Digital transformation key within industrial manufacturing

Marc Garner, vice president of the secure power division of Schneider Electric, examines the rise of digital transformation within industrial manufacturing.

The industrial world has come a long way from the early days of steam power and the advent of the manufacturing era. Industry 2.0 brought electrification and mass production in the 1800’s propelled by fossil fuels, and sometime later, in the 1980s, Industry 3.0 saw the dawn of electronics and mass information. This began the automation journey and the use of data to speed up production.

Today, we are in the midst of the fourth industrial revolution. Categorised by BCG, “Industry 4.0 is a transformation that makes it possible to gather and analyse data across machines, enabling faster, more flexible, and more efficient processes to produce higher-quality goods at reduced costs.”

This translates into truly automated, lights out environments where businesses can produce things at high-speed and on a massive scale. It is not just production, however, that’s driving transformation within industrial manufacturing, but efficiencies across the supply chain by harnessing technology to integrate different functions into the production process.

Distributed IT in manufacturing

Changes are far-reaching, affecting every aspect of the industrial process, and businesses must be proactive in its adoption. To gain a competitive edge, many companies are looking beyond the developed technologies to emerging, under-utilised technologies such as Artificial Intelligence (AI) and digital twins.

Robotics and automation have been in the manufacturing industry since the 1950s. Early automation systems had limited “intelligence”, autonomy, and operational degrees of freedom. They were mostly designed to perform one or two sets of repetitive tasks in a highly controlled environment. However, industrial automation and robotics are increasingly becoming more “intelligent” and versatile.

Warehouses are using robots and analytics to identify the fastest route to move goods for fulfilment. In many cases they are also being used to reduce the physical demands on human employees and reduce the number of workers required. According to McKinsey and Co, using Artificial Intelligence and robotics could lead to an estimated gain of 89% in incremental value over time for the transportation industry. 

These systems are expected to be able to collaborate with human resources to take on more aspects of a flexible manufacturing process.

The deployment of IoT sensors will help to ensure maximum uptime and a reduction in the costs of operation. For example, the technology is already being used in the Oil & Gas sector. Forbes reported that, “according to an MIT Sloan study, a single day of downtime for a liquefied natural gas (LNG) facility can cost $25 million. And a typical midsize LNG facility goes down about five times a year”. Sensors collecting maintenance and performance data in the field alert management promptly and proactively so actions can be taken to prevent downtime.

The revolution is also providing a demonstrable impact in the agricultural sector. Traditionally, farmers use herbicide liberally, spraying across a field of crop to destroy unwanted vegetation. However, new technologies can now identify the difference between a crop and a weed, which provides a range of benefits throughout the value chain. For example, the farmer can reduce the cost of operation and from a sustainability perspective, begin to reduce the use of chemicals on their land.

Need for local edge data centres

All of these processes require industrial manufacturers to utilise advanced processing power, with computing systems that offer low latency, are highly connected and secure, and have localised control. As a result, there has been a rapid deployment of edge data centres, which is key to bringing the promise of Industry 4.0 to reality. New research from IDC has also found that when organisations were asked why they were investing in edge computing architectures, 50% of respondents cited to improve cybersecurity, with a further 44% stating systems resiliency and reliability were key drivers.

Latency was also crucial and 32% of respondents had experienced a lack of connectivity or slow connectivity with their edge deployments.

The challenges

While the industry is well versed in the benefits of standardisation, at the industrial edge there are some additional key challenges.

Data security is a major focus due to the rise of regulations and the cost of data breaches, which are often caused by physical security issues. In fact, 29% of data breaches are due to physical security, and the average cost of a corporate data breach is $4M. Furthermore, with new regulations making physical security compulsory, it is not surprising that CIOs often cite security as a major area of focus.

Speed of deployment is also a critical consideration, as most companies are working at an incredible pace, with few resources and limited expertise. Gartner predicts that skills shortages are growing, and that 75% of organisations will experience visible business disruptions due to infrastructure and operations skills gaps.

Finally, the management of edge infrastructure, which in this environment is critical to ensure maximum performance and business success, remains a significant issue. IT staff in many enterprises are already at capacity, therefore, learning new skills, while balancing deployment and management strategies, and maintaining existing operations is a key challenge.

Accelerating digital transformation

Recently, Schneider Electric partnered with a large automaker, using edge computing-related software, hardware, and services, to capture massive amounts of test drive data from their autonomous vehicles.

This helped the customer address three main issues. The need to capture large amounts of test drive data for research and development; to capture a flood of information in real time, which makes low latency critical; and capturing the data in harsh, remote, or rugged environments, such as outdoor test tracks.

Schneider Electric’s solution was to provide a fully integrated, all-in-one, EcoStruxure Micro Data Centre complete with EcoStruxure IT Expert management software. This gave the customer the ability to collect data close to where it was generated, enabling fast and secure data acquisition and real-time decision making.

Performance of this kind is something which can only be achieved through the deployment of pre-configured, fully integrated, and secure IT Infrastructure, and via a standardised design which can be replicated across future sites and in different locations around the globe.

In conclusion, industry 4.0 has the potential to transform and enhance all aspects of the industrial manufacturing process. But it is essential that integrated edge computing solutions are installed to simplify IT /OT infrastructure as well as address data management, resiliency, latency, and cybersecurity concerns.

By embracing pre-configured edge computing solutions, manufacturers can utilise advanced compute to reduce the overall complexity of factory operations, while ensuring uptime, efficiency, and performance throughout the production process.