NeoBramPlan an AI project
    Industrial AI solution

    AI Turnaround Planning For Refineries & Petrochemicals

    Cut shutdown duration, optimize scope, and de-risk contractor coordination using AI on your Primavera P6, SAP PM and inspection data.

    Plan the use case Explore ROI tools
    AI turnaround planning at an oil and gas refinery

    Acceptance before scale

    Baseline, representative test set, failure conditions and a named human owner are defined before production approval.

    Direct answer

    NeoBram helps industrial teams evaluate and build ai turnaround planning for a defined workflow. The engagement starts with the operating decision, representative data, integration boundary, human owner and acceptance test. Deployment can be designed for offline, on-premises, edge or private-cloud operation when the selected components and licences support it.

    Private deployment available

    Why evaluate it

    Every Day of Shutdown Costs Millions.

    The decision is whether this capability improves a defined workflow safely not whether an AI demo looks impressive.

    AI changes the planning equation: scope optimization models rank work orders by risk and ROI, schedule risk AI flags slip-prone paths weeks ahead, and contractor coordination agents catch resource conflicts before they bite.

    We deploy turnaround AI that plugs into Primavera P6, SAP PM and your inspection history - not a separate tool. Planners keep their workflow; the AI runs in the background and surfaces risks they would otherwise miss.

    Uncited universal benchmarks from the legacy page were withheld. Define the baseline and acceptance threshold from customer evidence or a reviewable primary source.

    Candidate capabilities

    What a production solution may need to do.

    Each capability is validated against representative data and the customer's workflow. Product or model names describe possible components, not partnerships, certifications or guaranteed compatibility.

    Scope optimization

    AI ranks every work order by risk-of-not-doing, ROI and dependency, helping STO leads cut non-essential scope without compromising integrity.

    • Risk-based inspection scoring per equipment item
    • Work-order clustering to batch similar tasks
    • Defer / accelerate recommendations with justification
    • What-if scope scenarios with cost and duration impact

    Schedule risk prediction

    A candidate capability to test against a documented baseline, representative scenarios and agreed human-review workflow.

    • Per-activity slip probability with confidence
    • Critical-path risk heatmap updated daily
    • Monte Carlo end-date distribution
    • Suggested float buffers for high-risk chains

    Contractor & resource coordination

    Detects double-booked crews, missing permits, late material deliveries and crane conflicts before they hit the schedule.

    • Crew-availability conflict detection across contractors
    • Permit-to-work bottleneck forecasting
    • Material readiness tracking against schedule
    • Long-lead item alerts with escalation paths

    Lessons-learned capture & reuse

    Each turnaround feeds a RAG knowledge base so next-cycle planners get instant access to what worked, what failed and why - from prior STOs at this and other sites.

    • Auto-extracted lessons from STO closeout reports
    • Searchable by equipment, contractor, failure mode
    • Recommended risk register entries for new STO
    • Cross-site benchmarking on duration and cost

    Architecture context

    Select components after the boundary and test are clear.

    The list is a design vocabulary. Final selection depends on licences, data location, latency, security, existing systems and customer approval.

    Planning & scheduling

    • Oracle Primavera P6

      Schedule integration

    • SAP PM / S/4HANA

      Work order source

    • Microsoft Project

      Alternative scheduler

    ML & analytics

    • XGBoost / LightGBM

      Slip-risk prediction

    • PyTorch

      Schedule embedding models

    • Monte Carlo simulation

      Duration distribution

    Inspection data

    • Meridium / GE APM

      Risk-based inspection

    • OSIsoft PI

      Operating history

    • RBI databases

      Equipment condition

    Integration

    • Power BI / Tableau

      Planner dashboards

    • REST APIs

      Bidirectional sync

    • SharePoint

      Document control

    Planning ranges

    A standard path from decision to operation.

    01

    Discovery and qualification

    1-2 weeks

    Named workflow, owner, baseline, risks and go/no-go questions.

    02

    Readiness assessment

    2-4 weeks

    Data, integration, security, value and operating-readiness findings.

    03

    Technical proof of value

    4-6 weeks

    A bounded test on representative data with documented limitations.

    04

    Production pilot

    8-12 weeks

    One controlled workflow, integrated and evaluated with real users.

    05

    Enterprise or multi-site rollout

    3-6+ months

    Phased scale-out, monitoring, support and change management.

    06

    AI capability or CoE programme

    3-6+ months

    Governance, delivery methods, reusable assets and team enablement.

    These are planning ranges, not guarantees. Readiness, validation, hardware, integration, access and change management affect the schedule.

    Production safeguards

    Private deployment is one control, not the whole control system.

    Data boundary

    • Select offline, edge, on-premises or private-cloud deployment from the real operating constraints.
    • Document data flows, storage, deletion, backups and support access before production.
    • The customer approves every interface and any permitted external connection.

    Model and application controls

    • Evaluate representative cases, uncertainty and harmful failure modes before use.
    • Use suitable access control, input handling and output guardrails for the selected risk.
    • Treat grounding and citations as testable behaviours, not as a promise of perfect answers.

    Traceability and operation

    • Define identity, roles, logs, monitoring, updates, backup and incident handling.
    • Keep the evidence needed to investigate outputs and reproduce important decisions.
    • Assign a named business and technical owner for production operation.

    Human authority

    • Domain, quality, safety and regulatory owners retain decision authority.
    • Compliance depends on the implemented system and the customer's validated controls.
    • Escalation and safe fallback are part of the acceptance criteria.

    Limitations to plan for

    Performance can change with data quality, equipment, process, product mix, documents, users or operating conditions. Third-party model and software licences still apply. AI output does not replace the responsible engineer, operator, quality owner, safety professional, legal adviser or regulator.

    Buyer questions

    Direct answers before you plan a pilot.

    How much can AI turnaround planning cut shutdown duration?+
    This legacy draft included a quantitative benchmark that has not been published with a reviewable source or customer evidence. NeoBram now treats it as an open validation question and defines the baseline, test method, acceptance threshold and limitations during discovery.
    Does it replace our Primavera P6 scheduler?+
    No. AI sits alongside P6 and SAP PM. Planners continue working in their tools. The AI ingests the schedule, runs risk models, and writes recommendations back as flagged activities, risk register entries or what-if scenarios. No workflow disruption.
    How much historical turnaround data is needed?+
    Three to five past turnaround cycles deliver strong slip-prediction models. With less history, we start with scope optimization (which uses RBI and work-order data) and add slip prediction once we have one full cycle of NeoBram-tracked data.
    Can it handle simultaneous operations and multiple contractors?+
    Yes. The coordination layer is designed for typical refinery STO complexity: 20-80 contractors, 5,000-20,000 work orders, hundreds of permits and lifts per day. Conflict detection runs on full schedule updates daily or hourly.
    What's a typical engagement timeline?+
    A focused production pilot is commonly planned over 8 12 weeks after scope, representative data, owners and acceptance tests are ready. Discovery or a technical proof of value may be shorter; integration, validation, hardware, site access and change management can extend the plan. This is a planning range, not a delivery guarantee.
    Does our turnaround data leave our network?+
    No, unless you choose a managed cloud option. Default deployment is in your VPC or on-prem. Inspection records, contractor rates and schedule data stay in your environment with role-based access.

    Bring one workflow

    Define the evidence, boundary and acceptance test together.

    Your team supplies process authority. NeoBram supplies AI architecture, engineering, evaluation and operating handover.

    Plan the first project