Can better oversight prevent overstocking?
Overstocking 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. But there are ways to work smarter, explains Duncan Bain:
”Despite being more data-driven than ever, manufacturers still often lack the insights they need to be confident in 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.
Creating Detailed Insights
Inevitably, this means both cash flow and profit margins take a hit, at a time when many firms are already struggling due to ongoing high energy costs and skills shortages. They’re faced with a conundrum: either leave their cash tied up in fixed assets, like machinery and 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 costs millions of pounds and takes six months to produce. Manufacturers might sensibly 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 floor.
Businesses must weigh up the complexities – and potential unintended consequences – of maintaining existing machines versus buying new ones. Could they get more from their existing investments if they improve 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 efficient?
AIoT Provides Faster Responses
Coming to the right decision isn’t easy, since nobody can see into the future. But you can make it more confidently if you have detailed and forward-looking insights on asset performance by fitting sensors to machinery.
Data on machine speed, energy-efficiency, capacity and output, as well as 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 data, manufacturers can understand exactly 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, increasing the longevity of their machinery or highlighting issues that might indicate it’s time to replace it.
AI is Bringing New Ways of Thinking
AI of Things (AIoT) is the next step in the 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 different parts of the business to determine whether, for instance, the output of a particular machine will be sufficient to meet forecasted 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.
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 from coffee machines to cars – via a phone app, 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.”
By Duncan Bain, Senior System Engineer at SAS Institute
