ExxonMobil is moving its entire asset base onto one global operations stack and one data model, reshaping how supply is planned, routed, and optimised across the company.
In Brief
- ExxonMobil is replacing more than ten ERP systems and 65 million lines of custom code with a single, standardised platform and data construct.
- Global operations, supply chain, and procurement are now centralised around value chains, using shared processes to drive $15 billion in structural cost savings.
- Advantaged assets such as Guyana, the Permian, LNG, and upgraded refining and chemicals hubs are being run as one integrated network on this common stack.
The Strategic Break: One Company, One Operating Stack
ExxonMobil has been explicit about its next phase of operational redesign: a single enterprise-wide process and data platform that underpins every business, geography, and function. The company is migrating from more than ten legacy ERP systems and over 65 million lines of custom SAP code to a clean S/4HANA core, with 97% fewer profit centres and 70% fewer cost centres.
This is not a back-office IT refresh. It is a structural decision to run upstream, refining, chemicals, LNG, and emerging CCS and materials businesses on one operating stack and one data model. It sits on top of a prior organisational shift that aligned units into value chains and pulled out global functions in operations, supply chain, procurement, and business solutions.
The intent is clear in the language used around the programme:
- a single data construct and nomenclature for the entire corporation
- redesigned end-to-end processes
- connected data, transactions, and decision-making
- automation and AI to plot logistics across the system, starting with marine
This is the foundation for how ExxonMobil now plans and manages its supply network, rather than an adjunct to it.
How a Single Data Model Changes Supply Decisions
In operational terms, standardising on one data model and ERP platform changes several mechanics that sit underneath supply performance:
- Master data and item definitions: Upstream wells, refinery units, chemical grades, marine voyages, and CCS contracts are defined once, in a common structure. This is a prerequisite for cross-network planning, constraint modelling, and comparative performance analysis.
- Financial and operational dimensions: Collapsing profit and cost centres reduces the number of planning dimensions. That makes it feasible to run global S&OP-style cycles at enterprise scale, rather than reconciling local views through layers of manual consolidation.
- Transaction logic and workflows: Standard workflows for procurement, maintenance, shipping, and order management replace business-specific variants. Exceptions become visible at network level, not buried in local systems.
The company has already deployed supply chain technology around the new core to automate and optimise marine logistics. AI engines can route vessels, plan port calls, and balance demurrage risk against utilisation and schedule reliability. That capability only scales if all cargoes, vessels, ports, and contracts are referenced consistently across the portfolio.
At network level, this is implemented through:
- a single source of truth for volumes, capacities, and constraints across upstream hubs (Permian, Guyana, LNG), refineries, and chemical plants
- integrated planning cycles that can trade off crude allocation, product slate, and shipping options under one set of assumptions
- central visibility of inventory positions and in-transit stock, enabling different allocation logic in tight or oversupplied markets
The result is a shift from business-led optimisation to corporate-led orchestration, with the data and process architecture to support it.
Global Operations as a Manufacturing System
The data platform sits alongside a structural consolidation of operations. ExxonMobil has created a global operations organisation to support all businesses, building on earlier central support models that had already lowered maintenance costs and improved reliability.
The new construct moves the company closer to a single manufacturing system with shared standards for reliability, safety, and cost. The evidence is visible in asset performance:
- Upstream production averaged 4.7 million barrels of oil equivalent per day in 2025, the highest in over 40 years, with unit earnings more than double 2019 levels on a constant price basis.
- Guyana’s first four FPSOs are producing roughly 100,000 barrels per day above investment basis, with the latest project, Yellowtail, starting up ahead of schedule and helping lift gross production to about 875,000 barrels per day in the fourth quarter.
- Permian production reached 1.8 million barrels of oil equivalent per day in the fourth quarter, with plans to exceed 2.5 million beyond 2030, supported by more than 40 stackable technologies and accelerated deployment of lightweight proppant.
These assets are not being run as standalone successes. The company has said that advantaged hubs like the Permian, Guyana, and LNG, which have lower cost of supply and lower emissions intensity, are expected to account for roughly 65% of total production by 2030. The global operations organisation’s role is to bring best practices across this portfolio, using the common data model to identify and replicate what works.
In practice, that requires:
- global reliability standards and KPI sets, aligned with the new ERP dimensions
- common maintenance regimes and spare strategies across refineries, chemical plants, FPSOs, and LNG trains
- shared contracting and supplier strategies, run through the central supply chain and procurement organisations
The claim that the global projects team executes three times as many mega projects as the nearest competitor, up to 20% cheaper and 20% faster than industry average, is an expression of the same system logic: a repeatable project delivery engine feeding assets into a common operating model.
Running One Stack In an Asset-heavy World
ExxonMobil’s decision to operate on one stack contrasts with a sector where many peers are still managing partial harmonisation.
Across the wider energy and industrial landscape, other operators are moving in similar directions but with narrower scope. One large service company has built a digital division whose annual recurring revenue now exceeds USD 1 billion, but it remains functionally separate from core field operations. A major power and renewables builder is partnering with a cloud provider on an AI programme for grid reliability, while still running transmission, generation, and customer businesses on a mix of legacy and modern tools.
ExxonMobil is attempting to fold upstream, refining, chemicals, projects, and corporate functions into a single process and data architecture. That is a more ambitious integration problem. It is also a harder one in a company with decades of local system build-out and highly customised ERP installations.
The structural cost savings figure of USD 15 billion, which management attributes largely to operating model changes, process standardisation, and shared services, suggests the programme has already moved beyond design into execution. The five-year average return on capital employed of 11%, two percentage points above the next closest peer, reinforces that this is not purely cosmetic.
Constraints and Friction In a Single-stack Strategy
The move to one global operations stack carries real constraints, which show up in both execution complexity and risk posture.
- Change bandwidth: Migrating from highly customised, business-owned systems to a standard platform requires absorbing significant disruption in local organisations. It competes directly with the capacity to run large capital projects, integrate acquisitions, and deliver new product technologies such as Proxxima and advanced battery materials.
- Standard versus local fit: A clean ERP core and 97% fewer profit centres favour standard processes. Some assets or jurisdictions may have regulatory, tax, or joint venture requirements that do not fit easily. Workarounds reintroduce complexity and can erode the benefits of simplification.
- Risk concentration: A single data construct and shared systems increase the blast radius of any failure, whether technical, cyber, or process design. ExxonMobil’s emphasis on clean core and easy upgrades mitigates some of that risk, but it does not remove the need for robust governance and segregation where appropriate.
There is also a strategic trade-off. A global stack makes it easier to enforce corporate allocation and cost standards. It can also slow down local experimentation and niche optimisation if not carefully governed. The company’s answer has been to redeploy people away from transactional tasks into roles that focus on where they can add unique value, such as network optimisation, contracting, and technology deployment. That shift is dependent on the quality of process and data design in the new system.
What The Operating Model Now Enables
Taken together, the organisational and systems changes mean ExxonMobil is operating a concentrated set of advantaged assets, high-graded through USD 25 billion of divestments since 2019, on a single operations and data stack governed centrally.
In supply chain terms, this enables:
- coordinated planning of crude, gas, and product flows across Permian, Guyana, LNG, and key refining and chemicals complexes
- standardised procurement and maintenance regimes that lower unit costs and support higher uptime across plants and fields
- AI-enabled optimisation of logistics, starting with marine but extensible to pipelines, rail, and inventory across the network
- faster replication of technologies such as lightweight proppant, cube development patterns, proprietary catalysts, and Proxxima-based materials across relevant assets
The model does not remove exposure to macro cycles, overcapacity in chemicals, or geopolitical risk in places such as Mozambique and Guyana. It does, however, give the company a coherent way to run its network as a single manufacturing and logistics system with shared data, processes, and governance.
For other large industrial networks, the significance lies less in the ambition of the technology and more in the choice to make one data model and one operations stack the backbone of capital deployment, portfolio design, and day-to-day execution.
