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Introduction

What are the main constraints that prevent manufacturing firms from achieving real-time optimization in their business processes and systems? According to an Everest Group study, 63% cited a lack of process visibility while 61% cited a complex process landscape.

At the 2022 AI+Data Summit organized by Databricks, an interesting session highlighted how smart manufacturing companies go through varying levels of maturity in process optimization. In their drive to modernization, manufacturers must understand the importance of real-time process optimization. This is an integral part of digital transformation in the modern manufacturing domain.

To address today’s challenges, manufacturers realize that they need to switch from batch-based data processing to real-time (or streaming) data processing. Real-time data streaming effectively provides them with the latest operational data, thus improving visibility and reducing complexity.

Through this blog, we shall discuss how modern manufacturers can leverage this approach efficiently.

What is real-time process optimization and why is it important?

Real-time process optimization is the process of optimizing any process (or system) in a manufacturing facility to respond efficiently to real-time data. Smart manufacturers realize the importance of technology-driven real-time process optimization to:

  • Reengineer their existing processes.
  • Meet constantly changing business requirements.
  • Implement real-time decisions to improve outcomes.

Why is real-time process optimization necessary for modern manufacturers?

The 2022 Everest Group study found that “mature” manufacturing companies with embedded process optimization were able to achieve the following benefits:

  • Standardizing their business processes (75%)
  • Identifying and removing inefficiencies (64%)
  • Implementing continuous improvements (50%)
  • Adapting business users to new systems (46%)

For instance, a leading Fortune 500 manufacturer of rolling aluminum stocks leverages real-time data to improve their cold rolling process and deliver defect-free products. Real-time process optimization enabled them to improve their productivity by over 20%.

Why is a Data + AI platform necessary for real-time process optimization?

In a manufacturing setup, operational managers leverage real-time data insights and analytics to optimize their production process. Similarly, maintenance teams can leverage real-time data analytics to monitor their production equipment and minimize shutdowns.

An intelligent “Data + AI” platform is essential for any manufacturer to optimize their business process. Using this technology platform, they can collect, organize, and integrate real-time data from diverse sources and create a unified data ecosystem.

With this framework, AI and machine learning (ML) algorithms can drive data-based decision-making by identifying any change in real-time data trends and patterns. For instance, an ML-based data model can provide accurate inputs and predictions to a cold rolling mill operator. This enables them to operate the machine with the correct inputs (required for the particular product profile and specifications).

Here’s an example:

A December 2023 survey by BCG on the role of Generative AI in the factory of the future identified AI (including Generative AI) at the top among 5 leading digital technologies. Based on the manufacturer’s maturity levels, Generative AI technology can drive the following 3 use cases:

  1. User assistance systems enable operators to perform manual tasks more efficiently. For instance, a GenAI tool can use textual inputs to generate code automatically, thus reducing the time for automation engineering.
  2. The user recommendation system is capable of identifying and recommending the best solution. For instance, GenAI can enhance predictive maintenance by providing step-by-step instructions to maintenance teams.
  3. Autonomous systems are self-adapting solutions designed to adapt to new production environments. For instance, GenAI-powered robots in production facilities can execute material handling tasks by translating the supervisor’s natural language prompts (for example, “Get me the spare part no. XXX”).

Using Databricks for real-time process optimization

Built on the Databricks Lakehouse platform, Databricks SQL (or DB SQL) is an effective tool for real-time analytics and optimization. This serverless data framework allows manufacturers to run business intelligence (BI) and SQL applications with:

  • 12x improvement in price-to-performance ratio
  • Unified data governance
  • Support for open formats and APIs

Using the Databricks tool, manufacturing companies can automatically optimize a variety of workloads. Here are some of Databricks’ runtime capabilities to enhance operational performance:

  • Dynamic file pruning improves SQL performance by avoiding directories without data files matching the SQL query.
  • Low shuffle merging reduces the volume of rewritten data files (using the MERGE command).
  • Table cloning improves SQL performance by creating copies of source datasets.
  • Cost-based optimizers can accelerate SQL performance using database table statistics.
  • Bloom filter indexes reduce the chances of scanning data files not containing records (that match a particular condition).

Additionally, with Databricks, manufacturers now have access to pre-built solution accelerators including:

  • Digital twins can process real-time data and extract insights to deliver to multiple applications for data-based decisions.
  • Overall Equipment Effectiveness (OEE) can ingest and process IoT sensor data automatically in a variety of file formats.
  • Computer Vision automates critical manufacturing processes, thus reducing wastage and rework.
  • Predictive maintenance can ingest real-time data from Industrial IoT-connected devices and complex time-series data processing.

Here’s an example of a Databricks-based technical solution architecture:

Conclusion

To stay relevant and competitive in the global manufacturing space, companies need to mandatorily implement digital transformation using the latest cutting-edge technologies. Data+AI solutions are the answer to the need for real-time optimization of manufacturing processes.

As a leading solution provider for the manufacturing industry, Pratiti Technologies can provide both consulting and implementation services for the Databricks platform. In the age of Industry 4.0, we are enabling manufacturers to meet growing challenges like process optimization and supply chain integration.

Our domain experts are here to help you implement a customized Databricks solution for your business needs. Contact us today!

Nitin
Nitin Tappe

After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

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