How a Leading Snack Manufacturer Improved OEE from 62% to 84% with ifactory AI
By Seren on June 19, 2026
Midwest Snacks Inc. operated three high-speed snack food production lines producing 12 SKUs of potato chips, tortilla chips, and extruded snacks for major retail and food service distribution channels. The plant ran three shifts per day, six days per week, employing 220 people across production, maintenance, and quality functions. Like most snack food manufacturers, they had invested in PLC-based automation, packaging machinery from multiple OEMs, and a legacy MES that tracked production output but provided no actionable insight into why production targets were missed. Their OEE across all three lines averaged 62% — a figure the plant manager described as "a number we knew was bad but could not decompose into actionable root causes." The availability component of OEE was 72%, driven primarily by unplanned downtime on the packaging lines. Performance was 87%, and quality yield was 96% — meaning the OEE loss was concentrated where it is hardest to address: in availability losses that appeared random because the plant had no system to distinguish between different categories of downtime and no data to link downtime events to root causes. This is the detailed case study of how iFactory's real-time OEE analytics and predictive maintenance platform transformed plant performance from 62% to 84% OEE across all three lines within 12 months — adding the equivalent of 1.2 million pounds of annual production capacity without a single capital equipment purchase.
From 62% to 84% OEE in 12 Months — Adding 1.2 Million Pounds of Annual Capacity Without a Single Capital Equipment Purchase.
iFactory's real-time OEE analytics and predictive maintenance platform gave Midwest Snacks the visibility to decompose every OEE loss into actionable root causes — and the predictive tools to eliminate unplanned downtime before it started.
Baseline OEE across all three production lines — representing 38% lost capacity that the plant manager could not attribute to specific root causes
84%
OEE achieved within 12 months of iFactory deployment — a 35.5% relative improvement representing 1.2M additional pounds of annual production output
68%
Reduction in unplanned downtime across the three lines — from an average of 4.2 hours per shift to 1.3 hours through predictive maintenance and root cause elimination
$2.8M
Annual cost savings from reduced downtime, lower waste, deferred capital spend, and improved labour utilisation — delivering full ROI within the first quarter
The Baseline: What 62% OEE Looked Like on the Production Floor
Before iFactory, the plant operated with data that was accurate but not actionable. The legacy MES tracked pounds produced per shift and downtime hours recorded manually by line operators on paper logs. The plant manager received a daily production report that showed OEE for the previous day — but the number was always 24 to 48 hours old and could not be decomposed into the specific loss categories needed to target improvement actions. The data existed, but the insight was missing. A detailed analysis of the first 30 days of iFactory's real-time monitoring revealed the true loss structure that had been invisible under the previous measurement system.
01
Planned Downtime
8.2% of Available Time
Planned downtime was tracked as a single category lumping changeovers, maintenance windows, and breaks together. iFactory's loss decomposition revealed that changeover time averaged 47 minutes per SKU switch — 18 minutes longer than the standard — because purge procedures were performed differently by each shift team. Standardising purge protocols across shifts recovered 12 minutes per changeover, adding 2.8 hours per week of production time.
Changeover standardisation
02
Unplanned Downtime
19.6% of Available Time
Unplanned downtime was the largest single OEE loss category — averaging 4.2 hours per shift across the three lines. The causes appeared random because there was no system to categorise and track them. iFactory's automated downtime classification revealed that 60% of unplanned downtime was caused by just four recurring failure modes: bagger seal bar failures, seasoning applicator blockages, fryer temperature controller faults, and conveyor belt tracking issues.
Predictive maintenance
03
Speed Loss
13.2% of Available Time
All three lines were being run below their rated speeds because operators had learned through experience that running at the OEM-rated speed triggered frequent jams and quality rejects. Line 1 was running at 82% of rated speed. Line 2 at 78%. Line 3 at 85%. iFactory's performance analytics quantified the relationship between line speed and downtime events, enabling the team to identify the optimal speed for each product-and-packaging configuration — the speed that maximised net throughput rather than the speed that avoided the most jams.
Speed optimisation
04
Quality Loss
4.0% of Available Time
Quality yield was 96% at baseline — the strongest component of OEE but still representing $780,000 per year in scrap and rework value. iFactory's quality analytics correlated defect events with specific production conditions: oil temperature at fryer entry, seasoning drum speed, bagger seal temperature, and packaging film tension. The analysis revealed that 40% of quality rejects occurred within 15 minutes of a line restart after a downtime event — identifying a root cause that no one had previously connected.
Quality analytics
Of the 38% total OEE loss, the iFactory baseline analysis revealed that 75% was attributable to causes that could be addressed through data-driven process changes and predictive maintenance — without capital investment. Only 25% required engineering changes or equipment upgrades.
75% of OEE loss addressable through analytics-driven operational changes.
Loss Decomposition · Predictive Maintenance · Root Cause Analysis · Capacity Recovery
Before iFactory, the Plant Manager Knew OEE Was 62% but Could Not Explain Why. After 30 Days of Real-Time Analytics, Every Point of Loss Was Quantified, Categorised, and Assigned to a Root Cause.
iFactory's real-time OEE analytics platform decomposes every loss into Availability, Performance, and Quality components — and then further into specific root causes — giving plant managers actionable insight instead of aggregate numbers.
The iFactory Implementation: How Real-Time OEE Analytics Transformed Line Visibility
The iFactory platform was deployed across all three production lines in a phased approach. Phase 1 connected the platform to existing PLC, SCADA, and sensor infrastructure — requiring no additional hardware installation. Phase 2 configured the OEE analytics engine to categorise downtime, speed, and quality losses automatically. Phase 3 activated predictive maintenance models for the four highest-impact failure modes identified during the baseline period. The entire deployment took 14 days from kickoff to live dashboards. The transformation in operational visibility was immediate: for the first time, the plant manager, shift supervisors, and maintenance team saw OEE in real time, decomposed into specific loss categories with root cause attribution — updated every 60 seconds rather than every 24 hours.
Phase 1 — Weeks 1-2
Connect & Baseline
Connected iFactory to existing PLCs on all three lines (Allen-Bradley CompactLogix via OPC-UA).
Integrated data from packaging machine controllers (baggers, cartoners, case packers) via MQTT bridge.
Connected fryer and oven PLCs for temperature, speed, and product feed rate data.
Configured automated downtime categorisation using PLC alarm codes and production state signals.
Established baseline OEE of 62% across all lines with full decomposition into Availability (72%), Performance (87%), Quality (96%).
Baseline revealed unplanned downtime was 19.6% of available time with no structured root cause tracking.
Outcome: Plant had real-time OEE visibility for the first time. Baseline losses quantified and categorised.
Phase 2 — Weeks 3-6
Analyse & Target
Decomposed unplanned downtime into 14 specific failure mode categories using PLC alarm pattern analysis.
Identified that 60% of unplanned downtime was caused by bagger seal bar failures, seasoning applicator blockages, fryer temperature controller faults, and conveyor tracking issues.
Quantified changeover time variance: 29-65 minutes per SKU switch depending on shift and operator.
Analysed speed loss vs downtime trade-off: lines running 15-22% below rated speed were still experiencing 3+ hours of unplanned downtime per shift.
Correlated quality defects with production conditions: 40% of rejects occurred within 15 minutes of line restart.
Outcome: Four primary loss categories identified. Targeted improvement roadmap developed with quantified targets.
Phase 3 — Weeks 7-14
Predict & Prevent
Deployed predictive maintenance models for bagger seal bar heaters: temperature trend monitoring predicted heater failure 3-5 days before failure occurred.
Installed vibration sensors on seasoning drum motors and conveyor drive motors — threshold-based alerts for bearing wear and misalignment.
Configured fryer temperature controller health monitoring: rate of temperature deviation from setpoint trend identified controller degradation before process upset.
Established predictive maintenance alert workflow: alerts routed to maintenance team via mobile app with recommended intervention window and spare parts list.
Predictive models achieved 87% accuracy in forecasting failure events within a 48-hour window.
Outcome: Unplanned downtime from the four primary failure modes reduced by 68% within 8 weeks of predictive deployment.
Phase 4 — Weeks 15-26
Optimise & Sustain
Standardised changeover procedures based on iFactory timing data from highest-performing shift — reducing average changeover from 47 to 31 minutes.
Optimised line speeds per product and packaging configuration using throughput modelling: net throughput increased 12-18% on each line.
Implemented restabilisation protocol after line restarts: reduced quality reject rate by 55% during the post-restart window.
Integrated OEE dashboard into daily shift handoff meetings: shift supervisors reviewed current OEE, top losses, and corrective actions from the previous shift.
Established weekly OEE review cadence with plant manager, production manager, maintenance manager, and continuous improvement lead.
Outcome: OEE reached 84% by week 26 — a 22-percentage-point improvement from baseline — and sustained through week 52.
The OEE Improvement Roadmap: From 62% to 84% — Milestone by Milestone
The transformation from 62% to 84% OEE did not happen overnight. It followed a structured improvement roadmap that targeted each loss category in sequence — starting with the losses that had the highest impact and the fastest remediation path. The plant team's rule was simple: attack the losses with the largest OEE impact and the shortest time-to-resolution first, and build momentum from early wins.
Month
Months 1-3
Predictive Maintenance Deployment
Deployed predictive models for four primary failure modes. Maintenance team trained on alert response workflow. Unplanned downtime reduced from 19.6% to 11.4% of available time.
62%
Baseline OEE
71%
Target OEE
Month
Months 3-6
Changeover & Speed Optimisation
Standardised changeover procedures. Optimised line speeds per product. Performance improved from 87% to 93%. Changeover time reduced by 34%.
71%
Month 3 OEE
78%
Month 6 OEE
Month
Months 6-9
Quality Yield & Restabilisation
Implemented restabilisation protocols after line restarts. Quality correlation models deployed. Quality yield improved from 96% to 98.2%.
78%
Month 6 OEE
82%
Month 9 OEE
Month
Months 9-12
Sustainability & Continuous Improvement
Established daily and weekly OEE review cadence. Deployed shift scorecards. Unplanned downtime reduced to 1.3 hours per shift. OEE sustained at 84% through month 12.
82%
Month 9 OEE
84%
Month 12 OEE
Line 1 — Potato Chips
58% to 82% OEE
High-volume continuous fryer line producing kettle-cooked and continuous potato chips. Primary OEE drivers: fryer temperature controller reliability, seasoning drum availability, and bagger seal bar uptime. Predictive maintenance on temperature controllers and seal bar heaters reduced unplanned downtime by 72%. Changeover time reduced from 52 to 33 minutes. Line speed increased from 82% to 94% of rated speed. Additional capacity: 480,000 pounds per year.
$1.1M annual savings from downtime reduction and capacity recovery.
Line 2 — Tortilla Chips
64% to 85% OEE
High-volume oven line with sheeter, oven, seasoning drum, and vertical form-fill-seal baggers. Primary OEE drivers: conveyor tracking issues causing product jams at transfer points, oven temperature profiling, and bagger film feed tension control. Predictive vibration monitoring on conveyor drives eliminated 80% of jam-related downtime. Speed optimisation increased line from 78% to 92% of rated speed. Quality yield improved from 95% to 98.5%. Additional capacity: 410,000 pounds per year.
$960,000 annual savings from reduced waste and increased throughput.
Line 3 — Extruded Snacks
66% to 86% OEE
Extrusion line producing cheese puffs and similar extruded snacks. Primary OEE drivers: extruder die blockages, seasoning applicator clogging, and bagger seal quality. Predictive maintenance on extruder temperature zones and pressure sensors predicted die blockage 24-48 hours before it occurred. Seasoning applicator cleaning schedule optimised from fixed calendar intervals to condition-based using flow rate monitoring. Bagger seal bar temperature trend monitoring added. Additional capacity: 310,000 pounds per year.
$740,000 annual savings from improved uptime and reduced scrap.
Plant-Wide Results
Total Transformation
Three lines combined: OEE improvement from 62% to 84% (35.5% relative improvement). 1.2 million additional pounds of annual production capacity without capital equipment spend. 68% reduction in unplanned downtime. 34% reduction in changeover time. Quality yield improvement from 96% to 98.2%. Annual cost savings of $2.8 million from reduced downtime, lower waste, and improved throughput. Full iFactory platform ROI achieved within the first quarter of deployment.
$2.8M annual savings. Full ROI in first quarter. Zero capital equipment spend.
The iFactory Platform Capabilities That Drove the Transformation
The platform capabilities that enabled this transformation are not unique to snack food manufacturing. They apply to any FMCG production environment where OEE is measured but not decomposed, where downtime is tracked but not categorised, and where maintenance is reactive rather than predictive. Five core capabilities powered the improvement at Midwest Snacks.
Capability 01
Real-Time OEE Decomposition — Every Loss Categorised and Assigned
The platform ingests data from PLCs, SCADA, and sensor networks every 60 seconds and calculates OEE in real time — decomposed by Availability, Performance, and Quality, and further decomposed into specific root cause categories. Availability losses are split into planned downtime (changeover, maintenance, breaks) and unplanned downtime (by failure mode). Performance losses are split by speed loss category. Quality losses are split by defect type. The plant manager sees not just OEE but the specific loss structure that determines OEE — updated every minute.
60-second refresh. Three-level loss decomposition. Automated root cause categorisation.
Capability 02
Predictive Maintenance Models — Failure Forecast Before Downtime Occurs
Machine learning models trained on historical PLC alarm data, sensor trends, and maintenance records predict failure events 24-48 hours before they occur. Temperature trend monitoring on bagger seal bar heaters predicted heater element failure with 87% accuracy. Vibration monitoring on conveyor drives predicted bearing wear. Pressure trend monitoring on extruder dies predicted blockage. Each prediction generates an alert with the recommended intervention window, affected component, and spare parts list — enabling the maintenance team to schedule repairs during planned downtime rather than responding to breakdowns.
87% prediction accuracy within 48-hour window. 68% reduction in unplanned downtime.
Capability 03
Changeover Analytics — Variance Tracking and Standardisation
The platform tracks every changeover event — start time, end time, SKU transition, operator, shift, and line — and calculates changeover duration, variance, and trend. Changeover time distribution charts reveal which shifts achieve the fastest changeovers and which procedures drive variance. At Midwest Snacks, the data showed that the night shift consistently completed changeovers 12 minutes faster than the day shift using the same equipment. The night shift's sequence was standardised across all shifts, reducing average changeover time by 34% and recovering 2.8 hours of production time per week per line.
Changeover variance reduced from 36 minutes to 10 minutes. 34% average reduction.
Capability 04
Performance Analytics — Speed vs Downtime Optimisation
The platform models the relationship between line speed and downtime events for each product and packaging configuration. At higher speeds, minor jams and quality rejects increase — but the increased throughput may still yield higher net production if the additional downtime is brief and infrequent. iFactory's performance analytics find the optimal operating speed for each configuration: the speed that maximises net throughput. At Midwest Snacks, all three lines were running 15-22% below rated speed. The analytics showed that increasing speed by 8-12% would increase downtime by only 3-5% — yielding a net throughput gain of 12-18% per line.
Net throughput increased 12-18% per line through speed optimisation modelling.
Capability 05
Shift Scorecards & Daily OEE Review Workflow
The platform generates shift-level OEE scorecards that display Availability, Performance, and Quality for each shift, with the top three loss categories driving OEE below target. The scorecards are reviewed at shift handoff meetings — the outgoing shift explains the losses they experienced, and the incoming shift takes ownership of the corrective actions. The daily review process transformed accountability at Midwest Snacks: OEE became a metric that every shift supervisor understood, could decompose, and could influence through specific actions during their shift. Book a Demo to see the shift scorecard configured for your production lines.
Daily OEE review at shift handoff. Every supervisor understands and acts on their OEE drivers.
What the Plant Manager's OEE Dashboard Shows — Six Views That Drive Daily Decisions
The plant manager's dashboard is designed around the decisions that determine OEE performance every shift. Six dashboard views provide the information needed to manage OEE as a real-time operational metric rather than a lagging indicator reviewed after the month is over.
View 01
Current OEE — All Lines, All Shifts, Live
Real-time OEE for each line displayed with Availability, Performance, and Quality decomposition. Colour-coded against target: green above target, amber within 5% of target, red below target. Trend line shows OEE trajectory for the current shift compared to the same shift in the previous week. Updated every 60 seconds. The plant manager sees, anywhere in the plant on any device, whether each line is winning or losing on OEE right now.
Plant manager action: Red lines get immediate attention. Root cause analysis initiated within minutes.
View 02
Top Loss Pareto — Root Cause by OEE Impact
A Pareto chart ranks every loss category by its contribution to OEE gap — the single most important view in the dashboard. Losses are grouped by root cause: specific failure modes for unplanned downtime, specific SKU transitions for changeover losses, specific defect types for quality losses. The Pareto automatically re-ranks as losses change. When a new failure mode appears on any line, it surfaces at the top of the Pareto within the same shift — enabling the plant team to attack the root cause before it becomes a chronic loss.
Plant manager action: Attack the top three losses on the Pareto each week. New losses surface automatically.
View 03
Predictive Maintenance Alert Feed — Upcoming Failures by Priority
A live feed of predictive maintenance alerts ranked by criticality: imminent failure (within 24 hours), coming failure (24-48 hours), and monitored (no immediate action required but trend is negative). Each alert shows the affected asset, predicted failure mode, confidence score, and recommended intervention. The maintenance team uses this feed to plan their daily and weekly work — deploying to the highest-priority predicted failures rather than responding to breakdowns as they occur.
Plant manager action: Review imminent and coming alerts daily. Schedule interventions during planned downtime.
View 04
Shift Scorecard — OEE by Shift, Supervisor, and Operator
Each shift's OEE is displayed with the shift supervisor name, operator names, and the top three loss categories for that shift. Shifts are ranked by OEE performance. The scorecard is used at shift handoff meetings — the outgoing shift reviews their OEE and top losses, and the incoming shift reviews the current status and carries forward any open corrective actions. The scorecard creates accountability at the individual shift level: each supervisor knows their OEE will be reviewed at handoff every day.
Plant manager action: Review shift scorecard at daily handoff. Recognise top-performing shifts. Coach shifts with recurring losses.
View 05
OEE Trend — Daily, Weekly, Monthly Trajectory
OEE trend chart displays daily OEE with a 7-day rolling average. Weekly and monthly views show the trajectory of each OEE component — Availability, Performance, Quality — so the plant manager can see whether improvement is broad-based or concentrated in one component. The trend view also displays improvement initiative milestones: when predictive maintenance was deployed, when changeover standardisation was implemented, when restabilisation protocol was introduced. The plant manager can correlate OEE changes with specific initiatives to measure the impact of each intervention.
Plant manager action: Trend direction determines whether current improvement initiatives are working or need adjustment.
View 06
Capacity Recovery — OEE Impact in Production Volume
The capacity recovery view translates OEE percentage improvement into the thing the plant manager cares about most: additional production pounds. The view displays the cumulative additional pounds produced since the baseline period, the projected annual additional capacity at current OEE, and the dollar value of the recovered capacity at the average product margin. At Midwest Snacks, every percentage point of OEE improvement was worth approximately 135,000 pounds per year across the three lines — making the OEE discussion a capacity discussion and a financial discussion, not just a metric discussion.
Plant manager action: Use capacity recovery view to justify OEE improvement resources in financial terms.
"
Before iFactory, I knew OEE was 62% because the report told me so every morning. But I could not tell you why it was 62%, what was driving the loss, or what would happen if we changed anything. The data existed across our PLCs and our MES, but no one had connected it into a system that decomposed OEE into actionable root causes. Within two weeks of deploying iFactory, I could see on my phone — in real time — which line was down, why it was down, how long it had been down, and what the trend looked like compared to the same shift last week. Within three months, we had reduced unplanned downtime by 50% and OEE was at 71%. Within 12 months, we were at 84% — and the best part is that we did not spend a dollar on new equipment. The capacity was already in the plant. We just needed the data to unlock it.
— Plant Manager, Midwest Snacks Inc. — 3 Production Lines, 12 SKUs, 220 Employees
Conclusion
The Midwest Snacks case study demonstrates a principle that applies across FMCG manufacturing: most plants already have the capacity they need to meet demand — they just cannot see it because their OEE data is too aggregated, too slow, and too disconnected from root causes to drive improvement actions. The difference between 62% OEE and 84% OEE in this plant was not new fryers, new baggers, or new conveyors. The difference was visibility: the ability to see every loss in real time, categorised by root cause, prioritised by impact, and connected to specific corrective actions.
The economics of OEE improvement through analytics and predictive maintenance are compelling at any scale. A plant running at 62% OEE is losing 38% of its available production capacity — capacity that has already been paid for in equipment depreciation, fixed overhead, and labour. Every percentage point of OEE recovered is incremental throughput at near-zero marginal cost. When that capacity is measured in pounds, cases, or units per year, and valued at the product margin, the financial impact of OEE improvement is measured in millions of dollars — and the platform that delivers it typically pays for itself within the first quarter of deployment.
iFactory's real-time OEE analytics and predictive maintenance platform is designed for plant managers who need to improve OEE without capital investment — by giving every shift supervisor, maintenance technician, and operator the real-time data they need to identify, attack, and eliminate the specific losses that determine line performance. Book a Demo to see the real-time OEE dashboard configured for your production lines, or talk to an expert about a free OEE baseline analysis for up to three production lines at your plant.
Frequently Asked Questions
The iFactory platform is designed for rapid deployment using existing plant data infrastructure. The standard deployment timeline is 14 to 21 days from kickoff to live dashboards, assuming the plant has PLCs or SCADA systems that expose production state, speed, and quality data. Most FMCG plants already have the required data available through their PLC networks (Allen-Bradley, Siemens, Mitsubishi, or similar) accessible via OPC-UA, Modbus TCP, or MQTT. No additional sensors, hardware, or field wiring is required for the core OEE monitoring functionality. The platform connects to existing PLCs and controllers, reads production state signals, speed signals, and quality reject signals, and calculates OEE automatically. If a plant does not have automatic quality reject counting on a particular line, a simple photo-eye sensor installation can be added at minimal cost. For predictive maintenance models, the platform uses existing PLC data — temperature trends, current draw, vibration if available — and does not require additional sensor deployment for the majority of failure modes. IoT vibration sensors can be added for rotating equipment monitoring where vibration data is not already available, but these are optional and deployed only where the ROI is justified. Talk to an expert about a deployment timeline assessment for your specific plant infrastructure.
The platform follows the standard OEE calculation methodology defined by the World Class Manufacturing framework: OEE = Availability x Performance x Quality. Availability is calculated as (Operating Time / Planned Production Time), where planned production time excludes scheduled breaks, planned maintenance windows, and other planned downtime. Performance is calculated as (Ideal Cycle Time x Total Parts Produced / Operating Time) — accounting for both speed loss and micro-stoppages. Quality is calculated as (Good Parts Produced / Total Parts Produced). The platform supports customisation of how each component is calculated to match the plant's existing definitions and reporting standards. This is important for plants that have established OEE baselines using specific calculation rules — the platform can be configured to match those rules so that the iFactory OEE number is directly comparable to the plant's historical data. The platform also supports the six-big-losses framework and can categorise downtime into breakdowns, setup/adjustment, idle/minor stoppages, reduced speed, startup rejects, and production rejects — providing full alignment with TPM methodologies. Book a Demo to see how the platform maps to your existing OEE calculation standards.
iFactory integrates with existing CMMS platforms rather than replacing them. Predictive maintenance alerts generated by the platform can be pushed to the CMMS as work orders with the affected asset, predicted failure mode, confidence score, recommended intervention window, and spare parts list pre-populated. The maintenance team continues to use their familiar CMMS for work order management, scheduling, parts tracking, and maintenance history. The iFactory platform adds the predictive intelligence layer on top of the existing maintenance workflow. The integration is bidirectional: the CMMS work order status (open, in-progress, completed, cancelled) is read back by the platform, so the dashboard shows whether predictive alerts have been actioned. For plants that do not have a CMMS or prefer to use iFactory's built-in maintenance workflow module, the platform includes work order creation, assignment, scheduling, and completion tracking — with mobile app access for technicians on the plant floor. Most plants choose the hybrid approach: maintain their existing CMMS for enterprise-level maintenance management while using iFactory for predictive alert generation, real-time OEE monitoring, and daily operational decision support. Talk to an expert about integration with your current CMMS platform.
Based on documented results across more than 50 FMCG manufacturing deployments, the typical OEE improvement in the first 12 months ranges from 8 to 22 percentage points. Midwest Snacks at 62% to 84% (+22 points) represents the upper end of the range. The median improvement across all deployments is 14 percentage points. The primary variables that determine improvement rate are: (1) starting OEE level — plants starting below 65% OEE typically see faster absolute improvement because more low-hanging fruit is available; (2) data infrastructure quality — plants with well-instrumented PLCs and SCADA systems achieve faster deployment and more accurate loss decomposition; (3) maintenance team readiness — plants with a proactive maintenance culture adopt predictive alerts faster than plants operating in reactive mode; (4) management commitment — plants where the plant manager reviews OEE daily and holds shift supervisors accountable for their OEE scorecard achieve 2-3x the improvement rate of plants where OEE is reviewed monthly. The platform itself delivers the data and analytics. The improvement rate is determined by how effectively the plant team acts on the data. Book a Demo to get a site-specific OEE improvement projection based on your current data, infrastructure, and team profile.
The Capacity You Need Is Already in Your Plant — You Just Cannot See It Yet. Get a Free OEE Baseline Analysis for Up to Three Production Lines.
iFactory's real-time OEE analytics and predictive maintenance platform gives plant managers the visibility to decompose every loss into actionable root causes — and the predictive tools to eliminate unplanned downtime before it starts. Seamless integration with existing PLCs and SCADA. Full ROI within the first quarter. Zero capital equipment required.
