Machine learning quality control

How Machine Learning Slashes Quality Control Costs in Manufacturing


Machine learning (ML) and artificial intelligence (AI) continue to capture media attention and business investments. IDC estimates machine learning and AI spending to increase from $19.1 billion in 2018 to 
$52.2 billion by 2021.

Now:

With billions of dollars invested in machine learning and AI, it’s no surprise that tech giants Google, Microsoft and Amazon are investing billions in cloud infrastructure and development tools to accelerate the delivery of custom machine learning applications. Case in point, machine learning was the third-highest category for the number of patents granted between 2013 and 2017.

So, what exactly is machine learning?

Machine learning is a form of artificial intelligence, with algorithms that automatically recognize patterns in big data sets to derive meaningful insight and take action. What’s more, these algorithms are self-learning - they improve over time as more data is fed through their algorithms - without the need for explicit programming.

You may be wondering:

“How is machine learning relevant to manufacturing, and quality assurance in particular?”

As the manufacturing sector embraces industry 4.0 technologies to optimize production, we’re already seeing large scale deployments of ML-powered systems that improve production throughput while maintaining adequate quality levels and reducing costs.

This is crazy:

It’s estimated that the global smart manufacturing market -  which combines IoT and AI technologies in manufacturing - will reach a staggering $395 billion by 2025.

In this article, we will elaborate on the leading use cases of machine learning in quality management, specifically in the domains of predictive quality assurance and control, and downtime prevention.

 

The role of machine learning in quality management

Depending on the industry, the manufacturing process can be complex and prone to errors. For instance, automotive manufacturing has a completely different set of quality assurance challenges and complexities than, say, printed circuit board (PCB) manufacturing.

Although manufacturing processes across industries can widely vary, manufacturers share common operational goals centered around overall equipment effectiveness (OEE) and overall line efficiency (OLE).

OEE is calculated as follows:

OEE = Availability x Performance x Quality

And OLE is the weighted average of OEE per machine in a production line.

The importance of quality as a fundamental operational metric is clear. Let’s take a look at the two leading use cases of machine learning in minimizing production faults.

 

Predictive Quality Control

Quality control is integral to manufacturing processes, where defective products are weeded out from the rest, as early on in the production process as possible.

Now:

While quality control identifies defects, and prevents faulty products from reaching the market, the process of identifying the root causes that lead to the production of the defective products is time-consuming, and often requires multiple disciplines to collaborate - process engineering, quality assurance, mechanical and electronic engineering, to name a few - to collaborate.

In other words, quality control and its root cause analysis is costly and lengthy.

Let’s take food manufacturing as an example:

Food manufacturing requires stringent quality control measures at every step in the manufacturing process to ensure food safety. Over the years, regulations and standards have been imposed on food manufacturers by governments worldwide, making food quality control critical, and costly.

Good Manufacturing Practices (GMP), Hazard Analysis Critical Control Point (HACCP), Hazard Analysis Risk-based Preventive Controls (HARPC), Codex Alimentarius, and ISO 22000 - are just a few of the regulations and standards.

Here’s where machine learning can provide amazing value to quality assurance:

By implementing machine learning, machinery and product data can be monitored throughout the manufacturing process to predict quality faults - before they arise. Quality and maintenance teams are alerted, together with the precise root causes of the anticipated faults.  

Integrating machine learning into the quality management process - often referred to as predictive quality - reduces quality issues and waste, cuts manufacturing costs, and minimizes product recalls to protect brand reputation.

What’s the bottom line?

It has been reported that machine learning can increase fault detection rates by up to 90% while slashing the time to pinpoint the root cause of quality issues to minutes from days.

 

Predictive Maintenance

According to recent studies, unplanned downtime of industrial machines costs manufacturers an estimated $50 billion each year. And equipment failure accounts for a whopping 42% of this unplanned downtime.

Predictive maintenance is the condition monitoring of equipment and predicting when maintenance is required based on leading indicators of fault development.

Advanced machine learning techniques are increasingly applied to optimize predictive maintenance outcomes. Such machine learning techniques involve modeling the relevant manufacturing processes and assets in a production line,  and then applying the most appropriate machine learning algorithms in the context of the production process and the specific products being produced.

Here’s the best part:

With machine learning, predictive maintenance algorithms do not have to be fed with preassigned threshold values, rather they is “trained” to recognize data patterns and anomalies from standard behaviors and historical faults. While initially, the algorithms have to be trained, over time they self-optimize with minimal human intervention.

And the end result:

Manufacturers predict unplanned downtime events, and prevent them from occurring, by taking corrective and timely action.

 

Final words

Maintaining high quality in production is a strategic goal for manufacturers since it directly affects the top and bottom lines for a company.

Integrating machine learning technologies into the quality management process can minimize product faults and reduce production costs. What’s more, as machine learning is a self-learning system, it promises to continually improve results.

 

 


How the Precision of Process-Based Machine Learning Solves Manufacturing Disruptions

How the Precision of Process-Based Machine Learning
Solves Manufacturing Disruptions

Predictive maintenance, predictive quality and automated root cause analysis are industry 4.0 initiatives driven by the power of AI and machine learning. However, many implementations - specifically in the process manufacturing industry - fall short in delivering the promised value of industry 4.0 due to inaccurate insights and too many false positives.

The solution? Production process context.

With process-based machine learning, the specific characteristics of the manufacturing process and its assets are taken into consideration within the algorithms.

Specific sensors in machines and mechanical equipment, production recipes, production process flows, and the facility’s environmental factors - all contribute to the algorithm’s accuracy.

 

Too Many Anomalies, Too Many Alerts

In process manufacturing, data from thousands of tags (sensors) are typically captured in a data historian. This data is heavily influenced by production context: which product is being manufactured, and how - product recipe, machine settings, production flow, and manual interventions.

For this reason, applying machine learning algorithms to raw data, without factoring all the relevant contextual information, will result in an abundance of alerts, most of which will be false positives.

Unfortunately, for many manufacturers, this is where the credibility of their industry 4.0 efforts begins to crumble:

Generic Vs. Process-Based Machine Learning

What about weights?

One method used as an attempt to shape data towards actionable insight is to use formulaic weights. In this method, weights are attributed to certain behaviors (measurements/groups of measurements) in an attempt to define problematic states.

While introducing weights to a process will indeed add a contextual layer to the data collected from a production line, it will also introduce new problems. This is because the logic behind weight allocation is based upon human rationale.

 

Process-based Machine Learning to the Rescue

The good news is that this type of problem is perfectly suited to the application of industrial AI. That’s why machine learning and artificial neural networks are considered a key part of Industry 4.0, especially when it comes to driving predictions as in the cases of predictive maintenance and predictive quality.

Including the Process

With process-based machine learning, the topology of the production floor is modeled precisely to include all the lines, manufacturing stages, machines, and the flow of the product through the system.

This model provides an accurate representation of all the assets via directional graphs and the movement of the product through each stage of the production process.

Process-based Machine Learning

Enter the Algorithms

The production context, critical for closing the gap between the representative data and the real-world manufacturing environment, is added by means of feature engineering. With this contextualized data in place, machine learning algorithms become extremely powerful predictors. While more conventional ML algorithms such as Random Forest and XGBoost may be used, applications designed for industrial process data such as the Seebo platform, use graph-based models such as Hidden Markov (HMM) and Directed Acyclic Graph (DAG).

Since the data fed into the ML algorithms includes the context of the entire operation, analytics produce accurate predictions regarding maintenance, quality, and the supply chain.

These predictions are then presented to personnel and management with enough time to be acted upon strategically, significantly improving the performance of the entire plant.

 

Get in touch for a 1-on-1 demo to see how process-based machine learning can significantly improve your operation’s uptime and output quality.

 

 

 


IoT Platform Solutions - choosing a platform per business solution

Pick solutions, not platforms!
How to choose an Industrial IoT platform

“If it ain’t broke, don’t fix it.”

That common phrase once defined manufacturing’s approach to optimization. But today, enterprises are painfully aware that smarter production lines and more efficient processes can save them millions in downtime and lost yield.

So, plant managers – people in what traditionally is one of the more conservative roles –  are actively looking to implement industry 4.0 solutions based on an Internet of Things (IoT) platform.

But are they looking in the wrong places?

Don’t make this mistake

Industrial IoT (IIoT), or Industry 4.0, touts itself as an innovative means to increase production efficiency and quality in the factory, with a compelling return on investment (ROI). The problem, though, is that every IoT platform is different, and not necessarily suited to each company’s business needs.

Companies generally start  by deploying pilots for one or two key IIoT solutions, but they don’t understand that some IoT platforms are focused on providing the underlying technical infrastructure for business solutions, while others are built to provide specific business solutions. 

So how do you know which IoT platform is best-suited to your needs?

In order to choose the right IoT platform to get started, there are a couple of things you need to know:

Different types of Industrial IoT platforms

Before choosing an IoT platform, you’ll have to define which type of platform fits your solutions.

There are over 450 IoT platforms today, and each one offers something slightly different. Broadly speaking, they fall under the categories of IoT development platforms or IoT runtime platforms. We’ve already covered the differences between the IoT platforms in this blog post.

Choosing the right type of  Industrial IoT Platform depends on your internal resources. To deploy Industry 4.0 solutions, you’ll need to capture machinery data in a central repository  to analyze that data, and visualize the resulting insights in an intuitive way.

Some companies already have sensors integrated into their assets and data connectivity solutions that capture their machinery data; they’re looking for a data analytics solution to derive and visualize actionable insights for their teams. Others need an end-to-end service, but plan on using their own data scientists to translate the data into a solution.  

It’s generally best to look for an industrial IoT platform that provides solutions for the use cases that are relevant to your business. Look for a platform that is built on standard, proven technologies for its services, such as IoT device management, data security, and data storage.

In fact, the best match between business needs and Industrial IoT platform is made by orienting your search in line with the solutions you’re looking for.

Get the manufacturer’s guide to navigating the Industrial IoT Platform landscape

Industrial IoT Platform Solutions

While industrial IoT platforms must deliver the technical infrastructure to ensure that the delivered IoT solutions are scalable, secure, and easily managed in production, the business value of the platform is derived from its solutions.

Some platforms do NOT claim to deliver solutions. Instead, they provide the tools for manufacturers to build and test the solutions themselves, requiring staff skilled in data security, data connectivity, data management, data science, and data visualization – a tall order for most manufacturers.

Other IoT platforms are focused on targeted business solutions, such as predictive maintenance, predictive quality, and condition monitoring.

Before checking out an Industrial IoT platform’s features, you should define the primary solution or solutions you want to employ.

The top 8 Industry 4.0 solutions:

  1. Predictive Maintenance

  2. Predictive Quality

  3. Digital Twin

  4. Condition Monitoring

  5. Automated Root Cause Analysis

  6. Energy optimization

  7. Transportation 

  8. Logistics 

Taking a solution-targeted approach to your IoT platform search helps you map out which capabilities to look for in the IoT platform and quickly narrow down the choices.

In fact, the solution capabilities are the prime consideration that should most inform your choice of the underlying Industrial IoT platform.

How to match the solutions you need to an Industrial IoT platform

Know thyself – and thy pain points

Examine your company’s main pain points. Identify where the drain in resources or revenue is.

You can pinpoint the right solution to launch as a test case by reviewing your overall equipment effectiveness (OEE) and choosing the aspect that can be improved on the most.

In a recent Seebo survey of process manufacturers, 62% of respondents said their main production failure root causes lay in the manufacturing processes; for another 25%, it was mechanical failures in motors and bearings of specific assets in the production line. Energy and supply chain pains took a distant third and fourth place.

Optimizing manufacturing process or preventing machine breakdown through better condition monitoring – or even preventative maintenance – require different technologies. There are platforms that are better suited for process manufacturing vs. discrete manufacturing, and vice versa. It’s clear, though, that not every Industrial IoT platform will suit every solution, so before you research platforms, verify within your company what needs to be worked on the most.

What type of services does the industrial IoT platform offer?

Going back to the different types of platforms – any solution implementation involves sensors, connectivity, data acquisition, data analytics, and visualization. You may already have some of these capabilities in-house, but any Industrial IoT solution will require all of these, from sensors embedded in million-dollar machinery to an on-cloud or on-premise data repository with advanced analytics .

Many platforms bill themselves as IoT platforms, but their solutions differ drastically – make sure you are targeting the link in the chain that is relevant for your production line.

Similarly, pay attention to which step or steps in the production lifecycle the platform focuses on.

Some solutions relate to the production line, while others focus on addressing pain points in the supply chain. Knowing this, you can look for specific capabilities in an IoT platform that address the stage where your pain points lie.

Contextualized vs. raw data

For some production lines, particularly in discrete manufacturing, it’s enough to collect data from the machines in the process. In more complex process manufacturing processes, where the materials used are ingredients that change shape and form throughout the process, there is a tremendous variety of variables that can influence results. To understand yield and product quality, production results must be contextualized via data about the production environment. For process manufacturers, it’s crucial to contextualize data in the framework of the manufacturing environment and process. Look beyond the buzzwords, and understand if the IoT Platform which offers insights based on more than data from the production assets alone.

Security

Data security is always a serious concern. Manufacturing assets are less vulnerable to hacking than consumer devices, but captured data must be secured, and you’ll need stringent measures in place to protect who can access which data, from what location, and at what times.

Data integration

Integration is multi-directional. In process manufacturing, you will not only need to capture the OT data from PLCs and data historians, you will need to contextualize it with data from MES, ERP, and quality management systems, to name a few.

Above all, you will want to ensure that the platform considers the production processes in its data schema, to accurately reflect your production processes, in addition to the assets.

How does the IoT platform manage its integrations? Will you need additional technical support to manage it?

In a solution-centric approach, you should ask yourself which integrations your solution will need. A platform that claims to to provide a production quality solution doesn’t have to include its own quality management software, but it should provide easy integration with at least the most common quality management tools.

Solutions for whom?

Most AI solutions require a plant to have in-house data scientists. The insights produced will often be incomprehensible to the people who need the insights they provide the most: quality investigation teams, process engineers, and even operators on the factory floor. Evaluate whether your solutions are for production line staff or for data scientists, and choose an Industrial IoT platform relevant for those users.

Understand who the users of the system will be – and ensure the chosen IoT platform can serve them directly.

First, understand who the users of the system will be. Ensure that the chosen solution can serve these users directly. These teams must be able to access and understand the data that you are collecting or receiving, in order to act on them. Then, check that any platform you review is indeed accessible to these teams. If they require a data scientist, you’ll have to find a way to translate the findings to the relevant teams.  

Alternatively, look for an IoT platform which doesn’t require that you hire data scientists, or which transmit actionable insights in a way that’s visually clear to the teams that need the results the most.

With these tips in mind, you should find it easier to define the solutions you want and create a checklist of what to look for in an Industrial IoT platform.

To get more ideas for Industry 4.0 uses cases, read our blog about digital twins for production lines.


Investing in Industrial IoT for data analytics? Read this first.

Industry 4.0 is all about the Internet of Things and Big Data Analytics. Analysts across the board are talking about the enormous economic opportunity of Industrial IoT, predicting 30 billion connected devices by 2020 and a potential impact of more than $14 trillion over the next 12 years.

According to Forbes, Industrial IoT data analytics is consistently a top driver for why companies invest in IoT. Companies look to data for improving product performance, making data-driven product decisions with a better understanding of their customers, and gaining visibility into system usage.

But while the hype surrounding IoT is focused on data analytics, the reality is that most companies are only getting a fraction of the value from the data of their connected systems. A study by McKinsey found that 54% of companies utilized 10% or less of this information.

This gap between data expectations and reality may seem extreme, but it can be bridged.

Understanding the data challenge

How do we explain why in a gas rig with 30,000 sensors, only 1% of data is being used for decision making? Or why Industrial IoT data management remains a leading challenge for companies year after year?

To answer these questions, we first need to understand how we got here.

IoT development and adoption has surged dramatically in a very short time frame. In the past 2 years alone, almost 5 billion new devices were connected worldwide.

And while the growth of IoT has been drastic, many of the early IoT adopters have been taking a “makeshift approach to their early IoT initiatives.” Companies are moving forward with industry 4.0 initiatives such as predictive quality and predictive maintenance, without an Industrial IoT framework for supporting it or the right partners to ensure successful implementation.

When it comes to data, a clearly defined strategy for how to collect the right data and get actionable insights from it, is rarely in place prior to product development.

Define your data strategy

The price tag of Industrial IoT or industry 4.0 is too high for companies to fail at extracting greater value from its data.

Ahead of deciding which analytics will be applied to production line data, companies must first ensure that they will be collecting the right data to analyze.

To do so, we have to rethink how to formulate a data strategy at the outset of industry 4.0 planning.

A successful data strategy must take into account 4 critical things:

1. Business goals- What are the benefits you want to get from your IIoT initiative and how do you plan to put your data to work for achieving those goals (e.g.: improved quality, reduced unplanned downtime, increased throughput)?

2. Start with the end in mind- Which data-driven insights will you need to extract in order to fulfill your needs? What will the dashboards look like?

3. Work backwards- What data needs to feed into your analytics in order to get to the data-driven insights?

4. Assess and define the data gap- What sensors do you have in place, and which are missing? How do you get the required data to a central repository at the required intervals and how do you get your PLC, SCADA, and data historians to communicate via the internet protocol while keeping it secure?

A model-driven approach  

Defining an Industrial IoT system’s functionality and its data strategy simultaneously requires a model-driven approach to IoT. This approach creates context between the Industrial IoT system spec, its planned data implementation, and visualization of data analytics - by displaying them all within the model.  

Such an approach allows you to simulate how users will engage with the system, in order to validate the system functionality and address any issues ahead of development.

Interested in learning more about how to leverage a model-driven IoT platform to get the most value from your data?

Get a demo of the Seebo Platform and see how we turn the data analytics hype into a reality.

Get Demo


“Fishbone Diagram” created by Kaoru Ishikawa (Quality Manager at Kawasaki) in the 1960s.

Root Cause Analysis in the Age of Industry 4.0

What is Root Cause Analysis in manufacturing?

On the production floor, Root Cause Analysis (RCA) is the process of identifying factors that cause defects or quality deviations in the manufactured product.

The term “root cause” refers to the most primary reason for a production line’s drop in quality, or a decrease in the overall equipment effectiveness (OEE) of an asset.

Common examples of root cause analysis in manufacturing include methodologies such as the “Fishbone” diagram and the “5 Whys”. The simplicity of these methods is also their strength, but how effective are they in dealing with the complexity of today’s manufacturing processes?

 

“Fishbone Diagram” created by Kaoru Ishikawa (Quality Manager at Kawasaki) in the 1960s.
“Fishbone Diagram” created by Kaoru Ishikawa (Quality Manager at Kawasaki) in the 1960s.

Root cause analysis is undergoing a new interpretation in light of the Industry 4.0 revolution. With the power of industrial IoT and artificial intelligence at our fingertips, it’s natural that manufacturers progress to more advanced root cause analysis methods.

 

Why do we look for the Root Cause?

While the symptom and immediate cause might be easy and quick to solve, failing to detect and treat the root cause will very likely lead to the problem recurring.

The challenge in RCA is distinguishing between a symptom or intermediate cause, and the true root cause of a problem.

 

Shortcomings of Traditional Root Cause Analysis

The general approach currently used by many manufacturers when it comes to root cause analysis is to rely on on-site expert knowledge.

Experience is indeed valuable, but some production lines are so complex that being simultaneously aware of every component and sub-process is humanly impossible.

Manufacturers that do collect data from OT and IT systems still need to be able to make sense of it in order to perform RCA. This requires time and a variety of professionals to perform - in most cases, process, quality and maintenance engineers.

It’s natural that even experts can be biased towards certain ideas. And, even if the root cause of the problem is roughly identified, there may be inaccuracies in the definition of the problem, making it difficult to come up with an intelligent and lean solution.

Another disadvantage of manual root cause analysis:

Currently, most RCA information isn’t shared across manufacturing sites, leaving factories/plants of the same company to repeat each other’s mistakes, leading to unplanned downtime that could have been prevented.

 

The Power of Automated Root Cause Analysis

Machine Learning is a subfield of artificial intelligence that focuses on developing and researching algorithms that learn from data. The algorithms exist in the form of models which are trained with historical data in a way that allows them to make predictions and decisions based upon new data.

Thanks to significant advances in machine learning and Big Data analytics, root cause analysis can be performed using automated methods. These methods are unbiased and based purely upon historic and real-time data from the production floor.

Anomaly Detection

To perform RCA using machine learning, we need to be able to detect that something is out of the ordinary, or in other words, that an anomaly is present.

The machine learning model is trained to analyze the equipment’s data output under regular “healthy” operating conditions. An anomaly can take the form of any pattern of deviation in the amplitude, period, or synchronization phase of a signal when compared to normal behavior.

The algorithm forms a prediction based on the current behavioral pattern of the anomaly. If the predicted values exceed the threshold confirmed during the training phase, an alert is sent.

Examples of anomalies detected using automated root cause analysis include:

  • Component failure
  • Abnormal process input parameters (eg. off-spec material composition)
  • Corrupt sensor values
  • Changes made to the control logic (eg. via the PLC)
  • Changes in environmental conditions

So, is this the end of industry expertise?

Automated root cause analysis reduces the overall dependency on expert knowledge, but it doesn’t diminish the value of on-site experts who are vital in monitoring, validating and managing the RCA process.

Additionally, automated root cause analysis is powered by machine learning and probabilistic graphical models that need to be trained in order to be able to perform inference. This makes on-site experience critical in ensuring a system that takes into account all relevant parameters.

Mutual Information

Another mathematical solution suited to RCA is the probabilistic strategy known as Mutual Information. In a manufacturing setting involving a high volume of data and parameters, this approach can be used to leverage complex statistical knowledge to search for patterns.

Mutual information is an investigative tool that aims to describe the mutual dependency between two random variables. When aiming to identify causal relationships - such as in root cause analysis - mutual information helps by identifying which information can be learned about one variable through data about another.

 

The Role of AI in Root Cause Analysis

Artificial intelligence, specifically in the form of machine learning, catapults root cause analysis into another realm of asset management.

It’s all about timing:

The ability of AI to formulate predictions relating to machine performance and health, instead of waiting for disaster to strike, introduces a whole range of benefits that affect the bottom line.

Some examples of the direct benefits of automated root cause analysis in manufacturing are:

  • Early detection of safety issues
  • Reduced emissions due to accurate monitoring of the entire production process
  • Identification of complex process disruptions eg. inefficiency of a reactor
  • More efficient electrical consumption through anomaly detection
  • Predicting quality deviations and adjusting processes to prevent the waste of raw materials

If you had to summarize the value of machine learning in root cause analysis, it would be:

Less time spent on figuring out the problem, more time spent on fixing and preventing it.

 

Example of Automated Root Cause Analysis in Manufacturing

A prime example of automated root cause analysis would be to look at how machine learning can be utilized to deduce the root cause of asset failure and quality deviations in manufacturing.

We can look at a manufacturing process as consisting of:

  • an input stage - in process manufacturing, feeding the raw materials into the production line;
  • the process - a sequence of steps performed consecutively;
  • a resulting output - the finished product.

For this basic example, we will describe the framework for an automated root cause analysis system by using a Bayes Network (see Figure 1.)

Figure 1 - A Bayes Network describing causal correlations between root causes and failures.
Figure 1 - A Bayes Network describing causal correlations between root causes and failures.

 

The example process consists of 6 processing steps (S1 - S6), each with a number of causal nodes (the blue circles) and 4 failures or quality deviation types known as failure nodes (the orange circles).

The failures are the result of errors in one of the six processing steps, although some failures can be the result of errors in more than one processing stage. The causal correlations are represented by the dotted arrows.

Building a Bayesian Network like the one in Figure 1 requires the involvement of relevant process experts since all process stages and failure points need to be carefully defined.

Expert knowledge that includes causal correlations based upon experience, as well as historic data of known causal relationships, can be added to the algorithm which can take into account this knowledge, without being biased by it.

Once the model has been trained, new data can be fed into it to discover the root cause of new failure incidents. With data only about the failure nodes, the machine learning algorithm can infer which causal nodes were likely involved in the failure.

An example of the algorithm’s output:

Probability (Causal Node) A = 0.01
Probability (Causal Node) B = 0.81
Probability (Causal Node) C = 0.03

As you may have noticed, the results don’t add up to 1 (the standard for statistical and probabilistic calculations). This is because the algorithm takes into consideration the fact that the exact root cause might not be described as an already-defined causal node.

Another important element of this type of model is what is known as a measurement node. Measurement nodes give specific readouts of observable information pertaining to the causal nodes such as pressure or vibration measurements taken in a specific step of the process.

In this way, measurement nodes add another data layer to the model, allowing for relationships that aren’t yet defined by the model to affect the outcome.

 

The result:

A data-driven automated RCA system that is accurate and predictive, offering actionable insights that can be shared between cooperating facilities.

Patterns and anomalies can be detected pointing to root causes that would normally be very difficult to identify based purely upon expert knowledge.

The fact that root causes of unplanned downtime and quality deviations can be predicted makes these methods of automated root cause analysis perfectly suited to Industry 4.0 use cases.

For an in-depth example of automated root cause analysis in manufacturing, be sure to check out our free case study here.

 


Industrial AI is Revolutionizing Manufacturing

5 Ways Industrial AI is Revolutionizing Manufacturing

There’s no doubt that the manufacturing sector is leading the way in the application of AI technology. From significant cuts in unplanned downtime to better-designed products, manufacturers are applying AI-powered analytics to data to improve efficiency, product quality and the safety of employees.

Here’s how:

Industry 4.0 and Smart Maintenance

In manufacturing, ongoing maintenance of production line machinery and equipment represents a major expense, having a crucial impact on the bottom line of any asset-reliant production operation. Moreover, studies show that unplanned downtime costs manufacturers an estimated $50 billion annually, and that asset failure is the cause of 42% of this unplanned downtime.

For this reason, predictive maintenance has become a must-have solution for manufacturers who have much to gain from being able to predict the next failure of a part, machine, or system.

Predictive maintenance uses advanced AI algorithms in the form of machine learning and artificial neural networks to formulate predictions regarding asset malfunction.

This allows for drastic reductions in costly unplanned downtime, as well as for extending the Remaining Useful Life (RUL) of production machines and equipment.

In cases where maintenance is unavoidable, technicians are briefed ahead of time on which components need inspection and which tools and methods to use, resulting in very focused repairs that are scheduled in advance.

The Rise of Quality 4.0

Because of today’s very short time-to-market deadlines and a rise in the complexity of products, manufacturing companies are finding it increasingly harder to maintain high levels of quality and to comply with quality regulations and standards.

On the other hand, customers have come to expect faultless products, pushing manufacturers to up their quality game while understanding the damage that high defect rates and product recalls can do to a company and its brand.

Quality 4.0 involves the use of AI algorithms to notify manufacturing teams of emerging production faults that are likely to cause product quality issues. Faults can include deviations from recipes, subtle abnormalities in machine behavior, change in raw materials, and more.

By tending to these issues early on, a high level of quality can be maintained.

Additionally, Quality 4.0 enables manufacturers to collect data about the use and performance of their products in the field. This information can be powerful to product development teams in making both strategic and tactical engineering decisions.

Human-robot Collaboration

The International Federation of Robotics predicts that by the end of 2018 there will be more than 1.3 million industrial robots at work in factories all over the world. In theory, as more and more jobs are taken over by robots, workers will be trained for more advanced positions in design, maintenance, and programming.

In this interim phase, human-robot collaboration will have to be efficient and safe as more industrial robots enter the production floor alongside human workers.

Advances in AI will be central to this development, enabling robots to handle more cognitive tasks and make autonomous decisions based on real-time environmental data, further optimizing processes.

 

Making Better Products with Generative Design

Artificial intelligence is also changing the way we design products. One method is to enter a detailed brief defined by designers and engineers as input into an AI algorithm (in this case referred to as “generative design software”).

The brief can include data describing restrictions and various parameters such as material types, available production methods, budget limitations and time constraints. The algorithm explores every possible configuration, before honing in on a set of the best solutions.

The proposed solutions can then be tested using machine learning, offering additional insight as to which designs work best. The process can be repeated until an optimal design solution is reached.

One of the major advantages of this approach is that an AI algorithm is completely objective - it doesn’t default to what a human designer would regard as a “logical” starting point. No assumptions are taken at face value and everything is tested according to actual performance against a wide range of manufacturing scenarios and conditions.

Adapting to an Ever-Changing Market

Artificial intelligence is a core element of the Industry 4.0 revolution and is not limited to use cases from the production floor. AI algorithms can also be used to optimize manufacturing supply chains, helping companies anticipate market changes. This gives management a huge advantage, moving from a reactionary/response mindset, to a strategic one.

AI algorithms formulate estimations of market demands by looking for patterns linking location, socioeconomic and macroeconomic factors, weather patterns, political status, consumer behavior and more.

This information is invaluable to manufacturers as it allows them to optimize staffing, inventory control, energy consumption, and the supply of raw materials.

Industrial AI will Continue to Transform the Manufacturing Sector

The manufacturing sector is a perfect fit for the application of artificial intelligence. Even though the Industry 4.0 revolution is still in its early stages, we’re already witnessing significant benefits from AI. From the design process and production floor, to the supply chain and administration, AI is destined to change the way we manufacture products and process materials forever.

 

This article was featured in CIO magazine.

 


Condition Monitoring

Condition Monitoring - The Foundation of Industrial IoT

What is Condition Monitoring (CM)?

Condition Monitoring forms the foundation of what has become known as Industry 4.0. In its basic form, the term is fairly self-explanatory and refers simply to the act of monitoring the condition of an asset.

Within the IIoT ecosystem, an integral part of Condition Monitoring is providing data that can then be used for Predictive Maintenance (PdM) and other smart factory applications such as Digital Twin.

 

Condition Monitoring Vs. Condition Assessment

When a technician performs a routine visual check of a component in a plant, it’s a pre-scheduled and momentary snapshot of that component’s health, regardless of historical performance data or previous inspections. This type of inspection is known as Condition Assessment.

With Condition Monitoring, we take into account a much broader set of granular data that includes sensor data from the asset, previous inspections, other components of the same type, location and condition of the plant, and historical trends. An analysis is made that not only defines the component’s current status but also predicts future issues and when they’re likely to occur, including when the part will need replacement.

The 6 Main Benefits of IoT Condition Monitoring

The 5 Main Benefits of IoT Condition Monitoring

Condition Monitoring offers multiple business benefits, including secondary advantages earned from the reduction in costs and resources that this technology enables.

The core benefits of condition monitoring can be summarized as:

Reduced maintenance costs

Maintenance becomes proactive and timely, cutting labor and travel costs, and repairs are done before critical damage occurs. Service time is reduced, and customer satisfaction improved.

Maximized production

With accurate and extensive readouts from sensors on production machines, combined with data analytics algorithms to gain visibility into production inefficiencies, new levels of productivity can be reached. This is especially true with condition monitoring in the oil and gas industry.

Optimized inventory of spare parts

Rather than overstocking the inventory of expensive spare parts, which impacts margins, or running low on inventory, which increases downtime, Condition Monitoring enables accurate forecasting of the demand for spare parts.

Accurate and relevant data for driving product development

Asset behavioral data collected over time can be aggregated and analyzed by engineering to identify product design flaws that can be rectified in subsequent product versions.  

Extended machinery lifetime

The health of a machine and all of its components is monitored in detail. Overheating, wear-and-tear, and other threats to the machine’s well-being are taken care of in a timely manner, lengthening the machine’s lifespan.

 

Condition Monitoring Techniques

The implementation of condition monitoring differs greatly from one manufacturer to another. This is largely because of how every product or asset has its own unique pattern of “normal” behavior which must be monitored and analyzed.

Here are some of the more commonly used Condition Monitoring techniques:

  • Vibration analysis
  • Lubricant analysis
  • Infrared thermography
  • Acoustic emission
  • Ultrasound
  • Motor current signature analysis (MCSA)
  • Model-based voltage and current systems (MBVI)

As an example, let’s take a look at this last method using the following illustration:

Condition Monitoring techniques
Condition Monitoring with MBVI

In the above flow diagram, after running a voltage through a motor, the current is measured and compared to that of a mathematical model that is fed with accurate real-time data from the same motor. The two current readings are summed and compared. In cases where no deviations are evident, the motor (or system) can be regarded as healthy. If there are discrepancies between the mathematical model and the actual motor, we move on to the analysis stage to find out exactly what the problem is. Once the problem has been identified, we can classify it and deploy the relevant solution.

In this example, the idea of the permanence of Condition Monitoring becomes clear. It makes sense to constantly be able to monitor and record the motor’s status, instead of only momentarily performing a diagnostics check. This way, historical trends are captured automatically showing us how mechanical, electrical and operational problems and their parameters change over time.

 

Condition Monitoring Software

With sensors in place to record the various parameters of the machines as they work, it would also be very useful to have an application to concentrate the information and communicate the required action. For this reason, the adoption of condition monitoring software is growing rapidly as manufacturers look for an easy and efficient way to interpret information collected by a CM system, and then take timely action upon it.

Seebo's Condition Monitoring system
Seebo’s Condition Monitoring system

The Seebo remote condition monitoring solution is an example of such software with the exception that it not only consolidates the Condition Monitoring data but also supports the planning and delivery of a complete condition monitoring system from scratch.

Where should the sensors be placed? What should they be measuring? How should they be calibrated? What alerts should they send out, and to where? All these questions and others can be answered using Condition Monitoring software, allowing stakeholders to weigh in on the design of a system at any stage.

Once remote Condition Monitoring has been implemented, the software continues to act as its hub, concentrating all the incoming data being reported by the sensors into a central repository, allowing for deep data analysis that drives corrective action.

 

Leveraging Condition Monitoring to Create Business Value

Condition Monitoring is really only the first phase in a larger cycle of industrial IoT maintenance. While monitoring the condition of an asset, data is collected and stored. If that data calls for an immediate action such as a repair or preventive maintenance of some sort, then a technician or team is deployed.

Regardless of the action taken, the state of the asset is stored together with its sensor data, in a big data repository. The data repository can be referred to for specific comparisons that require historical data, and can also be used to observe trends and formulate predictions.

The accuracy and depth of the data collected from Condition Monitoring, and its reach with regards to encompassing an entire factory or plant, provides manufacturers with extremely valuable information that can be leveraged to make informed business decisions regarding production efficiency.

Using Big Data analysis, trends can be observed that form the foundation for accurate predictions, helping both day-to-day operations as well as triggering creative and proactive strategies for further growth.

 

Machine + Human = Best Outcome

Condition Monitoring fits into the overall Industrial IoT framework as a foundation block for continuous improvement. Insights from Condition Monitoring must be acted upon by humans, embedding the new knowledge into customer service processes, aftermarket sales, production planning, and new product development. Through Condition Monitoring and other IoT use cases in manufacturing, we improve our daily operations by containing service and production costs, increasing sales, and boosting customer satisfaction.

 


AI in Manufacturing

Artificial Intelligence - The Driving Force of Industry 4.0

A lot of the hype surrounding artificial intelligence in manufacturing is focused on industrial automation, but this is just one aspect of the smart factory revolution; a natural next-step in the pursuit of efficiency. What artificial intelligence also brings to the manufacturing table is its capability to open up completely new avenues for business.

Below is an outline that covers both these aspects of artificial intelligence within the Industry 4.0 paradigm, and how this powerful technology is already being used by manufacturers to drive efficiency, improve quality and better manage supply chains.

 

Industrial AI’s Impact on Manufacturing

Artificial intelligence’s impact on manufacturing can be organized into 5 main areas:

  • Maintenance / OEE

Predictive maintenance has become a very sought-after use case for manufacturers looking to advance to Industry 4.0. Instead of performing maintenance according to a predetermined schedule, predictive maintenance uses algorithms to predict the next failure of a component/machine/system and then alerts personnel to perform focused maintenance procedures to prevent the failure, but not too early so as to waste downtime unnecessarily.

One of the most common applications of AI for manufacturing is machine learning, and most predictive maintenance systems rely on this technique to formulate predictions. The advantages are numerous and can significantly reduce costs while eliminating the need for planned downtime in many cases.

By preempting a failure with a machine learning algorithm, systems can continue to function without unnecessary interruptions. When maintenance is needed, it’s very focused – technicians are informed of the components that need inspection, repair and replacement; which tools to use, and which methods to follow.

Predictive maintenance also leads to a longer Remaining Useful Life (RUL) of machinery and equipment since secondary damage is prevented while smaller labour forces are needed to perform maintenance procedures.

Regression labeling for Predictive Maintenance.
Regression labeling for Predictive Maintenance. Each recorded instant (5Y, 4Y, 3Y etc.) describes the Remaining Useful Life of an asset before it is predicted to fail.

 

  • Quality 4.0

Manufacturers are finding it harder than ever to maintain consistently high levels of quality. This is due in part to a rising complexity in products (that integrate software, for example) and very short time-to-market goals.

Despite these challenges, managers are highlighting quality as a top priority, realising the importance of the customer’s experience of a product and the power of customers to push a brand forward as well as being aware of the pain point of high defect percentages and product recalls.

Using Industry 4.0 techniques, this new quest for quality has been appropriately named Quality 4.0 and artificial intelligence is at its forefront. Quality issues cost companies a lot of money, but with the use of AI algorithms developed through machine learning, manufacturers can be alerted of initially minor issues causing quality drops, similar to the way alerts are created for predictive maintenance.

Quality 4.0 allows manufacturers to continually improve the quality of their output while collecting usage and performance data from products and machinery in the field. This data becomes a vital source of information that forms the basis for product development and crucial business decisions.

 

  • Human-robot collaboration

According to the International Federation of Robotics, by the end of 2018, there will be 1.3 million industrial robots working in factories around the world. The general approach is that as jobs get taken over by robots, workers will be offered training for higher-level positions in programming, design, and maintenance. In the meantime, the efficiency of human-robot collaborative work is being improved as manufacturing robots are approved for work alongside humans.

As the adoption of robotics in manufacturing increases, AI will play a major part in ensuring the safety of human personnel as well as giving robots more responsibility to make decisions that can further optimize processes based on real-time data collected from the production floor.

 

  • Generative design

Manufacturers can also make use of artificial intelligence in the design phase. With a clearly defined design brief as input, designers and engineers can make use of an AI algorithm, generally referred to as generative design software, to explore all the possible configurations of a solution. The brief can include restrictions and definitions for material types, production methods, time constraints and budget limitations.

The set of solutions generated by the algorithm can then be tested using machine learning. The testing phase provides additional information about which ideas/design decisions worked, and which did not. In this way, additional improvements can be made until an optimal solution is found.

 

  • Market adaptation / Supply chain

Artificial intelligence permeates the entire Industry 4.0 ecosystem and is not only limited to the production floor. One example of this is the use of AI algorithms to optimize the supply chain of manufacturing operations and to help them better respond to, and anticipate, changes in the market.

To construct estimations of market demand, an algorithm can take into account demand patterns categorized by date, location, socioeconomic attributes, macroeconomic behavior, political status, weather patterns and more.

This is groundbreaking for manufacturers who can use this information to optimize inventory control, staffing, energy consumption, raw materials, and make better financial decisions regarding the company’s strategy.

 

Industry 4.0 Demands Collaboration

The complexity of using artificial intelligence in industrial automation requires that manufacturers collaborate with specialists to reach customized solutions. Attempting to build the required technology is costly and most manufacturers don’t have the necessary skills and knowledge in-house.

An Industry 4.0 system consists of a number of elements/phases that need to be configured to suit the manufacturer’s needs:

  • Historical data collection
  • Live data capturing via sensors
  • Data aggregation
  • Connectivity via communication protocols, routing and gateway devices
  • Integration with PLCs
  • Dashboards for monitoring and analysis
  • AI applications: machine learning and other techniques

To truly leverage AI, manufacturers will do well to partner with experts who understand their goals and who can help create a clearly defined roadmap with an agile development process that links the AI implementation to relevant KPIs.

 


quality 4.0 adoption zones

Quality 4.0 - Better Processes & Better Performance for Better Products

One would think that the acceleration of technological growth would automatically result in improvements in manufacturing quality, but it seems that the opposite is true in many cases. There are a number of factors making it more challenging than ever for manufacturers to maintain a high level of output quality.

Today’s products are complex, often incorporating or integrating with software, and the highly competitive nature of the manufacturing sector means that time-to-market goals are shorter than ever.

Managers are aware of this phenomenon, and quality is moving up to take its place as a top priority for many companies. With Industry 4.0 making such a huge impact on manufacturing, it’s only natural that these methodologies be leveraged to meet the new quality demands. And so, the birth of Quality 4.0 – a term used to describe a new focal point in industry – is finally upon us.

 

What is Quality 4.0?

Like Industry 4.0Quality 4.0 isn’t a closed-ended term that defines just one technology or activity. Instead, Quality 4.0 describes a new approach to manufacturing, where production is not just gauged based upon output rate and cost, but on the quality of the product, the quality of the process, and the quality of the services provided surrounding the product.

The “4.0” is a reference to Industry 4.0 and its associated technologies such as Industrial IoT, Digital Twin, AI in the form of machine learning algorithms and artificial neural networks, and others.

These are all technologies that can be leveraged to improve quality. For example, Predictive Quality Analytics is a use case that utilizes the aforementioned technologies to predict changes in production quality. This information is crucial to manufacturers who realize the importance of quality to customers, and who are interested in developing a much leaner operation while making better products.

 

Quality 4.0 – The Time is Now

In our current reality, Quality 4.0 is still in its early stages of adoption. In fact, most manufacturing facilities still rely on traditional quality evaluation methods; methods that in many cases are no longer relevant for current products. Companies that fail to take an innovative stance on quality, for current and new production processes, will find it hard to survive, let alone lead, in future markets.

The bottom line is that quality issues cost companies a lot of money, and in doing so, affect the potential longevity of a manufacturing operation especially in a market that is ever-changing and more competitive than ever.

 

The Opportunity Presented by Quality 4.0

Advancing to Quality 4.0 requires financial and organizational resources, but the process presents a huge opportunity for manufacturers. Searching for new innovative ways to optimize quality is an opportunity to nurture a culture of development which can lead to better products that cost less to produce.

Introducing Quality 4.0 can also help to strengthen and differentiate a brand within its market, and improve awareness among existing and potential new customers.

As with Factory 4.0, Quality 4.0 levels the manufacturing playing field since mid and even small-scale enterprises can leverage new technology to make significant advances in production efficiency and better meet the demands of customers.

 

Challenges in the Current Quality Arena

Today’s manufacturers face a number of quality-related challenges:

  • Maintaining a high level of quality amidst high expectations and changes in customer demands.
  • Allocating resources for innovation and for research into new methods of quality improvement.
  • Compliance with changes in regulation laws.
  • Agility: increases in product variety demand work on multiple products simultaneously (development and production stages).
  • Global standardization: companies producing from a number of locations have to offer consistent output quality regardless of differences in the standard of local raw materials and production conditions.

Industry 4.0, along with its suite of powerful use cases such as predictive quality & maintenance, remote monitoring, and digital twin, enables manufacturers to meet the above challenges head-on. For example, changes in regulations can be directly communicated to production lines or code can be modified remotely so that new and existing products comply with the new laws.

 

The Four Zones of Quality 4.0 Adoption

The Four Zones of Quality 4.0 Adoption

 

  1. Concept & Design

In the past, “quality” has usually been associated with production processes – raw materials used, assembly, finishing and packaging – but quality should be an integral part of the conceptualization/design and industrialization phases as well.

By including the quality perspective in the early stages of the product lifecycle all the way through production and delivery, manufacturers will be able to achieve higher levels of customer satisfaction. After all, the quality of a product’s concept is an attribute that affects how a customer experiences the use and value of that product.

  1. Production

This particular zone represents where most of the quality activity has taken place in manufacturing prior to the Industry 4.0 revolution. Traditional data analytics and process harmonization methods are being replaced by techniques that involve artificial intelligence such as Machine Learning, and advanced levels of monitoring and connectivity such as Digital Twin.

  1. Service & Performance in the Field

A unique characteristic of Quality 4.0 is that a product’s performance is monitored (and modified, if necessary, and possible) even after delivery.

By collecting and making sense of user data from the field, future failures can be prevented with minimum loss of materials in rejected batches. The time it takes from failure identification to elimination can be extremely short, reducing wastage and maintaining customer satisfaction despite temporary disappointments.

In software-integrated products, updates can be made remotely, eliminating bugs and adding features requested by users.

  1. Company Culture

Quality 4.0 is a broad field of activity, and companies should aim to instill the quality approach as part of the overall company culture.

Since every employee, and every interaction with the manufacturing process, can be considered within the quality paradigm, Quality 4.0 is not limited to a particular segment of manufacturing.

 

Industry 4.0 – Taking Quality into the Future

Technologies associated with smart factory – IIoT, Big Data, AI, Machine Learning etc. – can all be utilized to improve quality. However, methods for quality improvement are lagging behind the development of other production-enhancing technologies. This is especially true for methods involving B2C communication and feedback loops. In other words, the power of Quality 4.0 has yet to be fully unleashed.

The good news is that industrial IoT techniques – connectivity protocols, sensors, gateway devices, dashboards, analytics – provide the perfect toolbox for Quality 4.0 implementation.

One way of demonstrating this is through examining remote monitoring as a Quality 4.0 use case…

 

Remote Monitoring for Quality 4.0

Using sensors to collect data for root cause analysis, diagnosis techniques can be performed remotely. By gathering feedback from a number of devices, “swarm intelligence” can also be used as a method of further analysis into machine behavior or product performance.

By using predictive analytics on the collected data, we can identify correlative patterns and enable predictive maintenance. Beyond efficient maintenance and the prevention of malfunction, this analysis provides insight into parameters affecting output or performance quality.

A common argument is that only software or data-related issues can be handled remotely, but in the field it’s become evident that extremely often, service technicians are summoned for just that reason. In the automotive industry, for example, software issues represent a significant portion of all the reasons behind service requests. And in cases where an on-site visit is needed, technicians arrive informed of the details of the issue, and equipped with the necessary components, tools and methods for the specific repair.

Remote monitoring and maintenance allows manufacturers to continually improve quality over time while the usage and performance data collected from products or production machines provides an invaluable source of information for business and product development insights.


factory 4.0

How Factory 4.0 is Changing the Way we Make Products and Process Materials

What is Factory 4.0?

Around the world, and in practically every sector, manufacturing facilities are undergoing a major transformation. Digitization is changing the way we process materials and make products, and data is becoming the golden key that can open a door to technological possibilities with the power to completely reshape manufacturing.

The traditional manufacturing model is evolving into what is referred to as a “smart factory” or Factory 4.0 – a connected system that links machinery, personnel, maintenance activity, and analytics for a completely integrated approach to factory management.

Factory 4.0 leverages technologies and industry 4.0 components such as non-intrusive sensors, wireless connectivity, cloud computing, artificial intelligence, machine learning and others, to affect all phases of manufacturing business from raw materials processing, safety, and production, to quality assurance, packaging, and distribution.

The Building Blocks of Factory 4.0

There are numerous approaches for digitally transforming a manufacturing facility, but any typical Factory 4.0 solution will include the following core elements:

Sensors

Sensor technology has developed greatly in recent years and today’s market of sensor providers offers a wide variety of low-cost sensors that can measure parameters that include temperature, pressure, light, vibration, water/lubricant quality, chemical content, liquid/solid levels and many more.

Depending on what they’re monitoring, sensors can be placed on or inside machines, at designated workstations, on devices carried by personnel, or in part of the factory’s existing systems such as the HVAC or security network.

Some use cases for industrial IoT sensors include:

  • Tracking the movement and position of raw materials, components, finished products, and valuable equipment throughout the factory
  • Quality assurance (optical testing and analytics)
  • Inventory: monitoring the supply of raw materials and spare parts
  • Identifying equipment behavior anomalies that could result in quality issues
  • Safety: sensors on machinery to restrict activity near personnel; sensors carried by personnel that measure potential environmental threats, lack of movement etc.

Connectivity Protocols

IoT connectivity protocols form the language of an IoT system. These communication standards allow data to be transferred and understood by the various components of the system – from the sensors to the cloud via PLCs and gateway devices; and finally to a software program for analysis.

Deciding on the correct protocol early on is critical for building a successful smart factory.

Cloud Computing

The cloud represents Factory 4.0’s main data center. Here, information collected from the sensors is stored, processed and analyzed. The cloud is also utilized for edge computing to further optimize data processing by minimizing the reliance on centralized processing nodes.

Analytics & Machine Learning

The large amounts of data captured continuously from the shop floor, and collected from historian systems, including information about every aspect of production. Data can be analyzed using statistical algorithms as well as by implementing machine learning techniques which automatically derive actionable insights from root-cause analysis and historical data.

This continual analytical activity triggers insights that lead to improved machinery performance, more efficient processes (eg. on-site transport, production line configuration, maintenance etc.), and reduced downtime.

Use Cases Drawing Manufacturers to Factory 4.0

Factory 4.0 is Changing the Way we Make Products and Process Materials

Implementing changes to a manufacturing system is a complex and costly process, but the use cases driving companies to adopt Factory 4.0 are highly compelling, with the potential to begin positively affecting ROI within a single quarter.

Improving OEE (Overall Equipment Effectiveness)

Analytics-driven insights enable the identification of the root cause of system issues. This understanding, based directly upon machine output data, allows management to hone in on areas that require changes while taking into consideration real-time availability of equipment, performance levels, and the quality of output.

From Corrective to Predictive Maintenance

Using predictive analytics to leverage the data collected from machines, it’s possible to monitor asset health to the point where equipment failure can be predicted, improving reliability and greatly reducing maintenance costs.

With Factory 4.0, manufacturers receive automatic alerts when anomalies occur and can optimize maintenance schedules to completely side-step machine failure.

This method of preempting system errors does away with the need for corrective or preventive maintenance, cutting labor costs and building a strong sense of reliability amongst customers.

Remote Asset Monitoring

A powerful use case for management, Remote Asset Monitoring offers improved visibility of the factory floor as well as mobile assets regardless of their location. Alerts about the condition of individual machines, equipment, and the factory environment are sent to stakeholders who can make data-driven decisions to increase efficiency and maintain compliance with regulations.

Enter the Digital Twin

A Digital Twin is a digital representation of an asset, process or facility; a visual model that offers real-time data about its physical correspondent. Digital Twins are the culmination of a number of technological capabilities that fall under the Factory 4.0 umbrella.

Digital Twin software offers full visualization of its “real-life” twin, allowing management to experiment with parameters and explore ideas for further optimization, without the risk of harming performance or damaging equipment.

The Many Benefits of Factory 4.0

Every production facility is different, and the nature of process manufacturing differs greatly from discrete manufacturing. That being said, there are a number of benefits to Factory 4.0 that are relevant across the board.

The Constant Pursuit of Quality

Using artificial intelligence along with input from management, Factory 4.0 continually learns how to optimize itself, reacting to changes in conditions in real-time, and running entire manufacturing processes autonomously.

Besides detecting risks, predicting failures, and preventing unplanned downtime, Factory 4.0 and predictive quality can help detect decreasing quality trends (increases in defects) and can suggest areas for improvement by identifying human, machine, or environmental factors that are affecting the number of defects.

Cutting Costs & Impacting the Bottom Line

The improved optimization brought on by Factory 4.0 technology cuts costs in a number of ways, leading to a leaner operation overall. Inventory can be managed in a much more precise manner since maintenance is far more predictable.

Repairs are proactive and timely, keeping machine health optimal. Since technicians know ahead of time the exact type of malfunction they’ll be working on, secondary damage is prevented and repairs are much quicker.

Having data on all aspects of the process also enables better-informed decisions regarding staff. This allows for more accurate employee allocations per task, preventing unnecessary spending on labor.

The “Why” behind Factory 4.0 is Crystal Clear

Replacing corrective maintenance with predictive maintenance is just the tip of the iceberg.

Factory 4.0 represents a new paradigm in how we produce materials and products. The use of Big Data and the high level of connectivity and control offered by smart factories allows manufacturers to focus on taking their operations, products, and services to the next level.

Interested in Factory 4.0 technology for your operation? Book a free demo to learn about Seebo’s smart factory solution for manufacturers.