Production & Operations Analytics

OEE is the single most important metric for measuring manufacturing productivity. It combines three factors — Availability (was the machine running?), Performance (was it running at full speed?), and Quality (did it produce good parts?) — into one percentage that tells you how much of your theoretical capacity you are actually converting into sellable output.

Formula: OEE = Availability × Performance × Quality

A world-class OEE benchmark is 85%. Most Indian manufacturing plants operate between 55–70% OEE — which means 30–45% of production capacity is lost to downtime, speed losses, and quality rejections every single day.

What FireAI tracks:

• Real-time OEE by machine, line, and plant — updated every shift, not every month
• Availability loss breakdown: planned maintenance vs unplanned breakdowns vs changeover time
• Performance loss: actual cycle time vs ideal cycle time, micro-stoppages, and speed reductions
• Quality loss: first-pass yield, rework rate, and scrap rate by product and machine
• OEE trend over 30/60/90 days with automatic anomaly flagging when any component drops below threshold

Why it matters for Indian manufacturers: A Pune auto-component manufacturer running 12 CNC machines tracked OEE manually on paper. FireAI connected to their ERP production logs and revealed that changeover time accounted for 22% of total downtime — a problem invisible in their monthly summary reports. Targeted SMED (Single Minute Exchange of Die) improvements on 3 machines lifted plant OEE from 62% to 74% in one quarter, adding ₹28 lakh in recovered capacity without any capital investment.

Production throughput tracking measures actual output (units produced per shift, per line, per machine) against the planned production target. The gap between planned and actual throughput is where margin is lost — and without shift-level granularity, the causes stay hidden in averages.

What FireAI tracks:

• Units produced vs planned target — by shift (Day / Evening / Night), by line, and by product
• Shift-wise production rate (units per hour) with trend analysis
• Changeover time between products and its impact on net output
• Planned vs unplanned stoppages that reduced output during each shift
• Daily cumulative output tracking against weekly and monthly production plans

Why shift-level matters: A Rajkot FMCG packaging unit producing 15,000 pouches/shift discovered through FireAI that night shift output was consistently 18% lower than day shift — not because of machine issues, but because raw material staging was delayed by warehouse team handover gaps. The problem was invisible in daily aggregate numbers. After fixing the handover process, night shift output recovered within 2 weeks, adding ₹4.2 lakh/month in recovered production.

FireAI natural language queries:

• "Show me production vs target for Line 2 across all shifts this week"
• "Which products missed their daily target most often in April?"
• "What was the average changeover time on the filling line last month?"

Downtime is the single largest OEE killer in Indian manufacturing. But tracking that a machine was down is not enough — you need to know why it was down, how often the same cause recurs, and which machines are consistently the worst offenders.

FireAI categorizes every downtime event into a structured root cause taxonomy and builds Pareto charts automatically — so maintenance teams and plant managers can prioritize fixes that recover the most production hours.

Downtime categories FireAI tracks:

• Unplanned breakdowns — mechanical failure, electrical fault, hydraulic/pneumatic issues, tool breakage
• Planned maintenance — preventive maintenance, calibration, tool changes (included for OEE but tracked separately for analysis)
• Changeover / setup — product changeover time, die/mould changes, cleaning between batches
• Material starvation — upstream delay, raw material unavailability, staging gap
• Quality holds — machine stopped for quality investigation or adjustment
• Operator-related — no operator available, break overlap, training

Root cause analytics:

• Pareto chart of downtime causes by total hours lost — the top 3 causes typically account for 60–70% of all downtime
• Mean Time Between Failures (MTBF) by machine — identifies machines approaching replacement threshold
• Mean Time To Repair (MTTR) by cause — identifies causes that take longest to resolve
• Recurring failure pattern detection — flags when the same machine fails for the same reason 3+ times in 30 days

Real example: A Coimbatore textile manufacturer running 40 looms used FireAI to categorize 6 months of downtime logs. The Pareto analysis showed that yarn breakage accounted for 34% of all downtime — more than mechanical failures. Root cause investigation revealed that a single yarn supplier's batch quality had deteriorated. Switching supplier reduced loom downtime by 28% and saved ₹11 lakh/month in lost production.

Shift-level benchmarking compares the same metrics — output, OEE, downtime, reject rate, changeover time — across Day, Evening, and Night shifts on the same machines. The goal is to identify whether performance gaps are driven by equipment, people, process, or scheduling.

Most Indian plants know intuitively that night shift performs worse. FireAI quantifies how much worse, on which metrics, and on which machines — turning a vague sense into actionable interventions.

What FireAI benchmarks across shifts:

• Output per hour by shift — normalized for product mix differences
• OEE decomposition (Availability × Performance × Quality) per shift
• Downtime hours and causes per shift — isolates shift-specific patterns
• Reject rate per shift — catches quality issues tied to operator fatigue or material batch
• Changeover time per shift — reveals training gaps between shift teams
• Material staging delays per shift — highlights handover process issues

Typical findings in Indian manufacturing:

• Night shift output is 10–20% lower than day shift on identical machines
• The gap is usually split between: (a) longer changeover times due to less experienced operators, (b) more material staging delays due to warehouse understaffing, and (c) higher minor stoppage frequency
• Day shift often has higher planned downtime (maintenance scheduled during senior staff availability) but lower unplanned downtime

FireAI shift comparison dashboard: Side-by-side shift performance cards showing every metric simultaneously. Supervisors can drill from shift → line → machine → specific downtime event. Alert triggers when any shift's output drops more than 10% below its own 30-day average — catching degradation before it compounds.

Example: A Faridabad sheet metal plant running 3 shifts found through FireAI that evening shift had 35% more changeover time than day shift on the same press line. The cause was a training gap — evening shift operators were not following the optimized changeover SOP. Targeted retraining brought evening shift changeover time within 5% of day shift, recovering 45 minutes of productive time per shift — equivalent to ₹3.8 lakh/month in additional output.