FEATURE Automation & AI


AN INSIGHT AND OVERSIGHT OF ASSETS


Overstocking is a big drain on manufacturing businesses


increasing the longevity of their machinery or highlighting issues that might indicate it’s time to replace it.


‘Just in case’ stockpiling has become the norm, but there are ways to work smarter, says Duncan Bain, Senior System Engineer at SAS, a global leader in data and AI. The company has helped many manufacturers optimise their supply chains through the use of the AI of Things (AIoT)


O


verstocking is a big drain on manufacturing businesses – but it can feel like the only solution for manufacturers when supply


chain shocks are common and they work in an industry where the lead times on machines and components are long. Despite being more data-driven than ever, manufacturers still often  their forecasting decisions. That’s evident from the ‘just in case’ stockpiling of spare parts that has become the norm for many, rather than a leaner ‘just in time’ approach.


Asset conundrum    high energy costs and skills shortages. They’re faced with a conundrum: either leave their  vehicles or components for products, or free it up and risk being caught out by stockouts. It’s a major challenge for the industrial sector in particular, where a large machine  to produce. Manufacturers might sensibly


10 September 2025 | Automation


conclude that they should order a piece of equipment or keep spare parts sooner rather than later, to avoid a breakdown. The trouble is that it’ll take much longer for this asset to deliver value and it requires maintenance and servicing even before it gets to the shop 


 – and potential unintended consequences – of  new ones. Could they get more from their  maintenance processes and have the right spare parts in stock? Or is it better to switch to a more up-to-date model that is easier to use, improves output and is more energy 


Coming to the right decision isn’t easy, since nobody can see into the future. But  have detailed and forward-looking insights  machinery.


Collecting data


Data on machine speed, energy-  environmental factors like humidity and temperature that could impact performance can all now be captured at speed. In the case of a large-scale plant, there might be millions of machine sensors collecting vast amounts of data every hour. By applying AI-driven analytics to this


 what this means for asset performance, turning the data into something useful. It allows maintenance to identify potential issues in good time and prioritise tasks,


 evolution of IoT, and we’re already seeing what it could look like in practice. It’s faster, helping users get to the answers they need sooner and it’s also smarter, uncovering trends and details that would have otherwise been missed. Teams can also layer insights from  whether, for instance, the output of a particular  demand. We’re already seeing how AIoT could transform the management of critical assets in order to maintain uptime. At SAS, we supported Volvo Trucks and Mack Trucks, both subsidiaries of Swedish Manufacturer AB Volvo, with remote diagnostic and preventative maintenance services using IoT technology, analytics and AI. In an industry where uptime is a competitive advantage, this led to a 70% reduction in diagnostic times and 25% drop in repair times.


Being proactive


AIoT allows you to see beyond your own operations by proactively ordering parts, taking into account long lead times, and empowering end users to increase the longevity of their assets. Consumers could monitor the health and performance of their connected household devices – everything  which alerts them it’s time to buy replacement parts from the manufacturer. With the rapid advances we’re seeing in AI, and the growth of IoT-enabled machines and devices, AIoT represents a shift in how manufacturers manage every part of their business and their wider supply chains. It gives them insight and oversight of asset performance and its impact on sales, procurement and customer relations, reducing the need for stockpiling and promoting forward-looking decisions.


SAS www.sas.com


automationmagazine.co.uk


AN INSIGHT AND OVERSIGHT OF ASSETS


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