Historically, the apparel industry’s success has always relied on a combination of design and physics. Data has never really been a part of that equation. But the rise of the Fourth Industrial Revolution, has opened up a lot of new opportunities for fashion supply chains.

Digital supply chains help drive improvements to traditional supply chains by developing and implementing advanced digital technologies like IoT, machine learning, artificial intelligence, predictive analytics, etc. The overall consensus about these technologies is that they possess the potential to bring a disruption and lead innovation. But what do they mean?

The internet of Things, or “IoT” for short, is about extending the power of the internet beyond computers and smartphones to a whole range of other things, processes and environments. Those “connected” things (like sensors for example) are used to gather information, send information back, or both. (Source: IoT for All)

Artificial Intelligence (or AI) is the ability of a computer program or a machine to think and learn. It is also a field of study which tries to make computers “smart”. They work on their own without being encoded with commands. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. (Source: Wikipedia)

Machine Learning (or ML) is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves. It refers to computer algorithms that solve problems by experiencing them many times and figuring out how to solve them. (Source: Forbes)

Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about the future. (Source: PAT Research)

Data intelligence in Quality & Compliance Management

Quality and compliance digitization can unlock knowledge about manufacturing processes, supplier performance and inspection accuracy. “Which of my suppliers is performing the best? In which locations am I seeing the most problems? Is my quality improving or declining? Can I anticipate my corrective action plans?” The data generated allows brands and factories to evaluate and take action faster and more easily than ever before. It enables them to assess their risks and make decisions based on facts instead of intuition.

On top of that, predictive analytics can help them to look into the future and make even more informed decisions. In the case of quality and compliance data, machine learning captures the inspection data and use algorithms to make predictions about the product’s quality and supplier’s compliance. The more data is captured, the more accurate the predictions will be.

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