Eliyahu M. Goldratt wrote one of the best books on manufacturing—long before the Internet of Things (IoT) was heard of.
In his celebrated book, The Goal, Goldratt explains in one simple sentence what the supreme goal that every manufacturer can achieve is:
“To make money by increasing net profit, while simultaneously increasing return on investment, and simultaneously increasing cash flow.”
Take one of the variables out of your manufacturing processes—revenue, profit, cash flow—and you fail your manufacturing ROI.
Everything else—machines, automation, and manpower—that contributes to your manufacturing eventually maps to this goal.
But what does this mean for your business? Is your manufacturing ROI high, low, or is it break-even? And how do you go about measuring it anyway?
In this post, we will discuss a few factors that contribute directly to improve your manufacturing ROI and the consequences of what happens when your processes are sub-optimal.
Low Manufacturing ROI
A recent report by Aberdeen Group found that the average cost of unplanned machine downtime is $260,000 per hour.
When you add that up per year, the number can reach into the billions.
Look around in any manufacturing plant that has seen its fair bit of asset failures and unforeseen repair costs. I can guarantee that you will find the following things that lead to frequent machine downtimes:
- Ambiguous job instructions
- Poor equipment maintenance
- Human error
- Lackadaisical changeovers
- Early shutdowns
- Frequent personnel breaks
- Long set-up time
- On-machine press checks
- Lack of downtime data recording
However, these are just causes; the effects can be far more disastrous. For instance, the unplanned machine downtime can offset your manufacturing productivity by several days or lead you to the following setbacks:
- Increased repair cost
- Delayed production
- Overall equipment effectiveness (aka OEE)
Not to mention that these setbacks will inevitably result in the increased total cost of ownership (TCO) and low production value.
But there’s hope.
Top manufacturing plants have started using predictive maintenance systems to improve OEE and reduce overhead maintenance costs to avoid production delays.
But What Exactly is Predictive Maintenance?
Is it yet another machine that you plug into your manufacturing tech stack?
A predictive maintenance system helps manufacturing businesses with condition-monitoring tools to track the performance of any equipment in idle, normal, and peak performance states.
Think of it like an AI-powered crystal ball for manufacturers to predict the future.
The data you glean from machines operating at different conditions can help you plan future maintenance schedules and prevent sudden machine failures or downtimes.
And it doesn’t need babysitting like other machines in your plant do. IoT-powered means smart and self-sufficient (to a large extent).
It also means it doesn’t take water or smoke breaks every 30 minutes like your human personnel do.
How Predictive Maintenance Works
Predictive maintenance relies heavily on IoT. When you attach an IoT device and sensors to manufacturing equipment, it starts recording the machine’s real-time performance data.
Condition-based monitoring is not much different. It’s all about automating the painstakingly tough and manual job of collecting real-time machine data with the help of an IoT device.
For instance, here are a few equipment data that an IoT sensor capture by monitoring the machine in real-time:
- Chemical content
- Liquid/Solid levels
And here’s the best part about it. Once the sensors collect the above information, it automatically pushes the data to a cloud platform where it is fed into an AI- or ML-enabled system.
This is to analyze the processed data and predict future problems based on present and past data patterns.
Finally, the data reaches the maintenance specialists so that their team can make contingency plans around future downtimes.
What Does This Mean for Manufacturing Plants?
There is no reason why a manufacturing business shouldn’t use predictive maintenance. On the flip side, there’s a ton of reasons why they should.
For instance, using predictive maintenance guarantees the following:
- Capture accurate, real-time data
- Predict machine downtime
- Higher transparency
- Reduced/Avoid production delays
- Increase production volume
- Lower repair/maintenance costs
- Improve machine efficiency
- Improve operator safety
- Boost overall profits
With so many benefits to shave off costs and increase profit, what’s not to like about predictive maintenance?
For years, businesses have been spending huge amounts of money on fixing machine downtimes or cutting operational costs.
Take shop plant managers and operators, for instance. They schedule machine repair and maintenance at regular intervals thinking that will help them prevent the downtime.
What they don’t realize is no amount of money is going to get them to their profit goals if they keep barking up the wrong tree.
Preventive maintenance might not be perfect, but it’s a way more superior solution than guesstimating operational downtimes.
A Better Solution to the Persistent Problem
Simply put, implementing an IoT-based predictive maintenance strategy will put your operational efficiency in autopilot.
It will do all the grunt work for your business—identifying the downtime patterns, automating the real-time data across all teams/systems, and interpreting the data for you.
All you have to do is make fail-safe plans based on the predictive data. And that’s exactly what predictive maintenance for industry 4.0 is all about.
Shop floors can leverage predictive analytics in shop floors to monitor machines in areas that are difficult for humans to monitor and intervene.
Below, let’s look at a few examples of how you can apply predictive maintenance in different use cases.
Manufacturers across industry verticals use condition-based monitoring to gather real-time machine data to gauge their performance. The technology makes the process seamless, hands-free, and accurate.
Without an IoT solution system like predictive maintenance, it’s close to impossible for enterprises to employ humans to gather such data sets and analyze them at a breakneck speed.
If there’s any industry that’s rigorous about the tiniest bits of portion control and record-keeping, it has to be the chemical plants.
Chemical manufacturers also play around with a huge expanse of data sets that needs constant monitoring and analysis. For them, relying on anything less than accurate data processing leads to lethal consequences.
This means they have to consistently gather large streams of data for ensuring optimal equipment performance. The use of predictive analysis along with machine learning can give chemical plants the kind of digital reliability they need.
We live in a fast-moving world that literally runs on tires. And the popularity of tires is not slowing down anytime soon, given the increased complexity of manufacturing tires to meet the growing demands of electric cars and autonomous vehicles.
Tire manufacturers might be reinventing the wheel, but they are not doing it without the challenges of being innovative. For an industry that is in the driver’s seat of innovation, it’s very important to keep up with the speed of technological efficiency.
Tire manufacturing is a complex process: it starts with mixing and cooling rubber and passes on to more complicated processes such as extrusion, cutting, tire building, curing, and labeling. The entire manufacturing process requires process stability and energy efficiency.
The use of predictive maintenance and analysis helps them automate the processes with accuracy and avoid unnecessary pit stops in their operational efficiency.
Just like the tire makers, pipe manufacturers deal a lot with the change in temperatures, shapes, and sizes.
But the players in this industry also have a unique challenge. Pipe manufacturing is a highly competitive market because the barrier to entry is relatively lower compared to other verticals.
Therefore, it’s important for a pipe manufacturing business to produce top-notch products in order to develop a competitive advantage for themselves.
And while every pipe manufacturer fights for achieving that level of competency, the fortune lies with manufacturers who can use predictive analysis to get ahead of the game.
For instance, using predictive maintenance technology can help pipe manufacturers proactively monitor the production processes and avoid issues that can lead to defect batches.
Most manufacturing businesses today have already started implementing IoT-based predictive solutions to their production processes. Such businesses are enjoying an early-adopter advantage of improving their product quality and sales volume.
How are you placed in measuring your manufacturing ROI? Do you want to leverage the cutting-edge technology of predictive maintenance and IoT to improve your manufacturing productivity? Learn more about how you can automate your shop floor processes.