that can affect other risk factors that impact the return process on the FMCG logistics system. The result becomes a reference for decision makers to prioritize risk factors management that have a relationship with other risk factors, because the impact obtained will be maximal.

A new risk assessment framework for safety in oil and gas industry: Application of FMEA and BWM based picture fuzzy MABAC

As one of the riskiest sector, oil and gas production, transportation and storage operations create dangers for workers. Since health hazards and dangerous conditions can result in fatalities or damages, safety precautions are vital in oil and gas industry. As accident types, fire and explosions are among the most frequent primary coincidences and vary from different causes. The aim of this study is to identify and prioritize fire and explosion risks in the midstream section of the oil and gas industry. For this aim, the common causes of fire and explosions were evaluated to take preventive measures or mitigation strategies. To prioritize risk factors cause fire and explosions in the midstream section of the oil and gas industry, a new risk assessment framework that consists of Best-Worst Method (BWM) and Picture Fuzzy multi-attributive border approximation area comparison (PF-MABAC) method based on Failure Mode and Effect Analysis (FMEA) was proposed. Accordingly, while the weights of risk parameters in FMEA were determined using BWM, to follow proper safety precautions risk factors were evaluated using PF-MABAC method. To demonstrate the effectiveness and practicality of the proposed risk assessment approach, case studies were conducted. At the end, sensitivity analysis was performed to validate and prove robustness of the proposed risk assessment framework via results of a real case.

Prioritizing risk factors in sustainable supply chain using fuzzy Kano and interval-valued intuitionistic fuzzy QFD

PurposeThe purpose of the paper is to develop a hybrid method to prioritize risk factors (RFs) of sustainable supply chain (SSC) considering sustainable customer requirements (CRs) and uncertain evaluation.Design/methodology/approachIn the proposed method, fuzzy Kano model (FKM) is applied to prioritize sustainable CRs considering customer satisfaction (CS) and objective weight of each CR, the interval-valued intuitionistic fuzzy (IVIF) set theory is integrated with quality function deployment (QFD) to translate the sustainable CRs into RFs of SSC under uncertain environment and the IVIF cross-entropy is used to conduct objective analysis to prioritize RFs. Finally, a case in air-conditioner-manufacturing company is presented to demonstrate the proposed method.FindingsA case study of SSC risk management, the comparative analysis and associated discussions are conducted to illustrate the feasibility and effectiveness of the proposed method. The results obtained from the case study shows that RF5 (market share reduction) is the most important RF in the SSC. Compared with the existing methods, the proposed method can integrate sustainable CRs into SSC's RFs, handle uncertain information effectively and obtain objective importance of RFs.Originality/valueTheoretically, the paper develops a customer-oriented model based on the FKM, QFD, IVIF sets and entropy theory to prioritize RFs of SSC under uncertain environment. The model enables to integrate sustainable CRs into RFs managements and is efficient to deal with the subjectivity and conduct objective analysis to prioritize RFs. In practice, the systematic and correct RFs' priorities analysis provides reliable decision support for the managers to take measures to avoid or mitigate the critical RFs.

Assessment and Prioritize Risk Factors of Financial Measurement of Management Control System for Production Companies Using a Hybrid Z-SWARA and Z-WASPAS with FMEA Method: A Meta-Analysis

The management control system in an industry is managerial, directional, hindrance, and cohesive action in order to cohere and regulate various branches and sub-branches. In fact, it is a system that supports the real state of matters in the right way. This method is intended at assuring that the purposes and activities carried out have the desired outcomes and eventually lead to the objects and purposes of the company. In this matter, the financial and non-financial management control system is essential both when it comes to strategy community; Consequently, in this paper, the management control system is classified into financial and non-financial categories because such analysis gives a chance to get a broad assessment of a management control system relationship in organizations. In this paper, we evaluate the relationship between business strategy and management control system and their influences on financial performance measurement of a manufacturer (a case study of Maral Co.) with the use of Merchant’s theory. Furthermore, In this case, a decision-making strategy centered on the FMEA is used to identify and prioritize risk factors financial of the control system in companies. Nevertheless, because this strategy has some significant limitations, this research has presented a decision-making approach depending on Z-number theory. For tackle, some of the RPN score’s drawbacks, the suggested decision-making methodology combines the Z-SWARA and Z-WASPAS techniques with the FMEA method. The findings reveal that in the non-financial management control system element, customer satisfaction, and in the financial component, cost standards are at the largest level of weight. Furthermore, the strategic planning factor with a rate of 2.95 and the deviation analysis factor with a rate of 2.87 is at the lowest level, respectively. In sum, market or industry changes are the primary cause of risk in businesses, according to FMEA methodology and the opinions of three professionals.

Prioritizing Risk Factors of Hazardous Material Road Transportation Accidents Using Fuzzy AHP Method

Prioritizing Risk Factors of Hazardous Material Road Transportation Accidents Using Fuzzy AHP Method

A new model for risk assessment in glass manufacturing using Risk Matrix based IVIF-TOPSIS method

The glass manufacturing includes operations, such as batch forming using raw materials, melting, forming, annealing, quality check and package. Due to risky processes in glass manufacturing, significant health hazards for workers are present in the glass industry. Risk assessment is effective way to prevent accidents and protect workers from serious accidents during glass manufacturing. To assess health hazards associated with glass manufacturing, in this study Risk Matrix and The Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method are integrated under Interval-Valued Intuitionistic Fuzzy (IVIF) environment to prioritize risk factors and suggest required preventive and protective measures. Suggested preventive and protective measures provide technical, economic and environmental challenges for glass manufacturing firms. Once the importance weight of risk parameters in Risk Matrix’ are determined, the risk factors are assessed by performing IVIF-TOPSIS method during glass manufacturing. In order to verify the validity and stability of the proposed risk assessment model, sensitivity and comparative analysis are accomplished at the end of the study.

A multicriteria decision making methodology based on two-dimensional uncertainty by hesitant Z-fuzzy linguistic terms with an application for blockchain risk evaluation

Blockchain technology (BT), which provides to record transactions among many computers as a distributed ledger, is commonly utilized by companies to develop and manage business processes. Since it is a multi-process and multi-agent complex system, the implementation process of BT contains many risk factors. Identifying and analyzing these risks in order to minimize their adverse effects during the implementation process is also completely critical for companies. Since evaluating these risks must be handled by considering many different perspectives, the evaluation of risk factors for BT can be considered as a multi-criteria decision-making (MCDM) problem. To cope with uncertainty in this process, the fuzzy set theory (FST) can be effectively utilized with MCDM methods to increase their ability to obtain more concrete and realistic results. Therefore, this paper proposes an MCDM methodology consisting of Decision-Making Trail and Evaluating Laboratory (DEMATEL) and cognitive mapping methods based on FST. These methods have been reconstructed with hesitant fuzzy Z-numbers that enable not only to state impreciseness of the assessments but also to consider indeterminacy and hesitancy of the experts to evaluate risk factors with respect to BT implementation. While the dependencies of risk factors have been determined via the hesitant fuzzy Z-DEMATEL method, their weights have been obtained through the hesitant fuzzy Z-cognitive mapping method. As a result, this study aims to prioritize risk factors related to the BT implementation process with a novel fuzzy decision-making methodology and provide a road map for companies. With this study, which is the first paper in the literature in terms of the subject of the study and the method adopted, an integrated, comprehensive fuzzy approach is introduced for the first time in evaluating BT risks. As a result of detailed research, risk factors that companies may encounter in BT use and the degrees of their importance have been determined. Through that, essential decision analysis has been put forward that companies can apply for their strategic decisions. A sensitivity analysis based on changes in the initial vector values has also been adapted to analyze the effects of the risk factors.

Risk Assessment in ERP Project by COPRAS Method Under Fuzzy Environment

Enterprise resource planning (ERP) projects are prone to risk from the strategic, operational, technical and organizational perspective. The assessment of project risks is a challenging task for project managers and is considered in a class of a multi-criteria decision making (MCDM) problem. In this work, an MCDM approach has been presented for risk assessment in ERP project by COPRAS under fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by triangular fuzzy number. Fuzzy COPRAS (COPRAS-F) has been used to determine the weight of risk criteria and then prioritize the risk factor based on the calculated weight of criteria. The prioritized risk factors have been classified in scale from almost certain to rare risks. The proposed approach is illustrated with a real case studying fertilizer plant and the associated results have been compared with fuzzy TOPSIS technique. The result of this study demonstrates that excessive customization, ineffective consulting services experiences, complex architecture and high number of modules, poor project team skills, inadequate change management, inadequate ERP selection, ineffective strategic thinking and planning, poor leadership, lack of business process re-engineering and low-key user involvement are top ten risks that need to be mitigated to avoid failure of ERP project.