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SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+ SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+
Available CORE-PROD-RATE-AGT-001 AI Agent

Agentic Production Rate Agent (Performance Optimizer)

Continuously monitors production flow, identifies shifting bottlenecks, and provides explainable throughput optimisation recommendations that increase capacity utilisation — without overdriving equipment or compromising product quality.

ManufacturingMiningOil & GasEnergy & UtilitiesWater & Wastewater Production Optimisation

Target outcome · Sustainable production throughput improvement through intelligent bottleneck detection and coordinated line optimisation — reducing WIP variability, overtime costs, and missed delivery commitments.

Business problem

Manufacturing operations face constant pressure to maximise production output while maintaining product quality and equipment reliability. Production bottlenecks shift dynamically across equipment, lines, and processes — faster than static dashboards can track. Without intelligent optimisation, operators run equipment below capability to play it safe, leaving untapped productivity on the table, while efforts to increase throughput often lead to downstream quality or reliability issues that cost more than the gained output.

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Production, maintenance, quality, and energy teams frequently optimise in isolation — local gains from one team create global inefficiencies for another. Line speed increases trigger unexpected downtime or defect spikes that are difficult to anticipate without cross-system reasoning. Manufacturing systems generate rich data on throughput, cycle times, and WIP, but rarely in a form that enables actionable, system-wide optimisation decisions in real time.

What it does

The Production Rate Agent is an autonomous Decision Agent that uses Composite AI — combining process flow models, expert rules, causal reasoning, statistical process control, and machine learning — to continuously monitor production line behaviour, identify bottlenecks, and provide transparent throughput optimisation recommendations.

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It integrates with MES, SCADA, ERP, and operator dashboards and coordinates with the Equipment Performance Agent and Quality Control Agent within MAGS teams to ensure throughput gains are sustainable and do not compromise equipment health or product quality.

Current process vs. with AI Agent

TODAY · PRODUCTION OPTIMISATIONREACTIVE
×
Bottleneck identificationStatic KPI dashboards require manual interpretation; bottlenecks identified reactively after throughput loss
×
Line speed optimisationOperators apply conservative, fixed speed settings to avoid quality or equipment risk without data support
×
Cross-functional coordinationProduction, maintenance, and quality teams optimise independently, creating conflicting interventions
×
WIP and inventory managementWIP accumulates at constraint points without systematic real-time response

Outcomes and measurement

Production throughput

Baseline Capacity underutilised due to conservative fixed settings and undiagnosed bottlenecks
With agent Measurable throughput increase through systematic bottleneck resolution and optimised pacing

Overtime and unplanned shift costs

Baseline Overtime required to meet delivery commitments due to inefficient flow
With agent Reduction through improved flow efficiency and more predictable production output

WIP variability

Baseline WIP accumulation at constraint points increasing inventory cost and flow unpredictability
With agent Reduced WIP and cycle time variability through real-time load balancing recommendations

Delivery reliability

Baseline Missed delivery commitments from production shortfalls and reactive interventions
With agent Improved on-time delivery through consistent, data-driven production optimisation

*All figures are typical ranges. Achievable range depends on existing control maturity, data quality, and site-specific conditions.

Data inputs

Other

Ingests real-time and historical production data via XMPro Data Stream Designerincluding production ratescycle timescapacity utilisationWIP levelsequipment statusquality metricsshift plansdemand forecastsand ERP systems

and maintenance schedule data from MES

SCADA

SCADAPLC

*Categories only — no tag names or system-specific field references. Exact data mapping is scoped per site.

Scoping questions

Expect these questions in a first scoping conversation. They signal engineering discipline and help narrow the template to your specific site context.

  1. Which production lines and process areas are highest priority, and what data is currently available from MES and SCADA for real-time throughput visibility?
  2. What are the key equipment capacity limits and quality thresholds that must be treated as hard constraints in the optimisation logic?
  3. How are production bottlenecks currently identified, and what is the typical lag between bottleneck occurrence and operator response?
  4. What integration is available with downstream systems — ERP demand forecasts, maintenance schedules — to provide the agent with scheduling context?
  5. What autonomy level is appropriate — advisory recommendations only, or bounded autonomous adjustments to production parameters within defined safety envelopes?

Want our AI to walk you through these scoping questions?

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Get specialist advice on scoping this for your site.

Our specialists will help you understand how the Agentic Production Rate Agent (Performance Optimizer) fits your operations, what data you'd need, and what a scoping engagement typically looks like.

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