High Variety and Variable Quantity Production

When producing a large number of products with the same models and specifications, such as home appliances, we can combine industrial robots and servomotors to produce them on automated production lines. However, as the product life cycle becomes shorter and mass customization is required in recent years, it is necessary to prepare parts and production facilities according to the specifications of each customer, especially when products are manufactured differently. In other words, at the manufacturing site, workers are required to manually arrange parts and change the grippers.

However, in this case, it takes time to change the setup and you need to stop the line every time. In the manufacturing industry, if you don’t produce the same model at a certain volume, you won’t be productive. It is also a great challenge for the factory automation industry to flexibly implement set-up in a short time and labor-saving manner to meet individual customer needs.

These problems can be solved by adding redundancy to automated production lines using industrial robots, etc. and managing them using digital data. This will enable automatic setup changes without manual intervention, and enable the realization of high variety and variable quantity from a minimum of single unit. Our company’s factory that produce servomotors have had to produce about 20 units of the same models and specifications, but with thorough automation of set-up and utilization of data, we are now able to flexibly produce a single servomotor even if it goes into the middle of production schedules.

Other solutions

  • Production Production
  • Quality Quality
  • Maintenance Maintenance

Shorter installation time

AI picking

AI Picking

By utilizing the AI technology “Alliom” developed by Yaskawa Group, the installation time to actual operation is drastically shortened, and the accuracy to actual machine can also be improved.

Production Production

Flexible production

Autonomous distributed manufacturing

Autonomous Distributed Manufacturing

Digital data such as the torque value, vibration value, and temperature of the servomotor is absorbed into the controller, and the robot can think for itself how to move.


Accuracy improvement

 Accuracy improvement of defect cause analysis Yaskawa case

Accuracy Improvement of Defect Cause Analysis < Yaskawa Case >

By “visualizing” the operation status of equipment/devices with Yaskawa Cockpit, it is possible to identify the root cause by comparing the normal value and abnormal value of the data in the factor analysis for defects in production.

Quality Quality

Quality inspection

Automated product quality assessment with AI

Automated Product Quality Assessment with AI

When the quality inspection process is labor-saving, the use of an image judgement service that utilizes AI technology such as deep learning makes it possible to automatically determine complex No Good patterns with the same level of accuracy as humans.

Quality Quality

Failure prediction

Predictive failure diagnosis of equipment

Predictive Failure Diagnosis of Equipment

To reduce downtime to zero by performing planned maintenance in anticipation of equipment failure due to wear, etc., in response to concerns that production may become impossible due to the sudden shutdown or something else.

Maintenance Maintenance

Recovery support

Investigating the cause of equipment failure

Investigating the Cause of Equipment Failure

By acquiring quality data on when, with which equipment, and how it was processed, it is possible to accurately identify the cause of the problem between which equipment and equipment at the time of failure.

Maintenance Maintenance
Faster Recovery Simulation

Faster Recovery Simulation

The planning technology that Yaskawa developed automatically generates optimal paths, enabling simulation in a few minutes and dramatically reducing engineering time for recovery from sudden stop.

Maintenance Maintenance