“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.

 


Machine Learning Cornerstone

The Impact of Machine Learning
and AI on Manufacturing

It’s a timeless manufacturing goal: to produce high-quality products at a minimum cost. Factory 4.0 is already demonstrating its value by enabling manufacturers to reach this goal more successfully than ever, and one of the core technologies driving this new wave of ultra-automation is Industrial AI and Machine Learning.

Data has become a valuable resource, and it’s cheaper than ever to capture and store. Through the use of artificial intelligence, specifically Machine Learning, manufacturers can use data to significantly impact their bottom line by greatly improving efficiency, employee safety, and product quality.

 

Powering Predictive Maintenance with Machine Learning

Maintenance represents a significant part of any manufacturing operation’s expenses. For this reason, Predictive Maintenance has become a common goal amongst manufacturers, drawn by its many benefits, with significant cuts in maintenance costs being one of the most compelling.

While certain manufacturers do perform Predictive Maintenance, this has traditionally been done using SCADA systems set up with human-coded thresholds, alert rules, and configurations.

This semi-manual approach doesn’t take into account the more complex dynamic behavioral patterns of the machinery, or the contextual data relating to the manufacturing process at large. For example, a sensor on a production machine may pick up a sudden rise in temperature. A static rule-based system would not take into account the fact that the machine is undergoing sterilization, and would proceed to trigger a false-positive alert.

In contrast, Machine Learning algorithms are fed OT data (from the production floor: sensors, PLCs, historians, SCADA), IT data (contextual data: ERP, quality, MES, etc.), and manufacturing process information describing the synchronicity between the machines and the rate of production flow.

In AI, the process is known as “training”, enables the ML algorithms to detect anomalies and test correlations while searching for patterns across the various data feeds.

The power of Machine Learning lies in its capacity to analyze very large amounts of data in real-time, and propose actionable responses to issues that may arise. The health and behavior of every asset and system are constantly evaluated and component deterioration is identified prior to malfunction.

 

Enabling Predictive Quality Analytics with Machine Learning

Preventing downtime is not the only goal that industrial AI can assist us with. The quality of output is crucial and product quality deterioration can also be predicted using Machine Learning. Knowing beforehand that the quality of products being manufactured is destined to drop prevents the wastage of raw materials and valuable production time.

 

Supervised & Unsupervised Machine Learning

Machine Learning can be split into two main techniques – Supervised and Unsupervised machine learning.

Supervised Machine Learning

In manufacturing use cases, supervised machine learning is the most commonly used technique since it leads to a predefined target: we have the input data; we have the output data; and we’re looking to map the function that connects the two variables.

Supervised machine learning demands a high level of involvement – data input, data training, defining and choosing algorithms, data visualizations, and so on. The goal is to construct a mapping function with a level of accuracy that allows us to predict outputs when new input data is entered into the system.

Initially, the algorithm is fed from a training dataset, and by working through iterations, continues to improve its performance as it aims to reach the defined output. The learning process is completed when the algorithm reaches an acceptable level of accuracy.

In manufacturing, one of the most powerful use cases for Machine Learning is Predictive Maintenance, which can be performed using two Supervised Learning approaches: Classification and Regression.

These 2 approaches share the same goal: to map a relationship between the input data (from the manufacturing process) and the output data (known possible results such as part failure, overheating etc.)

  • Regression

Regression is used when data exists within a range (eg. temperature, weight), which is often the case when dealing with data collected from sensors.

In manufacturing, regression can be used to calculate an estimate for the Remaining Useful Life (RUL) of an asset. This is a prediction of how many days or cycles we have before the next component/machine/system failure.

For regression, the most commonly used machine learning algorithm is Linear Regression, being fairly quick and simple to implement, with output that is easy to interpret. An example of linear regression would be a system that predicts temperature, since temperature is a continuous value with an estimate that would be simple to train.

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.
  • Classification

When data exists in well-defined categories, Classification can be used. An example of Classification that we’re all familiar with is the email filter algorithm that decides whether an email should be sent to our spam folder, or not. Classification is limited to a boolean value response, but can be very useful since only a small amount of data is needed to achieve a high level of accuracy.

In machine learning, common Classification algorithms include naive Bayes, logistic regression, support vector machines and Artificial Neural Networks.

Predictive Maintenance makes use of multi-class classification since there are multiple possible causes for the failure of a machine or component. These are possible outcomes that are classified as potential equipment issues, calculated using a number of variables including machine health, risk levels and possible reasons for malfunction.

 

Unsupervised Machine Learning

With Supervised machine learning we start off by working from an expected outcome and train the algorithm accordingly. Unsupervised learning is suitable for cases where the outcome is not yet known.

  • Clustering

In some cases, not only will the outcome be unknown to us, but information describing the data will also be lacking (data labels). By creating clusters of input data points that share certain attributes, a Machine Learning algorithm can discover underlying patterns.

Clustering can also be used to reduce noise (irrelevant parameters within the data) when dealing with extremely large numbers of variables.

  • Artificial Neural Networks

In the manufacturing sector, Artificial Neural Networks are proving to be an extremely effective Unsupervised learning tool for a variety of applications including production process simulation and Predictive Quality Analytics.

The basic structure of the Artificial Neural Network is loosely based upon how the human brain processes information using its network of around 100 billion neurons, allowing for extremely complex and versatile problem-solving.

A basic schematic of a feed-forward Artificial Neural Network.
A basic schematic of a feed-forward Artificial Neural Network. Every node in one layer is connected to every node in the next. Hidden layers can be added as required, depending on the complexity of the problem.

This ability to process a large number of parameters through multiple layers makes Artificial Neural Networks very suitable for the variable-rich and constantly changing processes common to manufacturing. Moreover, once properly trained, an Artificial Neural Network can demonstrate a high level of accuracy when creating predictions regarding the mechanical properties of processed products, enabling cuts in the cost of raw materials.

 

Data Preparation

Machine learning is all about data, so understanding some key elements about the quality and type of data needed is extremely important in ensuring accurate results.

With Predictive Maintenance, for example, we’re focused on failure events. Therefore, it makes sense to start by collecting historical data about the machines’ performance and maintenance records in order to form predictions about future failures.

Since the operational lifespan of production machines is usually a number of years, historical data should reach back far enough to properly reflect the machines’ deterioration processes.

Additionally, other static information about the machine/system is also useful such as data about a machine’s features, its mechanical properties, typical usage behavior and environmental operating conditions.

Next, certain questions should be answered to help focus on the data that is most crucial to our needs:

  • What are the various types of failure that can occur with this component/machine/system?
  • Which failure events are we interested in trying to predict?
  • Is the failure a sudden, focused event, or is there a slow decline before complete malfunction?
  • Which components are typically associated with this type of failure?
  • Which parameters should be measured that most signify the state of component/machine health?
  • What is the required accuracy and frequency of the measurements needed?

The questions above should be answered by both domain specialists and data scientists, resulting in the final and most important two questions:

What question do we want the Machine Learning model to answer? And, is it possible to answer this question using the data that’s available?

 

The Groundbreaking Benefits of Machine Learning and AI for Manufacturing

The introduction of AI and Machine Learning to industry represents a sea change with many benefits that can result in advantages well beyond efficiency improvements, opening doors to new business opportunities.

Some of the direct benefits of Machine Learning in manufacturing include…

  • Cost reduction through Predictive Maintenance. PdM leads to less maintenance activity, which means lower labor costs and reduced inventory and materials wastage.
  • Predicting Remaining Useful Life (RUL). Knowing more about the behavior of machines and equipment leads to creating conditions that improve performance while maintaining machine health. Predicting RUL does away with “unpleasant surprises” that cause unplanned downtime.
  • Improved supply chain management through efficient inventory management and a well monitored and synchronized production flow.
  • Improved Quality Control with actionable insights to constantly raise product quality.
  • Improved Human-Robot collaboration improving employee safety conditions and boosting overall efficiency.
  • Consumer-focused manufacturing – being able to respond quickly to changes in the market demand.

Predictive Maintenance for Industry 4.0 - The Complete Guide

What is predictive maintenance?

Predictive maintenance for industry 4.0 is a method of preventing machine failure by analyzing machine data to identify patterns and predict issues before they happen.  

Until now, factory managers and machine operators carried out scheduled maintenance and regularly repaired machine parts to prevent downtime. In addition to consuming unnecessary resources and costing large sums in forfeited productivity, half of all preventive maintenance activities are ineffective.

Not surprisingly, leveraging industrial IoT technologies for predictive maintenance is a leading Industry 4.0 use case for manufacturers and asset managers. Implementing analytics to monitor asset health, optimize maintenance schedules, and gaining real-time alert to operational risks, allows manufacturers to lower service costs, maximize uptime, and improve production throughput.

 

How does IoT predictive maintenance work?

For predictive maintenance to be carried out, a product or machine requires the following base features:

  1. Sensors – data-collecting sensors installed in the physical product or machine
  2. Communication protocols – the communication system that allows data to flow between the unit and the data store
  3. Data store – the central data hub in which data is stored, processed and analyzed, typically a cloud-based data repository
  4. Data analytics – algorithms applied to the machine data to recognize patterns and generate insights in the form of dashboards and alerts

Machine data is streamed from the sensors to a central repository using communication protocols and gateways. Here, big data analytics techniques are applied to provide valuable insights for reducing downtime and improving product efficiency.
 
How does IoT predictive maintenance work?

Predictive maintenance architecture

 
To implement this system effectively, manufacturers need to map the parameters of failure for machines and create a blueprint for their connected system (the sensors, communication protocols, gateway, cloud, and predictive analytics).

Inside a visual modeling platform, product and engineering teams can graphically define the logic of the system, with a set of logic-based rules that monitor and alert to maintenance issues.

Once in-market, predictive analytics applied to the machine data analyze historic events and compare it back to the blueprint, in order to predict conditions of upcoming failure. A dashboard for predictive analytics synthesizes operational data, allowing manufacturers and operators to visualize the system operation in real-time and derive actionable insights.

 

The benefits of predictive maintenance

Manufacturers and their customers get a range of business benefits from IoT predictive maintenance. The advantages of PdM include:

  1. Reduced maintenance time- Automatic reports for strategic maintenance scheduling and proactive repairs alone reduces maintenance time by 20–50 percent and decreases overall maintenance costs by 5–10 percent. These insights save the manufacturer and their customers time and money.  
  2. Increased efficiency- analytics-driven insights improve OEE (overall equipment effectiveness) by reducing unnecessary maintenance, extend asset life and enable root cause analysis of a system to uncover issues ahead of failure.
  3. New revenue streams- Manufacturers can monetize industrial predictive maintenance by offering analytics-driven services for their customers, including PdM dashboards, optimized maintenance schedules, or a technician dispatch service before parts need replacement. The ability to provide digital services to customers based on data presents an opportunity for recurring revenue streams and a new growth engine for companies.
  4. Improved customer satisfaction- Send customers automated alerts when parts need to be replaced and suggest timely maintenance services to boost satisfaction and provide a greater measure of predictability.
  5. Competitive advantage- Predictive maintenance strengthens company branding and value to customers, differentiating their products from the competition and allowing them to provide continuous benefit in-market.

 

Take the free whitepaper with youWhy predictive maintenance is driving industry 4.0

 

Predictive maintenance tools

Once a manufacturer has chosen the right sensors, cloud, communication protocols and analytics platform for their products or machines, implementing predictive maintenance requires a baseline of common tools.

Predictive maintenance tools include an IoT development platform to model, test and deploy the predictive maintenance solution, data analytics algorithms to detect patterns based on historical and current machine data, and workflows to feed the data into existing ERP and CRM systems.

 

What is the difference between preventive and predictive maintenance?

Manufacturers have been carrying out different forms of preventive and predictive maintenance for years. Understanding the difference between them, however, is critical with the emergence of Industry 4.0.

Preventive maintenance depends on visual inspections, followed by routine asset monitoring that provide limited, objective information about the condition of the machine or system. In this process, manufacturers regularly maintain and repair a machine to prevent failure.

On the other hand, PdM is data-driven and relies on analytics insights for strategic maintenance and repairs ahead of disruptions in production.

 

How are companies using IoT predictive maintenance tools?

Organizations are implementing predictive maintenance analytics in a range of ways, from targeted solutions for a single machine part, to factory-wide deployments for increasing OEE throughout the production line.

For machine and parts manufacturers, a relatively common predictive maintenance use case is monitoring and analyzing the condition of a motor to get alerts about its productivity levels, power consumption, health status, and internal wear.

Manufacturers are also turning to predictive maintenance for Factory 4.0, or a connected factory, by installing sensors in machines, workstations, and other designated sites such as the HVAC, security cameras or worker equipment, to predict issues across the factory floor.
 

predictive maintenance use cases

 

Common approaches to IoT predictive maintenance

The two most common approaches to predictive maintenance are rule-based and machine learning-based.

Rule-based predictive maintenance

Also referred to as condition monitoring, rule-based predictive maintenance relies on sensors to continuously collect data about assets, and sends alerts according to predefined rules, including when a specified threshold has been reached.

With rule-based analytics, product teams work alongside engineering and customer service departments to establish causes or contributing factors to their machines failing.

Once common reasons for product or part failures are established, manufacturers can build a virtual model of their connected system. Here they define product use cases, with “if-this-then-that” rules which describe the behaviors and inter-dependencies between the various IoT system components.

For example, if temperature and rotation speed are above certain predefined levels, the system will send an alert to an operator dashboard, to address the issue ahead of failure.

These rules provide a level of automated, predictive maintenance, but they are still dependent on a product team’s understanding of what parts or environmental elements require measuring.

The condition monitoring dashboards can be integrated with insight from machine learning to provide a visually understandable heatmap of asset conditions in real-time.

Predictive Maintenance Machine Learning

Predictive maintenance with machine learning looks at large sets of historical or test data, combined with tailored machine-learning algorithms, to run different scenarios and predict what will go wrong, and when.

Predictive Maintenance Algorithms

In this scenario, algorithms learn a machine’s normal data behavior and uses this to identify and alert to deviations in real-time.

The algorithms required for machine learning must analyze input (historical or a training set of data) and output data (the desired result). A machine monitoring system includes input on a range of factors from temperature to pressure and engine speed. The output is the variable in question - a warning of a future system or part failure. The system will then be able to predict when a breakdown is likely to occur.

 

How can I offer new customer service with predictive maintenance?

Predictive maintenance offers OEMs with a business value proposition to offer data-driven customer services and get recurring revenue in return. With PdM services, companies can build a subscription-model for customers to access dashboards or reports that will improve their OEE and reduce maintenance costs. Additional opportunities for services can be found in dispatching technicians for repairs, setting up alert systems, and shipping parts that need replacement ahead of failure.  

 

How do I get started with IoT predictive maintenance?

Predictive maintenance is often restricted to the minority of companies whose machines have been collecting data for years, and can utilize advanced analytics platforms to sort through the data with machine learning.

But for companies implementing a connected system for the first time, with the ultimate goal of implementing machine learning for predictive maintenance, a pragmatic way to get started with IoT predictive maintenance.

With rule-based PdM, manufacturers can bypass the need for a large historical data set or advanced machine learning algorithms at the outset.

 

Predictive Maintenance Machine Learning

 

This gives companies quick business results and a stepping stone into machine learning. In this model, product teams start with basic assumptions or ‘rules’ based on ‘what if’ scenarios that can be easily defined, rather than a machine algorithm running possible scenarios.

Rule-based PdM is achievable, affordable, and delivers measurable business benefits. The easiest way to get started is with an IoT platform centered on a rule-based model, which enables teams to quickly define, simulate and deploy a predictive maintenance solution for their products.

 


Seebo integration with the IBM Watson IoT Platform

Seebo and IBM Collaborate to Simplify Smart Product Development

The article below was originally published on PR Newswire on February 16, 2017.

Seebo and IBM Collaborate to Simplify IoT Development for Product Companies

Bringing together IBM and Seebo IoT capabilities allows manufacturers to derive business value from smart products

seebo-and-ibm-integration

MUNICH, February 16, 2017 -- Today Seebo, the development platform for smart products, and IBM announced a collaboration to bring together the Seebo platform and IBM Watson Internet of Things (IoT). Connecting Seebo’s easy-to-use IoT development capabilities with IBM’s Watson IoT Platform and Bluemix empowers manufacturers to accelerate the delivery of smart products and gain insights from user data.   

A growing number of manufacturers are connecting their products to the IoT to enhance their product’s value and increase revenue streams. The complexity of developing smart products - including parallel software, firmware and hardware design, alongside new design and production considerations – often deters manufacturers from pursuing innovation through IoT. The addition of IBM’s Watson IoT Platform alongside Seebo’s SaaS platform helps to address these obstacles. The collaboration gives manufacturers access to tools to help build smart products quickly, efficiently and cost-effectively as well as out-of-the-box connection with the cloud.

“We are excited for Seebo to join with our Watson IoT Platform,” said Neil Postlethwaite, Director, Watson IoT Platform at IBM. “Seebo offers a unique set of tools that enable quick time to market for new IoT products in this fast-growing space. IBM sees this collaboration as one which will enable clients to rapidly design and develop their IoT products; while also having the ability to use cognitive capabilities from IBM’s IoT and Watson services.”

As an IBM Watson IoT business partner, Seebo joins IBM’s IoT ecosystem and links the two platforms together for easy use by developers and product managers.

Seebo customers can use IBM’s capabilities alongside Seebo’s innovative hardware simulator and testing app to reduce development costs and accelerate smart product time to market.

“In less than an hour of using Seebo’s platform, product managers see data from their newly-developed online prototype visualized on the cloud dashboard,” said Lior Akavia, Seebo Co-Founder and CEO.

The combination of hardware and app simulators, alongside the Cloud integration, empowers developers to build and test software independent of hardware, reducing the resources needed during smart product development.

“We are proud to be joining forces with IBM. Through our collaboration, we allow our customers to enjoy the synergy of IBM’s most advanced IoT, cloud and analytics tools and Seebo’s fast development capabilities to become active players of their sectors’ growing digital transformation trends,” said Akavia.

Seebo integration with Watson IoT
Integration with IBM Watson IoT. To learn more, see the Seebo recipe on Developer.seebo.com.

One company taking advantage of the integration is Greengage, a manufacturer of lighting systems. The company is currently developing a smart solution with Seebo to enable remote control and monitoring of livestock houses for farmers, improving productivity and animal welfare management. “We are excited to be using Seebo’s platform and the integration with the cloud and analytics from IBM Bluemix to launch a unique product solution that will revolutionize livestock management for farmers,” said Derek Liddle, Director of Engineering at Greengage.

About Seebo:

Seebo is the IoT development platform that makes it easy to develop smart, connected products. The SaaS platform empowers everyone from small startups to enterprise customers, experts or not, to get smart products to market quickly, efficiently and cost-effectively. Seebo simplifies the complex process of going smart by offering a set of innovation and functional design tools, a smart hardware engine, forecasting tools, cloud and app integration, IoT simulation and a third-party marketplace. For more information, visit seebo.com or follow @seebo.

The Seebo IoT Development Platform makes it easy to build smart products