US-based and Western industries are being hit with a dose of reality as they begin to recover from pandemic-driven slowdowns while simultaneously preparing for a post-pandemic surge. At the same time, one of the biggest challenges is in re-evaluating the global supply chain which was thrown into disarray as a result of global shutdowns, unavailable shipping and some suppliers going out of business completely. The pandemic brought to light the inherent vulnerability many companies faced in having an extended supplier ecosystem into which companies often have limited visibility. As a result, the gap that has always existed between planning and execution has become even more visible – and solving that gap has become urgent for companies as they emerge from their pandemic-driven malaise and begin to assess the damage.
Vast amounts of supply chain data exist, but too often companies fail to take full advantage of it so long as operations are functioning. But the disruptions faced over the past year have made it clear that companies need to better understand the data they have on hand, and they need ready and real-time access to that data in order to respond to unexpected disruptions. Most importantly, companies are only beginning to get a firm grasp on what really happened and how the pandemic disrupted their operations, and have come to understand that a significant gap exists between planning and execution.
This gap is not new. It existed before the pandemic, but solving it has become a matter of urgency. “Remaining competitive even during an unexpected global disruption requires, more than anything, the ability to understand from minute to minute what factors are affecting your supply chain,” said Ganesh Gandhieswaran, CEO of ConverSight.ai, an AI-driven, natural language platform that delivers immediate intelligence to decision-makers at all levels. “We saw during the pandemic just how quickly global supply chains can be affected by factors out of our control. Understanding those changes in context and in real time – and being able to respond quickly – in many cases made the difference for those companies which continued to thrive during this past year’s disruption.”
The biggest sustainable contributing factor to competitiveness is the underlying decision intelligence technology that Gandhieswaran describes, and a real-time focus that closes the gap between planning and execution. It is that technology, delivering decision support and predictive analytics in real time, which will position US based industries to effectively compete in the global market.
Big and bold points to a need for deeper insights and analytics
A rebalancing of global supply chains in a new blended onshore/offshore strategy that de-focuses single-region supply chain strategies will help companies lower risk and better withstand future disruptions like the COVID pandemic.
A post-pandemic surge in consumer spending and demand, coupled with new domestic infrastructure projects and a shift towards closer-to-home sourcing, will bring new opportunities for companies, but this will also impact the need for information and decision support technologies. “We have long understood that the need for supply chain intelligence and intentional visibility is critical when building a global supply chain with potentially hundreds of partners in both domestic and overseas locations,” said Gandhieswaran. “But that decision support and insight will be even more critical with supply chains closer to home, because when they do come home, it will be coupled with a new level of transformation at every level of the operation, from manufacturing to transportation.”
That may seem counter-intuitive at first. One might think that with a supply chain closer to home, visibility would be inherently better and intelligence would be easier to come by, but the opposite will be true. This is because to balance the labor arbitrage advantage they would otherwise have through an extended supply chain based on best-cost sourcing in China, Southeast Asia and other low-wage destinations, companies will need to turn to newer technologies like additive manufacturing, robotics, and 5G-enabled Industrial Internet of Things (IIoT); and on the transportation front, driverless car networks or even more ambitious, high-speed rail. This enhanced level of technology was of course, already moving forward, but onshoring of the supply chain will tip the scales towards ubiquitous use.
That advanced technology generates data, and lots of it. We were already facing a state of data overload and significant challenges to derive useful information out of it, and just as traditional data intelligence tools were beginning to solve the problem, the volume of data is now about to exponentially increase yet again.
Data is the currency of business
“We have already long since reached the point at which data is the currency of business,” said Gandhieswaran. “We understand that information, more than anything else, is what drives commerce forward, and we now find ourselves at yet another tipping point. The rapid advancement of sophisticated technologies that will drive the newly rebalanced supply chains of industry will take the already accelerated pace of data overload and take it even further. Navigating and understanding that data will require AI-based, natural language interfaces to make sense of it all.”
With the rapid increase in industrial data about to be unleashed, there is an even greater need for agility and responsiveness to supply chain changes, and this will require a new approach to data, analytics and decision intelligence. According to Gandhieswaran, ConverSight.ai’s natural language industrial interface, personified in the form of an AI business assistant called “Athena,” enables this level of responsiveness with a decision intelligence platform capable of abstracting multiple structured and unstructured data sources, conducting deep analysis and delivering insights and recommendations in real time.
“One of the most innovative aspects of the technology is that it allows decision-makers to take an unscripted path to discovery,” said Gandhieswaran. “It’s one thing for a data analyst to draw on data to tease out patterns they suspected were already there, but the AI component, real-time nature and contextual, conversational techniques take data science to the next level in delivering insights that are more actionable, more useful and more proactive than were previously possible through standard data analytics tools.”
Data is indeed the currency of business and is what will drive the success of industrial policy on a national scale. The flow of data will increase, and quickly, as manufacturers and supply chain partners leverage new technologies. Global competitive success requires several things – a competitive and flexible supply chain, and increasing sophistication in manufacturing and distribution technologies. The final component of success is being able to understand that this will result in a new rapid increase in data, and that understanding that data will require us to take advantage of newly emerging natural-language, real-time data analytics.
About the Author
Dan Blacharski is an author, consultant and industry observer who frequently writes about global affairs, next-generation technology and industry. He lives in South Bend, Indiana with his wife and inspiration Charoenkwan, and their Boston Terrier Ling Ba.