Navigating Digital Transformation in Supply Chain in Oil Industry
In this article, we delve deeper into the fundamentals of supply chain management within the oil industry. We expand our discussion to explore how global markets influence local supply chain management and provide a concise overview of the latest applications of digitalization in supply chains, including blockchain and artificial intelligence.
Introduction
The imperative of digitalization has prompted all stakeholders within local markets, including the O&G sector, to adapt swiftly. As a result, companies find themselves compelled to align their local supply chain management (SCM) practices with the requirements of the global market. This transition is essential for staying competitive in an increasingly interconnected world.
Concurrently, it's crucial to recognize that the specific format and implementation of the organization's SCM model play a pivotal role in defining the parameters of its growth and development. A well-structured SCM model provides the organization with a comprehensive view of its operations, helping identify potential bottlenecks and barriers that might otherwise limit growth opportunities. Thus, a well-managed SCM model serves as the foundation for strategic decision-making and sustainable expansion in the global business landscape.
Effect of Global Markets on Local SCM
Local supply chain management involves a set of activities aimed at achieving a company's performance goals within a specific market. On the other hand, global supply chain management is designed to enhance a company's competitiveness in the global market. It's important to note that the standards and requirements for supply chain management in local markets may differ significantly from those in the global market. As a result, businesses are often required to adapt their supply chain management practices to meet global market demands, as failing to do so could increase the risk of failure.
El Khatib et al. (2022) conducted a study to explore the impact of global supply chain management on local supply chain management. They presented various case studies from companies such as Shell, ADNOC, SLB/Cameron, and GE/Diamond Offshore. Additionally, they conducted interviews with supply chain management experts to highlight the substantial influence of global supply chain management principles on the transformation of local supply chain management processes within the Oil and Gas sector. Ultimately, their research underscored that local supply chain management should prioritize aspects related to disruptive technologies and digital transformation, including: (1) the digitalization of business operations, (2) effective knowledge management practices, (3) sustainability initiatives, (4) strategic partnership development, and (5) the utilization of innovative tools based on disruptive technologies.
Block chain in SCM
The most effective form of integration can be achieved through the use of real-time information systems, which leverage the latest technological advancements. These advancements include concepts like the Internet of Things (IoT), Robotics, Big Data, Artificial Intelligence, Information and Communication Technology (ICT), and Blockchain (Treiblmaier, 2018). However, the adoption of Blockchain technology in supply chain management is significantly constrained by global economic instability and the absence of a well-defined framework. Despite these challenges, Blockchain technology, with its capacity for secure and instantaneous information sharing, has garnered attention as a promising tool for enhancing organizational performance. It is particularly relevant for aspects such as real-time information sharing, cybersecurity, transparency, reliability, traceability, and visibility, as noted by Aslam et al. (2021).
Researchers have expressed their views on the incorporation of Blockchain into supply chains, examining how the theoretical concept of Blockchain aligns with supply chain operations and how it is poised to transform supply chain networks (Treiblmaier, 2018; Wang et al., 2019; Helo & Hao, 2019). They have emphasized that Blockchain offers significant benefits for fostering more effective inter-organizational relationships, especially in complex networks, by providing transparency, real-time information sharing, traceability, and ensuring the non-repudiation of data during the information flow. As demonstrated by Aslam et al. (2021), Blockchain emerges as a tool of paramount importance in this era due to its unique features, which include real-time information sharing, cybersecurity, transparency, reliability, traceability, and visibility, all of which contribute to enhancing supply chain performance.
Machine Learning and Optimization in SCM
Uncertainty plays a pivotal role in the decision-making process, particularly in supply chain planning, where there is a high degree of unpredictability concerning factors such as demand, costs, and lead times. Various approaches are available to address this uncertainty, including scenario-based analysis, stochastic programming, supply chain dynamics, and fuzzy decision-making techniques. In their study, Abdolazimi and Abraham (2021) applied a forward oil supply chain method for the downstream sector to minimize both shipping costs and the number of shipments under conditions of certainty and uncertainty. They employed two meta-heuristic algorithms, namely, particle swarm optimization (PSO) and multi-objective particle swarm optimization (MOPSO), along with the Mulvey approach to handle uncertainty.
Companies commonly devise and put into action supply chain strategies with the goal of minimizing expenses and increasing profitability. However, they often contend with significant financial losses and supply chain disruptions on an annual basis. Sakib et al. (2020) introduced a Bayesian Network (BN) model, which is a probabilistic graphical model frequently utilized in risk analysis. This model is employed to depict and evaluate the probabilistic associations among various variables, establishing these relationships among nodes (representing interacting variables) and edges (the connecting lines) within the network. Its primary purpose is to predict and assess potential disasters within the oil industry's supply chain based on a range of factors encompassing technical, economic, safety, and environmental considerations. The model was developed based on actual cases of supply chain disruption within the oil and gas sector and covers technical factors such as mechanical failures, scheduling issues, lead time, and delivery delays; economic factors, including fluctuations in international oil prices, shifts in consumption demand, and stock market volatility; safety-related factors like injuries, occupational hazards, and fatalities; as well as environmental factors, which involve accidental waste releases and natural disasters.
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