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Machine Monitoring

Learn how manufacturers are leveraging machine data to increase productivity and efficiency on the shop floor.


Think about the last time you made an important decision about your manufacturing operation. Did you trust your gut? Did you stop with intuition?

Or did you gather information to guide your actions?

Data is the foundation for success in manufacturing.

This statement is true across industry and vertical, and it’s particularly true in job shops, CNC manufacturing, and other machine-intensive operations. Data is the key to realizing your greatest improvements and maximizing profitability.

Digital technologies have made it possible to turn routine machine runs into granular data, ready at your fingertips. Also known as machine monitoring, it’s now possible to capture and display machine data in real-time for better visibility and more targeted improvements.

This guide will provide you with a comprehensive overview of machine monitoring in manufacturing. It’s designed to help you apply a data-driven approach to industrial assets.

Throughout, we’ll review use-cases, implementation tips, and offer our tested methods for creating a machine monitoring solution that delivers value for you.

Importantly, we’ll show you exactly how to locate your machines within a process, giving you a holistic view of machines and human performance in context.

Our goal is to help you imagine what you could do if you had the machine data you need.

Chapter One: What is Machine Monitoring?

Machine monitoring is the process of extracting, storing, and displaying machine data in an industrial context. It’s a fast, scalable technique for turning manufacturing machine data into real-time insights.

Machine monitoring works equally well in discrete and process industries. In the last five years, sensors have grown more affordable, edge devices more flexible, and connectivity more robust. There are more parameters to track and more opportunities to create value than ever before.

What is a machine monitoring system?

Machine monitoring systems are the hardware and software tools needed to process the raw information generated by machines.

They encompass everything from monitoring and analysis software to industrial sensors to the touch-screen interfaces mounted on machines. Usually, machine monitoring systems are coordinated by an IIoT Platform or Frontline Operations Platform, like Tulip.

Depending on the size, capacity, and age of your machines, a machine monitoring solution that works for you will have its own idiosyncrasies. With little initial investment, these systems can help you redefine what data driving success looks like, giving you the chance to define performance and define effectiveness in a way that reflects your operations.

Nevertheless, there are a few core elements that unite all machine monitoring systems.

Manufacturing Software

At its most basic, machine monitoring systems can be as simple as software that makes machine data intelligible. This could be a cloud-based software-as-a-service (SaaS), a more comprehensive platform-as-a-service (PaaS), or it could live on-premise within your organization’s IT infrastructure.

For manufacturers with machines that run standard network-based protocols like OPC UA, all you need is an ethernet connection to get started. Plug-and-play machine monitoring software contains everything you need to track, store, and analyze machine data.
The types of manufacturing software include Software as a Service (SaaS), Platform as a Service (PaaS), and on-premise.

Sensors, Edge Devices, and Gateways

For analog machines, brownfield transformations, or larger-scale projects, machine monitoring systems might also require basic hardware devices.

In these cases, machine monitoring systems will include the sensors and devices necessary to generate and translate machine performance into data. For example, many of the manufacturers we work with rely on analog machines built before the digital era.

These are workhorse machines that function perfectly, but lack native internet connections. Others use machines that communicate with proprietary, deprecated, or otherwise non-standard protocols.
An example of a machine monitoring implementation using sensors attached to a gateway.

In practice, implementation can include installing inexpensive industrial sensors, as well as edge devices for transmitting that data for storage and analysis. It can also include gateways for aggregating data from many machines, connecting to IoT devices, or setting thresholds and programs for filtering signals from noise.


Machine data is only helpful if it’s available for analysis. Machine monitoring systems, therefore, include the databases, no-code tables, and dashboards that enable analysis.

One of the most popular features of machine monitoring systems are production dashboards that display machine state and production metrics for every machine in a department.

Many of the engineers and managers we work with want to be able to walk into their machine shop and see exactly what’s happening on every machine at a given moment: which program is running, how many parts have been produced, how close a run is to finish, and objective measures of uptime and downtime.

Others want detailed time-series data for their business intelligence and data science departments. Machine monitoring can enable machine learning, AI, and other kinds of big data analytics, including predictive quality and anomaly detection.

Machine monitoring analytics make it possible to track your factory on a single screen while digging deep into machine data.

Configurable Manufacturing Applications

While machine data is essential, good data should be a baseline, not an end in itself.

This is where applications come in. On-machine manufacturing applications like digital work instructions for changeovers, SMED, and human-centric process visibility let you take your machine monitoring solution further. They enable you to control what happens around the machine, too, for a human-centric approach to machine-based processes.

With over 70% of mistakes in factories attributable to human error, applications give you insight into the most variable element of any process.

Chapter Two: Advantages and Benefits to Machine Monitoring

There are many obvious benefits to continuously sampling machine data. Not every benefit and use-case, however, works for every operation. Knowing the kinds of benefits machine monitoring offers can help you design a system that creates value for you.

Objective Measurements

Manual measurements are prone to error, and data taken by hand is inherently subjective. Machine monitoring is the only way to create a truly objective measure of your machine performance.

Instant Notifications

Machine monitoring systems don’t passively collect data. You can configure your solution to automatically notify a manager or technician if a particular threshold is crossed. This eliminates the possibility of unnecessary and costly machine damage while reducing the possibility of unplanned downtime.

Refined Maintenance Schedules

The data you collect through machine monitoring can help refine maintenance schedules. With a record of part and machine life-cycle, you can tailor your repairs to reflect actual wear-and-tear, no guesswork involved.
Benefits of machine monitoring include objective measurements, greater efficiency, a data-driven shop floor culture, and more.

Greater Efficiency

Machine monitoring gives you the information you need to balance lines and time production runs. With an accurate understanding of production, you can eliminate bottlenecks and perfect production schedules.

More Accurate Inventory and Resource Consumption

When you measure exactly what goes into a machine, what comes out, and how much energy it took to produce, you can refine your Just-in-Time manufacturing schedules and eliminate waste.

No Manual Data Entry

Manual data entry is time-consuming and prone to error. Machine monitoring automatically collects data and provides a source of truth for the whole operation.

Simplified Performance Reporting

Think of the last time you conducted an analysis. It likely involved pulling, compiling, and cleaning data from multiple sources before any real analysis was possible. Machine monitoring puts all of the data you need in a single, easily accessible location to expedite the entire analysis process.

Data-Driven Shop Floor Culture

As we’ve argued elsewhere, a data-driven digital culture is the least acknowledged–but most important–factor behind successful projects. Machine monitoring helps encourage everyone on the shop floor to consult data at every stage of the manufacturing process.

Chapter Three: Machine Monitoring Use Cases

Every factory is different. Therefore every machine monitoring program will need to track different parameters.

The manufacturers we work with often come to us with specific questions or hypotheses about their machines.

It’s common for conversations to open with a statement like, “My tools are aging out too quickly. I want to know why.” Or, “I think my operators are letting the machines idle more than they’re reporting and my OEE calculations are wrong.”

Depending on what you want to know, we recommend different machine monitoring use-case.

Here are some common ones that consistently deliver value for customers.
Use cases for machine monitoring include improving OEE, production tracking, condition monitoring, and more.

Modeling State for Improving OEE

How are you currently measuring uptime and downtime? How many minutes a day does each of your machines spend idling? How do you know your measurements are right?

Machine monitoring systems constantly and objectively measure machine state. With machine monitoring, you can see exactly how long each machine spends in each state. Operators can annotate downtime with reason codes, letting you document the root cause at the source.

Using a machine monitoring system to track state helps you balance lines, track work-in-progress, and plan production, and lays a foundation for calculating essential machine KPIs like quality, availability, utilization, and OEE, among others.
Taza Chocolate monitors machine uptime and downtime, production, and conditions, in order to gain a holistic view of their OEE.

Production Tracking and Job Route Optimization

With machine monitoring, you can track work-in-progress throughout the value stream. Dashboards can show you exactly how close a particular run is to completion, and whether or not you’re meeting production quotas.

With this data, you can better plan for production, and make sure that jobs are quickly and accurately routed to the correct station upon completion.

Condition monitoring

Maintenance schedules shouldn’t be subject to guesswork. Unplanned downtime and unexpected breakdowns are too expensive to leave to chance.

Machine monitoring helps you to track the condition of each of your machines for a more complete understanding of part and asset lifecycle. Sensors can help you monitor parameters like vibration, temperature, noise, and part displacement to measure the degradation of mechanical components in real-time.

You can also monitor ambient condition. Perhaps your factory is excessively humid. Maybe heat isn’t dissipating as expected from machines in one corner of your factory. There are innumerable factors that lead to premature machine degradation, and it’s impossible to get to root causes without the right data.

Condition information is essential for optimizing maintenance schedules, and for knowing how your machines behave on your shop floor. Because machine monitoring collects data about machine performance and health in your local conditions, it can create the foundations for predictive maintenance programs.

Resource monitoring

Many opportunities for cost savings require maximizing the efficiency of an asset’s resource consumption. Machine monitoring systems make it possible to measure power draw, current, or other resource usage, like coolant, injection wax, or water.

Understanding how your machine use resources in local conditions can help you hone usage, plan inventory and buffers, and reduce hard costs.

Program Optimization

There are several ways machine monitoring can be used for CNC monitoring. One of the best is tracking the success of a given program. The information collected by checking parts produced against tool path, tool lifecycle, and feeds and speeds can lead to more efficient programs and higher throughput.

Tool Life-Cycle Optimization

Machine monitoring allows you to account for all of the variables that impact tool lifecycle. It enables you to track the number of hours on a tool, which programs it ran, who performed the changeovers, whether or not the machine was adequately cleared, and more.

It gives you the data you need to identify which parameters are contributing to shortened tool life so you can maximize your investment in machining tools.

Chapter Four: How to Get Started with Machine Monitoring

Getting started with machine monitoring is simple. With plug-and-play software, it often takes little more than an internet connection to get started. Further, we work directly alongside customers to design machine monitoring solutions that get results, fast.

Here are a set of proven steps you can take to get started with machine monitoring.
The first step to getting started with machine monitoring is assessing your current capacities and needs.

Take Stock of Your Operations

The first step is to assess your current capacities and needs.

For example, how many machines are in use on your shop floor? How diverse are their outputs and production schedules? Are some (or perhaps all) of your machines analog? How important are operators to the machining process? Will you need to integrate with an MES? What protocols do your machines use? Are any of the protocols proprietary or deprecated? What are your security and infrastructure requirements?

Simply outlining your current operations will go a long way toward informing the kinds of machine monitoring strategies and solutions you prioritize.
It helps to have some hypotheses as to why your current performance isn’t where you’d like it to be. These hypotheses could be gut feelings, or they could be supported by your historical data.

Form Some Initial Hypotheses

You don’t need to know exactly which problems you like to solve from the outset.

Still, it helps to have some hypotheses as to why your current performance isn’t where you’d like it to be. These hypotheses could be gut feelings, or they could be supported by your historical data.

One of the virtues of machine monitoring is that it creates a new source of knowledge. Machine monitoring can reveal unknown-unknowns as well as providing clarity to your machining questions simply by broadening the amount of information available.
Establish a source of truth for your measurements.

Create a Source of Truth

From here, it’s all about implementation details. These will differ depending on your operations and needs.

Regardless, the first step in all machine monitoring journeys is to establish a source of truth.

In our experience, the simplest parameter to measure to this end is the state. Creating an objective understanding of uptime and downtime can shed light on a host of other issues. We’ve worked with manufacturers who thought they were running at 90% uptime. Once they started taking data, they realized that they were below 60%. This simple fact creates a reason to dig beneath the surface and figure out why.

If your machines communicate using protocols like OPC UA, MTConnect, Modbus, or MQTT, analyzing state is a simple matter of collecting and organizing data. If not, we can help you establish Kepware servers and outfit the necessary hardware to make your machine data intelligible.

  • Tulip supports OPC UA natively, and other protocols can be connected to the platform through Node-RED or a solution like Kepware.
After taking an initial sample of state data, you can validate your hypotheses and refine your questions.

Evaluate Your Findings and Add Nuance

After taking an initial sample of state data, you can validate your hypotheses and refine your questions.

For example, Frontline Operations Platforms like Tulip enable you to nuance your state data. With Tulip, you can create custom states, letting you record machine performance to your precise specification. When certain predetermined thresholds are exceeded, you can trigger an automatic notification to a supervisor or technician.
Machines never operate in isolation. There’s always a human in the picture, even in putatively “lights out” factories.

Bring the Operator into the Picture

Machines never operate in isolation. There’s always a human in the picture, even in putatively “lights out” factories.

Tulip lets you account for the human in your machine processes.

For example, operators can enter downtime reasons into applications running on a machine terminal, logging errors, and process completions at the source. The applications themselves can record which operator was on duty, which machine program was running, the number of hours on the tool, and more.

This kind of data can provide a holistic understanding of machine health and performance.
With adequate human and machine data, you can start to design applications that help you track quality and production throughout the value stream.

Build Applications for Your Unique Improvements

With adequate human and machine data, you can start to design applications that help you track quality and production throughout the value stream.

Machine monitoring can reveal the root causes of machine performance issues. With a complete Frontline Operations Platform like Tulip, you can configure applications to solve those problems.

For example, you can place work instruction applications on each machine terminal, guaranteeing that changeovers and SMED are performed correctly. If quality is impacting OEE, you can add inline quality checks on each machine, ensuring that each item that comes of the line passes all necessary tests.

This is a human-centric approach to machine monitoring, and it’s where manufacturers can realize the greatest improvements. When you have full visibility into human and machine performance, you can begin tracking Overall Process Effectiveness alongside OEE.
The ability to scale and transfer means that machine monitoring can be the single best investment you make in digital solutions.

Scale your Program

Machine monitoring scales better than most digital initiatives. This is because the solutions that work for one machine can be easily transferred to departments of 5, 10, 100, and even across plants and geographies.

This ability to scale and transfer means that machine monitoring can be the single best investment you make in digital solutions.

Chapter Five: 4 Questions to Ask Before Choosing a Machine Monitoring Platform
Make sure to ask these questions when choosing a machine monitoring platform.

How easy is it to access machine data?

The goal of machine monitoring is to increase data quantity and quality for more impactful improvements. For machine monitoring to fulfill this goal, data needs to be easily accessible.

You should look for solutions that don’t require extensive experience with SQL. Here, no code tables make it possible to store and access custom tables without needing having to write a single query. This way, manufacturing engineers and managers can work directly with the data for faster results.

Can I create custom states and track custom parameters?

Every shop floor is different. Not every machine shop will be interested in the information. So you should ask how difficult it is to track the parameters that matter most to you. Make sure that any solution enables you to add custom columns, create custom tables, and create a picture of machine performance that will enable your initiatives.

How easy is it for operators to use? Engineers? Supervisors?

Machine monitoring has the potential to positively impact your operations across the entire hierarchy. The question is whether and how different individuals and job functions will use it. When assessing any machine monitoring solution, make sure it will empower operators, engineers, and management. Make sure that it’s simple to implement and maintain.

Can you supplement your machine monitoring with applications?

If your machine monitoring solution can’t support additional applications, you’re missing opportunities for the greatest improvements. If your IT department is building applications, you’re likely to wind up in slow development cycles, risking a delay every time a feature needs to be updated. When evaluating a solution, make sure the platform you choose is enabled by applications.

Chapter Six: Conclusions

Understanding how machines fit into complete manufacturing processes is essential to improving those processes.

Holistic machine monitoring solutions that bring machines online and guide operator action to help you realize these improvements.

At Tulip, we work closely with our customers to design holistic machine monitoring solutions that create value fast and scale.

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