why manufacturers must prioritise data and optimisation

There are few industries facing as many complex and intersecting operational challenges as manufacturing. An ever-evolving business climate is disrupting the decision-making progress as much as it is impacting supply chains. Skilled labour shortages are limiting or delaying business growth (the Food & Drink Federation ‘State of the Industry Report Q4 2024’ stated the vacancy rate in food and drink manufacturing was 3.6%, while wider manufacturing was 2.3%) while rising costs are forcing manufacturers to find ways to achieve more, with less. Steve Adams, CEO, Lineview explains.

In response, manufacturers are adopting an increasing number of smarter, digital solutions that drive productivity and output improvements. The Factory of the Future – the industry vision for the implementation of advanced manufacturing technologies and processes – sets the scene for intelligent manufacturing. It will move traditional manufacturing into modern, optimised and sustainable operational practices resulting in highly efficient smart factories, and encourages bold and exciting moves in how manufacturers collect, analyse and interact with critical data points.

Identifying the right data

Manufacturers need access to the right data at the right time – and the ability to make business-level decisions based on this data – to improve production and operations. By 2035, we will see the adoption of new and emerging technologies, including artificial intelligence (AI), the industrial internet of things (IIoT), and Industry 4.0 technologies.

However, as new technologies are introduced and operational technology (OT) systems increasingly integrate with digital systems, manufacturers can expect to see their data volumes grow exponentially. In fact, ABI Research projects that industrial enterprises globally will generate a total of 4.4 Zettabytes (ZB) of data by 2030, up from 1.9 ZB in 2023. This will require a significant uplift in data capabilities over a relatively short period of time while other data related challenges will need to be addressed rapidly. For example, fragmented data silos across legacy systems make it nearly impossible to unify and analyse data points delaying data gathering and making reports unreliable while storage strain becomes another real concern as traditional on-premises systems weren’t designed for such scale.

Key performance metrics such as Overall Equipment Effectiveness (OEE), availability and efficiency are good starting points, as well as downtime, breakdown and minor stops which reveal the hidden causes of manufacturing costs and inefficiencies. Recent research from IDS-INDATA estimates that UK & European manufacturing will lose more than £80bn in 2025 due to unplanned downtime.

These insights help to deliver a strategic view of operational performance and losses across production environments, enabling factory teams to unearth opportunities that are often hidden in plain sight but have the potential to deliver huge financial gains. For example, a business with 50 production lines running at 50% OEE could create an additional five production lines worth of capacity – for free – by increasing their OEE by ten per cent.

The most valuable manufacturing data is accurate, up-to-date and provides a single source of operational truth across facilities. With access to both live and historical data, manufacturers can identify exactly where, when and why problems occur. Frequent changeovers, for instance, are one of the biggest causes of planned downtime and a major contributor to lost productivity. Strategic, data-driven adjustments made to changeovers alone could increase efficiency and reduce downtime, leading to significant cost savings.

Establishing a strong data foundation now is critical, particularly as AI and emerging technologies become increasingly integrated into operations. However, the right data is redundant without a workforce that can make business-level decisions based on data insights.

Empowering the people

In an industry struggling with skilled labour shortages, is data literacy a hiring priority, or should it be? According to a 2024 report by Barclays Corporate and The Manufacturer, almost 98% of manufacturers believe hiring and retaining skilled labour is a significant business challenge; 81% think digital skills are harder to acquire due to competition from other sectors.

The reality is that factory teams need improved knowledge of what the production lines are recording. Real-time analytics can only deliver a direct business impact if teams can interpret the data and, more importantly, remediate issues before they disrupt operations. For instance, factory teams should be empowered to view production line data on the programmable logic controller, identify what is affecting a bottleneck, and intervene quickly and accordingly to minimise disruption. Decisions like these should not require input from already time-pressured senior leadership teams, particularly as the C-suite and other executive stakeholders are likely more interested in studying higher level strategic outcomes that align with board-level priorities.

The Factory of the Future requires a data-driven, digitally competent workforce. That isn’t to say that factory teams need to have excellent data fluency. The days of manually collecting and analysing data are thankfully behind us as advancements in AI and automation tools have made great strides here. Yet the onus remains on the people to understand the data and then take relevant and timely action based on the information presented to them.

Today’s manufacturing intelligence tools are democratising data to reduce the data-skills gap. Supported by advanced technologies, like AI, these tools now do the heavy lifting, presenting and prioritising key data points to guide teams towards the most effective business decisions quickly. These tools also reduce the delays and errors associated with manual data entry and resolve any uncertainty around tabs that may be open to misinterpretation. This is particularly crucial for ensuring consistency of the data being recorded and enabling teams to draw, and act upon, accurate conclusions.

Investing the time into upskilling operators, engineers and team leaders in these areas will improve confidence in making data-driven, business-critical decisions. The most successful teams will be those that combine the best data, people and technology.

Bringing smart factories to life with AI

To help paint the picture: imagine choosing between a map or a GPS for a road trip. A map requires forward planning and navigation skills to identify the best route. Even then, a wrong turn or unexpected traffic could impact the journey. A GPS, on the other hand, guides users via the fastest, most efficient route based on live data. Users don’t need excellent navigation skills; they just follow the instructions and make informed decisions along the way. Think of manufacturing GPS as AI-powered data intelligence tools.

Intelligent production lines that use advanced technologies, such as IIoT devices and AI, are already delivering significant operational value by optimising processes and increasing efficiency. But these lines are capital-intensive, and even the smallest inefficiencies could still significantly disrupt performance and profitability.

With these tools, manufacturers can both detect and understand the causes of shifts in machine performance data. By continuously monitoring this data, AI can automate alerts when something moves outside of the expected range. Lineview customers are increasingly saying how important this early insight is in helping to bring any critical issues front and centre, ready to remediate. This is especially crucial for continuous-flow manufacturing operations where one minor problem can have huge consequences for many other tightly integrated lines and interdependent processes.

As one customer recently told Lineview: “My routine in the morning involves me having to get up, open my laptop, and look at every single line. There’s 22 of them. Some days I might only look at the top six or seven that are on the top of my mind. Now I don’t need to do that – I can look on the mobile app at what happened overnight and even take information around the planning, the customer service environment and say, ‘how can we optimise production in a particular place to remove transport costs from another’. I don’t need to spend hours trawling through that information anymore. I believe in the power that AI, on top of good data, can actually bring because it meets my criteria. This is about the quality of the data and the power of AI on top of that to bring us some really tangible productivity benefits.”

AI has the potential to deliver further value by providing recommendations based on historical performance. This added layer of context is key in helping teams understand exactly what is happening and why. Combining this data provides a 360 view of operations and helps manufacturers to shift operations from looking in the rear view mirror – analysing and discussing past problems to prevent them happening again – to predictive analytics and preventative operations. This is already underway; AI-driven tools are learning to collect machine and production line DNA that looks ahead and anticipates any potential issues before they become business critical.

By 2035, it’s likely that AI, automation and robotics will combine to double productivity across factories by achieving more from available assets and running more efficiently. However, such moves will require a new strategy. One that implements technological advancements and combines highly efficient and sustainable practices to reduce operational costs and drive market competition.

Bold moves deliver the biggest benefits

As the industry gears towards the Factory of the Future, manufacturers can begin to use these data intelligence tools as their own AI assistants to guide strategic decision making. With access to live and historical data, factory teams will better understand what is happening in their production lines and why, even when comparing the data across multiple sites and geographies.

All of these capabilities support broader business goals, particularly around the following three areas:

• Cost savings: Timely intervention, predictive operations and identifying hidden efficiencies all unlock cost savings. By achieving increased capacity in existing lines, manufacturers can drive additional revenue and, critically, delay the need for investment in new product lines. Advanced data insights are already optimising factories around the world. For example, Coca-Cola Europacific Partners increased OEE by 20% in 24 months, where a single percentage point improvement translates to millions in additional revenue.
• Sustainability: When AI is combined with techniques like Single-Minute Exchange of Die (SMED), automation, and pre-staging, manufacturers can reduce downtime, improve consistency, and increase productivity. These improvements directly support sustainability goals by reducing waste and using energy and water more responsibly throughout manufacturing processes. In Lineview’s recent benchmarking report, it was recorded that a leading manufacturer in Thailand saved approximately one billion litres of water in its operation as a result of improved efficiency and data-driven decision making.
• Better workplace culture: An improved understanding of data and how to use it can significantly boost team morale and engagement. When teams actively contribute to improved business results, they feel more motivated and confident, creating a stronger operating environment where continuous improvement becomes part of the culture. This also reduces the reliance on leadership teams to make business level decisions, freeing up time to focus on strategy and other more meaningful tasks.

These combined benefits will not only help manufacturers address some of the most pressing business issues during this critical time in operations but will also pay dividends in increased competitiveness and help attract investment.

Collaborating towards a more productive future

Making decisions based on yesterday’s data will not address tomorrow’s problems. Smart factories need to operate with the future in mind, which means real-time and predictive data that measures key metrics that inform and guide business-level decisions. These real-time technologies should also support cross-site collaboration and deliver on productivity and sustainability metrics by providing an agreed benchmark on what good looks like in the space.

Benchmarking data will become all the more critical in mapping progress and setting performance goals within the next ten years. With improved visibility, businesses can compare performance across lines, factories, and different companies, and identify what good performance should look like. Benchmarking is especially crucial as the industry navigates labour shortages, rising costs, and sustainability targets. This reinforces the central role of data in laying the foundations for the Factory of the Future.