Legacy factory equipment (manufacturing machines with no built-in data collection mechanism) presents a significant challenge when it comes to manufacturing data, particularly for companies that want to calculate OEE. That most basic of all manufacturing metrics isn’t the only reason manufacturing companies are eager to make older equipment IoT-compatible. Capturing the right data can transform manufacturing operations: it eases the disconnect between the factory and business processes, eliminates the lag time for management to access, analyze, and act on data, and resolves problems with planning, inventory control, the supply chain, and meeting customer expectations.
While manufacturing data collection from legacy factory equipment is less expensive than years ago, there are still many ways to go about it, and it can be complex or difficult without the right expertise. Most of all, it’s important to start with the end in mind, make a plan, and use the right approach to capture data that will properly feed your organization’s improvement program. Even executives that are far removed from the day-to-day production equipment should develop a high-level understanding of what’s involved, because the data requirements and data collection engineering will affect system implementation costs and timelines.
Even though we understand the mechanics of how machine data feeds the OEE metric, getting the right data out of older manufacturing equipment isn’t a quick or simple activity (although it is certainly a high-value, reasonably economical exercise).
The first step to capture the required data from legacy equipment is an industrial IoT (IIoT) device with the right connectors, protocols, and firmware in place on the machine connection side. Your operations IT and machine connectivity engineer might advise something like a Siemens LOGO! intelligent logic module, an Allen-Bradley Micro820 PLC, or a Moxa ioThinx 4530 series industrial controller. These PLCs and controllers can gather a large amount of data to feed the analytics system. This is quite different than a sensor on a machine that only monitors a single specific element, like vibration or temperature.
The next step in capturing data from legacy equipment is to put a suitable connection and protocol in place on the client connection side, like:
The connection will communicate or translate and transmit the data from the PLCs and controllers into the OEE system.
Finally, your company might want to consider the machines’ ability to receive signals/data and programs if recipe management and/or program transfer is a requirement. Skipping this step in the planning process may cause significant angst later if the organization decides to have data flow from other sources back into the production equipment.
There are a host of other concerns that your manufacturing IT or operations IT engineers will consider as they design how to capture data from old manufacturing equipment. These may include:
Certainly, ease or difficulty of installation, reliability, cost to manufacture, and overall effort and resources required are all considerations. There is usually more than one way to get data from legacy equipment, and it’s up to your operations IT or machine connectivity expert to guide the company on the most efficient approach that meets all or most business requirements.
When a company has decided how it will engineer data collection from older manufacturing equipment and gets everything connected, it’s ready to extract data for OEE and other metrics. While legacy machines cannot provide the same volume of data as newer, native IIoT-enabled machines in the factory, they can provide enough data to monitor machine efficiency and productivity using metrics like OEE and total effective equipment performance (TEEP).
Once your operations IT team gets the data flowing, it’s time to integrate into operational and improvement systems. Whether the effort to connect legacy manufacturing equipment was meant for an OEE system, a quality management system, or to feed a continuous improvement initiative, the organization can apply the data in many ways, not just for the original project.
For example, if your company has a digital transformation or Factory 4.0 initiative, you might plan to feed data into an OEE system. Once it’s up and running, why not also use the data to feed the production scheduling system? Or, if you started collecting legacy machine data to fuel the quality system and satisfy industry or regulatory requirements, what other systems might the company improve with this newly expanded data set? Finally, for companies worried about tackling the problem of capturing data from older machines, these early steps can help you decide if the effort is worthwhile: