In his final & 5th Blog of the series ’23 lessons to World Class Asset management’, Dr Mike Provosts summarises his thoughts on how to achieve World Class Asset Management.
Other blogs in the series are:
Part 3: Analysis and Visualisation
Part 4: The Hard Stuff that comes from Experience
Part 5: Concluding thoughts and case studies
During the last 30 years of professional life, a key lesson I have learned is that Asset Management has to be business focussed, because a business is affected by what an asset does, not what it is. It’s also very beneficial to take a broad view, since the benefits of Asset Management may not lie where you first think; building on this, data integration gives synergies that create unexpected value and delight customers. Asset knowledge is critical; physics-based models build the foundations for full understanding of asset and business dynamics. Things must be kept simple and visible, if you want your efforts to be accepted and acted on. It must always be remembered that cost is not value; the cost-cutters need reminding of this frequently. An Asset Management business cannot be created without developing the three key ingredients; people, processes and tools. Human factors must never be underestimated, because they will dominate your efforts. Finally, I’ve always found that perseverance pays off, both personally and professionally. Here are some of the stories and experiences I have come across where success has depended on the application of the 23 lessons we have discussed in this blog series.
- In the 1970’s, the chairman of a well-respected European airline, when hearing that an experimental aircraft engine Condition Monitoring program could have prevented a turn-back of a wide-body airliner if its output had been heeded, demanded that it be put into fleet-wide use immediately. He didn’t require formal justification; he knew that his airline’s technical and financial performance and reputation would benefit if this was done.
- Since the 1980’s, many airlines have used engine monitoring to optimally dispatch aircraft, sending those with ‘hot’ engines to cooler destinations and vice-versa. This strategy extends engine on-wing lives and results in fewer engine over-temperature events, avoiding service disruptions.
- Data collection doesn’t have to be expensive and complex. In the 1980’s, one major European airline equipped all their check-in desks worldwide with optical character readers, so that passenger service staff could feed engine and aircraft data to their main engineering base from cockpit printouts when they were not serving paying customers. A worldwide data gathering network, riding on the back of the ticketing system, was created for a few tens of thousands of dollars.
- Another major European airline has amassed so much data on the performance of the aircraft, engines and other sub-systems that they operate that suppliers regularly use this ‘treasure trove’ to initiate design changes to in-service aircraft. In one case, the hydraulic system of a wide-body aircraft was completely redesigned based on data from one take-off during which an uncommanded pitch down was recorded.
- In the 1980’s, one somewhat sceptical power station manager in the UK shut down a large steam turbine on the basis of the output from an experimental vibration monitoring system. When the turbine was opened up and inspected, a crack was found in the main shaft that would have resulted in catastrophic failure and potential fatalities had the turbine run for another thirty minutes. He was convinced!
- The aircraft gas turbine industry depends heavily on physical models, which have reached such a degree of accuracy and sophistication that they form the basis of operational and maintenance forecasts that can be produced for each customer covering the whole life of an engine fleet. Thanks to these models, engine development programs are now used to validate the engineering understanding the models have already produced, rather than generating that understanding ‘from scratch’, resulting in huge savings of time and money. The models also create foundations for a great many sophisticated analytical approaches to Condition Monitoring.
- One major gas turbine manufacturer found it necessary to create a separate company to develop Condition Monitoring and other aftermarket service capabilities in order to prevent the prevalent ‘manufacturing mind-set’ killing off the ideas being developed before they had a chance to prove themselves.
- There are many examples in the railway industry of sensors being fitted for one purpose generating more value when being used for something else. For example, air suspension pressures are used to produce estimates of passenger count, while electrical faults and wheel slip protection system activations observed across a fleet are mapped to indicate areas of the rail network that require maintenance action and data recorded for potential incident and accident investigations is used to find the causes of service delays and attribute penalty payments appropriately.
- One major UK rail operator has eliminated the need for passenger door fault-finding activities at their engineering depots by relying entirely on the data such as opening and closing times and door actuator motor currents from millions of door operation cycles gathered from the in-service train fleet to accurately predict and schedule any necessary door maintenance activities.
- Another UK train operator transmits a ‘mimic’ of each driver’s control panel to a central control room in real time, enabling support staff to give timely advice to drivers and other train crew.
- One major UK truck manufacturer discovered that the sensors used to monitor diesel engines can be used to monitor driver behaviour. They now offer a service that uses this data to progressively improve driving styles, producing significant reductions in trip delays, accidents, insurance premiums and fuel consumption. The customers and drivers share the benefits, producing the necessary positive feedback to ensure success.
- One major Formula 1 team uses Condition Monitoring data to model the performance of each and every car in a race in real time, using these models to predict race outcomes and run ‘what-if’ analyses to optimise their refuelling, choice of tyres and pit stops.
- Many van and truck businesses now use real-time GPS and other vehicle data to track fleet performance, thus reducing costs and improving customer service. At least two major car manufacturers are extending this philosophy into the consumer arena, to offer comprehensive real-time advice and support to private motorists. This is felt to be particularly useful for battery-powered private vehicles, to overcome ‘range anxiety’ and instil confidence in new technologies.
- Almost all road vehicles are now fitted with comprehensive on-board diagnostics, reducing maintenance times and costs. Even owners with the right smartphone ‘app’ can now access detailed real-time information on the performance of their vehicles.
- Remote diagnostics are crucial for both maintenance and operational planning of a wide variety of critical, high-value or difficult-to-reach plant, including petrochemical and other process plant, water, gas and electricity networks, wind turbines (particularly those placed offshore), power stations, backup power units for mobile phone masts, etc.
- Human health monitoring is becoming increasingly important, as populations grow older and healthcare resources are stretched. I have successfully monitored my own blood pressure, weight, urinary function, food intake and exercise for nearly a decade, using very simple tools and visualisations to achieve huge improvements in my health and wellbeing and possibly saving my life in the process.
These are just a small selection of the many success stories that are emerging as companies in many industrial sectors begin to appreciate the power of knowledge about how their assets they make and use are behaving in service and how this knowledge can be used to improve their businesses and satisfy their customers.
Note: This series of blogs is based on material originally produced for the Society of Automotive Engineers (Jennions 2014): it has also been presented as a paper at the IET/IAM 2014 Asset Management Conference in London, UK (Provost 2014). It is reproduced with permission from both the SAE and the IET.
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