The plan is in the master craftsman's head; the product lists are incomplete or outdated; the cost will be determined at the end of the year.
Establish product trees and routes, ensure inventory accuracy.
2
Registered
Work orders are created, production and consumption are recorded; basic reports can be generated.
Code the reasons for downtime and failure; set up field data collection.
3
Planned
Material requirements planning and capacity load are being conducted; equipment effectiveness is being measured.
Add finite capacity scheduling and setup optimization.
4
Controlled
Scheduling, quality, and maintenance are integrated; actual cost and variance analysis is performed.
Establish the master planning cycle and scenario simulation.
5
Predictive
Predictive maintenance, predictive planning, and AI-powered recommendations are now in effect.
Transform sustainability and energy indicators into planning criteria.
Proposed sequence of actions
STEP 1
Build the master data:Product trees, routes, work centers, standard times, and inventory accuracy. The most tedious but most crucial step.
STEP 2
Record the workflow:Work orders, material consumption, production reports, and goods receipts are all processed within the system.
STEP 3
Measure the loss.Coded downtime and waste causes, equipment effectiveness, loss tree analysis.
STEP 4
Planyour material requirements, capacity load outlook, and realistic delivery dates.
STEP 5
Scheduling:Finite capacity, setup optimization, and dynamic rescheduling.
STEP 6
Costing:Standard cost, actual cost, variance analysis, and feedback to master data.
The most common mistake: Starting with scheduling or advanced analytics. These produce impressive demos; however, if there isn't a proper product tree, realistic standard lead times, and reliable inventory management, the plans they generate are impractical in the field. The system loses credibility in the first few weeks, and teams revert to spreadsheets. The correct order is clear: first master data, then recording, then measurement, and finally optimization.
The human side of the application
Production systems fail more for behavioral reasons than for technical ones. If the field crew perceives the system as a tool for self-monitoring, data quality silently deteriorates: downtimes are entered late, causes of waste are randomly selected, and notifications are sent in bulk.
The way to prevent this is through three principles: facilitating data entry (automatic collection, multiple-choice lists), providing feedback in return (allowing the field to see its own performance), and not using data as a tool for punishment (situational records are input for improvement, not accusation). When these three principles are met, data quality improves automatically.