IT Science Case Study: New Data Platform Aimed at Industrial IoT

eWEEK IT SCIENCE RESOURCE PAGE: ALPLA needed to build a centralized “mission control” for all of its Industrial IoT (IIoT) data; successfully doing so would expedite the company’s ability to take insight-based actions to improve plant efficiencies, introduce smarter automation and reduce manufacturing costs.

Here is the latest article in an eWEEK feature series called IT Science, in which we look at what actually happens at the intersection of new-gen IT and legacy systems.

Unless it’s brand new and right off various assembly lines, servers, storage and networking inside every IT system can be considered “legacy.” This is because the iteration of both hardware and software products is speeding up all the time. It’s not unusual for an app-maker, for example, to update and/or patch for security purposes an application a few times a month, or even a week. Some apps are updated daily! Hardware moves a little slower, but manufacturing cycles are also speeding up.

These articles describe new-gen industry solutions. The idea is to look at real-world examples of how new-gen IT products and services are making a difference in production each day. Most of them are success stories, but there will also be others about projects that blew up. We’ll have IT integrators, system consultants, analysts and other experts helping us with these as needed.

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Today’s Topic: A New-Gen Data Platform for Industrial IoT Workloads

Name the problem being solved: ALPLA, a global packaging provider for brands such as Coca-Cola and Unilever (with 178 plants in 45 countries), underwent a massive data infrastructure migration. The challenge was achieving a centralized “mission control” for all of ALPLA’s Industrial IoT (IIoT) data; successfully doing so would expedite ALPLA’s ability to take insight-based actions to improve plant efficiencies, introduce smarter automation and reduce manufacturing costs. ALPLA originally deployed Microsoft SQL Server as the data store for its IIoT sensor data; however, when this solution proved unable to cope with ALPLA’s massive data requirements, the company needed to identify and implement a more appropriate, built-for-IIoT data solution.

Describe the strategy that went into finding the solution: ALPLA explored options compatible with its IIoT solution, and discovered that’s Crate IoT Data Platform (which runs on CrateDB, a scalable SQL time-series database) enabled the plastics manufacturer to reliably receive the required real-time, sensor-based insights.

List the key components in the solution: The Crate IoT Data Platform is designed to operate in high-complexity environments, such as ALPLA’s manufacturing infrastructure. The solution components feature:

  • The ability to leverage dynamic schema, enabling ALPLA to combine its 900 unique IIoT sensor readings into a single database table for faster queries and simplified maintenance.
  • CrateDB’s distributed nature enables scalability using elastic clusters on low-cost servers.
  • Using columnar indexes cached in memory, ALPLA mission control dashboard queries execute 250 times faster with CrateDB than with Microsoft SQL Server, improving completion time from minutes to milliseconds.

Describe how the deployment went, perhaps how long it took, and if it came off as planned: Deployment of the new system went according to plan. The system was developed in close cooperation with plant managers and operators working to ensure its functionality and ease of use.

Describe the result, new efficiencies gained, and what was learned from the project: With this solution in place, the most appropriate production personnel receive automatic, 24x7 notifications when action is needed to address real-time or preventative issues, along with instructions on how to solve the issue. Experts positioned at ALPLA’s new “mission control” centers are armed with up-to-the-second sensor knowledge to remotely monitor, troubleshoot and intervene to optimize factory equipment efficiency.

Describe ROI, carbon footprint savings, staff time savings, if any: ALPLA’s new IIoT data strategy has been replicated across its manufacturing plants, yielding reduced personnel costs, the ability to perform cross-plan analysis, immediate insights (versus next-day reporting), knowledge sharing, attraction of millennial talent to manufacturing, and meaningfully improved Overall Equipment Effectiveness (OEE).

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