Case Studies

What becomes possible
when operations become visible.

Real results from manufacturers. OEE +13 pts, energy -18%, downtime -40%. Browse all cases or filter by category — every outcome is real, every number is from a live deployment.

+13 pts
Average OEE improvement
across factories
84%
Faster incident response
minutes, not hours
18%
Average energy reduction
within first quarter
180+
Case studies
across 17 categories
Highlights

15 featured outcomes.

Filter by category. Click any card to see the challenge and key insight behind the result.

Browse all 180+ cases ↓
OEE8 weeks

Hidden Micro-Stops: 2.4 Hours of Lost Production Found

61% → 74% OEE

Pune, Maharashtra · 12 machines

OEE12 weeks

OEE Optimization via Real-Time Parameter Tuning

68% → 78.5% OEE

Multi-Cavity Mold Facility · 18 machines

OEE6 weeks

Shift-to-Shift Consistency: Replicating the Best Shift

OEE variance 6% → 2%

Multi-Shift Machine Shop · 3 shifts

OEE10 weeks

FMCG Line: Micro-Stop Pattern Eliminated

84% faster incident response

Ahmedabad · 6 machines, 3 shifts

MaintenanceFirst 3 months

Predictive Bearing Failure: 72 Hours Before Breakdown

₹28–32 lakhs/yr saved

Tier-1 Auto Supplier

MaintenanceOngoing

Recurring Failure Pattern: Same Problem, Same Fix

Diagnosis time 3.2 hrs → 12 min

Body Shop OEM Supplier

Maintenance6 months

From Reactive to Scheduled: Maintenance Planning

MTBF 240 → 680 hours

Multi-Spindle Spinning Mill

Maintenance8 weeks

Auto Component: Unplanned Breakdowns Eliminated

MTTR 3.2 hrs → 1.1 hrs

Chennai, Tamil Nadu · 28 machines

Energy90 days

Real-Time Anomaly Detection: 18 Hidden Energy Wastes

14.2% power reduction

Biscuit Production Facility

Energy3 months

Demand Forecasting: Predicting Peak Hours 48 Hours Ahead

24% peak demand reduction

Multi-Line Production Plant

Energy3 weeks to locate all leaks

Compressed Air Leak Detection: ₹18L Hidden Tax

21.5% energy reduction

High-Speed Bottling Line

Energy6 weeks

Compressed Air System Optimised

Compressor idle time 42% → 18%

Bangalore · 8 machines

QualityFirst quarter

Defect Detection at Minute 5, Not Minute 55

Defect rate 0.8% → 0.18%

GMP-Certified Pharma Facility

ProcessAfter 200-batch analysis

Multi-Parameter Correlation: Finding the Safe Zone

Batch yield variance ±8% → ±2%

Specialty Chemical Batch Plant

Process12 weeks

Injection Moulding Process Consistency

Scrap rate 7.2% → 2.1%

Mandya · 18 machines, 3 shifts

Full Library

Search 180+ case studies.

17 operational categories. Filter by keyword, industry, or type to find cases that match your challenge.

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Production Analytics (10)

18 Case Studies

Production OEE Intelligence

Real-time OEE visibility and component breakdown

OEE AnalyticsProduction VisibilityPerformanceBenchmarkingEfficiency
10 Case Studies

Downtime Intelligence

Detection, prevention, and root cause analysis

Downtime DetectionMaintenanceMTTR ReductionFailure PreventionAvailability
8 Case Studies

Micro-Stop Intelligence

Hidden losses and compounding patterns

Micro-Stop DetectionHidden Loss RecoveryPreventionConsistencyProduction Stability
8 Case Studies

Production Shift Analytics

Team capability and shift-wise benchmarking

Shift AnalyticsTeam PerformanceBenchmarkingMethodologyConsistency
6 Case Studies

Production Cycle-Time Intelligence

Variance reduction and throughput optimization

Cycle-Time TargetingVariance ReductionThroughputForecastingOptimization
5 Case Studies

Production Quality Intelligence

Parameter correlation and defect prevention

Quality AnalyticsDefect PreventionYield OptimizationParameter ControlPrevention
4 Case Studies

Production Operator Intelligence

Capability assessment and skill development

Operator PerformanceSkill DevelopmentCapabilityTrainingEffectiveness
4 Case Studies

Production Bottleneck Intelligence

Constraint analysis and focused improvement

Constraint AnalysisBottleneck IDThroughputTheory of ConstraintsOptimization
4 Case Studies

Production Trend Analysis

Anomaly detection and predictive control

Trend AnalysisAnomaly DetectionPredictivePreventionForecasting
4 Case Studies

Production Benchmarking Intelligence

Peer comparison and best-practice transfer

BenchmarkingPeer AnalysisBest PracticesTarget SettingPositioning
Deep Dives

Three stories, in full detail.

The complete picture: the problem, what changed, the results, and the insight that explains why it worked.

Featured Story 01

CNC Precision Machining

Pune, Maharashtra

12machines
3shifts/day

The Challenge

OEE was tracked manually — weekly — by a production manager working from supervisors' verbal reports. Machine stops were logged in a paper register, inconsistently and often after the fact. The factory had no line-level visibility into how much time was lost to micro-stops.

The plant ran 12 CNC machines across three shifts. At shift end, each supervisor compiled a handwritten log of breakdowns. Micro-stops — two to eight minutes of idle time — were not recorded because no one had a way to capture them. These small losses, multiplied across machines and shifts, were consuming nearly 2.5 hours of production every day. Nobody knew. Because OEE was calculated manually and only weekly, there was no context to challenge the number. "Around 62% seemed reasonable," the production manager noted. It wasn't.

What Changed

All 12 CNC machines became visible in real time. Every stop — planned or unplanned — was automatically detected, timestamped, and categorised. Shift-level OEE was calculated without any manual effort. Supervisors received alerts within minutes of a machine going down.

Paper logbooks were replaced with digital shift records from Day 1. Within the first week, the team could see — for the first time — exactly when each machine ran, stopped, and why. The morning production review, which previously relied on memory and summaries, became a data-led discussion. Micro-stop patterns became visible within days, enabling targeted fixes within the first month.

Results

OEE
61%74%within 8 weeks
Hidden daily loss uncovered
2.4 hrs

of previously invisible production time

Incident response time
52 min8 minaverage
Paper logbooks
Eliminated

replaced with digital records

Key Insight

The OEE gap between 61% and 74% wasn't found in a management review. It was hiding in the first 48 hours of live data. Losses that had been invisible for years became visible immediately — not because anything changed on the shopfloor, but because for the first time, it was possible to see what was actually happening.

Featured Story 02

Auto Component Manufacturing

Chennai, Tamil Nadu

28machines
2shifts/day

The Challenge

Unplanned breakdowns were stopping the production line 4–5 times a week. Each breakdown averaged over 3 hours to resolve, because there was no structured maintenance tracking, no work order system, and no failure history.

The maintenance team knew intuitively which machines were unreliable — they lived with the knowledge every day. But without data, they couldn't justify the cost of planned interventions to management. Breakdown investigations began from zero each time: which machine, when, what happened. Technicians carried the history in their heads. When they changed shifts or left the company, the knowledge left with them. Three machines were responsible for over 60% of all failures — a fact that became visible only after monitoring began.

What Changed

Every breakdown was automatically logged with a precise timestamp and the affected machine identified. Digital work orders were assigned to the maintenance team. Technicians were required to log root cause before closing a ticket. For the first time, the plant had a searchable failure history.

Accountability changed the culture as much as the data did. When breakdowns are anonymous and untracked, responsibility diffuses. When every breakdown has a timestamp, an owner, a root cause, and a closure record, behaviour shifts. Technicians began identifying patterns themselves. Within six weeks, the three highest-failure machines had targeted PM schedules — built from actual failure data rather than guesswork.

Results

Breakdown frequency
4.8/week2.8/weekwithin 3 months
Mean time to repair
3.1 hrs1.3 hrsaverage
Repeat failures
↓ 51%

fewer same-machine, same-cause breakdowns

Work order backlog
Eliminated

all maintenance tracked digitally

Key Insight

Fixing the visibility problem fixed the culture problem. Reactive maintenance isn't just a process issue — it's an information issue. When teams can't see patterns, they can't break them. Making breakdown history visible and searchable gave the maintenance team the evidence they needed to shift from firefighting to prevention.

Featured Story 03

Compressed Air Systems

Bangalore, Karnataka

8machines
2shifts/day

The Challenge

Compressed air was the facility's primary utility cost, yet the plant had no visibility into consumption patterns or efficiency. Air leaks, unregulated pressure, and idle compressor operation were consuming 25–30% more energy than necessary.

The facility ran 8 compressors across two shifts serving pneumatic tools, processes, and automated equipment. Energy bills showed rising costs year-over-year, but the plant manager had no way to identify what was driving the increase. Air leaks were widespread but undetected because they were silent and diffuse. Compressors ran continuously even during periods of minimal air demand. Pressure settings were set once and never adjusted. The facility was paying for full-capacity air generation for what amounted to partial-capacity demand.

What Changed

Real-time compressed air consumption was metered at the main line and per-branch. Pressure levels, flow rates, and compressor runtime were tracked continuously. Abnormal consumption patterns — including air leak signatures — became visible within days.

The moment metering was in place, a 15% baseline reduction came from simple adjustments: tightening connections that were known leak points, adjusting pressure regulators to optimal levels, and scheduling compressor shutdown during low-demand periods. Systematic leak detection — previously a manual, infrequent process — became automated. When a branch showed abnormal consumption spikes, the team could pinpoint the problem immediately. Within two weeks, five previously undetected leaks had been located and repaired.

Results

Energy consumption
baseline−21.5%within 8 weeks
Undetected air leaks
5 found

and repaired within 2 weeks

Compressor idle time
42%18%of operating hours
Annual energy saving
₹22 lakhs

from optimization and leak repairs

Key Insight

Compressed air leaks are the silent tax of pneumatic systems. A 3 mm hole in a line running at 6 bar costs ₹15,000–20,000 per year in wasted energy — and because the loss is diffuse across the network, nobody notices. Visibility reveals not just where the waste is, but how to eliminate it systematically.

Your factory has the same invisible losses.

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