A Compelling Case for the Cognitive Supply Chain

As Amazon transforms modern logistics, ‘smart’ technologies are becoming available to help all industries transform their own processes and keep deliveries on time.

By Gene Knauer

By Gene Knauer June 23, 2021

The largest river in the world by volume, the Amazon flows for 3,977 miles through nine countries at a rate of 200,000 cubic meters of water per second. Since ancient times, the river has been a vital trade route, connecting the jungle’s natural assets to the rest of the continent and the world.

"Do you know why Jeff Bezos called Amazon ‘Amazon?’" asked Jerome Pedreno, director of business development at Hardis Group, a European digital services and consulting firm and an independent developer of smart supply chain solutions.

"Because the river is not just a way of transporting things. Bezos understood that the river is the market itself. He saw that the way to succeed was by selling the efficiency of the supply chain ‘river,’ made up of all the logistics technologies, automation and networks.”


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Today, Amazon the company transports 350 million different products between producers and consumers on its supply chain river. The automation, real-time tracking, Internet of Things (IoT), AI, analytics, edge and cloud systems that have helped make the company’s business so efficient are now available to everyone. 

Pedreno and his Hardis Group colleagues focus on bringing these systems, known as cognitive supply chain solutions, to factories, warehouses, and retail stores.

The Power of New Insights

These technologies have converged to add real-time analytics to today’s supply chains. Edge computing, for example, collects and analyzes data from local IoT devices, sensors, and other data sources, using AI and machine learning algorithms. The edge applies the results to real-time applications, if warranted, then forwards only the results across the WAN for aggregation and further processing in the cloud or a private data center. Edge analytics has made machine learning and AI affordable for many companies because it consumes far less of the costly network bandwidth used in traditional methods of aggregating data first, then forwarding it all to a central site for processing and analysis.


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In addition to bar codes and radio frequency identification (RFID) tags for inventory and asset tracking, process data is being collected from factory machines as part of the IoT revolution to conduct proactive equipment maintenance, increase uptime, improve plant performance, and boost worker safety. Video surveillance cameras and drones are being repurposed to use image and audio recognition systems to track products, machinery, and even the identity, speech, and emotions of workers and customers.

The use of AI and machine learning applied to data used in logistics and supply chain systems mixed with external data, such as market trends, social media, holidays, and weather forecasts, has been called a cognitive supply chain. Analysis of continuous, real-time streams of diverse data inputs keeps those in charge updated about supply chain conditions so they can quickly make any needed changes to vendors, shippers, and suppliers to keep schedules on track and customers satisfied.


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The impact of this capability on business success was summarized well in an article on cognitive supply chains in manufacturing, in which KPMG analysts wrote: “Where armies of analysts once spent months coming up with ways to speed up delivery or find pain points on factory floors, a well-oiled cognitive decision center can identify anomalies, search for patterns that lead to unexpected disruptions, and make suggestions on how to solve them—almost instantaneously.”

Lagging Adoption

During the pandemic, the shift to online everything showed customers clearly that some companies could keep items in stock and deliver them quickly and others could not. Despite the impact of national shutdowns on supply chains caused by shelter-in-place restrictions, customers had high expectations and low tolerance for out-of-stock products and slow deliveries.

Recent research by the Baymard Institute, for example, found that 69% of shoppers will abandon their online purchases when one or more of their items isn’t in stock. Only 17% of shoppers will return. Another global study by the IHL Group indicates that retailers lose $984 billion yearly due to out-of-stock items in retail stores.

A survey of 1,000 executives by Oxford Economics and SAP in 2020 revealed a high awareness of the benefits of real-time insights for improved logistics, supply chain efficiency and collaboration with partners and suppliers. A supply chain and analytics survey by Deloitte, however, found that while 76% of respondents said developing sophisticated digital logistics and supply chain solutions were crucial, most underinvested in them due to a disconnect between IT and business leaders.

A Cognitive Future?

Prominent companies like IBM and Microsoft, along with the Hardis Group, are focused on eliminating that disconnect and selling the benefits of an enhanced version of logistics and supply chain management. The platform developed by the Hardis Group builds on the company’s existing warehouse and factory logistics systems used by 250 companies in 20 countries.

“In the warehouse, factory, and store, our platform creates a model of the environment,” said Damien Pasquinelli, Hardis Group CTO. 

“We use deep learning to analyze different data inputs ― visual recognition, point-of-sale, tags, ERP, CRM. The system can trigger alarms to factory or warehouse workers or salespeople in stores if a pallet waits too long on a loading dock, if a product is put in the wrong place in a store, or if stock gets low and needs replenishment.”

With 5G networks, the volume of data that can be sent locally will increase substantially, enabling cognitive supply chains to stream a lot more video, according to Pasquinelli. Hardis Group is working on software to help customers manage the complex network of IoT nodes likely to communicate using 5G in their cognitive supply chains.

“We want to give companies the tools to manage the link between those nodes and the global network in real-time,” he said. He envisions those tools working the same way that a real cognitive system ― a brain ― operates.

Gene Knauer is a contributing writer who specializes in IT and business topics. He is also the author of Herding Goldfish: The Professional Content Marketing Writer in an Age of Digital Media and Short Attention Spans.

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