Is your production schedule still built manually by a planner who knows the floor well but cannot optimise across 50 variables simultaneously?
Are you discovering bottlenecks at the end of the shift in the production report rather than in real time when you can still act?
Is your OEE improving or is the same constraint limiting throughput month after month?
AI for Production Optimisation
Production scheduling, bottleneck detection, and throughput improvement driven by AI -- not the intuition of your most experienced scheduler or the constraint of your slowest machine.
From demand-driven scheduling that minimises changeover time to real-time constraint identification that tells you where the line is losing output before it shows up in end-of-shift reports.
AI production scheduling that minimises changeover time and maximises equipment utilisation across your full schedule horizon
Real-time bottleneck identification from production data with the constraint surfaced before the shift ends
OEE analytics showing availability, performance, and quality losses by machine and shift
Demand-aligned production plans that connect sales forecast to production sequence automatically
RaftLabs builds AI production optimisation systems for manufacturing operations including AI-driven production scheduling that minimises changeover and maximises equipment utilisation, real-time bottleneck detection from production data, throughput optimisation recommendations, demand-driven production planning aligned to sales forecasts, energy consumption optimisation during production, and OEE (Overall Equipment Effectiveness) analytics. Production optimisation AI projects typically deliver in 10-14 weeks at a fixed cost.
10-20%Throughput improvement (typical range)
·20+Manufacturing AI systems built
·24+Industries served
·FixedCost delivery
The constraint your production system works around is not always visible
Most manufacturing operations have a primary throughput constraint -- the bottleneck that limits output for the whole line. It might be a machine with a slower cycle time, a changeover that takes longer than it should, a quality check that creates a queue, or a scheduling sequence that forces unnecessary idle time elsewhere.
Manual scheduling cannot optimise across more than a handful of variables simultaneously. An experienced scheduler optimises by heuristic and learns the floor's patterns over years. AI scheduling optimises across all constraints simultaneously and reruns when conditions change.
The throughput that is being lost is not from lack of capacity. It is from suboptimal use of the capacity you already have.
What we build
AI production scheduling
Optimised production scheduling using constraint-based AI that minimises total changeover time, respects machine capacity and maintenance windows, sequences jobs to reduce setup transitions between product families, and balances workload across parallel resources. Schedule horizon from shift-level to weekly planning. Reoptimisation when unplanned downtime, urgent orders, or material shortages require replanning -- minutes to a new schedule, not hours. Integration with your MES or ERP for order data input and schedule publication. The scheduling capability that your most experienced planner cannot match because the variable count exceeds human optimisation capacity.
Bottleneck detection and constraint analysis
Real-time bottleneck identification from your production data: machine cycle times, queue depths between workstations, WIP accumulation points, and throughput variance by station. Constraint visualisation that shows where the line is losing output and by how much. Time-series analysis of constraint location -- bottlenecks shift as demand mix and product sequence change. Identification of whether the constraint is a rate constraint (the machine is running at its maximum speed), a quality constraint (defect rework is absorbing capacity), or a scheduling constraint (the sequence is creating idle time at downstream stations). The specificity that makes corrective action targeted rather than general.
OEE analytics
Overall Equipment Effectiveness dashboards calculated from your production data. Availability losses: planned downtime (maintenance, changeovers) and unplanned downtime by machine and cause code. Performance losses: speed losses and minor stoppages that reduce throughput below theoretical maximum. Quality losses: defect-related production time and rework volume. OEE by machine, line, shift, and time period. Trend analysis showing improvement or degradation over time. Pareto of loss categories to direct improvement investment to the highest-impact opportunity. The visibility into where capacity is being lost that a production log cannot provide without significant data processing work.
Demand-driven production planning
Production planning aligned to sales forecast and actual demand signals rather than fixed production programmes. Integration with your demand forecast from your ERP or sales planning system. Demand-driven sequence planning that builds ahead of forecast demand for high-volume SKUs while minimising overproduction of slow-moving variants. Safety stock modelling by SKU based on demand variability and production lead time. Production plan adjustments triggered by demand signal updates -- the connection between what sales expects and what the floor produces that manual planning takes days to update when demand changes.
Energy optimisation
Production sequence optimisation that reduces energy consumption by scheduling high-energy-draw processes during off-peak tariff periods and batching machine startups to reduce peak demand charges. Equipment energy consumption monitoring by production run and product type. Energy cost per unit produced by SKU and line. Identification of equipment with degraded energy efficiency suggesting maintenance intervention. For manufacturing operations with significant energy costs, scheduling optimisation for energy can recover 5-15% of energy spend without any capital investment.
Production analytics dashboard
Real-time production dashboards for floor supervisors and operations leadership. Current shift output versus target. Machine status across the line. Queue depth between stations. Hourly production rate trend. Defect rate from inspection integration. Current OEE versus target. Shift end projections: if current rate continues, what will end-of-shift output be? Alert triggers when production rate drops below defined threshold. The operational visibility that moves supervisors from discovering problems in end-of-shift reports to acting on them during the shift.
Frequently asked questions
Throughput improvement from AI optimisation depends on how suboptimal your current scheduling is and where the primary constraint sits. For facilities with manual scheduling and significant changeover time, schedule optimisation alone typically delivers 5-15% throughput improvement by reducing changeover waste and removing sequencing inefficiencies. Constraint elimination (improving the bottleneck capacity or rate) can deliver larger gains but requires capital investment or process change beyond software. For facilities already using scheduling software, the improvement from AI scheduling is smaller -- the gap is in dynamic rescheduling when conditions change during the shift. We model expected improvement during scoping based on your current OEE, scheduling process, and constraint profile.
Production scheduling AI requires order data (what needs to be produced, in what quantity, by when), machine capability data (what each machine can produce, at what rate, with what changeover times between product families), and real-time production status (current WIP, machine availability, maintenance schedules). For OEE analytics, we need machine stop reason data -- either from MES event logs, SCADA systems, or manual downtime entry. For bottleneck detection, we need cycle time data by station or throughput counts by workstation. Most of this data exists in MES or ERP systems. The assessment phase maps your available data to the system requirements before design begins.
Unplanned disruptions -- machine breakdowns, material shortages, urgent priority orders -- are where AI scheduling provides the most value over static plans. When a disruption occurs, the optimiser reruns with the updated constraints and produces a revised schedule in minutes. The revised schedule accounts for: remaining production requirements, current machine availability, jobs that are mid-run and must complete before changeover, and any new priorities introduced by the disruption. The planner reviews the revised schedule, accepts or adjusts it, and publishes it to the floor. The replanning that currently takes a planner an hour (or three hours for a complex disruption) takes a few minutes. The production floor has a new plan to work from rather than operating on outdated instructions while the planner reconstructs the schedule manually.
Yes. Production optimisation systems are built to integrate with existing MES and ERP platforms rather than replace them. Order data comes from your ERP. Machine capacity and changeover matrices are configured in the optimisation system and updated from your MES. The optimised schedule is published back to your MES or ERP for floor execution. We integrate with SAP PP, Oracle Manufacturing, Epicor, SYSPRO, Infor, and custom MES platforms. Integration scope and data mapping are assessed during discovery. Known integration constraints are surfaced before development starts, not discovered mid-project.
