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Author's profile photo Owen Cook

How TinyML Can Revolutionize Supply Chain Visibility

If you’ve been a part of a manufacturing facility, you’d be well aware of how data monitoring and collection happens on the shop floor. However, the march of time has evolved these systems into something new. Most people within a company might see abbreviations as IoT and Edge computing and simply assume that it’s the new flavor-of-the-week buzzwords. But these technologies, in combination, can update and revolutionize a supply chain’s visibility. Edge computing offers a way to do processing far away from the central hub of a network. IoT provides a distributed methodology for collecting data from the shop floor. Together, these can be used alongside TinyML to change the supply chain’s visibility. But what is TinyML, and how can it help supply chains?

TinyML in a Nutshell

By now, most people who have dealt with artificial intelligence have heard of machine learning. TinyML is a machine learning algorithm designed to take advantage of IoT technology and edge computing. In essence, it can process collected data right on the device that collects it via edge computing. The result is that analytics and insights are generated a lot faster because of the rapid processing. In the past, ML was the domain of massive processing engines since it was very computationally intensive. However, with the advent of edge computing, this bottleneck has been resolved to a great extent. Businesses could benefit from implementing TinyML alongside their systems now and be at the cutting edge of innovation. TinyML is more than just for manufacturing businesses, however.

TinyML in Logistics and Inventory Management

Supply chains start with manufacturing, but the innovations from TinyML can filter down the chain quickly. Inventory management and logistics can both benefit from this innovation immensely. There are several distinct areas where TinyML can impact the supply chain, such as:

Warehousing: TinyML can be instrumental in collecting and aggregating data about packages within a warehouse. By tracing the movement of goods through the warehouse, a business can spot bottlenecks in its warehouse management. Additionally, the algorithm could collect safety data about workers within the warehouse to improve the company’s safety record. Using a centralized dashboard to deal with this data collection and processing is the ideal result.

Managing Inventories: Many inventory management systems already use IoT devices that collect data on shelves and occupancy. However, no matter how good inventory management is, there are still occasions when a stock goes “missing.” TinyML devices can sound alerts or warnings if the reserve is placed in the wrong aisle to protect against human error. With time, once automation gets up to speed, warehouse workers need just place the goods on the loader, and it would put it on the shelves itself. However, that all comes from establishing the TinyML framework from now.

Frameworks are Necessary for Innovation

For a business to remain competitive, it must innovate, but this innovation is based on the right frameworks being leveraged within the company. Moving from the typical masters in human resources to logistics and inventory management requires personnel to get all the help they can. Businesses can leverage TinyML to build a framework that can be expanded upon as time goes by. Not only would it result in cost-saving over the long term, but the insights can even help the business be more efficient.

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      Author's profile photo vinith moduguru
      vinith moduguru

      Well explained!

       

      Author's profile photo Mauricio Taylor
      Mauricio Taylor

      Nicely Explained. Thanks.

       

      TelltheBell