Abstract
Several suppliers of oil and gas (O & G) equipment and services have reported the necessity of making frequent resources planning adjustments due to the variability of demand, which originates in unbalanced production levels. The occurrence of these specific problems for the suppliers and operators is often related to the bullwhip effect. For studying such a problem, a research proposal is herein presented. Studying the bullwhip effect in the O & G industry requires collecting data from different levels of the supply chain, namely: services, upstream and midstream suppliers, and downstream clients. The first phase of the proposed research consists of gathering the available production and financial data. A second phase will be the statistical treatment of the data in order to evaluate the importance of the bullwhip effect in the oil and gas industry. The third phase of the program involves applying artificial neural networks (ANN) to forecast the demand. At this stage, ANN based on different training methods will be used. Further on, the attained mathematical model will be used to simulate the effects of demand fluctuations and assess the bullwhip effect in an oil and gas supply chain.
Highlights
The behavior problems experienced by the oil and gas (O & G) suppliers find one plausible explanation on the bullwhip effect, which is a well-known distribution channel problem [1]
Is it possible to forecast the bullwhip effect using artificial neural network techniques?. Is it possible to create mathematical models for O & G supply networks’ behavior, so that possible remedial measures for the bullwhip effect can be assessed prior to testing in real conditions?. Answering these questions will allow the researcher to comprehensively characterize the effects of demand variability onto a typical oil and gas supply network, draw conclusions about the existence of the bullwhip effect upon the interest group, distinguish such effects based on the company profile of that group, and be able to model, forecast, and manipulate the network behavior, with a special focus on the equipment and service suppliers
As highlighted in the historical brief regarding the state of the art of the bullwhip effect, its study has gained an increasing relevance as supply chains have evolved to supply networks, as this has led to the processes, stakeholders, and their connections becoming much more complex and the information flow achieving unprecedented dimensions
Summary
The behavior problems experienced by the oil and gas (O & G) suppliers find one plausible explanation on the bullwhip effect, which is a well-known distribution channel problem [1]. Taking into consideration that too-simple models have been a major hindrance for past bullwhip effect studies [14], this is clearly a case for using the current artificial intelligence capabilities Cutting edge techniques, such as the recent developments in artificial neural networks will provide the means to infer the meaningful relations within an oil and gas supply network. Beyond learning from data, the use of artificial neural networks (ANN) will allow attaining mathematical solutions with a generalization capacity This is yet another very significant advantage of implementing ANN-based analysis methodologies, since the results of the proposed research program will not cease in the bullwhip effect for one specific case, but rather are expected to provide tools for the community that can be efficiently applied to similar problems. Developing industry-specific or integrating broader [15] risk analysis tools is a possible outcome for the research output
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