Predictive maintenance helped win a war. Now, it can help you outpace the competition.
The year is 1943. Britain’s survival still hangs by a thin, precarious thread, despite America joining the war effort. Just a few months ago, in February 1942, two German battleships, the Scharnhorst and the Gneisenau, alongside the heavy cruiser Prinz Eugen, and their escort fleet executed Operation Cerberus — a daring raid passing through the English Channel from their French port of Brest at the western cape of Normandy. Britain is not merely humiliated — not even the Spanish Armada has been able to cross the Channel in 1588 — , it is also existentially threatened. If it cannot protect its coastal waters, it may lose the Battle of the Atlantic, and with that the transatlantic lifeline of shipping that has been keeping it in the fight. If not for regular transatlantic convoys, carrying anything from tanks to milk powder, Britain would have been under immense pressure to surrender amidst starvation and undersupply. And now, the Channel Dash, as the raid in 1942 will be known, has shown it cannot even keep its own littoral waters safe.
Along the Royal Navy, the responsibility for that lay with Coastal Command, the junior home command of the Royal Air Force (alongside the Bomber and Fighter commands), trying to provide anti-submarine warfare capabilities when anti-submarine warfare was still a novel art and its mainstay tools, radar and sonar (then known as ASDIC), were nascent at best. They were also plagued by equipment issues, with the Coastal Command’s B-24 Liberator anti-submarine bombers spending more time in repair than flying — in some squadrons, at any given time, less than half of the aircraft were operational, the rest awaiting scheduled or unscheduled maintenance.
At their headquarters in Northwood, the Operational Research (OR) unit of the Coastal Command turned its attention to this very problem. Its leader, C.H. Waddington, was an unlikely choice — a distinguished developmental biologist, poet and artist, he was drafted into service to oversee the Coastal Command’s OR, despite having no experience with aviation or aeronautical engineering. It was here that he made his greatest contribution to the war effort — his discovery of the Waddington effect. Paradoxically, he noted, unscheduled repairs were more, not less, frequent after scheduled maintenance.
This flew in the face of conventional wisdom, which was that frequent scheduled preventive maintenance, every 50 flight hours, would keep the squadron flying. This was regardless of whether the aircraft needed it or not, often putting perfectly airworthy aircraft out of commission for unnecessary scheduled maintenance. Waddington didn’t merely say scheduled maintenance was useless, he went further — it could, he writes in his diaries (declassified in the 1970s and published as O.R. in World War II),
…increase breakdowns, and this can only be because it is doing positive harm by disturbing a relatively satisfactory state of affairs. There is no sign that the rate of breakdowns is starting to increase again after 40–50 flying hours when the aircraft is coming due for its next scheduled maintenance.
His thinking, however counter-intuitive, has been hugely successful. An early version of condition-based maintenance (CBM), where aircraft underwent repairs based on reported issues and a quick post-flight examination resulted at a 60% increase in effective flying hours by the Coastal Command. Recognising the counterintuitive yet vital Waddington effect helped secure Britain’s coastal waters and with that, played a significant role in the Allied victory in the Battle of the Atlantic.
The rest, as they say, is history.
Predictive maintenance helped win a war. Today, it’s helping manufacturing companies outpace their customers. As the keystone of the Industry 4.0 stack, interest in predictive maintenance is almost ubiquitous among the entire manufacturing spectrum. A 2005 Nielsen survey of automotive manufacturers estimated the cost of unplanned downtime to be around $22,000 per minute — up to $50,000. A more recent figure by Aberdeen puts this, across all industries, to $260,000 per hour on average. More worryingly, this figure is rapidly climbing, rising by 60% just over two years as manufacturing processes became increasingly dependent on sophisticated, complex machinery that often requires specialist off-site services to fix. No doubt with the increasing use of lean factories and just-in-time manufacturing, this figure is bound only to skyrocket. Adding the risk that catastrophic unplanned failures pose to high-value capital equipment and the potential human cost, unplanned breakdowns have earned their place as one of the top concerns of manufacturing executives, over half of whom have named unplanned downtimes as their #1 concern.
Predictive maintenance goes counter to two extremes — reactive maintenance (‘if it ain’t broke, don’t fix it!’) and scheduled maintenance, which Waddington so astutely proved to be worse than useless. It could be better described as ‘data driven maintenance’: failure risk is calculated based on known indicators associated with failure events, and maintenance is initiated once so-called prodromic patterns — signs of still-normal operation that however indicate an impending breakdown — are detected. This allows for a controlled shutdown, safe operating procedures and a degree of flexibility in repair. Between under- and overmaintenance, predictive maintenance offers the golden middle road of ‘just right’. And with the ubiquity of unplanned downtime — according to a Vanson Bourne study of 450 field service decision makers across a range of industries, 82% of companies have suffered at least one unplanned outage over the preceding three years — , getting predictive maintenance right can be the keystone of outpacing the competition and operating a high-performance business: whether it’s manufacturing, operating costly capital assets like oil rigs or safety-critical operations like healthcare equipment or nuclear power plant, predictive maintenance must be a strategic cornerstone of all operations where there’s just no room for error.
Starschema’s data scientists have decades of combined experience with predictive maintenance tasks, including designing the best predictive maintenance strategy for your business regardless of your operations. With an in-depth understanding of not just the disciplines of operations research and data science but also of sensorics, equipment communication and industrial IoT, the Starschema team brings a powerful integrated approach to your maintenance tasks. You can download our white paper on predictive maintenance in manufacturing for free, which outlines our approach, methodology and track record of successful engagements.