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By 2023, digital transformation-enabling technologies and services are expected to touch the $2.3 trillion mark, according to a Statista study. Clearly, digital transformation in manufacturing is—and will be—the flavor of several seasons to come. Manufacturing companies are actively pursuing smart solutions to optimize their processes and output by leveraging next-gen technology. A key step is the adoption of an efficient manufacturing data and analytics solution—one that is capable of helping organizations take data-driven, informed business decisions.

At Columbus, we specialize in digitally transforming manufacturing companies by deploying such data and analytics-oriented solutions. At our recently conducted Manufacturing Summit 2020, our Data and Analytics Practice Director, Michael Simms, gave a detailed presentation on the data journey (and how data analytics can prove to be the game changer) of a manufacturing company through the example of a glass fiber manufacturing enterprise. The presentation also highlighted how this enterprise's partnership with Columbus helped it overcome the difficulties it was facing.

Michael’s talk included the following:

Introducing the client and the business challenges it was facing:

A medium-sized glass fiber manufacturing company wanted to enhance its quality control process and minimize the time it was taking to make corrections. The company had enlisted Columbus’ assistance for deploying Microsoft Power BI and Advanced Analytics in Microsoft Azure AI to fulfill its requirements. Other challenges the client was facing with its existing system included:

  • High quantum of dependence on Microsoft Excel to analyze data gathered on a seven-day rolling basis from IoT sensors. This data included factors influencing breakage rates like temperature, material, etc.
  • The time-consuming manual process of fixing issues which resulted in compromised accuracy — Took about eight weeks to identify and resolve production problems which kept persisting despite maintenance efforts.

The Columbus approach and solution:

In the absence of consolidated production processes-related data, the manufacturer was unable to measure its readings against production best practices. This made process improvements unachievable and unsustainable. Since the client was working with dated information, drawing actionable insights from this data was a challenge too. The Columbus data and analytics team offered the following solutions to address these issues:

  • Architected a solution with Microsoft Azure platform which included:
    • Data factory for data ingestion
    • Data lake for data storage
    • Data bricks for data manipulation
    • Power BI for visual representation
  • The aforementioned tools eased the data ingestion and manipulation processes, helping build data reliability by doing so. They also facilitated low-cost storage and visualization.

The Columbus process:

Columbus implemented the required transformation sequentially in the following manner:

  • Understood the problem statement correctly by setting up an initial call with the client
  • Carried out an analytics assessment to complete initial discovery and determine the scope of work
  • Leveraged the Proof of Concept obtained after the analytics assessment to automate complex manual processes

Additionally, the Columbus team developed a road map for the client to help visualize the data journey they were undertaking—one that would allow the client to keep track of achieved milestones and gauge the quantum of progress made. One of the main objectives of this road map was to provide the client visibility into crucial aspects of the data journey that could help it drive digital transformation in manufacturing processes. The client would also be able to modify this road map according to its future needs as its business priorities change.

Learn more about the Proof of Concept Columbus created for the client or view the demo. Watch our on-demand recording of the Summit to view Michael's presentation

Columbus Manufacturing Summit 2020 on-demand recordings

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