May 17, 2020

Unlocking predictive maintenance’s potential take partnerships

Maintenance
Take partnerships
aircraft
Predictive Maintenance
Nye Longman
5 min
Unlocking predictive maintenance’s potential take partnerships
What if an aircraft part could tell you ahead of time that it needs service or replacement before its regularly scheduled preventive maintenance time? W...

What if an aircraft part could tell you ahead of time that it needs service or replacement before its regularly scheduled preventive maintenance time? Well, it can… with predictive maintenance based on constant monitoring of operating conditions, activities and events. Predictive maintenance in aviation offers the promise of higher levels of reliability and safety while reducing waste and unnecessary aircraft on ground (AOG) downtime. But it can only deliver maximum benefits when there is a true partnership between an MRO and an airline’s operations, engineering and technical departments.

Preventive To Predictive—The Path Forward

Preventive maintenance, a common industry practice for two decades, is based on a stringent requirement to repair or replace parts and systems before they fail to assure an aircraft’s safe, continued operation. It relies on engineering data and operational experience to determine the appropriate point in time to make the repairs—such as replace a part after 5,000 flight hours because studies indicate reliability can’t be assured after that point.

While preventive maintenance in aviation is largely responsible for the outstanding safety and reliability of today’s aircraft fleet, its effectiveness is limited by the backward-looking nature of the analyses. Based on history and statistics, the practice is, in truth, a large step removed from the realities of everyday operational usage. Each aircraft and system is subject to its own set of conditions and experience, yet a preventive maintenance schedule is based on averages. Due to the variability of actual experience, some good parts are replaced too soon, while others may fail before the prescribed replacement schedule.

In contrast, predictive maintenance in aviation identifies a part’s risk of failure through analysis of actual operational data. It’s firmly embedded in the specific use and experience of aircraft and systems, and takes advantage of sophisticated analytics to identify risk and recommend appropriate preventive actions. It can also allow for a more optimised operation of a component, and expand the on-condition concept to enable the use of less expensive components and parts.

Thanks to an increase in sensor-enabled aircraft—fueled by a decrease in the price of sensors—predictive maintenance is growing in acceptance and use in the aviation industry. A sterling example of the growing influence of Big Data and the Internet of Things (IoT), predictive maintenance leverages these evolving technologies to take proactive maintenance, and the aircraft reliability and safety it offers, to an entirely new level.

The Team Approach

Today’s aircraft are loaded with sensors, generating valuable streams—upwards of 100MB of data per flight hour—of real-time performance data according to FLYHT Aerospace Solutions. The key to predictive maintenance is applying analytics to that vast data stream to identify patterns and trends that can direct the maintenance strategy.

Big Data analytics can help identify risk of future failure based on sensor feeds.  But additional context, such as engineering data and past maintenance history—for the part in general and for the specific aircraft—make the analysis much more meaningful. Once the predictions are in hand,  the ecosystem of aircraft builders, operators, maintenance professionals and logistics providers combine to make up a truly effective predictive maintenance program that reaps maximum value.

More data and better analytics allow airlines to have more accurate intelligence on when aircraft or its parts will need service or repair and what types of maintenance issues are likely to arise. Then they have the luxury of time: the ability to schedule the work at a time and place potentially more convenient and more economical than if the repair was required unexpectedly. And since predictive maintenance is more precise than average-based or historically based preventive maintenance schedules, it is more effective. Serviceable parts won’t be replaced unnecessarily simply because they meet historical criteria. Sensor data tracks wear, usage and performance for that specific part in that specific airframe.

This increased effectiveness and convenience is only achievable, however, when the aircraft operator and the related predictive maintenance program are tightly integrated with the MRO operation. Experienced MRO partners have the knowledge and skill to control the resource positioning necessary to quickly respond to maintenance requirements, getting the aircraft back into service without delay.

Additionally, the MRO is positioned to respond to the unexpected. Predictive maintenance, no matter how sophisticated, won’t identify every potential failure. Parts will still break, and additional repairs will be discovered in the course of scheduled maintenance and inspection, and repairs will be required.  But IoT data, combined with an MRO’s own repair and maintenance records, will provide additional information the MRO’s team can use to fine-tune the deployment of material and resources for maximum effect.

The more data available to an MRO and the deeper its experience with a broad range of aircraft, the more able it is to reduce the number of unscheduled events and deliver better outcomes. As one of the world’s largest independent MROs, AAR has extensive experience with a broad range of aircraft and can leverage its comprehensive knowledge and objective expertise with data as a value-added benefit to airline operators. This can lead to strategies that avoid unscheduled maintenance events and unnecessary spare-parts deliveries, as well as limit part failure and reduce total part costs by replacing components before they cause breakdowns. This mutually beneficial relationship helps an airline fully leverage the benefits of predictive maintenance.

Trust Remains Key

Given the breadth and depth of data necessary to fully reap the benefits of predictive maintenance, it is essential to build strong trust between an MRO and the flight operations, engineering and technical departments of airline partners. This type of relationship is critical, as the airline must provide thorough, accurate and timely data, while the MRO must protect the data’s confidentiality while utilising it for mutual benefit. When this team approach is a reality, only then combined organisations can find the right balance between maintenance services, inventory management, parts replacement and cost control. Most importantly, together the team can reduce costs, minimise AOG time, enhance safety and improve aircraft reliability for the benefit of the airline and its suppliers, partners and customers.

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John Holmes is vice president and COO of the Aviation Services division of AAR Corp (NYSE: AIR), a leading aviation support company that serves commercial airline and government customers worldwide. The Aviation Services division Holmes oversees employs approximately 5,000 of AAR’s 6,000 people in over 20 countries. 

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Jun 11, 2021

NTT DATA Services, Remodelling Supply Chains for Resilience

NTTDATA
supplychain
Supplychainriskmanagement
Procurement
6 min
Joey Dean, Managing Director of healthcare consulting at NTT DATA Services, shares remodelling strategies for more resilient supply chains

Joey Dean, the man with the coolest name ever and Managing Director in the healthcare consulting practice for NTT DATA and is focused on delivering workplace transformation and enabling the future workforce for healthcare providers. Dean also leads client innovation programs to enhance service delivery and business outcomes for clients.

The pandemic has shifted priorities and created opportunities to do things differently, and companies are now looking to build more resilient supply chains, none needed more urgently than those within the healthcare system. Dean shares with us how he feels they can get there.

A Multi-Vendor Sourcing Approach

“Healthcare systems cannot afford delays in the supply chain when there are lives at stake. Healthcare procurement teams are looking at multi-vendor sourcing strategies, stockpiling more inventory, and ways to use data and AI to have a predictive view into the future and drive greater efficiency.

“The priority should be to shore up procurement channels and re-evaluate inventory management norms, i.e. stockpiling for assurance. Health systems should take the opportunity to renegotiate with their current vendors and broaden the supplier channel. Through those efforts, work with suppliers that have greater geographic diversity and transparency around manufacturing data, process, and continuity plans,” says Dean.

But here ensues the never-ending battle of domestic vs global supply chains. As I see it, domestic sourcing limits the high-risk exposure related to offshore sourcing— Canada’s issue with importing the vaccine is a good example of that. So, of course, I had to ask, for lifesaving products, is building domestic capabilities an option that is being considered?

“Domestic supply chains are sparse or have a high dependence on overseas centres for parts and raw materials. There are measures being discussed from a legislative perspective to drive more domestic sourcing, and there will need to be a concerted effort by Western countries through a mix of investments and financial incentives,” Dean explains.

Wielding Big Tech for Better Outcomes

So, that’s a long way off. In the meantime, leveraging technology is another way to mitigate the risks that lie within global supply chains while decreasing costs and improving quality. Dean expands on the potential of blockchain and AI in the industry

“Blockchain is particularly interesting in creating more transparency and visibility across all supply chain activities. Organisations can create a decentralised record of all transactions to track assets from production to delivery or use by end-user. This increased supply chain transparency provides more visibility to both buyers and suppliers to resolve disputes and build more trusting relationships. Another benefit is that the validation of data is more efficient to prioritise time on the delivery of goods and services to reduce cost and improve quality. 

“Artificial Intelligence and Machine Learning (AI/ML) is another area where there’s incredible value in processing massive amounts of data to aggregate and normalise the data to produce proactive recommendations on actions to improve the speed and cost-efficiency of the supply chain.”

Evolving Procurement Models 

From asking more of suppliers to beefing up stocks, Dean believes procurement models should be remodelled to favour resilience, mitigate risk and ensure the needs of the customer are kept in view. 

“The bottom line is that healthcare systems are expecting more from their suppliers. While transactional approaches focused solely on price and transactions have been the norm, collaborative relationships, where the buyer and supplier establish mutual objectives and outcomes, drives a trusting and transparent relationship. Healthcare systems are also looking to multi-vendor strategies to mitigate risk, so it is imperative for suppliers to stand out and embrace evolving procurement models.

“Healthcare systems are looking at partners that can establish domestic centres for supplies to mitigate the risks of having ‘all of their eggs’ in overseas locations. Suppliers should look to perform a strategic evaluation review that includes a distribution network analysis and distribution footprint review to understand cost, service, flexibility, and risks. Included in that strategy should be a “voice of the customer” assessment to understand current pain points and needs of customers.”

“Healthcare supply chain leaders are re-evaluating the Just In Time (JIT) model with supplies delivered on a regular basis. The approach does not require an investment in infrastructure but leaves organisations open to risk of disruption. Having domestic centres and warehousing from suppliers gives healthcare systems the ability to have inventory on hand without having to invest in their own infrastructure. Also, in the spirit of transparency, having predictive views into inventory levels can help enable better decision making from both sides.”

But, again, I had to ask, what about the risks and associated costs that come with higher inventory levels, such as expired product if there isn’t fast enough turnover, tying up cash flow, warehousing and inventory management costs?

“In the current supply chain environment, it is advisable for buyers to carry an in-house inventory on a just-in-time basis, while suppliers take a just-in-case approach, preserving capacity for surges, retaining safety stock, and building rapid replenishment channels for restock. But the risk of expired product is very real. This could be curbed with better data intelligence and improved technology that could forecast surges and predictively automate future supply needs. In this way, ordering would be more data-driven and rationalised to align with anticipated surges. Further adoption of data and intelligence and will be crucial for modernised buying in the new normal.

The Challenges

These are tough tasks, so I asked Dean to speak to some of the challenges. Luckily, he’s a patient guy with a lot to say.

On managing stakeholders and ensuring alignment on priorities and objectives, Dean says, “In order for managing stakeholders to stay aligned on priorities, they’ll need more transparency and collaborative win-win business relationships in which both healthcare systems and medical device manufacturers are equally committed to each other’s success. On the healthcare side, they need to understand where parts and products are manufactured to perform more predictive data and analytics for forecasting and planning efforts. And the manufacturers should offer more data transparency which will result in better planning and forecasting to navigate the ebbs and flows and enable better decision-making by healthcare systems.

Due to the sensitive nature of the information being requested, the effort to increase visibility is typically met with a lot of reluctance and push back. Dean essentially puts the onus back on suppliers to get with the times. “Traditionally, the relationships between buyers and suppliers are transactional, based only on the transaction between the two parties: what is the supplier providing, at what cost, and for what length of time. The relationship begins and ends there. The tide is shifting, and buyers expect more from their suppliers, especially given what the pandemic exposed around the fragility of the supply chain. The suppliers that get ahead of this will not only reap the benefits of improved relationships, but they will be able to take action on insights derived from greater visibility to manage risks more effectively.”

He offers a final tip. “A first step in enabling a supply chain data exchange is to make sure partners and buyers are aware of the conditions throughout the supply chain based on real-time data to enable predictive views into delays and disruptions. With well understand data sets, both parties can respond more effectively and work together when disruptions occur.”

As for where supply chain is heading, Dean says, “Moving forward, we’ll continue to see a shift toward Robotic Process Automation (RPA), Artificial Intelligence (AI), and advanced analytics to optimise the supply chain. The pandemic, as it has done in many other industries, will accelerate the move to digital, with the benefits of improving efficiency, visibility, and error rate. AI can consume enormous amounts of data to drive real-time pattern detection and mitigate risk from global disruptive events.”

 

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