Each year, manufacturers lose thousands of dollars to waste. Every plant or line manager knows the usual suspects: unplanned line downtime, material waste, overproduction, quality-related losses, and poor use of capacity or assets.
But there’s an insidious form of waste many managers are unaware of: inconsistent or inaccurate data. Such data can lead to incorrect insights and poor decisions, and ultimately undermine efforts to move to a data-driven management culture. Finished-unit production numbers, length of downtime, line speed, and the number of rejected units all need to be monitored constantly and reliably—and not just at the end of the shift—if you really want results.
To help eliminate waste, manufacturers need access to consistent and reliable data that identifies opportunities for continuous improvement. Unfortunately, many plants still manually collect operations data. Manual methods lead to inefficiencies, delays, and poor data integrity because the way one person manually measures time, quality, or efficiency likely differs from how other colleagues do, even if only slightly. These differences in human interpretation mean data will be inconsistent, which leads to errors in analysis and financial losses. According to an infographic from Stratus, downtime can cost anywhere from $30-50k per hour.
However, data challenges also bring plenty of opportunities, one of which is the adoption and use of the Industrial Internet of Things (IIoT). A 2017 survey found that there was an 84% increase in IIoT network connections in the manufacturing industry that year, more than any other industry.
IIoT can be leveraged to collect data automatically, removing opportunities for human error and inconsistencies. You’re likely familiar with the elements of IIoT: sensors, machines, network gateways, the cloud, data lakes, security, machine learning, analytics and software. Now it’s possible to combine these technologies in a platform to efficiently collect, evaluate, transform, integrate, and analyze data. Your supply chain and enterprise business systems can serve as additional resources to complement operational data from equipment operating on the packaging lines.
As your equipment accrues data through attached sensors, insights can be pushed to a shared dashboard viewed by various stakeholders, or else to custom dashboards designed and populated for specific roles (e.g., Maintenance Engineer, Packaging Engineer, Plant Manager, Production Supervisor, Quality Manager and others). This gives packaging-line managers a bird’s-eye view of their operations and the ability to make waste-reducing decisions.
For example, data gathered from the IIoT can be used to improve how equipment is maintained, allowing for higher equipment uptime and lower maintenance costs. For equipment downtime analysis, data on production counts, line start times, line stop times, production rates, and individual equipment start and stop times are typically collected. For advanced analysis on overall equipment effectiveness (OEE), additional data on quality (e.g., number of rejects, yield), operating shift schedule, and target packaging line speed are also collected.
IIoT solutions combine data collected from sensors and machines with data from shop-floor manufacturing execution systems (MES) and other sources such as ERP to provide a data foundation upon which numerous analytics-driven applications can be built. These applications can focus on solving specific problems such as waste reduction, productivity improvement, quality improvement, or efficient capacity use. They can also assist with operational scenario planning and “what-if” analysis.
Line managers will receive consistent, reliable data on packaging line operations and, if they pair IIoT with computer vision technology, they can get real-time insights into package quality data. For example, the system can automatically reject low-quality units while at the same time log the number of rejections.
This is IIoT’s value proposition in manufacturing and packaging: Get better data and resulting insights from your equipment and operations by connecting via IIoT technologies. Line managers obtain meaningful insights from this data, allowing them to quickly make decisions that will save time and money and help sustain a competitive advantage.
At Videojet, engineers and plant managers have been connecting industrial printers to the internet and providing remote service since the early days of IIoT. Now, as the technology becomes mainstream, they are looking beyond just connecting the industrial printer to placing data collection devices along the packaging and extended production line, and even at downstream supply-chain nodes.
Manufacturers should start focusing on using IIoT to get a better view of overall equipment effectiveness (OEE) and operations productivity. A better view of OEE lets manufacturers dig deeper into issues regarding the availability, performance, and quality of their manufacturing and packaging operations to identify opportunities for continuous improvements. Manufacturers will also better understand how well they’re using their assets to meet customer demand, resulting in less waste, more uptime, and better productivity.
How can line managers start getting insights on their OEE? The first step is to get equipment connected and IIoT-enabled, meaning the printing and coding equipment will connect to the network, collecting data and storing it in the cloud. This step is essential: Without it, companies won’t have a platform on which to build additional data-driven insights.
After printers are connected and printing and coding equipment data is flowing, the second step is to combine data from IIoT-connected devices with additional sources of data and complementary software. This software will help line managers decipher the numbers, quickly identifying root causes of any issues as they arise.
IIoT doesn’t work in isolation. To take full advantage of it, take the third step of combining IIoT with the manufacturing IT infrastructure. This means connecting IIoT-enabled equipment with the customer relationship management system, ERP, shop-floor systems, supply chain management systems, and any other parts of the infrastructure. This step is where the benefit of data analytics will become apparent.
Now that the packaging line is connected to the IT infrastructure, managers will be working with a much broader, more consistent set of data, letting them identify areas of waste and eliminate them as quickly as possible.
Although the future of IIoT will greatly benefit manufacturers, we don’t have to wait for the future: IIoT continues to transform production and packaging lines right now. Those who adopt IIoT technology will be rewarded with improved productivity, less waste, better data, more consistent production levels, and—perhaps most importantly—more predictable and better business outcomes.
Arun Saksena is CIO of Danaher’s Product Identification Platform.
