Using Machine Learning for Enhancing the Understanding of Bullwhip Effect in the Oil and Gas Industry

Sousa, Ana M.; Ribeiro, Tiago; Relvas, Susana; Barbosa‐Póvoa, Ana Paula

doi:10.3390/make1030057

Cited by 14 publications

(7 citation statements)

References 127 publications

(152 reference statements)

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“…This article explores the use of ML in demand forecasting, its benefits, and provides case studies of successful implementations (Ahmad, et. al., 2022, Sousa, et. al., 2019, Ezeigweneme et al, 2023, Trevathan, 2020.…”

Section: Machine Learning In Demand Forecastingmentioning

confidence: 99%

Machine Learning's Influence on Supply Chain and Logistics Optimization in the Oil and Gas Sector: A Comprehensive Analysis

Agnes Clare Odimarha,

Sodrudeen Abolore Ayodeji,

Emmanuel Adeyemi Abaku

2024

Comput. sci. IT res. j.

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Machine Learning (ML) is revolutionizing supply chain and logistics optimization in the oil and gas sector. This comprehensive analysis explores how ML algorithms are reshaping traditional practices, leading to more efficient operations and cost savings. ML enables predictive analytics, demand forecasting, route optimization, and inventory management, improving overall supply chain performance. Supply chain and logistics in the oil and gas sector are inherently complex, involving numerous interconnected processes and stakeholders. ML algorithms are adept at handling this complexity by analyzing vast amounts of data to identify patterns and optimize operations. By leveraging historical data, ML can predict future demand, enabling companies to adjust their inventory levels and production schedules accordingly. ML algorithms also play a crucial role in route optimization, helping companies minimize transportation costs and reduce carbon emissions. By analyzing factors such as traffic patterns, weather conditions, and road conditions, ML algorithms can determine the most efficient routes for transporting goods and equipment. Furthermore, ML enables predictive maintenance, which is essential in the oil and gas sector to prevent equipment failures and downtime. By analyzing sensor data from equipment, ML algorithms can predict when maintenance is required, allowing companies to schedule maintenance proactively and avoid costly disruptions. In conclusion, ML is transforming supply chain and logistics optimization in the oil and gas sector by enabling predictive analytics, demand forecasting, route optimization, and predictive maintenance. By leveraging the power of ML, companies in the oil and gas sector can improve operational efficiency, reduce costs, and enhance overall supply chain performance. Keywords: Machine’s Learning, Supply Chain, Logistics, Optimization, Oil and Gas.

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Section: Machine Learning In Demand Forecastingmentioning

confidence: 99%

Machine Learning's Influence on Supply Chain and Logistics Optimization in the Oil and Gas Sector: A Comprehensive Analysis

Agnes Clare Odimarha,

Sodrudeen Abolore Ayodeji,

Emmanuel Adeyemi Abaku

2024

Comput. sci. IT res. j.

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“…The objectives are developing a classification model for HF features and forecasting the outcomes. This research was organized into five portions using the CRISP-DM frameworks [46][47][48][49][50] described. Process models are independent of the industry in which they are used, and the methodology implemented [51][52][53][54][55].…”

Section: The Cross-industry Standardized Procedures For Data Mining F...mentioning

confidence: 99%

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach

Nallathambi

Savaram²,

Sengan

et al. 2023

Sensors

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In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study’s objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions.

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“…The efficiency of the inventory decision-making process contributes significantly to the bullwhip effect. The bullwhip describes the consequences of minor interruptions within the demand side of the SC, escalating into significant disruptions, such as amplified demand creating variability in replenishment orders moving up the SC [16,17].…”

Section: Big Data Analytics Within Supply Chain Managementmentioning

confidence: 99%

A Quantitative Analysis of Big Data Analytics Capabilities and Supply Chain Management

Zitianellis

2023

Artificial Intelligence

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With the emergence of Big Data Technologies (BDT) and the growing application of Big Data Analytics (BDA), Supply Chain Management (SCM) researchers increasingly utilize BDA due to the opportunities from BDT and BDA present. Supply Chain (SC) data is inherently complex and results in an environment with high uncertainty, which presents a real challenge for SC decision-makers. This research study aimed to investigate and illustrate the application of BDA within the existing decision-making process. BDT allowed for the extraction and processing of SC data. BDA aided further understanding of SC inefficiencies and delivered valuable, actionable insights by validating the existence of the SC bullwhip phenomenon and its contributing factors. Furthermore, BDA enabled the pragmatic evaluation of linear and nonlinear regression SC relationships by applying machine learning techniques such as Principal Component Analysis (PCA) and multivariable regression analysis. Moreover, applying more sophisticated BDA time series and forecasting techniques such as Sarimax, Tbats, and neural networks improved forecasting accuracy. Ultimately, the improved demand planning and forecast accuracy will reduce SC uncertainty and the effects of the observed SC bullwhip phenomenon, thus creating a competitive advantage for all the members within the SC value chain.

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Using Machine Learning for Enhancing the Understanding of Bullwhip Effect in the Oil and Gas Industry

Cited by 14 publications

References 127 publications

Machine Learning's Influence on Supply Chain and Logistics Optimization in the Oil and Gas Sector: A Comprehensive Analysis

Machine Learning's Influence on Supply Chain and Logistics Optimization in the Oil and Gas Sector: A Comprehensive Analysis

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach

A Quantitative Analysis of Big Data Analytics Capabilities and Supply Chain Management

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