Harnessing The Power of TinyML in Supply Chain

What is TinyML ?

You are not alone if you have not heard of ” TinyML “. The good news is that you are probably already aware of the underlying technology behind this terminology but were unaware of this marketing term assigned to it, sometime in late 2019. So let us first start by understanding what TinyML is all about in the simplest way possible. There is an assumption in the subsequent sections that you are aware of concepts like the Internet of Things (IoT), edge devices, and edge computing. I believe some of the most innovative applications of the power of TinyML are in operations and marketing. In this article, we will explore examples of applications of TinyML in operations and supply chain.

Let us start by exploring an example of how a “vanilla” IoT setup works today in a manufacturing context. A key aspect we need to keep in mind is that sensors capturing data on manufacturing floors is not new technology. The core setup has existed for decades. The supporting technologies have evolved, and sensors are becoming increasingly sophisticated and cheap. Still, the gist is that sensors have been used to collect data on manufacturing floors before even the term “Industry 4.0” came on the horizon. But in a vanilla setup, these sensors capture a set of data/data points and relay that data, in near real-time, to a central server. So it is not difficult to infer that the true value of this type of setup is not in the hardware (sensor) functionality but in how you leverage the data you are collecting. While that is not within the scope of this article, data captured from the floor can not only be used for analytics, but this near-time data relay is fundamental to creating a digital twin.

Now let us visit another example from manufacturing. A camera deployed in a production line scans finished parts from the production line. An “inference” program is installed on this camera to flag any parts it believes do not align with the quality requirements/specifications. Notice the difference between this example and the previous one. In the previous example, the edge device (sensor) captured and relayed a data point. It was not applying any form of intelligence to the data point it was capturing. In the second example, our edge device (camera) has a Machine Learning (ML) algorithm installed, allowing it to apply an inference algorithm on the captured data. This second example is the example of TinyML. The letters “ML” in TinyML stands for Machine Learning but why the word “Tiny”?

As you may know, true Machine Learning algorithms are computationally intense. The training and deployment mostly happen on high-powered machines. Let us revisit our manufacturing sensor example. Say we want to leverage the data collected to implement predictive maintenance capabilities. The data collected will be leveraged to build an ML model that will be trained on a machine with high computation specs and run on a proper system. The ML algorithm, in this case, sits on a device with powerful computing capabilities. Now if we come back to our camera example, it is not difficult to imagine (where we are currently in terms of technology) that, as compared to a full-fledged system, a camera on edge is a low-power device and hence houses an ML algorithm that is not computationally intense but is impactful.

So, with that background, we can formalize a simple definition of TinyML:

TinyML broadly encompasses machine learning technologies capable of performing analytics on data captured by extremely low-power edge devices on those devices themselves.

Between advances in hardware & computing power and the actively engaged open source tools-based TinyML community (like TensorFlow Lite)  that has generated exponential innovation in machine learning on edge, it is now possible to deploy increasingly complex algorithms directly on edge devices. The possibilities will only expand from this point, so now is the right time to explore the opportunities to leverage TinyML capabilities in your operations infrastructure in detail.

TinyML can revolutionize supply chain visibility beyond manufacturing.

TinyML has made good in-roads in manufacturing. While in one of the examples I used in the previous section, the sensor was only capturing and relaying the data, sensors today are very capable of applying some forms of predictive maintenance algorithms on edge. You can magnetically attach sensors these days to the outside of the turbine, and it will analyze detailed data at the edge. These sensors can alert you regarding potential issues before they occur by analyzing the data they capture. The key areas that need to be explored deeper are leveraging TinyML for logistics and inventory management. Let us visit a few areas where TinyML, if designed and leveraged prudently, can create innovative capabilities.

Warehousing: Opportunities to leverage TinyML in warehousing range from monitoring dock bottlenecks to identifying safety issues in the warehouse. The best part is that you can build these capabilities incrementally and then integrate them into a platform to create a “Smart Warehouse” platform powered by TinyML. Many aspects currently housed across multiple systems can be centralized in this platform and, combined with TinyML-powered edge devices, can help control warehouse applications much more efficiently. While relatively simple analytics happens on the edge devices leveraging TinyML, the central algorithm takes care of the coordination and centralized planning, plus strategic planning.

Inventory Management: While smart edge devices that monitor and flag storage conditions are already being used on shop floors of many warehouses, opportunities to expand the usage of TinyML are plenty. When you think about possible applications of any technology, a good starting point is what are some of the challenges those on the shop floors run into. For example, no matter how sophisticated warehouse technology is, the misplacement of inventory within the four walls of a warehouse is a frequent phenomenon. TinyML-powered edge devices can alert putaway personnel in real time if a product is placed in the wrong aisle or within the wrong bay. This is the simplest of applications, and it is not difficult to imagine that solving more complex problems using TinyML will yield more significant benefits, with some having the capability to transform inventory management in warehouses.

Transportation: Transportation has been an early adopter of digital tracking. Telematic tracking devices have been in use for a long time in transportation fleets, and these have now been augmented with more advanced sensors and trackers. But like inventory management, there are many unexplored opportunities. We know that pattern recognition and incident detection are among the capabilities ML algorithms can help build, and these can be leveraged in TinyML-powered edge devices powered and supported by a central algorithm sitting on a central server. As an example, TinyML-powered edge devices, in tandem with a central algorithm, can help study traffic patterns to optimize and plan routes based on peak traffic hours, maintenance, and construction, thereby helping develop dynamic routing capabilities that do not exist today (or maybe exist in marketing white papers only).

What does this mean for SAP insiders?

• TinyML is not a new concept, the marketing term associated with the technology is relatively new. SAP ecosystem has partners that have helped clients leverage the SAP technology portfolio and open-source tools like TensorFlow Lite to build TinyML-based solutions. Opportunities to leverage this galore in both operations and marketing.
• There is a fundamental foundation that needs to be built before embarking on building this type of advanced analytics solution. Setting up that foundation regarding people, processes, and technology is critical. Think about it this way: You can’t leap from having BI capabilities to building a solution like this. While you may end up implementing the technology, your people and processes will not be able to support the solution, and the entire initiative may turn into a failure.
• Plan an incremental roadmap based on where you are in terms of your current state in the functions mentioned above. While external partners can provide support, your people are the ones who are closest to the process. Start leveraging their expertise much earlier in the planning process.