Unifying plant floor assets on a common platform allows manufacturers to simplify the effort of connecting the disjointed processes and eliminate as many manual steps that were previously employed in order to improve quality and throughput. Higher automation of individual processes also facilitates the data collection capabilities of your factory. However, in order to allow for communication between machines, they have to be connected to a common platform that can manage this data as well as analyze it and present it to management. With all of this in place, you will be enabled to leverage a mixture of intersecting technologies such as AI/machine learning, IoT and Edge computing to fully execute on this vision of smart manufacturing.
It is immediately clear that enabling smart manufacturing requires a large number of moving parts and complex processes to click together in order to yield results. However, the process of getting to that endpoint isn’t so much a massive, simultaneous overhaul of the entire organization but rather a series of incremental improvements that all fit into a larger picture down the road.
Here we will discuss how Edge Computing can have an immediate impact on optimizing your operations as well as the large scale implications and opportunities.
What is Edge Computing?
Simply put, Edge Computing centers around moving code that usually runs in the cloud to a local device or to its proximity. This is typically accomplished through what are called gateway devices that serve as a link between IoT enabled equipment and the cloud.
To better understand Edge it helps to segment these IoT solutions into three discrete components:
- • Equipment fitted with IoT sensors that generate data.
- • Analytics that process the data to extract insights.
- • Value derived from actions performed based on new information.
Properly implemented, these solutions improve manufacturers’ ability to deliver value to customers by harnessing data insights to increase efficiencies and reduce downtime in an increasingly competitive industrial landscape.
Automating inefficient manual processes
Currently, a dairy product manufacturer is in the process of reinventing production operations after more than 50 years in business by implementing Merit Solutions’ IoT infrastructure built on the Microsoft Azure platform. The solution enables streamlining of manufacturing processes, higher employee productivity and production insights on the factory floor.
Food manufacturers like the one in question, like all organizations regulated by the FDA, fall under strict requirements when it comes to supply chain visibility and product tracking. Each individual product needs to have a unique label containing information such as item ID, batch ID and catchweight among others.
This used to take place at a weighing station where the worker would manually enter all of the required information and print a label for the product. Since this wasn’t scalable with increasing production volumes, this process had to evolve.
The way this issue was able to be resolved came down to retrofitting existing equipment at the weighing station with IoT technology. An Edge gateway allowed for swift processing of each unit by communicating with the rest of the supply chain and automatically fetching labeling information.
The result: automating this step of the production process dramatically reduced the time that each unit needs to get held up at the weighing station, allowing for increased throughput while also making sure that production doesn’t become bottlenecked at this step.
How does Edge Computing enable Smart Manufacturing?
Still, process automation should be viewed as one of multiple components of smart manufacturing. And although it is necessary to streamline production, smart manufacturing at its core is about autonomy. The ability of any given plant asset or piece of equipment to make decisions based on what’s happening on the plant floor without the need for human input.
Consider this example: the weighing station that was automated to fetch labeling information and record catchweight can, down the line, be connected to other parts of the production line or other stations performing the same tasks. By using a machine learning model, the equipment would be able to monitor and compare performance and predict impending failures or automatically monitor supply levels by communicating production and demand volumes with the warehousing system, allowing automatic order placement to avoid downtime.
As with any new technology, our recommendation is to start small to quickly prove its value while limiting the risk to an acceptable level. Pick a business problem you want to solve and set up a team to work on it. Once a sufficient portion of your production facility is automated, you will not only achieve improved efficiency and lower costs – but also unlock the potential of digital manufacturing that can truly take your organization to the next level.