Imagine this: You’ve had back pain for a few months, a sign that there might be a problem, but you refrain from going to your local doctor or physio to avoid expense. While you can survive the pain, it could become a major health issue down the line without having a check-up.
If we look at this through the lens of predictive maintenance– swapping you for a logistics management company, your back for a truck, and the doctor for a maintenance workshop– you can see how a lack of predictive maintenance can cost you.
Predictive maintenance models, powered by artificial intelligence (AI), can anticipate potential problems related to battery and engine performance to ensure the reliability and security of vehicles. By 2022, it was one of the top trends in the automotive industry. And it remains an extremely relevant talking point for the whole of the logistics industry too.
The technology enables more efficient repair for mission-critical equipment across the supply chain. But more importantly, preventing trucks or machinery from breaking down and being out of action means reduced costs, minimization of downtime, and delivery optimization.
Traditional to predictive maintenance models: The timeline
We’ve come a long way since the early 1900s when the ‘reactive maintenance’ attitude was to fix things when they broke. Quality control and maintenance checks became a priority when we started mass-producing with large factories like Ford Motor Company– a more preventive approach. But it was only after the second world war that the global community accepted maintenance as a concept, which trickled down to car manufacturers. Maintenance was born from increasing resilience in the face of adversity.
Then, servicing models became modernized and digitized with computer control databases to record updates. But, the most significant revolution is happening now; we no longer say there must be a check after achieving a specific variable. Instead, we are opening up conversations with the assets themselves – we are practically communicating with trucks. Today, internet of things (IoT)-powered trucks can give supply chain professionals a signal to accurately pinpoint what parts need replacing or repairing a long time before they break down. This is state-of-the-art maintenance: predictive maintenance.
Real-time updates
Truckers don’t want to be swapping tires too late or any wear and tear going unnoticed, which can lead to drivers getting hurt or delayed shipments. That would mean next-day delivery is out of the window, and that’s where the demand is right now and predictive maintenance can give companies a competitive edge.
- Logistics companies can now build near real-time systems depending on expense and sophistication. Say a driver has to cross a desert, AI systems with driver monitoring can do a real-time risk assessment to avoid potentially dangerous emergencies. If we zoom into tire pressure or the alignment of wheels, predictive maintenance starts with having control states. Then, IoT sensors can monitor pressure or notice dodgy alignment, gathering real-time data.
- If the tire variables seem out of sorts for an extended period, the wheel sensors can communicate with the truck’s main frame connecting all the data, which is fed to a network directly or read out at the next depot. This information allows time for human operators to predict issues before they arise.
- Some systems can check every few hours or once a day. Logistics planning or asset management professionals will ping the fleet for a status report. That can then be converted into a visualization of a truck with green, yellow, and red lights highlighting the condition of different parts, and giving an overview of the current asset status to know if it needs to be sent to a workshop soon.
Specific maintenance stops
With predictive maintenance, fleet managers can dynamically choose where to fix their trucks, bringing down costs significantly: They can sort all necessary checks en route instead of drivers going well out of their way.
- If truck drivers had a service level agreement (SLA) of two days to get from city A to B, but it was only a 2-hour drive, they could stop for a maintenance check where there are more affordable labor costs and trusted services. Stopping by the side of the road means costs are usually higher than dynamically choosing a budget workshop to do the fixing. That’s because maintenance becomes unscheduled rather than planned.
- It’s a way to keep drivers safer when going through dangerous routes or mountainous areas. If the status report shows that the brakes are in poor condition (and lit up in red), fleet managers can call the drivers and ask them to stop and do some checks. Then, if the trip is across Europe, for example, through Poland to the UK, they can stop to perform maintenance, since it is more affordable in Poland and they are passing through anyway.
Save costs
Great predictive maintenance strategies can impact a company’s revenue capacity.
- A successful AI-powered platform for the predictive maintenance of engines, brakes, and tires can increase vehicle uptime by up to 25% and save $2,000 per vehicle per year by cutting maintenance costs. For instance, ensuring perfectly inflated tires or having a slightly lower tire tread reduces the rolling resistance, gaining up to 6% better fuel efficiency.
- The cost savings trickle down to other connected expenses. Preventing breakages on the road can save companies hundreds of thousands of dollars. That’s because every trucking company has a SLA, and if they fail to deliver on promises, such as next-day delivery, there’s a penalty involved. It can start at $50,000 for the first time and quickly escalate to millions.
- If a trucking company breaks a contract, their client may look elsewhere for different transport. This is trucking companies' worst fear since they need to plan business ahead, buy or rent a certain number of trucks, and sign exclusive agreements with a particular number of companies.
Companies that astutely place IoT-based measuring devices throughout their assets permit direct communication with their equipment and vehicles. From there they can gain real-time vehicle performance updates to enable accurate maintenance predictions before an issue arises. Rather than only fixing what’s broken, forecasting servicing needs means logistics providers can minimize petrol and damage costs. While factoring in SLAs and package arrival deadlines, they can schedule maintenance stops within delivery routes at optimal moments.
Ultimately, AI-powered data insights are increasing overall uptime with predictive maintenance, allowing the logistics industry to maximize productivity and confidently meet SLAs with fully functioning vehicles.
