www.hpmag.co.uk HYDRAULICS & PNEUMATICS February 2026 43 to-end approach is to make shop-floor data usable beyond the machine or maintenance silo. Information is intended to flow into enterprise environments such as MES, CMMS, EAM and OEE platforms, as well as enterprise dashboards, where it can support production management, maintenance planning, asset performance analysis and utilisation decisions. The underlying argument is about reducing supplier fragmentation: limiting the need for manufacturers to assemble and manage multiple vendors to move data from sensors to enterprise consumption. This focus on integration highlights a common weakness in many Industry 4.0 programmes. Proof-of-concept projects often succeed in demonstrating that data can be captured but fail to show how it will be embedded in existing operational processes. Coghlan’s criticism is direct: data that cannot be acted upon adds little value. The opportunity lies in ensuring that information collected on the shop floor can trigger work orders within CMMS or EAM systems, inform production decisions through MES, or contribute meaningfully to OEE analysis, rather than remaining confined to isolated dashboards. Artificial intelligence has intensified attention on these fundamentals. While AI is widely promoted as transformational, its effectiveness in manufacturing depends entirely on the quality and context of the data it consumes. Jonathan Parr, who leads the Manufacturing Operations practice at Accenture UK, is explicit on this point, stating that manufacturers “need to have the availability of the data … to be able to plug AI into and be able to utilise that”. Without reliable, contextualised shop-floor data feeding enterprise systems, AI risks producing outputs that are mistrusted or operationally unsafe. Concerns around cyber security inevitably accompany greater connectivity, particularly as data moves closer to enterprise platforms. The discussion, however, is shifting away from fear and towards design discipline. Winter emphasises that principles such as encryption, network segregation and careful protocol selection must be treated as integral to system design rather than afterthoughts. In retrofit scenarios, parallel data-acquisition architectures can, when properly designed, limit exposure of legacy control systems while still enabling enterprise-level data flows. Skills shortages Skills shortages continue to shape what is practical. Many manufacturers are losing experienced engineers faster than they can replace them, taking decades of tacit knowledge with them. There is cautious optimism that better integration between shop-floor data and systems such as EAM and CMMS can help capture operational knowledge, standardise responses and support less experienced staff. However, this depends on disciplined data definition and deployment, not on technology alone. Levels of digital maturity across UK manufacturing remain highly variable. Some operations are heavily automated and already feeding data into MES and OEE frameworks, while others rely on manual processes with limited visibility. That disparity creates different development paths. Highly automated sites face the complexity of retrofitting legacy systems at scale, whereas less automated facilities may be able to progress more quickly by deploying modern sensing and connectivity technologies that are now cheaper and easier to integrate with enterprise systems. Taken together, these perspectives point towards a more grounded understanding of digital transformation. Rather than pursuing idealised architectures, manufacturers are increasingly focused on value, scalability and speed to insight. Winter’s emphasis on simplification and Parr’s focus on data readiness, alongside Coghlan’s insistence on actionable data and end-to-end responsibility, reflect a broader shift towards pragmatism. Lasting progress will depend not on digitising poor processes or accumulating data for its own sake, but on ensuring that shopfloor information can move reliably into MES, CMMS, EAM and OEE environments where it supports decisions that materially affect performance. For further information please visit: https://www.turckbanner.co.uk https://www.accenture.com
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