Use cases

Case studies: artificial intelligence in the packaging industry

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Case study: Octavo Systems enhances edge and compact embedded processing performance 


Octavo Systems has released the OSD62x, a Texas Instruments AM62x-based System-in-Package (SiP) module to enhance the performance of edge and small form factor embedded processing in next-generation applications. 

It is suitable for a wide range of applications, including small-size human-machine interface (HMI) applications for building automation, industrial control, IoT gateways, and AI at the edge.   

Nature of Disruption: The OSD62x combines multiple components into a single package, reducing the complexity of system design and accelerating time to market. It employs advanced SiP technology to integrate the Texas Instruments AM62x Sitara processor, DDR4 memory, power management, and passives. 

This integration simplifies the design process, reduces the number of components needed, and minimises the board space required. Octavo Systems offers the OSD62x SiP family in two package options. The first option, known as Maximized Integration (-MI), includes everything necessary for an AM62x-based system in a 22mm x 22mm, 1mm ball pitch BGA. The second option, Size Optimized (-SO), offers the smallest AM62x-based module form factor, catering to those who need a compact solution. 


The OSD62x is set to address the challenges of system design in the industrial sector by providing a simplified and integrated solution. It can potentially disrupt the market by reducing the time and resources required for system design and development. 

The OSD62x is expected to be adopted by various industries, including industrial automation, robotics, and communication infrastructure, due to its versatility and ease of use. It is also ideal for other low-power systems that require high-performance Linux processing. 

The OSD62x SiP family integrates high-speed memory, power management, passive components, and more into a single BGA package, delivering the smallest AM62x module form factor. 

Case study: Locus Robotics provides end-to-end reverse logistics solution 


US-based robotic process automation company Locus Robotics and logistics technology company Optoro have collaborated to create a fully integrated, high-volume, end-to-end reverse logistics solution.

This partnership combines Optoro's returns optimisation platform with Locus Robotics' autonomous mobile robots (AMRs) to streamline the returns process and improve warehouse efficiency. 
Nature of Disruption: Optoro's platform uses data analytics and ML to determine the most profitable disposition path for returned items, while Locus Robotics' AMRs provide efficient, scalable, and flexible automation in the warehouse. 

The combined solution is designed to handle high volumes of returns, reducing the time and cost associated with processing returned items. This innovation aims to address the challenges of managing returns, which have become increasingly complex and costly due to the growth of e-commerce. 


The partnership between Locus Robotics and Optoro addresses a significant challenge in the logistics sector, as the rise of e-commerce has led to an increase in returns and associated costs. By streamlining the returns process and improving warehouse efficiency, this solution could potentially reduce costs and increase profitability for businesses. 

The solution is currently being used by Radial, US-based omnichannel commerce technology and operations leader, to manage its high-volume returns process.  

Case study: NVIDIA optimises supply chain routing with AI 


NVIDIA has introduced ReOpt, an AI-powered service designed to optimise supply chain routing. The service aims to alleviate the challenges faced by the global supply chain due to massive disruptions, providing AI software for tasks ranging from last-mile delivery vehicle routing to efficient picking and packing of warehoused goods. ReOpt is intended to serve a wide range of industries, including transportation, warehousing, manufacturing, retail, and quick-service restaurants.

Nature of disruption:

ReOpt leverages AI algorithms to provide customers with real-time data on road conditions, traffic, and route metrics, aiming to reduce miles, fuel cost, carbon emissions, and idle time. The service models the movements of vehicles with finite capacities and different costs, considering factors such as the need for refrigerated trucks for fresh produce. 

ReOpt also allows customers to create automated routines for the dynamic routing of robots for truck loading as new orders arrive. It can account for the number of available pilots, drivers, and workers to operate vehicles on a given day, incorporating maintenance costs. The service takes advantage of NVIDIA's parallel architecture to generate thousands of solution candidates and refine them to select the best one. 


The cost of last-mile delivery, delivering goods directly to a customer's door, has been a significant challenge for companies even before the pandemic disrupted the global supply chain network. ReOpt addresses this issue by providing real-time, accurate routing optimisation, thereby potentially improving efficiency and reducing costs. 

The service's AI capabilities allow it to analyse vast amounts of data quickly and accurately, identifying optimal routes and strategies. This enables companies to make informed decisions and provide timely delivery. ReOpt is currently available in early access, and NVIDIA plans to continue developing and expanding its capabilities to further enhance its performance and utility. 

GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article. 

GlobalData’s Thematic Intelligence uses proprietary data, research, and analysis to provide a forward-looking perspective on the key themes that will shape the future of the world’s largest industries and the organisations within them.