MES, IoT and Machine Data: the Gap Between a Signal and a Fact
One factory fits sensors to 412 machines, and you follow the numbers that come out. A sewing machine reports only a needle going up and down. Every piece count, efficiency, and downtime reason on the screen is an inference somebody wrote. You take each one apart and price what it costs when it is wrong.
Published by Merchandising Academy · First lesson free to read
Course value
What will you be able to do?
Work outcome
You can say what a real-time feed would change about a decision you make today, price the instrumentation honestly, and refuse the dashboard that reports a number nobody acts on.
Who it is for
Factory and supplier teams.
What you will produce
You build a working method for machine data. You write down an inference chain assumption by assumption. You choose a sensor by the question it can answer. You cut a downtime reason-code list to what the factory would act on. You map the names and price it before an integrator quotes. And you set a weekly hand count that catches a drifting piece count for the price of one supervisor-hour.
Learning format
6 lessons · 0 templates · workplace calculations and decisions.
Lessons
- 01What a sewing machine actually reports🔒20 min
- 02Three layers, and what each one is for🔒18 min
- 03Retrofitting: the sensor decides the question you can ask🔒18 min
- 04Downtime, and the reason a machine cannot give you🔒20 min
- 05The integration nobody budgets: one bundle, five names🔒17 min
- 06The count that drifts, and the hour a week that catches it🔒17 min