Lecture 1

Oil and gas procurement functions steward significant amounts of money each year, often overseeing purchasing for multiple business units and teams in diverse locations. Because of the complex scope and scale involved, many companies lack a macro view of their purchasing information, which is critical in understanding how to effectively manage and reduce costs to improve the organization’s bottom line.
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How data optimization can help mitigiate Supply Chain Costs & Risks

Our latest paper — How Data Optimization Can Help Mitigate Supply Chain Costs and Risks — outlines the steps you must follow to manage risk and costs simultaneously. The paper also reveals just how one major multinational transformed its data practices and became resilient in the toughest supply chain environment.

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A resilient supply chain is defined by its capacity for resistance and recovery. That means having the capability to mitigate most supply chain disruptions and greatly limit the impact of those that occur. Operational risk and interruption can threaten multiple areas of the supply chain, and ultimately business resiliency. Worldwide disasters, as we’ve seen with COVID-19, can have a far-reaching, global impact upon supply chain logistics, suppliers, and workforces. Other supply chain disruptions can come in the form of unexpected competition, sudden market trends, or even rapid changes in customer shopping behaviours.
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In the engine room of every successful business today, you'll find one common powerhouse: data. The supply chain, once driven by human intuition and manual processes, is now propelling forward into a future underpinned by data driven decisions made by figures, patterns, and insights. Picture this: A world where each product's journey— from raw materials to the hands of the end consumer—is optimized not by guesswork but by a symphony of data points. This isn't just imaginative thinking; it's the reality of a data-driven supply chain.
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Stepping Into Next-Gen Supply Chain Management with AIO

When managing supply chains, the decisions we make every day about things like promising sales orders, triggering production and placing purchase orders can make a huge difference. These supply chain decisions happen frequently and in large numbers and are essential for how well the business runs.However, the current system landscape does not support determining whether these decisions are truly beneficial or detrimental to the supply chain performance. The truth is that supply chains demand frequent and numerous decisions. These decisions influence outcome analysis and necessitate a manual evaluation process that consumes substantial time and effort.
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Data–Driven Techniques in Logistics & Supply Chain Management: A Literature Review

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Contemporary firms rely heavily on the effectiveness of their supply chain management. Modern supply chains are complicated and unpredictable, and traditional methods frequently find it difficult to adjust to these factors. Increasing supply chain efficiency through improved supplier performance, demand prediction, inventory optimisation, and streamlined logistics processes may be achieved by utilising sophisticated data analytics and machine learning approaches. Inorder to improve supply chain management efficiency, this study suggests a unique data-driven strategy that makes use of Deep Q-Learning (DQL). The goal is to create optimisation frameworks and prediction models that can support well-informed decision - making and supply chain operational excellence.
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The explosive impact of e-commerce on traditional brick and mortar retailers is just one notable example of the data-driven revolution that is sweeping many industries and business functions today. Few companies, however, have been able to apply to the same degree the "big analytics" techniques that could transform the way they define and manage their supply chains.

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