What drives faults, downtime and throughput across the system. The causal model inside Ceptron reads the system's own history and reports which variables drive which, at what delay and by how much.
The physics hereQueues and travel times. Loads arriving and leaving, lifts and shuttles moving them between aisles and levels, buffers that fill and drain, and the controls that release work.
An automated system is one material flow: totes come in through goods-in and lifts, shuttles store and retrieve them on every level, and elevators and pick stations feed them back out. A fault in one place shows up as a queue somewhere else, hours later.
The warehouse system releases work in waves, and faults, queues and throughput all rise and fall with it. Most metrics look related for that reason alone.
Release rules react to queues, so history can show that more work shortens a queue. It does the opposite. The causal model recognizes the rule and reports the real direction.
Elevators serve every level, so their faults touch everything and take the blame. The largest cause of lost throughput is often somewhere else.
Each trap is a test file with the answer planted first, told the way it shows up in an automated storage system. Click a card for the trap, its impact, what the model did, and the test files.
Fault, mission and order-release records, exported as they are. We return the drivers of downtime and throughput in a working session.
The test files are synthetic, with the answers planted in advance. Nothing here is a result on customer data.
Tell us about your system and the questions that matter and we will be in touch.