Make idle time visible. Before it gets expensive.
Are machines drawing energy without delivering productive output? Connect machine status, consumption, and production data to see when idle phases, waiting states, or poor cycling create avoidable cost.
- Manufacturing companies
- Medium-sized producers
- Mechanical engineers
- Production managers
The Problem
Idle time costs more than electricity alone. It also hides how inefficiently machines are really being used. Without linking energy and production data, it stays unclear when consumption creates output and when it does not.
- Idle phases stay invisible in day-to-day line operations
- Waiting states and short stops accumulate across shifts
- Energy use is not linked to output or machine state
- Loss-reduction actions are hard to prioritize
The Solution
dAIve Predict connects machine status, energy, and output into a clearer picture: expose idle time, compare cycle patterns, and focus on the worst loss phases first.
Detect idle phases automatically
Find time windows where machines consume energy while producing little or no useful output.
Link energy use to output
Review how consumption, status, and production volumes actually relate to each other.
Compare cycle patterns more clearly
See which shifts, jobs, or operating states generate the most loss time.
potential energy savings when idle phases, waiting states, and poor cycling become visible in data instead of staying stuck in assumptions.
Indicative value from experience, to be verified with your data in a pilot.
How it works
Collect data
Machine status, energy, production numbers.
Start analysis
dAIve Predict reveals patterns, state changes, and anomalies.
See loss phases
Identify idle and waiting periods with a high energy share.
Compare actions
Review where timing or workflow should be adjusted first.
Every hidden idle phase costs output and energy.
Start your 14-day trial and use your first dataset to see where your line is consuming energy without enough return.