Democratized Analytics Guides Industry 4.0 | Automation.com

Democratized Analytics Guides Industry 4.0

Democratized Analytics Guides Industry 4.0

By Edwin van Dijk, VP Marketing, TrendMiner

Digitalization is vital for process manufacturing organizations to remain market leaders and sustain future profitability. Traditionally, advanced analytics has been the realm of the data scientist -- but access to those in this position is limited and thus, there is a need for analytics to be democratized. Business outcomes can be controlled at each level of an organization by using applied analytics, machine learning, and artificial intelligence. It's all about aligning business objectives with direct business outcomes across departments, all while growing production knowledge.

 

Making the Most of Existing Data Points

For optimizing process and asset performance many process manufacturing companies are already performing data analytics to some extent. The process manufacturing industry is the proud owner of most of the existing data points in the world, even surpassing Amazon and Google. This same industry, however, hovers towards the bottom when it comes to translating these data points into actual information.

The process manufacturing industry is experiencing a number of crucial data analytics challenges:

  • Improper tools
  • Insufficient analytics knowledge
  • Insufficient embedding of analytics in work processes
  • Unclear economic benefits
  • Insufficient management support

As a result, engineers spent too much of their time stretching the limits of MS Excel trying to acquire and prepare data and visualize the problem, instead of actually analyzing the problem and gaining crucial insights from it. Secondly, the 'language' barrier between subject matter experts and data scientists is too big. So, the ultimate goal is to put critical analytical capabilities into the hands of the process and asset engineers, enabling them to solve problems on their own and passing the more complicated issues on to the data scientists.

 

Self-Service Analytics to Assess Production Performance

Instead of juggling spreadsheets and limiting themselves to the trend client of historians, engineers can work with advanced analytics solutions while not needed to be trained as a data scientist. We call this self-service advanced analytics, where the statistical methods of data scientists, machine learning and artificial intelligence are all applied. The users have a visual interface, showing the time-series data in a recognizable format so they can use their subject matter expertise for finding ways to improve process performance and even predict required maintenance.

Implementing self-service industrial analytics can enable engineers to get more robust and faster insights into their operational production data. It enables them to identify new areas for performance optimization with advanced root cause analysis capabilities, monitor production to avoid abnormal situations and even predict future evolutions of batch runs, transitions or equipment startups in minutes. It enables the business users, such as process and asset engineers to:

  • Solve previously unsolved process performance issues
  • Verify hypothesis and prove them to be either true or false, so they can be addressed or ruled out for the future
  • Find new ways to improve production performance, because data with captured events and early warnings provides new insights
  • Use contextual information from 3rd party business applications to get new insights in operational performance
  • Use actionable dashboards to monitor operational performance in real-time
     

Case in Point

ARLANXEO is a chemical company developing, manufacturing and marketing synthetic rubber for use in various industries. ARLANXEO decided to gradually roll-out the use of self-service industrial analytics over their sites after a successful pilot implementation at one site.

During the pilot, one of the cases was related to valve leaks. Engineers wanted to use self-service industrial analytics to find the root cause for valve leaks in a regeneration process of a dryer (for removing H20 from MeCl). These leaks cause huge losses of the expensive MeCl and as a result of the spill severe corrosion is caused of expensive assets in the regeneration circuit.

By using the search capabilities of the self-service analytics platform, the engineers could easily find the regeneration cycles very fast. By creating a ‘fingerprint” of normal flare behavior during regeneration, they could monitor operational performance and with the found root cause create early warnings to avoid valve leaks and schedule maintenance in due time. The quick detection of the valve leaks significantly reduced the corrosion in the regeneration circuit and the reduction in MeCl flaring had direct impact on the cost reduction while it also mitigated environmental and safety hazards.

Figure 1: ARLANXEO use cases: What can production analytics deliver?

The various use cases (see figure 1) during initial roll out helped ARLANXEO to truly understand what benefits self-service industrial analytics could bring them. Arlanxeo was able to:

  • Increase the quality of their analyses by using more comprehensive sets of data.
  • Get easy access to historical data.
  • Gain complex process insights by analyzing a broader set of parameters across multiple steps in the process.
  • Integrate root cause analysis and process monitoring to define future alerts, start looking for similarities and optimize processes.
  • Compare various scenarios to statistically find out the performance parameters to meet the best product quality. 

 

Attaining Operational Excellence

The benefits of self-service analytics have been very clear for Arlanxeo and many other industry leaders. It enables their teams to analyze issues that are too complex using conventional tools and helps them gain faster insight into process issues. It enforces alternative thinking and a new way of looking at their operational performance by using data.

Companies that want to work towards quantified results in their business objectives need identify those KPIs at each level of the organization starting from upper management, to plant, department all the way to the operators and engineers. With the aligned KPIs, this creates a backbone to analyze, improve and benchmark globally, as well as locally. On the other hand, the set KPIs can be achieved when the process experts are empowered with advanced analytics to fuel the increase of overall profitability and sustainability.

When engineers start using a systematic analytics-driven approach, a business environment is created of continuously improving operational excellence, where the large group of operational experts contribute to the business objectives and can even help over-achieve the targets.

 

Controlling Business Outcomes

Modern connected factories are obtaining all sorts of data directly or indirectly related to the production process. Some of the data is stored in historians, other data goes into the quality information system, maintenance management system, incident management system, etc.

Self-service analytics using time-series data sheds light on operational performance. But having all the available contextual information available, captured during production and leveraged from other applications, there is much better visibility into operations. This contextual information helps to better understand operational performance and give new starting points for optimization projects when using advanced analytics.

Ideally, all operational stakeholders would have what we refer to as a “Production Cockpit,” which is complete with an actionable dashboard, analytics suite, and agile communications facility. Users at each level of the organization can create and share complete and live overviews of their current process statuses and performances, enabling teams and individuals to immediately access production data, analyze situations at hand, and make decisions in an instant. 

Figure 2: Today's production cockpits move beyond a typical dashboard - it shows live operational performance data and early warnings allowing to control business outcomes.

Enhancing the current process status overview with the early warning capabilities that self-service analytics solutions provide, the “Production Cockpit” can provide operators the opportunity to be proactive and optimize operational performances even before issues arise. It also helps optimize the flow of information between shifts, from shift teams to engineers and between all related actors and production stakeholders, thereby boosting the organization's collaborative agility. These are just a few of the ways that self-service industrial analytics is able to cater to the needs of engineers today, while business outcomes are directly controlled and communication between departments is optimized. 

 

Conclusion

When all business users are empowered with self-service advanced analytics, process manufacturing companies have the best change to remain a market leader and sustain future profitability. Subject matter experts have a very good understanding of the data that is directly related to the production processes they are responsible for ad are able to turn this into operational information. Today this information can be provided in an analytics-driven production cockpit with live data and early warnings, resulting in a business environment of continuous improvement. In this way all users at each level in the organization can proactively contribute to business outcomes in areas such as energy reduction, waste reduction, quality control, yield and predictive maintenance.

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