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As Industry Fully Embraces , What Happens Next?

Businesses from across industry are scrambling to implement automation plans. Those who get it right will be poised to own the future.

While the world put itself on hold for much of the past two years, I found myself busier than ever – collaborating with various global companies to urgently accelerate their automation strategies. Now, with plenty of interested parties keen to play catch up, I’m often asked for my take on the state of the nation in automation. My answer? It’s exciting times, but there are plenty more challenges to face.

Let me explain. In recent years when we talked about automation in industry, the conversations were predominantly focused on manufacturing, specifically in high-volume lines in the automotive industry. That world is history. Industry is all-in on automation as businesses scramble to realise new use cases from retail, logistics, wholesale and warehousing to pharma, medical devices and the broader life sciences arena.

The latter is a particularly interesting one. Automation in areas such as surgical robotics has been a big focus for a while of course, but now attention is shifting towards the exciting intersection of biology and engineering. AI-powered automation solutions can bring heightened levels of control to the lab and propel manufacturing at scale for life-changing medicines. The pathway to the mainstream market for radical new cell and gene therapies can be smoothed and scaled by the creation of rigorously controlled manufacturing environments.

The potency of this trend towards cross-industry adoption – this democratisation of automation – was really brought home to me during my recent gig as a panellist at the Automate 2022 show in Detroit. We are emerging from global pandemic restrictions and lockdowns with all the drivers for change in place: maturing technology capabilities, sky-high consumer demand, and an unprecedented global labour shortage. “We’re ready for what’s next!” was the shared sentiment in the conference, which had an unprecedented high attendance and strong representation across sectors and automation supply chain.

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That begs the big question of course – where do we go from here? As with all innovation curves, we’re faced with new opportunities and fresh challenges. The big opportunity right now lies in the consolidation of technologies and platforms that are concerned with automation. Look at it this way: if you’re a technology developer, you’ve now got the chance to apply your systems thinking approach in a way that cuts across industry sectors. Those who seize the moment can own the future.

It goes without saying that a manufacturing assembly line, for example, is vastly different from a logistics environment. For a start, the former did not need the level of human interaction, nor the flexibility and task complexity, demanded by the latter. In fact, there’s a whole host of specific checks and balances to consider across all sector applications, including the cost of technology deployment against the available profit margin. That’s where various business models that allow for flexible cashflow management (for example, the use of CAPEX/OPEX strategies) become key to meeting business needs.

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The RaaS (robots as a service) option certainly has its benefits, depending on the use case. It’s particularly suitable for applications such as standalone autonomous vehicles or autonomous mobile robots, which are easier to plug into an existing operation. RaaS adoption also means users are always benefitting from the latest technology, which is maintained and kept up to date by the technology supplier.

If you’re going to put billions of dollars into building your own technology, you’re investing in something that’s going to take at least five years to deploy at scale, which means you miss out on more than five years of technology development. Nevertheless, many of the global logistics giants will continue to opt for the CAPEX route as automation in their environment requires more existing infrastructure customisation and so may not be suitable for the XaaS model. Here, the strategy must be to design robust, mission-critical applications based on a systems-thinking approach that pays heed to crucial factors such as convergence and interoperability.

This is an exciting period for automation. Innovative tools such as digital twins and simulated environments are ready to de-risk projects and slash the costs of real-world machine learning development. Meanwhile, a wave of start-ups is arriving on the scene offering potential partnerships, differentiating technology and a springboard to the future. Watch this space, we’re in for quite a ride.

 

Source: E&T

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