You may associate automation more with Western country manufacturing but automation indeed has a significant history in Asia-Pacific also.

From the Japanese car factories of the 1980s to todays advanced manufacturing floors using imaging and AI for quality control, the region has seen some of the most impressive gains from leveraging technology.

The difference this time is that the automation that is transforming manufacturing will go hand in hand with more intelligence gained from data sensing and analytics.

A tire manufacturer, for example, may be able to detect mere millimetres of error in a product and have that data fed back into the system to make constant near-real-time adjustments.

Not only is this more cost-efficient in saving staff and materials costs, but the system also delivers a whole new level of quality control. Quality control used to be a checking process after the goods are manufactured. With this next-generation automation, quality control is built right into the process when the goods are being manufactured.

With constant refinement, the manufacturer may even be able to make new products that were not possible without this feedback loop continually driving improvement.

In 2021, we can expect this trend to grow steadily. With Industry 4.0 on the agenda, industry leaders across different verticals are fast-tracking their transformation efforts with foundational technologies.

Among those surveyed by McKinsey in 2020, 39% have implemented a nerve-centre, or control-tower, approach to increase end-to-end supply-chain transparency. Around a quarter are fast-tracking automation programs to stem worker shortages arising from Covid-19.

To get there, of course, you need to have the right tools. This is where edge computing will play an increasingly important part in the years ahead.

In the tire manufacturer example, what is needed is a fast analysis of the data that is constantly being produced by the sensors inside the tire making machine.

For this to be analysed on the spot, a round trip to the data centre at a centralised location may involve too much latency. Thats not to mention the quality of broadband connections that may vary greatly in different parts of a country.

The data eventually has to be stored in a data centre, but the important analysis that is carried out in the field has to be accurate and timely. For that, you need adequate computing power at the edge to digest the data and to make parameter changes in real-time for optimal production.

Indeed, there are many other ways in which the edge will make a difference. Besides running data analysis, it could be used to orchestrate and operate complex machines remotely, a scenario that the pandemic has forced on many manufacturers. The ability to operate remotely has tremendous value and companies are allocating more budget to make edge orchestration a corporate priority.

Edge computing resources could also help drive the adoption of AI on the manufacturing floor.

While a simple sensor or camera can give you the raw image data, what is needed is a compute unit right next to the sensor, or on the edge, to analyse that. It also has to complete this task quickly because there could be hundreds or thousands of devices to be checked in a short period of time.

Lets not forget automated guided vehicles (AGVs), either. While each of these smart vehicles can navigate its way around a warehouse with its own sensors and onboard processors, they still need to relay information, say, on stock levels to human operators.

You still need a capable compute unit located near to the action to make sense of the data from these AGVs and present a coherent picture of what is happening on the ground. Again, this is where the edge has an advantage relative to the cloud.

Not every edge computing platform will do, of course. What is needed is a setup that not only brings the compute performance but also the robustness to work in a tough environment.

Another quality to look out for in an edge computing device is the ease of maintenance. Are the units easy to upkeep, say, by operators who are not IT savvy?

After all, with factories often distributed across a country, it might take an IT team hours or even days to get to a site to fix a simple maintenance issue.

Security is of utmost importance as well. Any edge computing unit that is connected in the field has to have security baked in from the start, not added on as an afterthought. It is essential to have a host-based firewall that allows users to blacklist or whitelist specific IP addresses, domain names, protocols, or ports. In addition, all data should be sent through secure, encrypted channels.

Like many other technologies that came to the forefront during the pandemic, edge computing has seen an acceleration in terms of adoption.

This is the foundation that many businesses will build on as they boost their automation efforts in the years ahead. The good news for those that have invested early is that they will be more ready for the recovery, better prepared to scale up when demand returns and taking more market share from the competition.

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NG automation to catapult productivity and profitability with edge computing - FutureIoT