63 research outputs found
Risk Management in Environment, Production and Economy
The term "risk" is very often associated with negative meanings. However, in most cases, many opportunities can present themselves to deal with the events and to develop new solutions which can convert a possible danger to an unforeseen, positive event. This book is a structured collection of papers dealing with the subject and stressing the importance of a relevant issue such as risk management. The aim is to present the problem in various fields of application of risk management theories, highlighting the approaches which can be found in literature
Supply Chain Risk Assessment for Perishable Products Applying System Dynamics Methodology - A Case of Fast Fashion Apparel Industry
With the fast progress of science and technology and with the continuously growing customer expectations, share of merchandise exhibiting characteristics of perishability is on the rise. Perishable products, through their own nature, are subject to decay, deterioration or obsolescence. As a result, their usefulness, value or functionality is gradually reduced or even lost in a short window of time and cannot be regained if it is not used or sold within a specific time window. When producing perishable products, all stages of the supply chain are exposed to much higher uncertainty than in the case of durable products, which directly means higher risk. The phases of inventory planning, lead time control, and demand forecasting for perishable products play a critical role in the overall effectiveness of the supply chain. For this reason, the system dynamics methodology, a simulation and modeling technique developed specifically to address the long term and dynamic management issues, is adopted in this study. The focus of the proposed model is on the interaction between physical processes, information flows and managerial policies of a three-level supply chain for perishable products, in general, and fast fashion apparel supply chain, in particular, so as to create the dynamics of the variables of interest. The values of supply chain key factors such as, for example, inventory, backlogs, stock-outs, forecast error, cost, and profit for each time period are some of the outputs of the proposed model. Moreover, the Conditional Value at Risk (CVaR) measure is applied to quantify and analyze the risks associated with the supply chain for this type of product and also to determine the expected value of the losses and their corresponding probabilities. With the focus on three prominent categories of risks including risks of delays, forecast, and inventory, multiple business situations for effective strategic planning and decision making are generated and analyzed
Agribusiness supply chain risk management: A review of quantitative decision models
Supply chain risk management is a large and growing field of research. However, within this field, mathematical models for agricultural products have received relatively little attention. This is somewhat surprising as risk management is even more important for agricultural supply chains due to challenges associated with seasonality, supply spikes, long supply lead-times, and perishability. This paper carries out a thorough review of the relatively limited literature on quantitative risk management models for agricultural supply chains. Specifically, we identify robustness and resilience as two key techniques for managing risk. Since these terms are not used consistently in the literature, we propose clear definitions and metrics for these terms; we then use these definitions to classify the agricultural supply chain risk management literature. Implications are given for both practice and future research on agricultural supply chain risk management
Qualitative and quantitative analysis of scientific contributions in agribusiness
The agribusiness is a major generator of employment and income worldwide and contributes to food security and nutrition. Therefore, the objective was to perform a qualitative and quantitative analysis of the scientific contributions in agribusiness. A bibliographic consultation was made in Scopus and "Agribusiness" was used as keyword. A textual analysis was performed on 407 scientific papers from 2020, through Nvivo 12 software using the following analysis codes: Mega trade agreements and institutional harmonization, farm-level technology pricing and contracts, market power related to the mega consolidation of companies, new agricultural technologies, emergence of agrocorporations, institutional land access rules, property rights regimes and their consequences, private enforcement of property rights, farmer class action studies and territorial reconversion. Two more codes emerged in the analysis process: Environmental impact and human health impact. Current scientific contributions in agribusiness are focused on new agricultural technologies (24%), environmental impact (17%) and local actions of farmers (14%). A qualitative improvement of the contributions is observed as more elements that support the complex processes agribusiness generates are increasingly incorporated. From focusing on economic and financial aspects, sustainability-oriented and social commitment domains are now considered. A modern and innovative concept defines agribusiness as economic activities with different forms or models of production, derived from or linked to agricultural products. It considers production-consumption processes and farmers are inserted in a differentiated way according to their economic rationality. These activities are not only focused on the generation of monetary value, but also on the social processes it produces, where multiple actors are involved
Risk Management
Every business and decision involves a certain amount of risk. Risk might cause a loss to a company. This does not mean, however, that businesses cannot take risks. As disengagement and risk aversion may result in missed business opportunities, which will lead to slower growth and reduced prosperity of a company. In today's increasingly complex and diverse environment, it is crucial to find the right balance between risk aversion and risk taking. To do this it is essential to understand the complex, out of the whole range of economic, technical, operational, environmental and social risks associated with the company's activities. However, risk management is about much more than merely avoiding or successfully deriving benefit from opportunities. Risk management is the identification, assessment, and prioritization of risks. Lastly, risk management helps a company to handle the risks associated with a rapidly changing business environment
Green Technologies for Production Processes
This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies
A study in the financial valuation of a topping oil refinery
Oil refineries underpin modern day economics, finance and engineering – without their refined
products the world would stand still, as vehicles would not have petrol, planes grounded without
kerosene and homes not heated, without heating oil. In this thesis I study the refinery as a financial
asset; it is not too dissimilar to a chemical plant, in this respect. There are a number of reasons for
this research; over recent years there have been legal disputes based on a refiner's value, investors
and entrepreneurs are interested in purchasing refineries, and finally the research in this arena is
sparse. In this thesis I utilise knowledge and techniques within finance, optimisation, stochastic
mathematics and commodities to build programs that obtain a financial value for an oil refinery. In
chapter one I introduce the background of crude oil and the significance of the refinery in the oil
value chain. In chapter two I construct a traditional discounted cash flow valuation often applied
within practical finance. In chapter three I program an extensive piecewise non linear optimisation
solution on the entire state space, leveraging off a simulation of the refined products using a set of
single factor Schwartz (1997) stochastic equations often applied to commodities. In chapter four I
program an optimisation using an approximation on crack spread option data with the aim of
lowering the duration of solution found in chapter three; this is achieved by utilising a two-factor
Hull & White sub-trinomial tree based numerical scheme; see Hull & White (1994) articles I & II
for a thorough description. I obtain realistic and accurate numbers for a topping oil refinery using
financial market contracts and other real data for the Vadinar refinery based in Gujurat India
A systematic literature review on machine learning applications for sustainable agriculture supply chain performance
Agriculture plays an important role in sustaining all human activities. Major challenges such as overpopulation, competition for resources poses a threat to the food security of the planet. In order to tackle the ever-increasing complex problems in agricultural production systems, advancements in smart farming and precision agriculture offers important tools to address agricultural sustainability challenges. Data analytics hold the key to ensure future food security, food safety, and ecological sustainability. Disruptive information and communication technologies such as machine learning, big data analytics, cloud computing, and blockchain can address several problems such as productivity and yield improvement, water conservation, ensuring soil and plant health, and enhance environmental stewardship. The current study presents a systematic review of machine learning (ML) applications in agricultural supply chains (ASCs). Ninety three research papers were reviewed based on the applications of different ML algorithms in different phases of the ASCs. The study highlights how ASCs can benefit from ML techniques and lead to ASC sustainability. Based on the study findings an ML applications framework for sustainable ASC is proposed. The framework identifies the role of ML algorithms in providing real-time analytic insights for pro-active data-driven decision-making in the ASCs and provides the researchers, practitioners, and policymakers with guidelines on the successful management of ASCs for improved agricultural productivity and sustainability
A study in the financial valuation of a topping oil refinery
Oil refineries underpin modern day economics, finance and engineering – without their refined
products the world would stand still, as vehicles would not have petrol, planes grounded without
kerosene and homes not heated, without heating oil. In this thesis I study the refinery as a financial
asset; it is not too dissimilar to a chemical plant, in this respect. There are a number of reasons for
this research; over recent years there have been legal disputes based on a refiner's value, investors
and entrepreneurs are interested in purchasing refineries, and finally the research in this arena is
sparse. In this thesis I utilise knowledge and techniques within finance, optimisation, stochastic
mathematics and commodities to build programs that obtain a financial value for an oil refinery. In
chapter one I introduce the background of crude oil and the significance of the refinery in the oil
value chain. In chapter two I construct a traditional discounted cash flow valuation often applied
within practical finance. In chapter three I program an extensive piecewise non linear optimisation
solution on the entire state space, leveraging off a simulation of the refined products using a set of
single factor Schwartz (1997) stochastic equations often applied to commodities. In chapter four I
program an optimisation using an approximation on crack spread option data with the aim of
lowering the duration of solution found in chapter three; this is achieved by utilising a two-factor
Hull & White sub-trinomial tree based numerical scheme; see Hull & White (1994) articles I & II
for a thorough description. I obtain realistic and accurate numbers for a topping oil refinery using
financial market contracts and other real data for the Vadinar refinery based in Gujurat India
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