General Template for the FMEA Applications in Primary Food Processing

Özilgen, Sibel; Özilgen, Mustafa

doi:10.1007/10_2016_52

Cited by 5 publications

(5 citation statements)

References 85 publications

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“…Many articles have been published on food supply chain risk assessment and the application of FMEA in this area. Moreover, the method has been applied in controlling food production risk [21,22,59].…”

Section: Discussionmentioning

confidence: 99%

“…FMEA is a technique that is applied in a many various areas. Moreover, FMEA has been widely used in the food industry to assess risks in the food production process [21]. This method has been used in determining the risks in the agricultural supply chain for identifying the risk of damage or lost quality and product contamination throughout the entire supply chain [22].…”

Section: Introductionmentioning

confidence: 99%

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Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)

et al. 2020

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Failure mode and effects analysis (FMEA) is a popular technique in reliability analyses. In a typical FMEA, there are three risk factors for each failure modes: Severity (S), occurrence (O), and detectability (D). These will be included in calculating a risk priority number (RPN) multiplying the three aforementioned factors. The literature review reveals some noticeable efforts to overcome the shortcomings of the traditional FMEA. The objective of this paper is to extend the application of FMEA to risk management for agricultural projects. For this aim, the factor of severity in traditional FMEA is broken down into three sub-factors that include severity on cost, the severity on time, and severity on the quality of the project. Moreover, in this study, a fuzzy technique for order preference by similarity to ideal solution (TOPSIS) integrated with a fuzzy analytical hierarchy process (AHP) was used to address the limitations of the traditional FMEA. A sensitivity analysis was done by weighing the risk assessment factors. The results confirm the capability of this Hybrid-FMEA in addressing several drawbacks of the traditional FMEA application. The risk assessment factors changed the risk priority between the different projects by affecting the weights. The risk of water and energy supplies and climate fluctuations and pests were the most critical risk in agricultural projects. Risk control measures should be applied according to the severity of each risk. Some of this research’s contributions can be abstracted as identifying and classifying the risks of investment in agricultural projects and implementing the extended FMEA and multicriteria decision-making methods for analyzing the risks in the agriculture domain for the first time. As a management tool, the proposed model can be used in similar fields for risk management of various investment projects.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)

et al. 2020

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“…The incorporation of FMEA was verified to the procedure of the HACCP system in the bakery industry for better food safety assurance and fault detection [28]. Furthermore, a general structure of FMEA was suggested to detect the potential faults and their effects in primary food processing [29]. A summary of the literature, the application of FMEAs in the food sector can be divided into several topics such as analyzing the risks, finding the critical points, improving the quality and safety, and selecting the maintenance activities.…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes

et al. 2022

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Fault diagnosis and prognosis methods are the most useful tools for risk and reliability analysis in food processing systems. Proactive diagnosis techniques such as failure mode and effect analysis (FMEA) are important for detecting all probable failures and facilitating the risk analysis process. However, significant uncertainties exist in the classical-FMEA when it comes to ranking the risk priority numbers (RPNs) of failure modes. Such uncertainties may have an impact on the food sector’s operational safety and maintenance decisions. To address these issues, this research provides a unique FMEA framework for risk analysis within an edible oil purification facility that is based on certain well-known intelligent models. Fuzzy inference systems (FIS), adaptive neuro-fuzzy inference systems (ANFIS), and support vector machine (SVM) models are among those used. The findings of the comparison of the proposed FMEA framework with the classical model revealed that intelligent strategies were more effective in ranking the RPNs of failure modes. Based on the performance criteria, it was discovered that the SVM algorithm classifies the failure modes more accurately and with fewer errors., e.g., RMSE = 7.30 and MAPE = 13.19 with that of other intelligent techniques. Hence, a sensitivity FMEA analysis based on the SVM algorithm was performed to put forward suitable maintenance actions to upgrade the reliability and safety within food processing lines.

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“…Agriculture feedstocks undergo a series of various mechanical, thermal, chemical and/or biochemical transformations (simple or complex, small or large scale), known as food processing. Food processing aims at converting the inedible raw material to foodstuff, ensuring the safety of the product, and enhancing its preservation and shelf life [1][2][3]. Any by-product resulting from processing or any discarded material resulting from the food chain in general, is considered as a waste.…”

Section: Introductionmentioning

confidence: 99%

Bio-Refinery of Oilseeds: Oil Extraction, Secondary Metabolites Separation towards Protein Meal Valorisation—A Review

et al. 2022

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Edible oil extraction is a large and well-developed sector based on solvent assisted extraction using volatile organic compounds such as hexane. The extraction of oil from oilseeds generates large volumes of oilseed by-products rich in proteins, fibres, minerals and secondary metabolites that can be valued. This work reviews the current status and the bio-macro-composition of oilseeds, namely soybean, rapeseed, sunflower and flaxseed, and the refining process, comprising the extraction of oil, the valorisation and separation of valuable secondary metabolites such as phenolic compounds, and the removal of anti-nutritional factors such as glucosinolates, while retaining the protein in the oilseed meal. It also provides an overview of alternative solvents and some of the unconventional processes used as a replacement to the conventional extraction of edible oil, as well as the solvents used for the extraction of secondary metabolites and anti-nutritional factors. These biologically active compounds, including oils, are primordial raw materials for several industries such as food, pharmaceutical or cosmetics.

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General Template for the FMEA Applications in Primary Food Processing

Cited by 5 publications

References 85 publications

Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)

Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)

Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes

Bio-Refinery of Oilseeds: Oil Extraction, Secondary Metabolites Separation towards Protein Meal Valorisation—A Review

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