Eliminate Data Access Uncertainty

EP Editorial Staff | February 1, 2024

Data collection and analytics projects work best when you follow proven, traditional automation concepts.

By Kit Thompson & Jan Edler, Emerson

One of the best ways for manufacturers to move beyond a simplistic and superficial view of production is to obtain high-integrity data from the factory floor and then analyze and visualize it to obtain data-driven operational insights. Effectively gathering and assimilating data from a variety of assets, from different sources and vintages of automation technology, has typically been a challenge.

Simply throwing technology at the situation is not the answer. Instead, manufacturers need to define their goals and choose a platform that will lead to rapid and sustained success. Specifically, they should streamline ways to rapidly connect field-sourced data and turn it into useful information.

Objectives and scope

For production lines, and the assets that support them, designers are already familiar with creating functional specifications defining just how the automation needs to work. They may be less familiar with how to define objectives and scope around data-based, digital-transformation projects. The steps are largely the same.

Building a digital database is a foundational requirement for any data project. As field devices and other equipment gain intelligence and become IIoT networkable, vast amounts of data are becoming available. While initial project objectives may not include using this data for direct closed-loop control of equipment functions, they can incorporate items such as:

• improving visibility and establishing a knowledge base that is accessible across manufacturing teams
• streamlining communication and handoffs between shifts
• optimizing resource utilization and efficiency
• maximizing quality
• minimizing waste.

While specific objectives will vary by industry and project, they should be defined in as much detail as practical. The best way to know if objectives are being met is to quantify the results using KPIs such as:

• Lower the response/solution time when certain production problems arise by a given percentage.
• Increase productivity by a given percentage.
• Reduce scrap by a given percentage.
• Identify and eliminate wasteful air leaks.

To support these objectives, identify a digital platform capable of:

• performing real-time data collection from a variety of sources
• incorporating common and custom lean production and energy-efficiency analytics
delivering clear operational and informational/performance visibility using HMI and SCADA software, with convenient and secure access for local and remote/cloud users
integrating with Power BI dashboards and other IT resources to provide enterprise-wide perspectives.

A complete solution must address the entire journey of data from the plant floor to the cloud, providing value as manufacturers meet their objectives, and then prove results with KPI metrics.

Convenient access to quality data is necessary so users can fully evaluate their operations. In this case, OEE can be examined over the past month, past year, and by shift. Courtesy Emerson

Data into action

Emerson’s factory in Lodz, Poland, which manufactures Asco valves, is just one of many sites where the company has implemented their own solutions to improve their product manufacturing. Much like any other manufacturing enterprise, the organization wanted to collect and record data from operating assets, visualize real-time and analyzed conditions, and determine overall equipment effectiveness (OEE), along with other KPIs associated with efficiency and performance. The solution would need to connect with a variety of machines and it had to be scalable so they could start in a few operational areas, then expand to all operating assets and be able to grow as new machines were put into service.

The factory produces pneumatic automation and monitoring components, and the internal IIoT solutions team had several goals to help the facility gain insights and improve operation:

• Calculate OEE and measure downtime to understand production capacities and drive efficiency.

• Track product test results and analyze them to determine good-versus-scrap output.

• Monitor and evaluate energy and air consumption.

• Enable extended reporting so staff could investigate other areas of interest.

Based on the needs and requirements, the team implemented Emerson Movicon.NExT HMI/SCADA software for real-time data collection and visualization, combined with the Pro.Lean module to provide the desired analytics and reporting capabilities.

The HMI/SCADA software includes provisions to communicate with almost any type of operational asset, storing the data in a historian and logging it to an SQL database for more extensive evaluation. The cloud-based architecture for energy- and air-consumption monitoring uses time-series databases to effectively manage the otherwise overwhelming data using carefully applied compression techniques.

In this case, live data streams into the database and is maintained at full accuracy—typically a sample rate from 100 msec. to 5 sec.—for 30 days. The database can compress this data at a 1:25 factor. After 30 days, the data undergoes further compression to around a 1-hr. sample rate (by keeping minimum, maximum, and mean), yielding an additional compression factor of 1:1,200. The resultant overall data reduction is about 1:30,000, which makes it practical to store many years of data.

While some users might think about creating their own analytics from scratch, in most cases it is preferable to use standardized solutions for lean manufacturing and energy analysis. Production is typically monitored using common approaches associated with OEE, availability, performance, and quality, so the analytics solution should incorporate the background calculations and visibility/reporting views needed to represent these factors. Complete access to high-quality data lets users explore any aspect of their operation and to communicate with each other using a common basis.

The Lodz team installed edge controllers and devices to connect with existing automation assets. This scalable architecture allows the team to add on-premise servers and/or connect with Oracle cloud resources as the needs at any given site grow, or as it becomes advantageous to the solution to span many sites.

While analytics and visualization are important in the Lodz control room, the engineering department, and the front office, Emerson Pro.Lean can also provide valuable dashboard information on the factory floor. Courtesy Emerson

Enabling insights

As the data analytics solution was progressively deployed, factory personnel realized the many benefits of real-time visibility and analytics. They could examine the efficiency of each machine and associated interconnections throughout the overall manufacturing process. With clear information available, it became possible to set up goals for OEE and other KPIs. They installed marquee displays in production areas to provide immediate feedback for those closest to the work.

Improvement actions are now planned based on this information and the results are clearly measured for activities such as improving machine availability, performance, and production quality. Downtime analysis has enabled the team to determine the leading reasons for equipment stoppages so they can direct their efforts to resolve root problems. In addition, costly compressed-air leaks were identified and eliminated. EP

Kit Thompson is the IT EMEA director, Discrete Automation, at Emerson, St. Louis ( He works predominantly in ERP migrations and new implementations focused on delivering improved change to employees through the next generation of IT processes, technology, and value creation programs. Jan Edler is an IIoT manufacturing applications leader, Discrete Automation at Emerson. He leads Emerson’s global IIoT onboardings.


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