Automation, Artificial Intelligence, and Robotics are hot topics in the manufacturing space. The “Lights Out Factory” — the factory where the entire process is automated — is around the corner. Some may believe this idea is the eventual future of all manufacturing, and the robots will replace us all. We argue that this conclusion is incorrect. The current goals of automation should be to:

1) Increase efficiency in repetitive, mundane tasks.
2) Reduce the possibility of errors.
3) Increase the amount of information available in order to make intelligent decisions.

Notice that these goals are both achievable and do not reduce labor. They aim to move human labor to areas where the business will gain more value from that labor.
Toyota, the company mimicked all over the world for its Lean principles, has continued a strategy of putting humans first. In a continuous improvement culture, there must be humans on the production floor.
We want to place humans where their intellect can help improve the process. We also want to give them the relevant information to streamline and optimize their process. We believe that the ideal manufacturing environment will be a combination of machine, software, and human intelligence. Some examples will be discussed below.

Example 1: Error proofing
In 1711, Alexander Pope famously wrote “To err is human”. It is over 300 years later, and humans are still making mistakes. Manufacturing veterans understand this fact and know that humans are eventually prone to error. We choose to accept this truth, and work around our limitations. Rather than expecting our people to never make mistakes, we design the process so that our human errors are no longer possible.
This can be done in many ways. Depending on the problem, we can do this in the PLC, in the MES, or in a “middle-ware” solution that integrates the systems. Recipe management provides an easily illustrated example. Let’s say your process is making a cake. Are you adding the proper input materials? What is stopping your operators from adding chocolate instead of vanilla? Are the eggs expired?
Software can check against your inventory, quality and production systems and prevent mistakes before they occur. Eliminating these mistakes will make people’s jobs simpler and smoother.

Example 2: Data Automation
Think of data automation as easing the flow of data throughout your systems. Wherever possible, use machine and process data rather than relying on humans to enter the correct data at the correct time.
One example is downtime data. When your process line is down, you want to get accurate information about why it is not making product. It is much better to rely on machine data for this reasoning, so that you can accurately make decisions on how to improve your process. If you rely solely on human operators for this process, it is likely that your downtime chart will display the largest amount in the category of “other” or “miscellaneous”. This information is not useful to improve your process.
To truly understand your process, you need high quality data — preferably directly from the source. Human memory is not accurate enough for this task. High quality data helps you to utilize your human assets to improve the process!
The people will still be needed, and still be valuable. Once you have automated the data reporting, you can task your skilled people with using the data to further enrich your factory.

Example 3: Condition Monitoring
Despite advanced manufacturing techniques around the world, many facilities still run their equipment until failure. This disrupts the factory — it must now scramble to repair the equipment (hopefully there are spare parts on hand). The schedule must now be adjusted, and other production orders may be delayed, upsetting customers.
In an ideal manufacturing state, this would never happen. The maintenance team would understand when equipment performance started to shift downward and would work with the scheduling team to properly schedule the maintenance procedure.
The “P to F Interval Curve” helps to understand this concept. By responding at the first sign of equipment trouble, everyone’s job is made easier. However, this practice is difficult without using some automation. In this case, the “automation” is the flow of good data (or information about the condition of assets) through the factory.

Conclusion
Manufacturing personnel should be looking for ways that automation can enhance their jobs. By not embracing the wave of available technology, factories could be risking their business. Consider this fact: Research shows that since 2000, 52 percent of companies in the Fortune 500 have either gone bankrupt, been acquired, or ceased to exist as a result of digital disruption.
The best manufacturing strategy will be the one that effectively blends human inventiveness with machine and computer efficiency. Automation will be used alongside the people to help them in their jobs. The need for individuals in manufacturing will not go away — at least not for a very long time.