In a recent article in IndustryWeek (“Sewing Digital Transformation into the Fabric of Textiles”), I discussed the digital transformation in the textile industry, and the role of Industry 4.0 and the IoT (internet of things) as an enabler.
I strongly believe that digital transformation, and Industry 4.0, needs very much an industry-specific approach. While some of the IT enablers may apply across industries, the challenges and the solutions require domain expertise.Therefore, I would like to highlight here one example of digital transformation in the textile industry. The above-linked article talks about several more.
Getzner Textile is a beautiful showcase to explain how to approach this digital journey. Getzner, like many other mill customers, operate machines from various vendors. As a first step, they wanted to be able to connect their machines into a cross-vendor standardized manufacturing execution system (MES) – to drive visibility, uptime, and efficiency by comparing and benchmarking information across boundaries of an individual system. Their benefits include a greater uptime, faster root cause analysis across maintenance, quality, operations – even tracking and optimizing their energy usage to reduce consumption and cost.
How did they do this? Check their story for more details.
Step one is always connectivity: Connect machines and sensors, and “free” information that was otherwise hidden in a single data silo. Often this also means to connect the plant domain (OT) with the business domain (IT).
Step two is merging business data and operational data. Enter big data challenges like heterogeneous data sources – from image files, to historian and streaming data, to textual shift notes, maintenance records, and quality and complaint information.
Keep in mind the internal supply chain including weaving, knitting, dyeing – with many combined operations, merges and split which make traceability a unique industry-specific challenge e.g. how lot link multiple sensor values to the same area and piece of final fabric.
This is not a pure big data challenge but requires a partner that know the industry processes, and surely some involvement from your operations experts.
Step three is to apply machine learning and the big data analysis toolbox to detect anomalies, track down root causes.
Finally, in step four, these insights are connected into better business decisions in maintenance, quality, and production processes.
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My personal favourite – beyond Getzer – is the Pacfic Textiles customer story.