Article By Richard Gerdis, VP APAC, LogicMonitor
Unplanned downtime costs Australian businesses an average of $349,000 AUD per hour, significantly surpassing the global average of $194,000 AUD. Despite this substantial financial impact, many manufacturers continue to rely on reactive maintenance strategies, leaving their operations vulnerable to unexpected disruptions.
Today, manufacturing uptime depends less on mechanical reliability and more on catching small system-wide failures before they bring operations to a stop. Traditional monitoring reacts only after problems occur, offering too little visibility to keep production running at full speed. Unified observability changes this by giving manufacturers a live, connected view across their operations, exposing risks early enough to act before anything breaks down.
Aggregating telemetry from information technology (IT) and operational technology (OT) systems gives teams the complete picture they need to move fast. Reading behaviours across machinery sensors, industrial control systems (ICS), networks, and applications highlights the early signs of trouble that isolated systems miss. This means that issues that wouldn’t even trigger an alert in a traditional setup start to stand out, including pressure fluctuations, network latency spikes, and minor control errors. Machine learning (ML) helps sort through the noise, picking up patterns that hint at deeper problems. This kind of visibility shifts manufacturers from relying on fixed maintenance schedules to addressing real risks as they emerge, saving time, cost, and unnecessary downtime.
AI-powered observability builds on this by automatically grouping related incidents across systems, prioritising the most critical issues, and reducing alert noise. Teams can focus their efforts where it matters most, respond faster, and spend less time investigating false positives or disconnected alerts.
Keeping production moving isn’t just about what happens inside the factory. Supply chain dynamics play a direct role in operational resilience, and unified observability tracks supplier deliveries, inbound logistics, and material usage right alongside production telemetry. Production managers that detect supply delays or transport bottlenecks early can shift schedules, reallocate resources, and reroute workflows before shortages slow the line. The ability to model disruption scenarios in real time means manufacturers no longer scramble to react after the fact; instead, they build flexibility into their operations and stay ahead of problems.
Manufacturers can use observability to monitor supplier performance and logistics in real time. Teams can improve forecasting accuracy and make proactive scheduling changes when they identify trends in delivery delays or fluctuating inventory levels.
Connecting IT and OT systems is also essential for fast, accurate problem resolution. Isolated monitoring often misses how system slowdowns or equipment issues relate to software performance. Unified observability integrates both, giving teams the ability to troubleshoot holistically and resolve issues that cross technical domains. This correlation becomes more powerful when additional machinery is connected and digitised.
However, the real advantage emerges when manufacturers use observability for more than fault detection. Analysing how equipment, maintenance schedules, production rates, and supply chains interact uncovers problems that simple monitoring would miss. Root cause analysis becomes faster and sharper when teams can see the full chain of events, rather than focusing only on the last failure point, and spotting patterns like repeated micro-failures across similar machines flags design weaknesses or operational mismatches that can be corrected across the fleet. Improving efficiencies based on real operational intelligence lifts yields, cuts costs, and raises system reliability without relying on heavy new investments.
Observability also reduces manual ticket handling. Support teams spend less time sorting through repetitive incidents when alerts include relevant context and are grouped automatically. This improves productivity and lets them focus on fixing the problem instead of managing the process.
Keeping production running depends on more than strong equipment. It requires live visibility across every part of operations so teams can spot issues early and act before they turn into bigger problems. Unified observability gives manufacturers the insight they need to protect output, adjust quickly when supply chain pressures build, and keep production lines moving despite everyday challenges. Staying ahead of problems, rather than reacting after the fact, separates manufacturers that stay competitive from those that fall behind as pressures on margins grow.
Manufacturers that still rely on siloed, reactive monitoring expose their operations to greater risks of downtime, production delays, and rising costs while those that move to unified observability spot disruptions earlier, make faster decisions, and keep production stable even when conditions change.
