Triple bottom line analysis of oil and gas industry with multicriteria decision making

2013 ◽  
Vol 52 ◽  
pp. 289-300 ◽  
Author(s):  
Carlos Eduardo Durange de C. Infante ◽  
Fabricio Molica de Mendonça ◽  
Paula Michele Purcidonio ◽  
Rogerio Valle
Keyword(s):  
Decision Making ◽  
Oil And Gas ◽  
Triple Bottom Line ◽  
Multicriteria Decision Making ◽  
Oil And Gas Industry ◽  
Bottom Line ◽  
Gas Industry ◽  
Multicriteria Decision ◽  
Line Analysis

scholarly journals A Fuzzy Multicriteria Decision-Making (MCDM) Model for Sustainable Supplier Evaluation and Selection Based on Triple Bottom Line Approaches in the Garment Industry

Processes ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 400 ◽  
Author(s):  
Chia-Nan Wang ◽  
Ching-Yu Yang ◽  
Hung-Chun Cheng
Keyword(s):  
Decision Making ◽  
Production Process ◽  
Supplier Selection ◽  
Triple Bottom Line ◽  
Multicriteria Decision Making ◽  
Garment Industry ◽  
Supplier Evaluation ◽  
Bottom Line ◽  
Multicriteria Decision ◽  
Decision Making Unit

Vietnam’s garment industry is facing many challenges, including domestic competition and the global market. The free trade agreement, which Vietnam signed, includes environmental barriers, sustainable development, and green development. The agreement further requires businesses to make efforts to improve not only product quality but also the production process. In cases when enterprises cause environmental pollution in the production process and do not apply solutions to reduce waste, save energy, and natural resources, there is a risk of no longer receiving orders or orders being rejected, especially orders from the world’s major branded garment companies. In this research, the authors propose a multicriteria decision-making model (MCDM) for optimizing the supplier evaluation and selection process for the garment industry using sustainability considerations. In the first stage of this research, all criteria affecting supplier selection are determined by a triple bottom line (TBL) model (economic, environmental, and social aspects) and literature reviews; in addition, the fuzzy analytic hierarchy process (FAHP) method was utilized to identify the weight of all criteria in the second stage. The technique for order preference by similarity to an ideal solution (TOPSIS) is a multicriteria decision analysis method, which is used for ranking potential suppliers in the final stage. As a result, decision-making unit 10 (DMU/10) is found to be the best supplier for the garment industry. The contribution of this research includes modeling the supplier selection decision problem based on the TBL concept. The proposed model also addresses different complex problems in supplier selection, is a flexible design model for considering the evaluation criteria, and is applicable to supplier selection in other industries.

DECISION TYPE—A KEY TO REALISING THE POTENTIAL OF DECISION MAKING UNDER UNCERTAINTY

2007 ◽  
Vol 47 (1) ◽  
pp. 309 ◽  
Author(s):  
S.I. Mackie ◽  
S.H. Begg ◽  
C. Smith ◽  
M.B. Welsh
Keyword(s):  
Decision Making ◽  
Real World ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Decision Making Under Uncertainty ◽  
Gas Industry ◽  
The Past ◽  
Decision Outcomes ◽  
Decision Type ◽  
Definition Of

Business underperformance in the upstream oil and gas industry, and the failure of many decisions to return expected results, has led to a growing interest over the past few years in understanding the impacts of decisionmaking tools and processes and their relationship to decision outcomes. A primary observation is that different decision types require different decision-making approaches to achieve optimal outcomes.Optimal decision making relies on understanding the types of decisions being made and tailoring the type of decision with the appropriate tools and processes. Yet the industry lacks both a definition of decision types and any guidelines as to what tools and processes should be used for what decisions types. We argue that maximising the chances of a good outcome in real-world decisions requires the implementation of such tailoring.

Decision Making under Risk and Uncertainty in the Oil and Gas Industry

2017 ◽  
pp. 181-207
Author(s):  
Christopher Boachie
Keyword(s):  
Decision Making ◽  
Oil And Gas ◽  
Focal Point ◽  
Oil And Gas Industry ◽  
Energy System ◽  
Risk And Uncertainty ◽  
Decision Making Under Risk ◽  
Gas Industry ◽  
Decisions Under Risk ◽  
Oil And Gas Companies

The energy system studies include a wide range of issues from short term to long term horizons. The decision making chain is fed by input parameters which are usually subject to uncertainties. The art of dealing with uncertainties has been developed in various directions and has recently become a focal point of interest. Decision making is certainly the most important task of Oil and Gas managers and it is often a very difficult one. The purpose of this chapter is to review and investigate the decision making processes under risk and uncertainty of Oil and Gas companies. Questionnaires were distributed to eight Oil and Gas companies in Ghana to solicit their view on decision making under risk and uncertainty. Results indicate that most managers use Maximax, Minimax Regret and Expected Value when making decisions under risk and uncertainty.

Human Factors in Domain Adaptation Within the Oil and Gas Industry

2021 ◽  
Author(s):  
Iraj Ershaghi ◽  
Milad A. Ershaghi ◽  
Fatimah Al-Ruwai
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Oil And Gas ◽  
Deep Understanding ◽  
Digital Technologies ◽  
Oil And Gas Industry ◽  
Petroleum Engineering ◽  
Gas Industry ◽  
Starting Point ◽  
Domain Expertise

Abstract A serious issue facing many oil and gas companies is the uneasiness among the traditional engineering talents to learn and adapt to the changes brought about by digital transformation. The transformation has been expected as the human being is limited in analyzing problems that are multidimensional and there are difficulties in doing analysis on a large scale. But many companies face human factor issues in preparing the traditional staff to realize the potential of adaptation of AI (Artificial Intelligence) based decision making. As decision-making in oil and gas industry is growing in complexity, acceptance of digital based solutions remains low. One reason can be the lack of adequate interpretability. The data scientist and the end-users should be able to assure that the prediction is based on correct set of assumptions and conform to accepted domain expertise knowledge. A proper set of questions to the experts can include inquiries such as where the information comes from, why certain information is pertinent, what is the relationship of components and also would several experts agree on such an assignment. Among many, one of the main concerns is the trustworthiness of applying AI technologies There are limitations of current continuing education approaches, and we suggest improvements that can help in such transformation. It takes an intersection of human judgment and the power of computer technology to make a step-change in accepting predictions by (ML) machine learning. A deep understanding of the problem, coupled with an awareness of the key data, is always the starting point. The best solution strategy in petroleum engineering adaptation of digital technologies requires effective participation of the domain experts in algorithmic-based preprocessing of data. Application of various digital solutions and technologies can then be tested to select the best solution strategies. For illustration purposes, we examine a few examples where digital technologies have significant potentials. Yet in all, domain expertise and data preprocessing are essential for quality control purposes

Cost Element Modelling, Prediction and Optimization in a Dual-Completion Well During a Coiled Tubing Unloading Operation

2021 ◽  
Author(s):  
Merit P. Ekeregbe
Keyword(s):  
Mathematical Model ◽  
Decision Making ◽  
Cost Estimation ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Correct Prediction ◽  
Reservoir Pressure ◽  
Gas Industry ◽  
Coiled Tubing ◽  
The Mathematical Model

Abstract In an era where cost is a significant component of decision making, every possibility of reducing operational cost in the Oil and Gas industry is a welcome development. The volatile nature of the Oil market creates uncertainty in the industry. One way to manage this uncertainty is by the ability to predict and optimize our operations to reduce all of our cost elements. When cost is planned and predicted as accurately as possible, the operation optimizations can be managed efficiently. Practically, all new drills require CT unloading of the completion or kill fluids to allow the natural flow of the wells. Hitherto, there is no mathematical model that combines information from one of the wells in an unloading dual completion project that can be used to aid decision-making in the other well for the same unloading project and thereby result in an effective cost-saving. Deploying the mathematical model of cost element prediction and optimization can minimize operational unloading costs. The two strings of the dual completion flow from different reservoirs. Still, the link between the two drainages post completion is the kill fluid density, and can aid in cost estimation for optimum benefit. The lesson learned or data acquired from the lifting of the slave reservoir string can be optimized to effectively and efficiently lift the master reservoir string. The decision of first unloading the slave reservoir string is critical for correct prediction and optimization of the ultimate cost. The mathematical model was able to predict the consumable cost elements such as the gallon of nitrogen and time that may be spent on the long string from the correlative analysis of the short string. The more energy is required for unloading the short string and it is the more critical well than the long string because it is the slave string since no consideration as such is given to it when beneficiating the kill fluid to target the long string reservoir pressure with a certain safety overbalance. The rule for the mud weight or the weight of the kill fluid is the highest depth with highest reservoir pressure which is the sand on the long string. With the data from the short string and upper sand reservoir, the lift depth and unloading operation can be optimized to save cost. The short string will incur the higher cost and as such should be lifted last and the optimization can be done with the factor of the LS.

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