Summary: In 1913, the Ford Model T production line reduced assembly time by 90%. Today, the Internet of Things (IoT) has the potential to make as big of a difference, driving smarter advances in production and maintenance throughout the manufacturing industry. The combination of availability and affordability networking technology components and low-cost connectivity and data storage through the cloud, mean that IoT communication and real-time data analysis is fast becoming a key component of manufacturing. However, there are challenges to overcome before these benefits can be realised. For example, long capital cycles and constrained time and budgets make it difficult for manufacturers to turn to a new way of doing things and not deploy the available technology. In addition, new systems brought in with IoT solutions required specific skillsets – skills that existing workers may not possess.
In 1913, the Ford Model T production line reduced assembly time by 90%. Today, the Internet of Things (IoT) has the potential to make as big of a difference, driving smarter advances in production and maintenance throughout the manufacturing industry.
The IoT the concept of linking objects to the internet via a network. Born from the emergence of the fourth industrial revolution, IoT has boomed in the past decade with exponential growth in the number of internet-connected IoT devices or nodes. Gartner forecasted that the total number of consumer and business connected devices would grow by 11.9 billion by 2018 with vertical-specific business devices rising from 1.64 billion in 2017 to 3.17 billion in 2020. Indeed, such is the potential of the ability to network almost every object, that entire industries have emerged due to the ability to embed sensors in an increasingly wide range of machines, surfaces and textiles.
The combination of availability and affordability networking technology components and low-cost connectivity and data storage through the cloud, mean that IoT communication and real-time data analysis is fast becoming a key component of many processes. In the manufacturing industry, a Version study found an 84 percent boost in IoT network connections, more than any other industry surveyed, including Transit, Energy & Utilities, Smart Cities and Healthcare. The same report also found that 73 percent of executives are either researching or currently deploying IoT solutions for their businesses.
Using IoT technology, it is possible to connect equipment across the factory shop floor to drive effectiveness of the production process. In doing so, it is possible to solve technical issues before they cause stoppage time, collect data to help identify ways to improve efficiency and help manage delivery and orders in a more time efficient manner.
More devices mean more data. This influx of data means manufacturers now have access to a range of tech solutions on the plant floor that can be connected to devices and sensors to facilitate real-time analytics. However, the challenge is to incorporate device management into the core of a manufacturer’s processes in a structured way. To do this, management are likely to turn to augmented reality (AR) and artificial intelligence (AI) to bridge the gap by offering an intelligent solution. These two forms of intelligent devices have already helped manufacturers with complex assembly, automation and improved quality assurance, networking these efforts to assess efficiency and spot errors will be game-changing in terms of productivity levels.
Considering maintenance of the plant and machinery, IoT provides a way of moving from basic “fault-based maintenance” towards “prescriptive maintenance”, using IoT to monitor production and identify issues of opportunities to enhance production through the information the sensors send back to the main network. This approach ensures that any deviation from the process sends up an alert, indicating the equipment is behaving abnormally and requires action or investigation. This approach helps prevent costly mechanical failures and allows the equipment to be stopped only when there is an identified need.
By collecting data from equipment as it is up and running and allowing it to correlate it is the essence of ‘machine learning’. This use of computing power can make the production process more flexible can enable increased ‘meant time between failures’ (MTBF), shorter planned downtime create a positive impact on equipment effectiveness.
Whilst effectiveness depends on the quality of data the system can collect and the relevance of this information, the impact on a factory’s maintenance strategy has been observed to be positive and can enhance site productivity overall. However, there are challenges to overcome before these benefits can be realised. For example, constrained time and budgets make it difficult for manufacturers to remove their old system and implement an IoT system – a costly and time consuming exercise. In addition, IoT solutions require specific skillsets, often requiring existing workers to re-train. As a result, a growing skills gap is becoming apparent. A Deloitte study found that over the next decade, 2 million manufacturing jobs will go unfilled due to this skills gap. The same study found that 82 percent of executives believe the gap will impact their ability to meet customer demand.
Another barrier to the wide implementation of IoT across the manufacturing sector is a lack of understanding of how IoT works and its potential. For instance, despite growing investments in IoT solutions, a recent PwC survey found that only 30 percent of US manufacturing executives plan to increase spending on IT in the coming year. This demonstrates the need for a significant education-drive to communicate the benefits of IoT.
Just a few years ago, the idea of integrating IoT technology into the manufacturing process was hindered by being prohibitively expensive, and the technology was not yet capable of delivering effective results. However, the rapid advancement of 4IR means that IoT can be applied almost everywhere, from households to factories. Combining data from sensors with machine learning has the ability to revolutionise the efficiency of operations and significantly increase asset utilisation on the shop floor. In order to remain competitive, those within the manufacturing industry must turn towards solutions offered by the 4IR, balancing the cost and time barriers with the long term potential for cost savings and longevity of the business.
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