Digital Transformation Elevates Chevron Phillips Chemical Operations

Recommendation: implement an integrated, cloud-based data platform to unify asset, process, and supply data across office and site networks. This approach delivers real-time visibility, reduces waiting time during changeovers, and directly improves operational reliability and throughput across their facilities. Start with a one-site pilot and then expand to other plants to build momentum and confidence.

Early results from a controlled pilot across three lines show as much as 12% lift in OEE and a 7% reduction in energy per unit produced. This came from data-driven control loops, standardized asset models, and operational dashboards that highlight deviation before it cascades into quality issues. The источник feeds from PLCs and ERP systems enabled teams to act within minutes rather than hours, enabling us to implement fast improvements.

To scale, assign clear organizational governance across the organizational structure and data ownership. A cross-functional team led by operations and IT aligns what the business needs with what the tech can deliver. This alignment reduces friction and empowers office staff and field crews to produce faster decisions that satisfy customers. Our partner gislason helped define care points, data contracts, and implement milestones.

The approach centers on documented standard work and a practical phased plan that keeps limited scope in the initial phase while continuing expansion. In practice, teams track KPIs such as throughput, batch quality, and maintenance effectiveness to ensure the program yields tangible business value. The plan includes harvesting data from the source источник systems and turning it into prescriptive guidance for operators.

Across the enterprise, the data-driven shift reduces cycle times for upgrades and changeovers, improves safety by catching anomalies earlier, and provides a durable stream of insights for customers and suppliers. The data platform surfaces metrics at the office level and pushes alerts through mobile and desktop channels, ensuring teams respond with through continuous feedback loops rather than waiting for monthly reports.

Chevron Phillips Chemical Digital Transformation: Operational Roadmap

Start with a two-site pilot to prove value and set a scalable template. Implement data harmonization, predictive maintenance, and supply-visibility modules over 90 days. capgeminis leads the project planning and provides a developed data model, three integrated dashboards, and a shared tool through which teams access data. The team focuses on good collaboration and three fast wins: reducing unplanned downtime, improving first-pass yield, and cutting safety stock. It draws on decades of plant experience and helps employees access the needed insights and prevents overload by surfacing only the metrics. This approach also centers on cross-functional alignment. Our focus remains safety and reliability across sites. Completed milestones will include a baseline OEE, energy usage, and material waste benchmarks, plus a documented playbook for rollout.

Phase 1 delivers a unified data foundation: master data, sensor streams, and product specifications brought into a single model with limited sources. Phase 2 automates planning and execution with a standard tool, three critical use cases (batch scheduling, energy optimization, predictive maintenance). Phase 3 scales across plants, also expanding to all products and supply chains. Capgeminis supports governance, change management, and a focused training program for employees; milestones include a data model completed in 20 weeks, dashboards deployed in 12 weeks, and an automation layer covering 60% of repetitive tasks. The plan also notes a couple of cross-site pilots, three playbooks, and a cadence for risk reviews every quarter.

Measure and sustain: The operational dashboard tracks OEE, first-pass yield, inventory turns, and on-time delivery; maintain data quality with a 2-week refresh cycle. Care for frontline teams is built into dashboards and standard data views. The team ensures supply reliability by mapping critical spare parts and establishing a buffer for limited runs; plan for load-balancing of workloads during peak periods to avoid overload. This drive keeps actions aligned with what matters. Team members think in terms of root-cause data to guide improvements. The project assigns a three-person data governance group and a six-person plant-automation team; employees receive targeted training and coaching from capgeminis.

Governance and next steps: finalize the vendor-neutral data model, complete MES and ERP interfaces, and lock the change-control process. We will complete the first full-scale rollout in 24 months, with a couple of regional expansions and three new product lines integrated. The shared toolkit and process benchmarks become the standard for all teams, ensuring consistent planning, development, and execution across sites.

Real-time data pipelines from plant floor to control room

Recommendation: Start with a unified edge-to-enterprise pipeline that streams critical sensor data to the control room within 500 ms, enabling operators to act now and supporting automated control loops. This initiative, championed by gislason, aligns organizational resources with a standardized data model across plants. Leverage your expertise across process engineering, data science, and control systems to guide design choices; expand your capabilities to respond to events in real time. Build the pathway with an edge layer, a high-throughput streaming bus, and a centralized analytics tool that runs machine-learning inferences close to the source.

Define data contracts and a common data model across sites to ensure same units, timestamps, and event types; document schemas in a single repository to speed onboarding for new plants and reduce rework.
Install edge gateways on each line to pre-process and compress data, cutting your data footprint by 30-60% in pilots and reducing bandwidth costs for central processing.
Use a reliable streaming layer that preserves temporal order, targeting latency within 200-500 ms for critical signals and seconds for routine telemetry; partition data by plant and line to parallelize processing.
Route real-time signals to control-room dashboards and to the asset-management and historian systems, with separate pipelines for alerts to avoid fatigue and for predictive analytics to drive optimization.
Apply machine-learning models for anomaly detection and predictive maintenance; start with a small suite of models focused on the top 5 risk indicators and scale as you validate benefits; machine-learning makes detection faster and more accurate, improving your incident response time.
Embed governance and security into the pipeline: role-based access, encrypted data in transit, and immutable audit trails; align with organizational policies and ensure compliance for employees and contractors.
Track benefits with concrete metrics: time-to-detect events, reduced unplanned downtime, improved yield, and an increased rate of proactive interventions by operators and engineers; this work demonstrates the initiative’s impact and helps allocate resources.
Invest in skills transfer: run hands-on training with plant-floor staff, documenting best practices so employees can operate and tune pipelines; reuse playbooks across the same processes to reduce ramp time.
Design user-focused interfaces, delivering clear, actionable insights to the people who act on them; keep dashboards readable and alerts actionable to support the team doing real-time decisions.
Simplify things by consolidating telemetry into a focused set of high-priority metrics to avoid overload and improve operator response.

Phase-wise rollout plan to scale across sites:

Pilot in one plant with 1000+ sensors, measure latency and footprint, and establish a baseline for time to detection.
Refine data contracts and dashboards, then replicate the architecture with standardized templates across two additional plants.
Scale to the full enterprise footprint, consolidating data into a central lakehouse and expanding machine-learning use cases to cover additional processes.

Edge computing implementation to speed alerts and maintenance decisions

Deploy an edge gateway cluster at field sites to pre-process critical signals and trigger alerts locally within milliseconds, then forward only actionable information to central systems.

At chevron, focused analytics run on edge devices near key assets such as reactors, compressors, and pumps. They execute lightweight models that detect abnormal vibration, temperature spikes, and fluid leaks, and they issue alerts within 100–200 ms. Think of the edge as a local decision layer that operates on things from the plant floor; this setup makes alerts faster and reduces the load on core networks, which they rely on for deeper insights.

The data footprint drops in pilots by 60–75% as only anomalies travel to the cloud, helping avoid bandwidth saturation and lowering storage costs. The edge retains raw streams locally for deeper analysis when needed, while the cloud handles long-term trends and management dashboards together with local systems, providing a unified view for production teams.

Operationally, edge alerts empower technicians and managers to act quickly. By implementing focused, rule-based workflows, the manager can approve repairs while assets are offline or in protective modes. In early deployments, time-to-action declined from 2–4 hours to 30–90 minutes, depending on asset criticality, and some devices delivered even faster responses on limited hardware.

To scale from pilot to production, chevronPhillips Chemical defines focused data pipelines, metadata catalogs, and clear roles for employees and contractors. The approach provides dashboards that blend information from edge and cloud, delivering a single view for products, processes, and customer commitments to both customers and internal management, while reducing the footprint of the monitoring layer.

Implementation steps include selecting a small set of assets for a limited pilot, installing edge devices with compatible tools, codifying data policies, and training management and operators. Some key metrics: latency under 200 ms, data footprint reduction 60–75%, and MTTR improvements 40–60%. Start with 3–5 assets, then scale in waves across production lines, always aligning with your safety and reliability targets and keeping the footprint manageable while you improve things together with the corporate teams.

Digital twin models for process optimization and yield improvement

Begin with a 12-week pilot on the main process line to quantify yield gains by running real-time simulations with a digital twin.

Led by jacquie, manager, with limited resources, the team includes employees from operations, control, and reliability and will provide input from the customer side to define KPIs and acceptance criteria.

The twin ingests data from DCS, SCADA, PLCs, and the ERP system, modeling unit operations, catalytic beds, heat transfer, and mass balance closures. It uses machine-learning to capture aging effects, feed variability, and nonlinear interactions, allowing operators to run what-if scenarios without interrupting production. This approach drives improved yield, reduces waste, and supports expanded scale-up as you move from bench testing to expanded production.

That capability helps realize gains faster and fosters a creative, data-driven culture across teams, enabling them to learn and adapt while keeping customer requirements in focus. The model provides a transparent basis for decisions, so planners and operators can align on what to change, when, and why.

Continuing adoption focuses on scaling the model across beds and downstream units, while incrementally adding sensors and data sources to sharpen accuracy and reduce uncertainty. The approach permits rapid testing of feed strategies, catalyst loading, and heat-duty adjustments, with measurable impacts on throughput and product quality.

Data governance and competencies are embedded from day one: the plan includes targeted training to build machine-learning capabilities, clear roles for jacquie and other leaders, and ongoing model maintenance routines. This structure ensures the twin remains aligned with product specifications, regulatory expectations, and the needs of the customer.

Next steps emphasize providing a repeatable path to scale, integrating lessons learned from the pilot, and accelerating the transfer of the digital twin to other sites while maintaining governance and risk controls.

KPI	Baseline	Pilot	Target	Notes
Yield	92.1%	94.3%	95.5%	Assumes stable feed quality; catalytic beds optimized.
Throughput (kg/h)	1200	1260	1280	Heat and mass balance improvements.
Energy intensity (kWh/kg)	1.9	1.75	1.68	Enhanced heat integration.
OEE	78%	85%	88%	Reduced downtime via predictive maintenance.

Integrated MES, ERP, and analytics in a scalable cloud architecture

Adopt a couple of pilot integrations that connect MES and ERP with analytics on a scalable cloud platform. Establish a single источник for data truth across operations. Use a swift, API‑first approach with event‑driven data flows to provide real‑time visibility into batch and unit operations. Build an engineering‑led foundation that is reliable and easy to extend.

Choose a cloud‑native stack that interconnects MES, ERP, and analytics through a lightweight, service‑oriented layer. Leverage microservices and containers to evolve individual processes without destabilizing others. Implement a data lakehouse to unify structured production data with analytics‑ready datasets, enabling rapid modeling and crisp dashboards. Put in place clear data governance, access control, and lineage to reduce risk for leadership oversight.

For frontline staff and engineers, offer a common, role‑appropriate set of dashboards and alerts. The platform must maintain high reliability and resilience, with offline‑capable modes and automatic retry of failed data transfers. Use asynchronous pipelines to minimize latency and ensure accurate daytime decision‑making across sites.

To build capability years ahead, begin with a cross‑functional effort spanning engineering, operations, and IT. Develop a stepwise plan: foundation, integration, and optimization. In the foundation, standardize data models, establish data quality checks, and set up governance. In the integration phase, connect MES modules, ERP modules, and analytics workloads; in the optimization step, tune dashboards and deploy predictive analytics across additional sites.

Measure impact with concrete KPIs: data latency under 5 minutes, automated reconciliation time reduced by up to 40%, and dashboard uptime above 99.9%. Track data quality metrics and the share of automated workflows to show progress. This approach provides a cohesive solution that yields a consistent user experience while lowering total cost and risk through scalable governance.

Cybersecurity governance and data access controls across the stack

Implement a centralized cybersecurity governance council to provide clear data access ownership across the stack and establish least-privilege, just-in-time access, and continuous audit with quarterly reviews. There, for each project and initiative–from pods to products–the council ties governance to the engineering and operational footprint, treating security as a catalytic chemical that strengthens the technology and business outcomes, and also standardizes controls.

Enforce cross-stack access control with RBAC for individuals and ABAC for context such as project, data sensitivity, and environment. We will not rely on the same checklist; each layer uses tailored controls. Ownership ensures teams protect them. Require approvals for elevated access and automatic revocation within 24 hours of inactivity. Apply access controls at the network, compute, storage, and data layers, including API gateways, service meshes, and data catalogs.

Classify data into public, internal, confidential, and restricted. Map data flows from source to product to analytics, and link classifications to access policies in microsoft Purview to ensure consistent governance across on-premise and cloud assets.

Protect data in motion and at rest using strong encryption and key management. Use tokenization or data masking for sensitive fields in analytics, and store secrets in a centralized tool with rotation and access revocation. This is critical for engineering workloads, pipelines, and the products lifecycle. Layered controls are more robust than siloed approaches.

Improve observability with a single footprint for logs: collect identity, access, and data lineage events; feed to a SIEM; set alerts on anomalous access patterns; run quarterly incident response drills; establish a no-blame culture to learn from incidents. Policy updates occur about evolving threats twice a year.

Roadmap and metrics: launch a pilot focusing on two critical projects in Q4, scaling into three cloud environments by the next year. Target: 95% of data assets classified, 90% of access requests reviewed within 15 minutes, and 99% of secrets rotated within 30 days. Track footprint reduction, operational resilience, and the ability to realize business outcomes and deliver secure products through this initiative.