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Using Next Generation Industrial Analytics To Improve Process Plant Profitability

As disruption escalates in 2017, industry faces increasing pressure to transform in order to remain competitive. This means finding ways to leverage new technologies and identify opportunities for optimisation.

By: Edwin van Dijk, VP of Marketing, TrendMiner

Author: Edwin van Dijk, VP of Marketing, TrendMiner
As disruption escalates in 2017, industry faces increasing pressure to transform in order to remain competitive. This means finding ways to leverage new technologies and identify opportunities for optimization.
TrendMiner Edwin Van Dijk

One of the best ways to gain insight is to apply advanced analytics to the data generated by your revenue-generating assets and processes, but until now the challenges associated with achieving this goal have prevented many companies from enjoying the potential benefits.

In this article, we’re going to show how a new generation of industrial analytics solutions has solved many of the historical challenges, why this has democratised access to analytics-based insights and how this can help all manufacturers to improve plant profitability.

What are industrial analytics?

Industrial analytics refers to the collection, analysis and usage of data generated in industrial operations. This covers a very wide range of data captured from all kinds of sources and devices, whether an asset or a production process. Anything with a sensor creates data – and industrial analytics looks at all this data.

Naturally this means Big Data analytics – but industrial analytics differ from generic big data analytics systems in that they are designed to meet the exacting standards of the industry to which they apply. This includes the ability to process vast quantities of time series data from various sources, and turn it into actionable insights for the users. This makes industrial analytics relevant to any company that is manufacturing and/or selling physical products.

The problem with industrial analytics

Many organisations can see the potential benefits of such an analytics solution, but the time and money needed to achieve it have historically put it out of reach. That’s because historically industrial analytics have been run like generic big data analytics projects.

The traditional and most common approach to industrial analytics involves data scientists building an analytics model. Data scientists must understand the use case and then gather, transform, optimise and load the data in the developed data model, which needs to be validated, optimised and trained to get to business value. The completed data model provides the answers to the initial questions.

Aside from the long time needed to realise results and the high cost associated, this way of working has another major disadvantage: it leaves companies completely dependent on their data scientists, and results in a solution that the subject matter experts (engineers and operators) may not fully understand.

Solving the industrial analytics stalemate

Luckily, as disruptive technology creates new challenges, so it also creates new solutions. In the last few years, there has been a growing trend towards self-service applications. This next generation of software utilises advanced search algorithms, machine learning and pattern recognition technologies to make querying industrial data as easy as using Google.

No data scientist required

With self-service industrial analytics, there is no need to model data. Companies do not require a data scientist to use the software, and there is no long project timeline or high cost. Instead, the subject matter experts directly query their process data at any time in a self-service application.

Using pattern recognition and machine learning algorithms permits users to search process trends for specific events or to detect process anomalies. By combining search capabilities on both the structured time series process data and the data captured by operators and other subject matter experts, users can predict more precisely what is occurring or what likely will occur within their continuous and batch industrial processes. For example, an operator can compare multiple data layers or time periods to discover which sensors are more or less deviating from the baseline then make adjustments to improve production efficiency.

One of the best ways to gain insight is to apply advanced analytics to the data generated by your revenue-generating assets and processes, but until now the challenges associated with achieving this goal have prevented many companies from enjoying the potential benefits.

In this article, we’re going to show how a new generation of industrial analytics solutions has solved many of the historical challenges, why this has democratized access to analytics-based insights and how this can help all manufacturers to improve plant profitability.

What are industrial analytics?
Industrial analytics refers to the collection, analysis and usage of data generated in industrial operations. This covers a very wide range of data captured from all kinds of sources and devices, whether an asset or a production process. Anything with a sensor creates data – and industrial analytics looks at all this data.

Naturally this means Big Data analytics – but industrial analytics differ from generic big data analytics systems in that they are designed to meet the exacting standards of the industry to which they apply. This includes the ability to process vast quantities of time series data from various sources, and turn it into actionable insights for the users. This makes industrial analytics relevant to any company that is manufacturing and/or selling physical products.

The problem with industrial analytics
Many organizations can see the potential benefits of such an analytics solution, but the time and money needed to achieve it have historically put it out of reach. That’s because historically industrial analytics have been run like generic big data analytics projects.

The traditional and most common approach to industrial analytics involves data scientists building an analytics model. Data scientists must understand the use case and then gather, transform, optimize and load the data in the developed data model, which needs to be validated, optimized and trained to get to business value. The completed data model provides the answers to the initial questions.

Aside from the long time needed to realize results and the high cost associated, this way of working has another major disadvantage: it leaves companies completely dependent on their data scientists, and results in a solution that the subject matter experts (engineers and operators) may not fully understand.

Solving the industrial analytics stalemate
Luckily, as disruptive technology creates new challenges, so it also creates new solutions. In the last few years, there has been a growing trend towards self-service applications. This next generation of software utilizes advanced search algorithms, machine learning and pattern recognition technologies to make querying industrial data as easy as using Google.

No data scientist required
With self-service industrial analytics, there is no need to model data. Companies do not require a data scientist to use the software, and there is no long project timeline or high cost. Instead, the subject matter experts directly query their process data at any time in a self-service application.

Using pattern recognition and machine learning algorithms permits users to search process trends for specific events or to detect process anomalies. By combining search capabilities on both the structured time series process data and the data captured by operators and other subject matter experts, users can predict more precisely what is occurring or what likely will occur within their continuous and batch industrial processes. For example, an operator can compare multiple data layers or time periods to discover which sensors are more or less deviating from the baseline then make adjustments to improve production efficiency.
Industrial Analytics

Designed to be used

Self-service analytics tools are designed with end users in mind. They incorporate robust algorithms and familiar interfaces to maximise ease of use without requiring in-depth knowledge of data science. No model selection, training and validation are required; instead users can directly query information from their own process historians and get one-click results. Immediate access to answers encourages adoption of the analytics tool as the value is proven instantly: precious time is saved and previously hidden opportunities for improvement are unlocked.

This self-service analytics approach puts the power into the hands of the process experts, engineers and operators, who can best identify and annotate areas for improvement.

Why give this power to end users?

Working with time-series data is best done by the subject matter experts (such as process engineers and control room staff) because they have the knowledge of what to look for in case of anomalies in process behaviour and finding root causes. They can also identify best performance regimes that can be used as define ideal production and identify conditions for live process monitoring and performance prediction. These subject matter experts are, in fact, the key to improving the company’s profitability – all they need is the tool.

By democratising access to analytics insights, actionable information becomes available at all levels of the plant. This means the ability to achieve incremental improvements at all stages of the production process.

The real benefits of industrial analytics

With an industrial analytics solution, you can gain insight into all your assets and processes. Previously hidden trends and patterns become clear and can inform your decision-making. From high-level performance monitoring to the most granular investigations, industrial analytics deliver insight where it is most needed: in safety, efficiency and performance.

If you use an industrial analytics solution that focuses on self-service, you can gain benefits in the day-to-day running of your plant. This includes improved root cause analysis, objective performance prediction, automated monitoring, and knowledge retention. By sharing analytics insights with users, they are able to take action immediately when a trend appears. This allows users to directly contribute to improving overall plant performance.

When you give the power of self-service analytics insights to the people running your plant, you’ll open the door to improvements at all levels of production from day one.

Enabling a modern engineering analytics organisation

Just as technology has evolved to create connected plants, so engineers must be empowered to manage these factories. This is a critical shift in business culture as the entire organisation must be educated and made aware of the potential of analytics as it applies to their role.

Instead of relying solely on a central analytics team that owns all the analytics expertise, subject matter experts such as process engineers should be empowered to answer their own day-to-day questions. Not only will this spread the benefits to the engineers involved in process management, it will also free the data scientists to focus on the most critical business issues.

Enabling engineers does not mean asking them to become data scientists – it means providing them with access to the benefits of process data analytics. Process engineers will not easily become data scientists because the education background is different (computer science versus chemical engineering). However, they can become analytics aware and enabled. This process is sometimes referred to as the “rise of the Citizen Data Scientist,” a growing trend in which experts in their own disciplines (such as engineering) add analytics capabilities to their work, rather than splitting the analysis from the data.

By bringing engineers closer in their understanding of analytics, they can solve more day-to-day questions independently and enhance their own effectiveness. They will in turn provide their organizations with new insights based on their specific expertise in engineering. This delivers value to the owner-operator at all levels of the organization and leverages (human) resources more efficiently.

No time for analytics? Time to think again

Many people wonder if this type of analytics is worth the time needed to get started, and the answer is a resounding yes. With a self-service industrial analytics tool, the benefits may be big – but the time investment is very small.

With a self-service industrial analytics solution like TrendMiner, you don’t need to wait while a data model is being selected and built – you can start immediately after deployment by analysing the historical and live performance data from your own assets and processes.

You don’t need a long implementation project either – the software is plug and play, and can be implemented by your own IT team within an hour. Likewise, long trainings are not required, because the interface is designed for users: a simple interface that is easy to operate and quick to learn. So you do not need to invest time to start saving time.

Designed to be used
Self-service analytics tools are designed with end users in mind. They incorporate robust algorithms and familiar interfaces to maximize ease of use without requiring in-depth knowledge of data science. No model selection, training and validation are required; instead users can directly query information from their own process historians and get one-click results. Immediate access to answers encourages adoption of the analytics tool as the value is proven instantly: precious time is saved and previously hidden opportunities for improvement are unlocked.

This self-service analytics approach puts the power into the hands of the process experts, engineers and operators, who can best identify and annotate areas for improvement.

Why give this power to end users?
Working with time-series data is best done by the subject matter experts (such as process engineers and control room staff) because they have the knowledge of what to look for in case of anomalies in process behaviour and finding root causes. They can also identify best performance regimes that can be used as define ideal production and identify conditions for live process monitoring and performance prediction. These subject matter experts are, in fact, the key to improving the company’s profitability – all they need is the tool.

By democratizing access to analytics insights, actionable information becomes available at all levels of the plant. This means the ability to achieve incremental improvements at all stages of the production process.

The real benefits of industrial analytics
With an industrial analytics solution, you can gain insight into all your assets and processes. Previously hidden trends and patterns become clear and can inform your decision-making. From high-level performance monitoring to the most granular investigations, industrial analytics deliver insight where it is most needed: in safety, efficiency and performance.

If you use an industrial analytics solution that focuses on self-service, you can gain benefits in the day-to-day running of your plant. This includes improved root cause analysis, objective performance prediction, automated monitoring, and knowledge retention. By sharing analytics insights with users, they are able to take action immediately when a trend appears. This allows users to directly contribute to improving overall plant performance.

When you give the power of self-service analytics insights to the people running your plant, you’ll open the door to improvements at all levels of production from day one.

Enabling a modern engineering analytics organization
Just as technology has evolved to create connected plants, so engineers must be empowered to manage these factories. This is a critical shift in business culture as the entire organization must be educated and made aware of the potential of analytics as it applies to their role.

Instead of relying solely on a central analytics team that owns all the analytics expertise, subject matter experts such as process engineers should be empowered to answer their own day-to-day questions. Not only will this spread the benefits to the engineers involved in process management, it will also free the data scientists to focus on the most critical business issues.

Enabling engineers does not mean asking them to become data scientists – it means providing them with access to the benefits of process data analytics. Process engineers will not easily become data scientists because the education background is different (computer science versus chemical engineering). However, they can become analytics aware and enabled. This process is sometimes referred to as the “rise of the Citizen Data Scientist,” a growing trend in which experts in their own disciplines (such as engineering) add analytics capabilities to their work, rather than splitting the analysis from the data.

By bringing engineers closer in their understanding of analytics, they can solve more day-to-day questions independently and enhance their own effectiveness. They will in turn provide their organizations with new insights based on their specific expertise in engineering. This delivers value to the owner-operator at all levels of the organization and leverages (human) resources more efficiently.

No time for analytics? Time to think again
Many people wonder if this type of analytics is worth the time needed to get started, and the answer is a resounding yes. With a self-service industrial analytics tool, the benefits may be big – but the time investment is very small.

With a self-service industrial analytics solution like TrendMiner, you don’t need to wait while a data model is being selected and built – you can start immediately after deployment by analyzing the historical and live performance data from your own assets and processes. You don’t need a long implementation project either – the software is plug and play, and can be implemented by your own IT team within an hour. Likewise, long trainings are not required, because the interface is designed for users: a simple interface that is easy to operate and quick to learn. So you do not need to invest time to start saving time.

Process Industry Informer

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