Demystifying Industry 4.0: How to Connect Your Equipment Fleet Profitably

Introduction

Industry 4.0 is a comprehensive concept encompassing multiple aspects of manufacturing. It can be challenging to understand the various themes surrounding this major evolution in the Quebec industrial landscape. The main pillars of this transformational movement revolve around artificial intelligence, robotics, and automation.

This article aims to demystify the foundations of this fourth industrial revolution and the transformation it brings to industries. It is crucial to understand why and how to approach this transformation in your factory and maximize its benefits.

Foundations of Industry 4.0: What Differentiates It from Industry 3.0?

Industry 4.0 is often called the fourth industrial revolution. It's an imposing term that doesn't clearly explain what this industry actually is. Think of Industry 3.0 as a muscle performing an operation. It can be represented by a PLC (Programmable Logic Controller) that automates a process. It performs the operation efficiently, but sometimes needs breaks or repairs (especially the older ones 😉). Now imagine Industry 4.0 as the brain or nervous system operating the body. It can sense when tensions manifest in the muscles and can predict when the muscle will need a break or repair.

Let's apply this analogy to our factory. With Industry 3.0, our production line must regularly undergo shutdowns and maintenance to maintain the machinery. Generally, the process is repeated periodically because it's difficult to predict the right time to service or replace parts.

In our Industry 4.0 scenario, the PLC is connected via IIoT (Industrial Internet of Things) modules and communicates with a cloud server. It can now transmit data that was previously unexploited. For example, it can communicate its temperatures or downtime. Thanks to ERP/MES software, it's now possible to use the collected data to establish reference points and make comparisons.

These reference points now serve as guidelines for our production line. We use them to determine what a healthy production line looks like. When the PLC sends data that deviates too much from defined intervals, it's a sign that maintenance is needed on our machine. We can now easily plan downtime and anticipate future failures that might occur.

In summary, what differentiates Industry 4.0 from 3.0 is the ability to collect information from production machines and use that data. It can be used to automate processes that previously couldn't be. It can also be used to train artificial intelligence to perform tasks or make decisions.

Why and How to Connect Your Equipment Fleet?

New technologies and the idea of transforming industrial processes are exciting. However, the first step toward successful Industry 4.0 adoption begins with strategic and thorough reflection on these changes. Each process improvement must be quantifiable and measurable. You must view each piece of collected data as a tool that allows you to optimize your processes, with the goal of saving time, money, and potentially labor.

Don't view connecting your equipment fleet as a "big bang" that overnight connects everything from A to Z. This approach is costly and risky because you might connect non-priority machines and suddenly find yourself overwhelmed with more data than you can process. It's advisable to start with a targeted project, known as a "pilot project."

How do you choose the right pilot project? You can start by selecting a machine or production line that represents a known bottleneck. When this machine or production line is down, its unavailability costs the company significantly. This way, you can quickly see the value of connecting your factory.

Now that you know which machine will bring the most value when connected, how do you proceed if you don't have an internal development team or hundreds of thousands of dollars to invest in R&D? This is where it's crucial to partner with an IIoT expert, like Artemis Intelligence. Their role will be to connect your machines by installing external sensors or microcomputers in parallel with your existing processes. This method allows "talking" to your machines that aren't naturally communicative without being intrusive to the existing processes and controls on the machine. This way, the controller programming remains unchanged and there's virtually no risk of disrupting production.

When you're ready to connect your entire factory, it's advisable to map all machines by validating the type of controller (fixed, embedded programmable, etc.), its native communication protocols (Modbus, Ethernet/IP, Profinet, etc.), and the data it already generates. This exercise will give you a global picture of your fleet's heterogeneity and help you quickly identify the key data that will be accessible and the additional sensors that will need to be integrated into the controllers or production lines.

How to Exploit Acquired Data and Transform It into Opportunities?

Investing in connecting your factory shouldn't become a financial drain. It's important to define clear objectives and performance indicators. Using the SMART methodology (Specific, Measurable, Attainable, Realistic, Time-bound) is strongly recommended to maximize returns quickly. For example, a vague objective like "monitor the machine" should be transformed into a SMART objective: "Increase the OEE of bottling line No. 3 from 65% to 75% by the end of the second quarter."

Choosing the right KPIs is your priority (Key Performance Indicators), as they will give meaning to your data and decisions. Among the interesting KPIs to calculate and track when connecting equipment are:

• Overall Equipment Effectiveness (OEE): It measures the overall efficiency of equipment. It combines availability, performance, and quality. It evaluates actual performance compared to theoretical capacity.
• Mean Time Between Failures (MTBF): It measures equipment reliability. It provides a concrete indicator of machine health.
• Mean Time To Repair (MTTR): It measures maintenance efficiency when equipment fails. It helps you easily identify whether resolution time is increasing or decreasing.

Now you can exploit acquired data to be proactive and improve these KPIs. Is bottling line No. 3 experiencing significant temperature increases over the past few days? Perform preventive maintenance during a planned shutdown to avoid future failures. Analyzing MTBF trends, for example, combined with data collected by your new sensors (vibration, temperature, running time, etc.) now allows you to predict machine reliability rather than suffer its consequences. You can therefore use this data as leverage to transform it into equipment profitability and reliability.

Conclusion

Ultimately, the transition to Industry 4.0 is neither easy nor magical. It's important to have a well-defined plan and proper support. As this article explains, the success of the transition doesn't lie in accumulating new data, but in the ability to transform that data into concrete and profitable actions. These actions are diverse and can be seen through the implementation or automation of new processes thanks to the data obtained.

This transition and industrial revolution will also prepare for an inevitable shift in the Quebec industrial landscape in the coming years: the arrival of AI in factories. Indeed, without data that artificial intelligence can learn from, make decisions with, and fuel its decision-making engine, it remains limited and of little use.