Circular food supply chains – Impact on value addition and safety
زنجیره های تأمین مواد غذایی دایره ای - تأثیر بر ارزش افزوده و ایمنی-2021
Background: The “linear” manufacturing processes generate waste materials and products that after their use end up in landfills and incinerators. Circular supply chains implement one of the basic concepts of the bioeconomic, i. e., collecting waste streams in order to recycle them into new products, thus achieving a more sustainable production system. Scope and approach: This paper is focused on the application of a circular supply chain concept within the food system, with the aims to: a) outline the advantages of this approach in terms of value addition; b) discuss the impact of the increased complexity of circular supply chains on food safety; and c) propose management solutions. To link theoretical principles with empirical data, winemaking was chosen as a case study because of the high volumes of valuable byproducts produced globally. Key findings and conclusions: Circular food supply chains can potentially generate added-value foods. However, new loops in the food supply chains could also bring about new risks. The main challenges are likelihood of food contamination and loss of identification due to batch dispersion. Hence, a holistic approach of research is needed to integrate the value addition strategy with risk analysis and to apply forecasting and optimization studies to the whole supply chain. At the operational level, Internet of Things could represent a powerful management tool. Moreover, the management system within a circular supply chain should be conceived and implemented beyond the company level, involving all the trading partners in order to ensure high transparency, interconnectivity and thus efficacy.
Keywords: Circular supply chain | Value addition | Food safety | Risk analysis | Traceability
An assessment of probabilistic disaster in the oil and gas supply chain leveraging Bayesian belief network
ارزیابی فاجعه احتمالی در زنجیره تأمین نفت و گاز با استفاده از شبکه اعتقادی بیزی-2021
The oil and gas supply chain (OGSC) is considered to have one of the most significant stakes in the U.S. economy because of its interconnectedness with supply chains in other sectors, such as health and medicine, food, heavy manufacturing, and services. While oil and gas development is expanding exponentially, various factors ranging from man-made to natural disasters can hinder OGSC processes, which, in turn, can result in inefficient and costly operations in other sectors. This study presents a Bayesian Network (BN) model to predict and assess disasters in the OGSC based on seven main factors: technical, economic, social, political, safety, environmental, and legal. BBN is a probabilistic graphical model that is predominantly used in risk analysis to illustrate and assess probabilistic relationships among different variables. To draw meaningful managerial insights into the proposed model, sensitivity analysis and belief propagation are used. The results indicate that of the seven factors responsible for OGSC disasters, technical factors have the highest impact while legal and political factors have the lowest.
Keywords: Oil and gas | Supply chain | Disaster assessment | Bayesian network | Resilience
Risk assessment of agricultural supermarket supply chain in big data environment
ارزیابی ریسک زنجیره تأمین سوپرمارکت های کشاورزی در محیط داده های کلان-2020
Article history:Received 20 November 2019Received in revised form 30 June 2020 Accepted 14 July 2020Available online 16 July 2020Keywords:Big dataAgricultural super-docking Supply chainRisk analysisWith the application of big data in all walks of life, big data thinking is effectively improving the cir- culation efﬁciency of agricultural products supply chain by driving management changes in business decision-making and f¨armer-supermarket dockinga¨ s an innovative mode of agricultural products circu- lation. Based on the current situation of China’s agricultural supermarket supply chain development, this paper makes an in-depth study on the supply chain risk of agricultural products of large retail enterprises under the mode of a¨ gricultural supermarket docking¨, and then introduces the agricultural supermarket docking supply chain under the big data environment. This paper uses big data to analyze the risks that may arise in the supply chain of a¨ gricultural supermarket docking¨in large retail enterprises. This paper from the aspects of production, processing, distribution, retail and consumption, introduces the new risks of agricultural supermarket supply chain after introducing big data. Secondly, Qualitative analysis and quantitative calculation are combined to conduct risk assessment. Through empirical analysis, the ranking of all risk factors is obtained, and the relevant fuzzy evaluation grade and risk evaluation criteria are given. Through expert evaluation, a new risk ranking is also obtained, which is not much different from the results of empirical analysis, and the empirical results are also veriﬁed. Therefore, develop this study is helpful to prevent the risk of agricultural supermarket supply chain connection. At the same time, the information integration, sharing and feedback of the big database provide a new idea for the optimization of the supply chain connecting agricultural production.it also has reference signiﬁcance for other supply chain risk management.© 2020 Elsevier Inc. All rights reserved.
Keywords: Big data | Agricultural super-docking | Supply chain | Risk analysis
Economic feasibility valuing of deep mineral resources based on risk analysis: Songtao manganese ore - China case study
ارزیابی امکان سنجی اقتصادی منابع معدنی عمیق بر اساس ریسک تجزیه و تحلیل: سنگ معدن منگنز Songtao - مطالعه موردی چین-2020
The exploitation of deep mineral resources is an inevitable choice under economic development and resource shortage. Assessing the economic feasibility of deep mineral resource exploit projects is a prerequisite for resource industry development. Mining industry have some problems influence its economic feasibility, including long mining period, high infrastructure investment and lack flexibility, and have risks of geology instability and economic reserve degrade. On the other hand, with the increase of the buried depth of mineral resources, some problems have intensified the uncertainty of the profit of deep resource utilization project, such as high stress, high lithology, high temperature environment, and increase of upgrading cost. Net Present Value (NPV) and Internal Rate of Return (IRR) are traditional economic evaluation means which difficult to identify and assess risks precisely. Decoupled Net Present Value (DNPV) provides an efficiency tool to separate the time value and risk cost which is helpful to finds the real value of projects. A manganese mining project which is located Guizhou province, China is analyzed, paper choices several mainly risks of influence expected revenue to analysis project feasibility based on the DNPV technology, which includes the thickness of ore body, ore grade, market price, operation cost and nature disaster. The cost of potential environmental risk (carbon emission cost) also is analyzed. Paper constructs a risk management framework by risk identify, assess and classification, and analyzes the corresponding measures to reduce risk costs. The mainly risk cost of study case from market price shock and unexpected ore grade decline, which accounting for 80% of the total risk cost. In the process of deep mineral resources exploit, effective cost control measures can reduce the risk cost to a certain extent, including improving productivity, reducing unit cost of ore, improving mine sustainability and exploration accuracy. Green mineral construction is a feasible direction of deep resource utilization. For improve the accuracy of economic feasibility evaluation of deep mineral resources utilization, further improvement is needed in the selection and construction of different risk assessment model.
Keywords: Deep mining | Risk value assess | DNPV | Risk management | Songtao manganese
Multi-criteria decision-making considering risk and uncertainty in physical asset management
تصمیم گیری چند معیار با توجه به ریسک و عدم اطمینان در مدیریت دارایی های فیزیکی-2020
In this work we present a method for risk-informed decision-making in the physical asset management context whereby risk evaluation and cost-benefit analysis are considered in a common framework. The methodology uses quantitative risk measures to prioritize projects based on a combination of risk tolerance criteria, cost-benefit analysis and uncertainty reduction metrics. There is a need in the risk and asset management literature for a unified framework through which quantitative risk can be evaluated against tolerability criteria and trade-off decisions can be made between risk treatment options. The methodology uses quantitative risk measures for loss of life, loss of production and loss of property. A risk matrix is used to classify risk as intolerable, As Low As Reasonably Practicable (ALARP) or broadly tolerable. Risks in the intolerable and ALARP region require risk treatment, and risk treatment options are generated. Risk reduction benefit of the treatment options is quantified, and cost-benefit analysis is performed using discounted cashflow analysis. The Analytic Hierarchy Process is used to derive weights for prioritization criteria based on decision-maker preferences. The weights, along with prioritization criteria for risk reduction, tolerance criteria and project cost, are used to prioritize projects using the Technique for Order Preference by Similarity to Ideal Solution. The usefulness of the methodology for improved decision-making is illustrated using a numerical example.
Keywords: Risk analysis | Uncertainty | Asset management | Multi-criteria decision analysis | Risk matrix | Cost-benefit analysis
Program evaluation of highway access with innovative risk-cost-benefit analysis
ارزیابی برنامه دسترسی بزرگراهها با تحلیل نوآورانه تجزیه و تحلیل ریسک-هزینه-سود-2020
Access management is used to control vehicular ingress and egress to adjacent property, where the main goal is to preserve the safety and capacity of the transportation network. Access management can assist in protecting billions of dollars in current investments in the transportation infrastructure, yet it is common for transportation planners to have limited resources, including budgets, equipment, time, human resources, and others, and thus they need principled approaches for allocating their limited resources across a large network of highways. This research develops a framework that can be used to prioritize competing needs for access management among thousands of access points. A key innovation of this research is the integration of risk analysis and cost-benefit analysis with data uncertainties. This will be accomplished by introducing three risk components—hazard intensity, exposure, and vulnerability—that can be used to evaluate roadway performance and to monetize the potential benefits and costs of access management projects. These components are then presented in a threedimensional diagram to facilitate tradeoff analysis and to allow for risk-cost-benefit analysis with data uncertainties and tradeoff analysis to complement one another. The developed framework is demonstrated by applying it to four major U.S. highways with a combined length of 321.95 km.
Keywords: System safety | Resource allocation | Risk-cost-benefit analysis | Priority setting | Data uncertainty | Engineering systems
Quantitative assessment of microbial quality and safety risk: A preliminary case study of strengthening raspberry supply system in Chile
ارزیابی کمی از کیفیت میکروبی و خطر ایمنی: یک مطالعه موردی اولیه از تقویت سیستم تأمین تمشک در شیلی-2020
National governments are moving to integrate risk analysis frameworks into food safety management systems at the country level. However, this process is less advanced in developing countries. In this context, the Chilean Livestock and Agriculture Service (SAG), Food Quality and Safety Agency (ACHIPIA) and the University of Nebraska-Lincoln (UNL) collaborated on a project to control generic Escherichia coli and Hepatitis A virus (HAV) contamination in both fresh and frozen raspberry products destined for export. The objectives of this study were to 1) identify along the raspberry supply chain the most influential factors of E. coli and HAV contamination in the final products; and 2) evaluate the efficacies of possible interventions to control these influential factors. To achieve these objectives, a unified quantitative model of microbial contamination in raspberries was developed to describe the impact of factors in a continuum from the farm to the destination of importation on E. coli/HAV contamination in fresh and frozen raspberry products. Multiple surveys were conducted to obtain countryspecific data on current common practices of producing and processing raspberries in Chile for inputs into the simulation model. The model estimated mean bacterial loads of−1.64 and−5.46 logCFU/g for E. coli and mean viral loads of −6.45 and −6.51 logPDU/g for HAV in fresh and frozen raspberries, respectively. Sensitivity and scenario analyses indicated that reduction of E. coli contamination in the end products can be effectively achieved by improving the quality of water used for pesticide application, as well as by controlling the transport and storage time and temperature along raspberries supply chain. By contrast, to control HAV contamination in the end products, efforts should be focused on improving the hygiene practices of berry handlers on the farm and at the packing plant. This project provides straightforward recommendations for Chilean food safety authorities to effectively prioritize their financial and human resources to proactively prevent microbial contamination in raspberries. Moreover, this project provide a framework that can be extended to other countries to promote capability building for applying risk-based food safety management systems for public health protection.
Keywords: Risk analysis | Quantitative simulation | Raspberry | Escherichia coli | Hepatitis A | Intervention
Analyzing green building project risk interdependencies using Interpretive Structural Modeling
تجزیه و تحلیل وابستگی متقابل پروژه ساختمان سبز با استفاده از مدل سازی ساختاری تفسیری-2020
Green building (GB) projects have attracted wide attention in the construction industry in recent years owing to numerous benefits of green practices for sustainable development. However, existing research efforts on GB project risk management are very limited, and no prior in-depth research has focused on studying the risk interdependencies in GB projects from the perspectives of both the project life cycle and multiple project risks. This paper begins by identifying and distinguishing GB project constraints from multiple GB project risks using a systematic literature review and then investigates, based on the Interpretive Structural Modeling (ISM) method, the risk interdependencies taking into account the identified 16 constraint factors, 22 risk factors and 11 objectives throughout a GB project life cycle. The importance of constraints and risk factors associated with GB project objectives was calculated based on the influence transmission through network paths in the established ISM-based model. In addition, the Matrice d’Impacts Croises Multiplication Appliquee a un Classement (MICMAC) approach was used to analyze the drive and dependence powers of risk interdependency elements. Critical constraints and risk factors in the implementation of GB projects can be obtained from the proposed risk analysis model, which contributes to an in-depth risk perception of GB projects for industry practitioners and facilitates GB project risk management in a more effective way
Keywords: Green building projects | Risk management | Risk interdependencies | Interpretive structural modeling (ISM) | Matrice d’Impacts Croises Multiplication | Appliquee a un Classement (MICMAC) | analysis
Deep learning for symbols detection and classification in engineering drawings
یادگیری عمیق برای تشخیص و طبقه بندی نمادها در نقاشی های مهندسی-2020
Engineering drawings are commonly used in different industries such as Oil and Gas, construction, and other types of engineering. Digitising these drawings is becoming increasingly important. This is mainly due to the need to improve business practices such as inventory, assets management, risk analysis, and other types of applications. However, processing and analysing these drawings is a challenging task. A typical diagram often contains a large number of different types of symbols belonging to various classes and with very little variation among them. Another key challenge is the class-imbalance problem, where some types of symbols largely dominate the data while others are hardly represented in the dataset. In this paper, we propose methods to handle these two challenges. First, we propose an advanced bounding-box detection method for localising and recognising symbols in engineering diagrams. Our method is end-to-end with no user interaction. Thorough experiments on a large collection of diagrams from an industrial partner proved that our methods accurately recognise more than 94% of the symbols. Secondly, we present a method based on Deep Generative Adversarial Neural Network for handling class-imbalance. The proposed GAN model proved to be capable of learning from a small number of training examples. Experiment results showed that the proposed method greatly improved the classification of symbols in engineering drawings.© 2020 Elsevier Ltd. All rights reserved.
Keywords: Deep learning | YOLO | P&ID | Engineering drawings | Symbols recognition | GANs
Managing the dangers in Lake Kivu – How and why
مدیریت خطرات موجود در دریاچه کیوو - چگونه و چرا-2020
Lake Kivu is probably the Worlds largest natural freshwater digester of algae to produce biogas. Its resources in situ may generate power for generations. Extracting gas is essential to avert a future limnic eruption. Undisturbed, the reservoir formed by salinity-based chemoclines, keeps biogenic CH4 and CO₂ in solution. This is stored in lower strata of the lake. Gases accumulating ever closer to saturation levels, threaten to cause a future limnic eruption. An eruption as occurred at Lake Nyos in Cameroon in 1986 with 1746 casualties, can result if not prevented. But Lake Kivu has potential and inventory for consequences 1000 times larger. Based on novel hypotheses on vertical transport and the lakes history, we used multidisciplinary analyses of this situation. One can foresee its outcomes and recognise system constraints. Therefore, initiating gas extraction enables the vital outcomes; (a) society can enforce extraction methods that ensure prevention of future disasters while, (b) minimizing environmental impact, (c) maximizing useful energy output, and (d) developers pursuing economic projects. The key to safety is management of the chemoclines while producing gas. Achieving safety and production needs the right specification of plant design. For gas production facilities, it is designing to achieve what must be done technically. After our primary concern for public safety, we examine ways of minimizing any environmental impacts. Changes are caused by natural upwelling of saline meteoric water from lava strata into the deep monimolimnion of nutrient-rich water bodies. Raw gas extracted must be washed with water from the mixolimnion to make the gas fit for use in power-generation and domestic gas. For maintenance of chemoclines, we discuss how a fraction of the degassed water must be evacuated from the resource strata and re-injected into the mixolimnion to maintain chemoclines. The challenge lies in how to minimize this safety-driven impact on the mixolimnion from toxic effects of H₂S, from CO₂-induced acidity, and oxygen depletion by CH4 and H₂S.
Keywords: Lake Kivu | Limnic eruption | Gas extraction | Best available technology | Risk analysis | Gas processing | Gas resource