Machine timeline
A chronological record of every running and stopped period — with reason codes overlaid, duration precise to the second, and a complete picture of where the shift was lost.
The problem
Why manual downtime logging fails
Paper-based downtime tracking is always incomplete, always reconstructed, and never detailed enough to drive maintenance decisions.
01
Short stops disappear from the record
A 3-minute stop at 11:40. A 5-minute stop at 14:15. Neither makes the shift log because neither felt significant at the time. By Friday, those stops have added up to hours of lost production that never appeared in any report — and never will.
02
Causes are reconstructed, not recorded
At the end of a 10-hour shift, operators write what they think caused the stops — not what actually did. "Machine fault" covers everything from a tooling issue to a material problem to a PLC alarm. That level of detail is useless for root-cause analysis.
03
Maintenance prioritizes by instinct, not impact
Everyone knows machine 4 "breaks a lot." But without structured stop data sorted by production time lost, maintenance can't tell management which failure type to fix first — or build the business case for doing it.
How it works
Every stop detected at the machine signal — before anyone touches a logbook
hopit Manufacturing reads the machine state in real time. The moment a machine stops, a downtime event is created with a precise timestamp. The operator adds a cause. The data is immediately structured, searchable, and feeding the analytics.
Shift at a glance
Every stop this shift — total count, total time lost, and the top cause, updated continuously.
Stops this shift
7
Total downtime
1h 43m
Top cause
Tooling
Live machine states
See which machines are running and which are stopped — and for how long — updated every few seconds across the whole plant.
Downtime analytics
Rank stop causes by total production time lost — by machine, shift or period. The Pareto that turns "it breaks a lot" into a documented case for action.
See what your downtime actually looks like
Try our free online demo — the same dashboards, live.
TRY OUR FREE DEMOFeatures and what they change
Each capability changes something specific
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Automatic stop detection — every stop recorded, including the short ones that never make the paper log. hopit sees the machine state change the moment it happens — not when someone writes it down.
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Reason codes confirmed in seconds — operators tap a cause on the shop floor while the context is fresh, not reconstructed at shift end. Structured data instead of free-text memory.
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Pareto by cause, machine and shift — rank which failure type is costing the most production time so maintenance works on what matters, not what's most visible. Filter by any time period and export for reviews.
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Complete stop history per machine — know whether "it breaks a lot" is a recurring bearing failure or an operator training issue, with timestamps and durations to back it up. The data that makes a replacement or overhaul case undeniable.
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Planned downtime separated — maintenance windows and scheduled changeovers excluded from the Availability calculation so OEE reflects unplanned losses only.
Who it's for
Roles and industries
Roles
✓
Plant Managers & Operations Directors — need to know what downtime is actually costing them, in production hours by cause, to make maintenance investment decisions with numbers behind them.
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Maintenance Managers — need structured cause data to prioritize the backlog and build business cases for repairs, additional headcount, or machine replacements.
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Continuous Improvement & Lean Leads — need a Pareto of downtime causes to focus Kaizen events and track whether interventions actually reduced stop frequency over time.
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Production Supervisors — need to see which machines are currently down and for how long, so they can escalate or reassign work before the shift target is missed.
Industries
hopit is purpose-built for small and medium-sized manufacturers in the DACH region running machines or production lines. Common sectors:
Metalworking and precision machining
Plastics and injection moulding
Electronics and PCB assembly
Food and beverage processing
Packaging and converting
Automotive components and subassembly
Medical device manufacturing
Frequently Asked Questions
Paper logs capture what operators remember at shift end — not what actually happened. Short stops get omitted. Timestamps are approximate. hopit detects every stop at the machine signal level, with sub-minute precision, the moment it occurs.
Yes. Reason codes are fully configurable per machine or machine group. You can organize them into categories like Mechanical, Electrical, Setup, Waiting for material, and more.
No. Planned downtime — maintenance windows, scheduled changeovers — can be excluded from the Availability calculation so your OEE reflects unplanned losses only.
hopit shows total lost production hours by cause, machine and shift. When you can show that one machine caused 140 hours of unplanned downtime last quarter — and the cause was a recurring bearing failure — the maintenance investment justifies itself.
hopit Edge runs on your local network and collects machine data on-premises. That data is sent to hopit's EU-hosted infrastructure, where the Manufacturing dashboard runs. An internet connection from the Edge machine is required.
Machine data is collected by hopit Edge and sent to hopit's EU-hosted infrastructure over an encrypted connection. Access is role-based — admins, managers, and operators each see what's relevant to their role. All connections use TLS encryption.
Find out what downtime is costing you.
Try the live demo to see what structured downtime data looks like. Or talk to us about connecting your first machines.