For FMCG
Capture every micro-stoppage. Cut energy 10-18%. Improve yield 5-10%.
High-throughput lines produce 1000+ units per shift. Operators react to visible problems. Micro-stoppages (5-30 seconds) bleed energy, time, and yield but are never logged. wiseDo captures every stoppage, every changeover, every scrap event, with timestamps and root cause.
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The Problem
The micro-losses that hide in plain sight.
On a 1000-unit/shift line, a 5-second stoppage happens 50 times a day. 250 seconds. Over 4 minutes of invisible waste. Nobody logs it. The supervisor doesn't know until the daily report says output is 5-10 units short.
Energy costs scale with production. If half your stoppages are changeover-related, you're running the heater/cooler for changeover duration when it could be in standby. That's 10% of your energy budget on invisible waste.
Scrap and rework get logged at end-of-shift. Was it a material batch? Line calibration? Operator error? By then, nobody remembers.
The wiseDo Solution
Continuous vision-based supervision. Pareto analysis on the micro-losses.
Every micro-stoppage, every changeover, every scrap event is captured, timestamped, and correlated against shift, line, and product. Pattern analysis identifies the high-impact improvements.
Result: 10-18% energy reduction per unit of output. Changeovers optimized. Scrap and rework traced to root cause at the moment it happens.
Energy Reduction
Yield Improvement
Stoppages Logged
Why FMCG Plants Choose wiseDo
From micro-losses to measurable improvement.
SMED timing and changeover analytics
Measure setup and changeover times for each product variant. Identify which changeovers slip and why. Benchmark shift-to-shift performance.
Energy cost optimization
Correlate stoppages to auxiliary system runtime (heating, cooling, vacuum). Shift loads to off-peak tariff windows. Track cost per unit produced.
Scrap root-cause tagging
Every scrap event linked to line state at that moment: changeover, temperature, material lot, operator. Patterns emerge quickly.
How wiseDo is deployed here
Six weeks, step by step.
A packaging or filling line running high speed and frequent SKU changes. The losses are small, constant and invisible in a daily total.
- 1
Line walk and loss agreement
We map filler, capper, labeller and case packer, and agree the numbers: micro-stoppages per hour, changeover time, energy per unit, giveaway.
- 2
Cameras and machine signals connected
Existing cameras watch the transfer points where jams start; machine run signals and energy meters are read at the panel. Edge box installed inside your network.
- 3
Micro-stoppage capture calibrated
VisionOS learns normal flow at each transfer point, so every stop under thirty seconds is logged with its location. These are the stops nobody writes down.
- 4
Changeover timing live
SMED clock starts and stops on what the cameras see, not on when someone remembers to press a button. Every changeover is compared to its best-ever time.
- 5
Energy and giveaway tied to production
Energy per case and fill-weight giveaway are computed against live output, so a slow shift shows its real cost, not just its count.
- 6
Review and scale
At week six: micro-stops, changeover time, energy per unit against baseline. Next line follows on the same box.
What wiseDo brings
The platform components doing the work.
One platform, one data model. Each component below reads and writes the same event spine, so nothing is re-typed and nothing is reconciled at month-end.
VisionOS
Every micro-stoppage captured and located, and changeovers timed from what actually happens.
MES
Pareto of losses by station, shift and SKU; scrap and giveaway root-cause tagging.
Scheduler
SKU sequencing that minimises changeovers across the shift.
Energy module
Consumption per unit against load and tariff, so the cheapest way to run the day is visible.
What changes on your floor
The two hundred small stops that were never logged become a ranked list with a location. Changeover time stops being an estimate. And energy becomes a per-case number the plant manager can act on.