AI-enabled decision-making is the next step in the supply chain digitalization journey

The global supply chain crisis is not a passing phase

March 3, 2022

4 Min Read

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The global supply chain crisis is not a passing phase

The supply chain industry, which until recently operated sufficiently by using disparate spreadsheets, phone calls and even paper-based records, was exposed during the pandemic for its archaic processes.

As a result, companies have gone through a decade's worth of digital transformation in just a matter of months, with the pandemic forcing them to refresh archaic processes with AI, machine learning, and data science technologies.

Such technological advancements will continue to evolve and further establish themselves as a critical component to managing complex logistical landscapes – from improving efficiency and mitigating the effects of a global labor shortage, to identifying more robust and dependable ways to move commodities.

In a world where uncertainty is the only certainty, AI-enabled order and inventory visibility across shipments will also be vital to ‘keep the wheels in motion.’ Most importantly, to provide real-time updates on changes to arrival times and to identify potential disruptions before and as they occur.

Take the recent congestion issues at the Port of Los Angeles, for example. For a brand that has always shipped into Los Angeles, it could take weeks to fully examine alternative options. They will ask, should I consider a different port? What happens if I ship into the Port of Seattle and then engage train transportation for onward carriage? Perhaps there is a route through the Panama Canal over to New York? An AI-enabled machine-learning model would quickly assess all transportation options, enabling logistics managers to access real-time recommendations and take action in hours, not weeks.

This symbiotic relationship between AI and human intelligence will continue to develop in 2022, with AI-enabled decision-making now a critical component of modern, resilient supply chain management. But how can supply chain leaders strike a balance between harnessing new technologies with hesitancy from the wider business?

Balancing high hopes with pragmatism

As illustrated by Gartner, the supply chain industry is now in a phase of AI adoption where the technology is robust and advanced enough to greatly enhance decision-making.

Predictive analytics, for example, is now a proven advantage, with such technologies advanced enough to recognize anomalies and learn how to select carriers and preferred re-bookings in real-time.

The next phase for AI is hyper-automation, where AI delivers predictive insights, automated workflow, and decision-making across all modes and regions.

While AI-enabled decision-making may appear a done-deal, any new approach will always take time to win the industry’s trust – and rightly so. We know from the reaction to other next-generation technologies that all it takes is one bad example to regress the conversation, negate the value proposition and dampen well-placed enthusiasm.

Companies are right to balance enthusiasm with skepticism, but caution should not hinder progress. Evidence of impact, integrity, value, and resilience must be the primary considerations in adopting any new solution.

As Gartner warns, “Emerging supply chain management technologies are often overhyped, and leaders must aim to fully understand the risks and opportunities associated with each new technology… Supply chain technology leaders must conduct careful due diligence to identify the right technology partner to work with on visibility projects.”

While Gartner has raised a warning flag, they also urge for more investment in advanced analytics, AI, and data infrastructure, meaning there is no doubt that supply chain organizations must continue on their path to digitalization.

Fortunately, it appears that a balance is being struck, with half of supply chain leaders planning to invest in applications that support AI and advanced analytics capabilities within the next two years.

In practice, this requires the adoption of automated, real-time decision-making alongside skilled talent that can align with and make the most of developing technologies. To enable this, companies should place focus on the use cases that present the highest potential within the business.

Clear communication around the benefits of using innovative technologies, particularly from an individual and departmental productivity perspective, will be essential to secure buy-in from the luddites within the industry. As part of this, supply chain leaders must consider any cultural changes that might come with further AI adoption and train employees on how to incorporate these technologies into their daily decision-making processes.

The global supply chain crisis is not a passing phase. While human skill will always be at the heart of logistical operations, supply chain executives must now explore how AI technologies can enable human specialists to make real-time, data-driven decisions to traverse this complicated landscape. Companies that future-proof their operations against tomorrow's difficulties and establish long-term resilience will be those that hardness AI-driven decision-making capabilities.

Matt Dzugan is director of data science at project44 and has over a decade of experience solving complex business problems with data, modeling and simulation.  During his tenure at project44, Matt has been scaling the data science team from a few disparate efforts to a full department of 30 team members around the globe.

The project44 data science team uses the billions of shipments that are tracked through project44’s platform to extract insights that help customers make data-driven decisions: everything from “estimated time of delivery” to “impact of the latest disruptions.” project44’s data science team uses state-of-the-art Machine Learning techniques to capture the dynamic trends and patterns of today’s supply chain.

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