How Emerging Technologies Work Together for a More Effective Supply Chain

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Just about every day, customers ask me which digital supply chain technology is the most important and if they had to choose one, which is the best bet. The answer isn’t that simple. First, we need to look at the roles that different digital technologies play in an end-to-end supply chain orchestration process.

Blockchain, for example, is a revolutionary technology that has evolved since its birth in cryptocurrency and has found a strong footing in the supply chain. For organizations with many transactions and trust challenges, while dealing across multiple vendors, blockchain could help with secure and reliable data quality. However, as blockchain provides traceability across the network, it still relies on the enablement of the Internet of Things (IoT) to generate and capture data from various sensors and machines, and then the use of machine-learning techniques to realize value from this data.

Of course, machine learning and artificial intelligence (AI) are increasingly valuable, especially when data is in abundance. And that is the real challenge: it’s the abundance of data, and how to convert raw data into meaningful information for transformational change. It is in such situations that machine learning thrives. However, machine learning and AI need reliable, near-real-time data across domains and networks to help enable effective decision-making.

Let’s also look at IoT, in which multiple devices are connected into a network to generate real-time data, such as location, temperature, pressure, whether they are on/off, and more. This can help determine the location of a truck, for example, or the heat generated by a machine. But IoT provides data — not information or intelligence. Without technologies like machine learning or AI that can help make sense of this data, IoT’s value is diminished.

Then there’s the cloud, which provides a scalable infrastructure to store and analyze very large volumes of data. An enterprise that has IoT, AI and machine-learning technologies, but lacks cloud computing resources, will eventually run into scalability and cost issues as data growth continues its exponential pace.

Finally, there’s robotic process automation (RPA), which helps automate mundane and well-defined tasks to avoid repetitive human work, which in turn can save costs and minimize mistakes. The virtual robotic workforce is transforming how organizations move data, operate and engage with customers.

Based on the above examples of blockchain, machine learning, IoT, cloud and RPA, we see how individual disruptive technologies can offer value. But what if we harnessed the power of all these technologies on a single platform for end-to-end supply chain orchestration?

A digital control tower can offer a disruptive platform that engages technologies like blockchain, AI, machine learning, RPA, IoT and cloud to provide real differentiation for a company’s supply chain. For example, with IoT, companies can track exactly where their trucks are located. With blockchain, it makes it easier to trust your inventory data, so you can see your goods as they flow through the supply chain. Using machine learning or AI, you can leverage IoT data that might tell you whether a shipper will be late given certain weather. You can then use a combination of RPA, AI and machine learning to decide where to move inventory to meet your customers’ needs so that you don’t miss service levels. All this data capture and decision-making can be performed in the cloud. This convergence of digital technologies provides end-to-end supply chain orchestration.

So here’s what I typically recommend to clients who ask for guidance on the “best” digital technology. I advise them to not to focus on “doing digital” — on implementing one or more technologies — but to concentrate on “being digital,” which is creating a culture where digital solves real business needs and technologies are always built to work together.

This article was first published on Vivek Chhaochharia’s LinkedIn account.

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