IoT in Manufacturing: Improving Efficiency and Reducing Downtime with
The ‘sci-fi-like’ benefits that IoT promised in its early days are beginning to show. One area the impact is resounding is manufacturing.
So huge is the impact that many experts have opined that for manufacturing, the Internet of Things (IoT) revolution in manufacturing is akin to the Industrial revolution. McKinsey is already projecting the industrial Internet of Things (IIoT) market to grow to a $500 Billion market by 2025. That's not only huge, but also extremely fast considering that IoT is a very recent trend.
It’s not hard to see why Industrial IoT is surging. Take manufacturing as an example. In the not so distant past, human labor was heavily used for identifying functional challenges in manufacturing equipment. Remember that this is post-downtime, meaning the equipment malfunctions, then the human technicians have to go looking for the root cause of the problem. This process is tedious, time consuming and costly. But then there was no alternative, and this is the best way manufacturers could deal with it.
The entry of IoT in manufacturing through solutions like IoT application development has changed this completely. Instead of post-downtime diagnosis, IoT has ushered in the era of predictive maintenance.
So, how is IoT being applied in predictive maintenance across the manufacturing industry? Let’s address this in detail.
If you are new to IoT in Manufacturing, you may want to start with this guide about IoT based digital transformation for the manufacturing industry.
What is predictive maintenance?
Predictive maintenance is an approach that uses data analysis to determine in advance when equipment is likely to fail.
Potential defects are detected early enough before they actually occur. The defects are then acted on before they can cause downtime.
This approach has emerged as a better solution compared to preventive or reactive maintenance.
Preventive maintenance is when equipment is stopped deliberately so that maintenance can be performed on scheduled basis. Reactive maintenance is when the equipment suddenly stops due to an emergency defect. These traditional approaches are costly and disruptive.
Predictive maintenance, on the other hand, is precise and backed by data. This means that predictive maintenance is driven by factual foresight, and not just reactions or assumptions.
A variety of data sources are used in predictive maintenance. The top ones include historical data, sensor data, and machine learning algorithms.
Once this data has been analyzed, the predictive maintenance software will issue alerts that show when a particular equipment is likely to fail. These alerts then trigger the maintenance process. These processes could include repairing the machine or replacing it. All this needs to happen before the predicted date of failure.
IoT-driven predictive maintenance in manufacturing
Estimates have shown that on average, a typical manufacturer suffers up to 15 hours of machine down time per week. This translates to about 800 hours of downtime per year. Overall, industrial downtime is estimated to cost manufacturers up to 50 billion annually. A staggering amount!
But IoT is changing this through predictive maintenance.
Below are the key elements that bring IoT powered to life in manufacturing
- Sensors connected to equipment
- Intelligent connected networks
- Remote monitoring
- Real-time collection and analysis of data
- Detecting machine conditions
- Downtime prediction
Obviously, the foundation of predictive maintenance in manufacturing or indeed any other industry is DATA. Where does the data come from? The primary source is sensors connected to the machines. Other sources could include
- Operator notes
- Operational information
- The environment
- Machine specifications
- Business systems such as ERP
This data is then sent to a special software (the IoT application), where it is analyzed. The results of the analysis inform maintenance actions .
IoT applications can be used to predict when exactly a machine is likely to malfunction. The sensors that are attached to the machines collect various types of information. This may include temperature, vibrations, pressure, and energy consumption. Any type of change in performance is sensed and logged.
The data is transferred in real-time to the IoT application for advanced analysis. The application uses algorithms to process this data alongside other data that is coming from the other sources we highlighted earlier. Patterns are analyzed and the potential of failure is established based on historical failures or industry standards.
If the potential for failure is high, the IoT application issues alerts to the responsible teams.
Benefits of IoT predictive maintenance applications in the manufacturing industry
Maintenance is a core process for the manufacturing industry, and this applies across all industries where heavy equipment runs the show. Hence, any approach or technology that streamlines maintenance processes and enhances their efficiency is of paramount importance.
If you are planning to invest in IoT applications for your manufacturing plant, these are some of the benefits you can look forward to.
When you are able to identify potential challenges before they strike, you can take early action and avoid downtime altogether .
This has been proven to save manufacturers enormous money considering that unplanned downtime is one of the greatest causes of revenue loss.
With reduced unplanned downtime, you can meet production targets consistently and ensure timely delivery to customers. Customer satisfaction is enhanced and brand reputation gets a huge boost.
A key outcome of predictive maintenance is optimized maintenance. When you know when a particular machine is likely to fail, you can comfortably schedule maintenance during off -peak periods. The best case scenario is to schedule the maintenance when the machine is not in use.
Instead of relying on fixed time intervals for maintenance, which may lead to over-maintenance or missing critical issues, predictive maintenance allows tasks to be scheduled precisely when they are needed. This approach optimizes resource allocation.
The outcome is that your equipment will never be disrupted during peak time, meaning it'll always be fully utilized. You only repair when all factors are favorable. The rest of the time the machine is at work, and this delivers optimum efficiency.
The more you prevent failure, the more you increase uptime. This is exactly what predictive maintenance achieves for manufacturers.
Increased uptime translates to faster time to market, which ultimately gives you a competitive edge.
Reduced time to market also gives you the opportunity to capitalize on emerging trends and consumer preferences.
Downtime is a money drainer. Maybe you will bring in extra specialized personnel to look into the problem. Maybe you will need to hire short-term contractors to mitigate delays caused by the downtime. All these setbacks will cost you money.
Predictive maintenance eliminates all this by ensuring that maintenance is proactive. The lifespan of the equipment is also extended by ensuring that wear and tear does not develop into more significant problems. This reduces the overall strain on the equipment and extends its operational life, saving you even more money.
The sensors attached to the equipment collect important data that can be used to predict danger and warn workers to keep off the machine.
As an example, let’s consider a conveyor belt system that transports products from one section to another. Sensors attached to the conveyor belt collect data on its speed, temperature, and vibrations. Through data analysis, the IoT application can detect potential faults or abnormalities. Is the conveyor belt's temperature rising above a safe threshold? Are there unusual vibrations? Are there any irregular power fluctuations affecting the equipment's performance?
This could be symptoms of possible mechanical issues. What does the predictive system do? It issues an immediate warning to workers. They’ll keep off the machine until the maintenance team can address the problem.
Implementing IoT based predictive maintenance in manufacturing: key steps
While it may sound complicated, it’s really not that difficult to implement predictive maintenance with IoT in your manufacturing plant. The process is simple, two critical steps:
Identify the critical machines equipment
Which machines do you want to onboard to the IoT based predictive maintenance system? Why not all the machines? You may ask. It may never be practically possible to implement the system across all machines. For this reason, you need to select the most important machines - those that the plant can't just do without.
Consider factors such as:
- The amount of money you stand to lose when a particular machine is experiencing downtime
- The level of expertise of your technicians or engineers
- The implications of downtime from specific machines
- Is it expensive equipment?
Consult, develop and deploy
Now that you are clear about the specific machines or equipment you want to prioritize in predictive maintenance with IoT, it’s time to talk to experts. They must be professionals who have experience in developing applications and implementing IoT systems for the manufacturing industry.
They will study your prioritized machines, then recommend the best way to tailor and install the system.
The experts will advise you on both software and hardware. Here, software means the application or platform on which the predictive maintenance activities will be coordinated from. The IoT application will coordinate all the activities from the sensors and other data sources, process the data, analyze it, and issue predictive alerts.
Because manufacturing is quite dynamic depending on the type of products, it’s highly likely that the experts will advise going for custom IoT predictive maintenance applications.
They will also advise on the right sensors to use for each machine. If your manufacturing operation is complex, consider consulting both hardware and software experts.
We could go into more steps, including establishing equipment parameters, developing the model and even installing the sensors and much more. But that will be too much ‘boring’ technical details, plus the consultants will take care of all this anyway.
Also Read: Strategies for Connected Devices
Exciting trends shaping IoT based predictive maintenance in manufacturing
If you are the type that hates being caught out off guard on emerging trends, then this is your moment. Of course, IoT is not very old. So you are still within good timing to make the most of this technology.
But it always pays to look around and position your operations to take advantage of emerging opportunities.
The trends we are about to unveil have already caught our attention, what do you think?
Predictive Maintenance as a Service (PMaaS
This is a hot one. Here is why: .
As IoT adoption grows, we’ve begun to witness the emergence of specialized service providers in this niche. These providers are offering predictive maintenance solutions on a subscription or pay-as-you-go basis.
While predictive maintenance is about detecting potential defaults and alerting, prescriptive maintenance is about providing recommendations on the best course of action to prevent failures.
IoT operating systems such as Siemen’s Mindshare are enabling industries to run maintenance systems that can resolve issues autonomously. It’s not hard to see where this is going. Imagine a scenario where you need not worry about which machine is faulty and how to fix it. What if the entire repair process can be automated for critical equipment!
Also Read: Interesting Trends in IoT Outsourcing
Integration with Digital Twins
Well well. It seems like ‘Digital Twins’ is the new kid in the ‘tech hype land’. But don’t write it off. This is how things start in technology, almost all the time. So what’s the deal here?
Digital Twin technology will most likely become more prevalent. Manufacturers will be able to create virtual replicas of their equipment. IoT data will be used to monitor and simulate the behavior of these digital twins, and this will facilitate predictive maintenance in a virtual environment.
The growth of the Internet of Everything (IoE)
The IoE is an upgrade of IoT - the interconnection of everything, including people, machines, and devices. This will make it possible to collect data from a much wider range of sources.
It means the predictive systems will have much more data to work with, and this will translate to even more accurate predictive maintenance models.
Traditionally, maintenance has been carried out through periodic inspections and reactive responses to breakdowns or malfunctions. This reactive approach has its drawbacks, mainly unexpected downtime. In contrast, predictive maintenance offers a proactive and data-driven alternative, which improves efficiency and reduces downtime significantly.
But of course this new shift in maintenance is not without challenges. One challenge that manufactures and developers have to deal with is the threat of cyber attacks.
As IoT devices and sensors become interconnected, the attack surface for potential cyber threats expands. This calls on us to factor in strong protection in the IoT ecosystems we are building for the manufacturing sector and indeed all other industries.
So be sure to discuss cybersecurity with your IoT application developers, right from the early stages.
For budgeting, please read this comprehensive guide covering how to budget for a business app.