AWS for Industries
Top-performing supply chains: When AI meets energy industry experience
With the rapid evolution of AI and all that it can do, it is important for Energy customers and partners to be having discussions about how it can be used to transform supply chains, and slashing costs while unlocking billions in new opportunities.
Introduction
AI is emerging as a key accelerator for top line revenue growth, operational resilience, and cost optimization. McKinsey research shows companies using AI in their supply chains achieve 15% lower logistics costs, 35% reduced inventory levels, and 65% improved service levels. We will share Think Big use cases to spark meaningful discussions with senior executive decision-makers (EVP or VP-level), either in the central Supply Chain, Contracting and Procurement division, or in the business units.
Top line revenue growth
Any company aspiring for a top-performing supply chain should have a market demand signal mechanism to identify net new growth opportunities in emerging markets. The World Energy Model (WEM) of Shell plc is a proven example of such a mechanism. Shell uses WEM as a core tool in exploring the evolution of energy demand in different countries, and in different sectors under varying assumptions in policy, economy, technology, and consumer choices.
The WEM is a suite of linked Excel spreadsheets, with data handling and model runs governed by Visual Basic. The core engine comprises over 55,000 lines of code and the output engine can produce a wide variety of custom tables and graphs. It has a large repository of historical data, starting from 1960, on both energy demand and the drivers. It runs in yearly time-steps, out to the year 2100 if required.
Leading Amazon Web Services (AWS) Energy customers, with complex market demand signal models like WEM, are increasingly looking for pathways to modernize and enhance their demand sensing.
AWS can assist customers in:
- Building or modernizing market demand signal models using Amazon SageMaker to build, test, deploy, and monitor custom models and Amazon Q Developer for software development lifecycle acceleration.
- Developing artificial intelligence and machine learning (AI/ML) services to deploy and fine-tune foundation models (FMs) using the organizational data.
- Leveraging generative AI services to build AI-based interactive applications.
- Deploying multi-modal agents to combine data from multiple sources, such as supply chain operations, market indices, news, social media, policies, and so on.
A relevant use-case would be an emerging markets team (within an integrated Energy producer) using a generative AI-powered demand signal model. They could use it to proactively identify and respond to liquefied natural gas (LNG) demand in a South Asian country, due to a new government policy, to quadruple its clean energy usage. As a result, the organization inks a multi-year, multi-billion-dollar agreement in a response to a public tender to supply LNG. They can use their strategically placed floating LNG technology, making production, liquefaction and storage of natural gas possible at sea.
A forward-facing demand signal model provides the emerging markets team a way to use long-range output in combination with near real-time market data to proactively identify new markets. This automates the otherwise manual process of consolidating data manually from multiple sources to find demand signals, shortening time-to-signal by multiple folds.
Operational resilience
Nearly 80% of organizations experienced 1-10 adverse supply chain event in the last 12 months, costing $182 million in average losses to each organization. As a result, 81% of organizations plan to increase their supply chain risk and resilience investment over the next 12 months.
Increasingly, Energy customers are engaging with Amazon and AWS to build or modernize their supply chain resilience solutions. These organizations are keen to use their existing supply and demand model with new technologies. For example, the use of agent-based models (ABM) on AWS to build a new capability to run what-if interactive scenarios. Such scenarios are used to stress test value chains and evaluate mitigation plans in the face of unexpected events (such as tariff and trade variances, or geopolitical tensions, cyber-attacks, natural disasters, or commodity volatility).
Traditional supply chain risk management approaches often rely on historical data and static planning, leaving organizations ill-prepared for long tail events. Augmenting traditional supply and demand models with ABM provides a way to parameterize and run global demand-supply data against the real-world data from operations. Risk managers can recreate how products, assets, people, and information would flow through the supply chain in case of a domestic or a global event. They can then experiment with different response strategies to determine the most effective mitigation plans.
For example, this type of capability could proactively mitigate supply chain disruptions by rerouting shipments before the Israel-Iran regional war closes the Strait of Hormuz, avoiding millions in losses from over-stocking, labor shortage, and so on. ABM can transform supply chains by creating digital twins that simulate complex stakeholder interactions. ABM simulation-optimized strategies deliver 15-25% supply chain efficiency gains, cut disruption response times by 30-50%, and improve sustainability metrics by 10-20%. Through the use of ABM, executives can also test strategic decisions in a risk-free environment, predicting outcomes before implementation. This helps create a more resilient, adaptive supply networks that can respond dynamically to disruptions.
Cost optimization
Companies lose millions annually through inefficient contract management, inconsistent terms, and disconnected visibility between contracts and actual spend. Through continuous learning from human feedback and decisions, an agentic AI solution could help identify millions in value leakage or deviations. Agents can assist in tying contracts to invoices and provide a complete view of the overall supplier network actual versus planned performance based on enterprise resource planning and third-party data sets.
Amazon operates a large, sophisticated supply chain focused on fast, individual customer orders with wide product optionality. In doing so, Amazon brings a wealth of best practices and a culture of continuous innovation to supply chain strategic collaborations with AWS. Their example and expertise can significantly enhance the performance of different company’s supply chain operations.
Through such collaborations, AWS can assist Energy companies in establishing a Supply Chain Incubator with a goal of lowering operating costs (specifically, the cost for each barrel of oil equivalent (BOE)). This could help the company become one of the top performers operationally in the Exploration & Production industry. Incubators use the two-pizza team concept to ideate, incubate, build, and scale Minimal Lovable Products (MLPs) with meaningful outcomes.
For example, an incubator can be used to deliver an Amazon Bedrock AgentCore-based track and trace MLP to identify and monitor individual items from their origin to their final destination, and trace them backward. It could then be scaled across the company to reduce global spare parts inventory and redundant stock, leading potentially to hundreds of millions in annual savings. The purpose of an incubator or a similar mechanism is to embed innovation deeply into the organization’s supply chain DNA.
Conclusion
Supply chain excellence is no longer optional—it’s a strategic imperative for sustainable growth and competitive advantage. By leveraging the operational expertise of Amazon and the cutting-edge AI capabilities of AWS, companies can transform their supply chains. They can identify new revenue opportunities through sophisticated market demand modeling, build operational resilience through advanced risk simulation, and optimize costs through innovative incubation programs.
To explore more about the AWS Supply Chain, contact a Representative for a discovery deep dive and demonstration. To learn more about how AWS is helping its Energy and Utilities customers and partners innovate, please visit AWS for Energy & Utilities. To know more about how AWS approaches generative AI, please visit Transform your business with generative AI.
We invite supply chain executives to explore the art of the possible and use cases most relevant to their organization with Amazon global supply chain experts at the AWS Executive Briefing Center (EBC).