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Industrial internet of things improves smart factory productivity

Industrial internet of things improves smart factory productivity
The manufacturing sector is more productive and efficient than ever thanks to the industrial Internet of Things (IIoT). And it couldn’t come at a better time — global manufacturing output growth has been decelerating since 2018, according to quarterly reports published by the United Nations industrial Development Organization (UNIDO). The UNIDO attributes deceleration to increased risks and uncertainties including tariffs and trade tensions that have affected the world’s industrialized economies. Despite a slowdown in manufacturing growth, the rise of smart factories and adoption of IIoT and cloud technologies have grown. By using the vast amount of data generated from the Internet of Things, IIoT and cloud, manufacturers are able to predict equipment breakdowns, prevent unplanned downtime and reduce costs. However, due to this, understanding what to deploy, how to implement it and what the benefits of IIoT and cloud technologies are is critical.
Today’s smart factories are growing thanks to the growth of the internet of things (IoT), which has brought about the industrial internet of things (IIoT). As global manufacturing output slows, productivity and efficiency have become increasingly important. By using IIoT technologies, manufacturers and smart factory operators are able to collect and analyze data that enable them to optimize operations.

More data, more benefits

Richard Howells, VP,
Solution Marketing, SAP
Keeping up with technological innovation comes down to the aggregation, integration, processing and analyzing of data on IIoT platforms, said to Richard Howells, VP of Solution Marketing at SAP. “This is why factories are investing in IIoT in order to realize business benefits across the entire company. Many factors, applications and technological developments drive these business benefits and therefore demand for IIoT,” he said.

Nowadays factories are adopting digitization strategies that use IIoT technologies to capture additional sensor-based data (e.g., vibration, environmental, etc.) to augment their existing manufacturing data sources and provide additional insights. “This additional insight identifies opportunities to improve the operational efficiency of the asset or process as well as the health of the asset… We also see IIoT technologies being used to obtain data from older manufacturing equipment, that may be 20 to 30 years old,” said Enrique Herrera, Industry Principal for Manufacturing at OSIsoft.

However, manufacturers don’t always know exactly what type of data to collect when they want to start collecting it. Patrick Smits, Marketeer at Ixon explained, “Objectives are not always clear from the start but evolve during the process. Using an established IIoT provider with roots in manufacturing obviously helps lowering entry in Industry 4.0.”

IIoT in practice

The entire lifecycle of production can benefit from the many features and functions of IIoT solutions. This ranges from product design to monitoring of inventory levels in the supply chain.  

Howells explained that predictive maintenance data gathered from IIoT can help minimize production downtime, which can cost a manufacturer tens of thousands of US dollars a minute, depending on the industry. Utilizing predictive maintenance not only reduces downtime, it increases productivity by alerting operators to maintenance needs before problems occur.

Remote access that allows factory operators to connect to machines for remote support and remote assistance is another way IIoT can help optimize production processes. Smits pointed out that the ability to troubleshoot and monitor machines, as well as deploy new programmable logic controller (PLC) software over VPN, becomes much easier and saves a lot of unnecessary expenses when able to do it remotely. 

More advanced use cases of remote access involves monitoring machine production or factory production, or using metrics and KPI's to improve overall equipment effectiveness (OEE), Smits added. To do this, factories must start by logging machine data and then combine and analyze this data in order to optimize the production process.

Using IIoT solutions can also help manufacturers identify the root causes of quality issues in their production, which can also cut into productivity and lower customer satisfaction. Howells pointed to edge-to-cloud closed-loop machine learning and advanced manufacturing execution systems (MES) to reduce quality issues.

“An enterprise can leverage IoT usage and performance data to continuously improve its products. Right now, this requires engineers to analyze the data, but as more products get connected and companies leverage more AI techniques, generative design software could automatically create improved designs based on IoT data,” Howells explained.

Future of IIoT in manufacturing

While adoption of IIoT projects is growing, IHS Markit reported that currently half of all deployments fail; failure of a project is defined as not meeting the customer’s expected payback. High failure rates are often attributed to inflated expectations and a failure to gather support and cooperation from critical personnel within the company. Half the companies deploying IIoT projects expect to see payback within one year and are not getting the payback they expected, as many of these projects can take much longer to generate returns, according to IHS Markit.

Still the annual IIoT node shipments are expected to hit 224 million units in 2023, a 100 million unit increase from 2018, as reported by IHS Markit. However, to ensure more successful deployments of IIoT projects, providers must work together with manufacturers and smart factory operators to manage expectations and develop projects that can be executed successfully.

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