Reimagining Energy Supply, Trading & Risk Management for Oil & Gas

Create a data-centric digital ecosystem across Supply, Trading, and Risk to unlock value by increasing agility, streamlining business processes, and enabling next-generation decision support and portfolio optimization capabilities.

Market Changes Have Accelerated the Case for Transformation

Opportunity Created by Complexity and Volatility

Global energy markets are becoming increasingly complex and experiencing unprecedented levels of volatility. Energy supply is constrained due to years of underinvestment, and its growth is being challenged by the financial impacts of the energy transition and rising inflation. Additionally, geopolitical unrest in Europe has exacerbated supply chain disruptions and reinforced the demand shift that began during the pandemic.

However, with complexity and volatility comes a unique opportunity to create exponential value by ensuring your organization has the systems and processes in place to respond with agility.

Adapting to the Evolving and Interconnected Commodity Market Landscape

The rapid evolution and increasingly interconnected nature of global energy markets has accelerated the case for transformation. To thrive during the energy transition, Supply and Trading organizations must adapt quickly and develop new capabilities and digital pipelines to capture opportunities across geographies and asset types.

External Factors Impacting Supply & Trading

Geopolitical unrest and the energy transition are shifting global energy market dynamics and the Energy Supply & Trading portfolio mix.
Hydrocarbon supply growth is challenged by rising costs, longer lead times, increasing regulation, and fiscal constraints associated with rising inflation, increasing carbon taxes, and long-term reserve devaluation.
Oil & Gas and Power & Utility markets and infrastructure are becoming increasingly interconnected, driving the need for greater transparency and risk management across commodity value chains.
New energies and new energy markets are rapidly emerging and changing the commodity trading landscape.
Emissions data is becoming increasingly intertwined with trading data, and carbon credit trading is becoming a core capability.
Energy companies are increasingly committing to net-zero emissions targets, driving the need for greater transparency, auditability, and full-cycle cost analysis.

Evolution of the Commodity Market Landscape

Upstream: Rigs, Wells, Oil, Gas & Condensate
Midstream: Oil, Natural Gas, Processing Plant, Crude Storage
Downstream: Refineries, Diesel, Plastic
End Customer: Industrial, Commercial
Biofuel, RNG, Agro, Minerals, LNG, Power Generation, Hydrogen Manufacturing, Batteries, CCS & CO2, Solar, Alternative Fuels, Ammonia, Hydrogen Fuel, Credits, Wind, Steel, EV, Green Transportation, Cement, Agro, Credits

Addressing aging systems, manual business processes, and the proliferation of data silos across Supply, Trading, and Risk to increase agility and harness the power of artificial intelligence (AI) is a critical step in capturing value during the Energy Transition.

Conducting Business Across the Front, Middle, and Back Office

Managing with Legacy Infrastructure and Manual Processes

Supply and Trading organizations rely on a suite of specialized commodity/energy trading and risk management (C/ETRM) packages, shadow systems, and extensive manual processes to manage their business.

These specialized C/ETRM packages are effective for single commodity, single market operations, but they were not designed to support cross-commodity, multi-jurisdiction trading and risk analysis. Additionally, most C/ETRM systems lack integrated deal capture, contract management, scheduling, and reporting capabilities, and have limited front-office and mid-office capabilities.

Common efforts to address C/ETRM system capability gaps have resulted in complex business architectures that are inherently inflexible, inefficient, and costly to support. The prevalence of disconnected, highly customized solutions, complex system integrations, and manual data entry and reconciliation processes in these C/ETRM-centric business architectures restrict agility, hinder innovation, and increase the level of risk associated with security breaches and human error.

Internal Challenges for Supply & Trading Organizations

Prevalence of inflexible, commodity-specific, on-premise core C/ETRM systems with overlapping functionalities causes data silos and process inconsistency.
Data silos, limited automation, and a lack of collaboration tools restrict timely, data-driven trade and risk analysis and hinder the use of artificial intelligence to unlock next-generation business capabilities.
‘System of record’ functionality of C/ETRM systems limits the ability to support front office requirements including forecasting, pricing, and negotiations.
Commodity-specific system nuances drive over-customization and complex, multi-system architectures which are costly to support, difficult to integrate, and result in latency issues.
Capability gaps, limited solution options, and the slow pace of C/ETRM modernization hampers the ability to optimize, innovate, and support the Energy Transition.
Extensive manual intervention in data aggregation, analysis, and reporting across the front, mid, and back office increases the risk of human error and security breaches and limits the time available for higher value-add activities.

Common Supply, Trading, and Risk Personas and Tools

VCO* / Front Office

Supply Chain / Marketer
Trader
Scheduler & Operator
Value Chain Optimizer

Middle Office

Trading Operations Manager
Market Risk Manager
Credit Manager
Contract Manager

Back Office

Accounting Manager
Regulatory Manager

External Interfaces: Trading Exchanges, Market Data, eConfirm, Carbon Credit Registries, Reporting, Taxes

Shared Tools: C/ETRM Packages (commodity focused, region focused, business focused, hybrid), Master Data Management (Systems of Record)

Function-Specific Tools

VCO* / Front Office:

Pipeline Bulletin Boards
ISOs / GATs / Transmission
Scheduling Tools
Facility, Terminal & Fleet Management
Forecasting
Optimization

Middle Office:

Confirmations (eConfirm)
Risk Consolidation / Reporting
Credit: Dotz / Solutions
Contract Management
Office Systems

Back Office:

ERP
Inventory Valuation
Tax Calculation & Reporting
Equity / Royalty Settlement
Recalculation Reporting
Regulatory Reporting

Corporate Functions: Accounting, Legal, Governance, Sustainability, Procurement, HR, IT, and others

Other Business Units

*Value Chain Optimization

Moving away from a C/ETRM-centric architecture and streamlining business processes across the front, mid, and back office is necessary to reduce cost, complexity, and risk and address the rapidly evolving needs of the business.

Unlocking Value by Creating a Connected Data Landscape

A digital ecosystem enables organizations to break down silos, streamline business processes, and unlock the value of data to drive agility, efficiency, and innovation across the front, middle, and back office.

Digital Ecosystem Value Drivers

Gain agility to maximize value in a rapidly evolving commodity market
Accelerate decision-making and enhance collaboration and innovation
Automate to improve efficiency and reduce risk
Optimize portfolios and achieve sustainability goals by enabling end-to-end visibility

Agility

Gain the agility to maximize value in a rapidly evolving commodity market by enabling real-time access to data, automating business processes, and supporting new business models and market opportunities.

Collaboration

Accelerate decision-making and enhance collaboration and innovation by providing a single source of truth, breaking down silos, and enabling cross-functional teams to work together more effectively.

Visibility

Optimize portfolios and achieve sustainability goals by enabling end-to-end visibility across the value chain, providing actionable insights, and supporting data-driven decision-making.

Innovation

Unlock new business capabilities and drive innovation by leveraging advanced analytics, artificial intelligence, and machine learning to support next-generation decision support and portfolio optimization.

Efficiency

Automate business processes to improve efficiency, reduce risk, and free up resources to focus on higher value-add activities.

Maximizing Value While Maintaining Critical Operations

Our approach to modernizing Supply and Trading builds upon existing capabilities to minimize disruption to the business while building a strong digital foundation to unlock value now and in the future. These objectives will be achieved by:

Leveraging existing C/ETRM systems
Embracing open, modular development
Following foundational data integrity and security principles
Enabling seamless business process automation to free up resources to focus on exceptions, validation, and analysis
Integrating comprehensive artificial intelligence capabilities to accelerate data-driven decision-making and enhance trade and risk analysis
Drastically improving the user experience to enable modern, mobile, real-time decision support and collaboration tools
Reducing integration complexity to allow the business to unlock higher-value workflows without disrupting day-to-day business
Enabling transformative capabilities and the creation of new revenue streams to accelerate value creation
Delivering continuous value by employing agile methodology

Next-Generation Supply and Trading Solution Framework

Transform Your Business with a Data-Centric Digital Ecosystem

Our vision for a data-centric Supply and Trading digital ecosystem includes three core architectural components:

Service and integration layer leveraging comprehensive AI and low-code capabilities to create next-generation business services and digital user channels.
Unified commercial analytics platform to securely bring together data and analytics capabilities across Supply, Trading, and Risk and unlock high-value, AI-enabled workflows.
Common commercial infrastructure layer with core systems to execute and record business-critical processes across Supply, Trading, and Risk.

Accelerate Value with Artificial Intelligence-Enabled Workflows

One of the most significant benefits of moving to a data-centric architecture across Supply, Trading, and Risk is the ability to unlock AI and Generative AI-enabled use cases.

Imagine traders using simple queries to analyze third-party demand forecast predictions, generate simulated forecasts, and get recommendations on hedge positions and value optimization. Demand forecasting is just one of a long list of potential use cases for real-time decision support across the front, mid, and back office.

AI and Generative AI-Enabled Use Cases Across Supply, Trading, and Risk

VCO* / Front Office

Demand forecasting
Market simulation
Price forecasting
Schedule generation
Sentiment analysis
Policy and regulatory impact
Optionality and arbitrage opportunities

Middle Office

Credit ratings and scoring
DOA & credit limits assessment
Trade reviews and comparison
Contract review and analysis
Contract generation
Risk policy violations
Bid and offers
Automated hedging

Back Office

Automated reconciliation reporting
Invoice matching
Multi-variable data quality checks
Management reporting
Financial statement generation
Regulatory reporting
Independent monitoring of changes in regulation

Supply, Trading & Risk AI + Generative AI-enabled Use Cases

Corporate Generative AI-enabled Use Cases

Connected Workforce, i.e., Employee Chat & Help Desk
Information Discovery, i.e., expedite analysis by querying disparate document types and sources such as contracts, financials, etc.
Co-pilots for M365, BI, Development, etc., i.e., prompt co-pilots to create content, visualizations, apps, etc.

*Value Chain Optimization

AI-enabled business use cases will define the next generation of Supply, Trading, and Risk operations. Organizations that embrace artificial intelligence will accelerate their ability to thrive during the Energy Transition by improving margins, dramatically enhancing productivity, and developing transformative capabilities and new revenue streams.

Embarking on the Transformation Journey

A critical first step in shifting from a C/ETRM-centric architecture to a data-centric digital ecosystem across Supply, Trading, and Risk is to define a framework for your transformation journey—one that prioritizes business outcomes and maps to a relevant value case.

Foundational activities to consider when defining the framework to move from a C/ETRM-centric architecture to a data-centric Supply and Trading ecosystem include:

Decoupling front, middle, and back-office systems
Identifying and reducing/eliminating shadow systems
Using containers to migrate from in-house storage and compute to the cloud
Federating and contextualizing data within the cloud to enable extensive analysis

Executing these steps while identifying, automating, and eliminating low-value add tasks will reduce complexity and risk, and deliver more than enough cost savings to fund the next phase of transformation: implementing platform-level AI and Generative AI, innovation, and collaboration capabilities to unlock new business use cases such as:

Creating dashboards with real-time insights based on streaming market and peer data and intraday visibility to key trading and risk, portfolio, and financial metrics
Leverage AI and Generative AI to enable trader decision support and trade recommendations
Automating trend and trade analysis, contract reviews, and terms and conditions evaluation
Developing a user-friendly, mobile, integrated trading view with co-pilot capabilities
Optimizing and automating scheduling recommendations and contract generation
Integrating open-source risk analytics
Cross-commodity, multi-jurisdiction portfolio optimization
End-to-end auditability and full-cycle cost analytics
Carbon credit marketplace development and new energies trading
Transaction management with shared ledgers
Data, intelligence, algorithms, and application monetization

Finally, your organization can start creating incremental value for the business by developing transformative capabilities and new revenue streams:

The key to success is to first unlock the value that already exists (H1) and then to build upon it to enable new capabilities (H2), and finally to create incremental value for the business through transformative capabilities and new revenue streams (H3).

Transformation Journey Framework

The transformation journey progresses from tactical to strategic, and from incremental to evolutionary, across three main phases:

H1 (Experimental):

Automate manual front/mid/back-office tasks
Decouple primary systems and reduce shadow systems
Migrate storage and compute to the cloud
Federate and contextualize data in the cloud
Real-time insights based on streaming market and peer data
AI + Gen AI-enabled trader decision support and recommendations
Automate trend and trade analysis, contract reviews, and T&Cs evaluation

H2:

Automate and optimize activities such as scheduling, contract generation, and reconciliation reporting
Real-time visibility into trading, risk, position, and P&L

H3 (Aspirational):

End-to-end auditability, full-cycle cost analytics, and carbon credit marketplace development
New revenue streams by monetizing intelligence, algorithms, apps, data, etc.
New energies trading
Mobile, integrated trading view with co-pilot capabilities
Agile proprietary algorithms and applications
Automate transaction management with shared ledgers

Target Business Outcomes:

Improve Efficiency and Increase Agility
Enhance Collaboration and Drive Innovation
Enable Advanced Trading and Risk Practices
Gain Full Cycle Visibility including Sustainability Impacts
Grow Revenue through New Business Models

Relative Impact: Low, Medium, High (increasing as you move from H1 to H3)

Business Principles

H1: Optimize the Core through Data and Operational Efficiency and Resiliency
H2: Capitalize on Investments in New Technology to Secure Strategic Advantage
H3: Partner and Co-Innovate to Change Market Position

Prioritizing where to start and how far to take your journey should be based on your organization’s unique market position, ambitions, and transformation vision.

Enabling the Future with Publicis Sapient and Microsoft

Leading Energy companies around the world are partnering with Publicis Sapient and Microsoft to drive strategic digital transformation initiatives across their business.

Examples of how Publicis Sapient and Microsoft are helping our Energy clients rethink and evolve their business models to respond to the complexities and volatility of the global energy market include:

How a Top Global Oil and Gas Company Adopted Infrastructure as a Service (IaaS) on Azure Cloud to Reduce Their C/ETRM Total Cost of Ownership (TCO)

Imperative for Change

The U.S.-based oil and gas company wanted to improve the scalability and operational efficiency of its existing C/ETRM systems and infrastructure footprint. The organization also wanted to reduce total cost of ownership and optimize hardware utilization and investment.

Transformative Solution

Publicis Sapient implemented infrastructure-as-a-service (IaaS) to host and manage its existing systems using Microsoft Azure Cloud, SQL Database, Table and Blob storage. This service aimed to optimize the provisioning and cost of applications and hardware, while increasing capacity, performance, and scalability.

Business Impact

25% reduction in TCO with license-as-a-go service software and pay-as-you-go hardware utilization models
80% decrease in time to provision new hardware while optimizing capital investment with actual utilization trends

How Chevron Unlocked Business Value by Migrating Their Supply Data Platform to Azure

Imperative for Change

Chevron manages 200+ data pipelines and ingests supply data from various internal and external sources to standardize and share it across functions responsible for managing the flow of crude oil and refined products. Chevron needed to replace their legacy on-premise supply data platform and wanted to implement a solution that would improve collaboration and decision-making, reduce upgrade and disruption costs, and give them the power to scale.

Transformative Solution

Publicis Sapient and Chevron designed and implemented a cloud-based solution in Azure, successfully converting 200+ data integration jobs in Azure Data Factory, modeling and migrating 400 data tables, storing 450 procedures and queries, and migrating a data quality engine without disrupting the business.

Business Impact

45% of queries being completed faster
Launch of self-service business intelligence for seamless data exploration and analysis
Minimized support and disruption costs
Improved ability to develop, test, and deploy changes
Improved capability to enhance and scale the platform

As noted by Troy Engstrom, Senior Manager, Digital Carbon Management at Chevron: "We can now easily deploy advanced analytics services, including AI, quickly and easily on top of our existing data assets. Integrating those on-premise would take significantly longer."

Ready to Get Started?

Contact our Energy & Commodities team at: energyandcommodities@publicissapient.com

About Publicis Sapient

Publicis Sapient is a digital business transformation partner helping established organizations get to their future, digitally-enabled state, both in the way they work and the way they serve their customers. We help unlock value through a start-up mindset and modern methods, fusing strategy, consulting and customer experience with agile engineering and problem-solving creativity. As digital pioneers with 20,000 people and 53 offices around the globe, our experience spanning technology, data sciences, consulting and customer obsession – combined with our culture of curiosity and relentlessness – enables us to accelerate our clients’ businesses through designing the products and services their customers truly value.

For more information, visit https://www.publicissapient.com

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