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Home » Blogs » Think Tank » How AI Is Transforming Global Supply Chains

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How AI Is Transforming Global Supply Chains

factory
December 28, 2020
Doug Mefford, SCB Contributor

The modern supply chain is an intricate animal that incorporates many different organizations, people, processes and technologies. Add the word “global” to the equation, and those complexities become even more difficult to manage. Moreover, with disruptions like the global pandemic wreaking havoc on the world’s supply chains, the need to balance resiliency with profitability has become mandatory for most organizations.

Fortunately, technology has advanced to the point where it can help organizations more effectively manage their global supply chains without the need for additional labor, physical space, or expertise. From predictive to prescriptive, from decision support to decision automation, artificial intelligence is transforming supply-chain activities in new ways.

Performing tasks in a manner similar to human brains, AI-enabled technology can sense and respond to specific features within its environment. It can learn to solve problems such as delivery delays and weather-related disruptions in unexpected ways, recognizing the nuances of speech and exhibiting some form of humanlike creativity.

The Perfect Blend

As companies deploy new solutions that both address current supply-chain challenges and prepare those intricate networks for the post-COVID-19 world, AI is taking center stage in many of these implementations. From driverless trucks to delivery route optimization to demand forecasting, AI is being applied in many aspects of the supply chain. The transformation has been accelerated by the pandemic, as companies deploy new technologies within the span of months to meet the new demands of commerce.

Widely expected to transform supply-chain operations over the next five years, AI processes the wealth of data generated by operations, enabling quality control, predictive maintenance and supply-chain optimization. In recent years, all manner of cloud-based applications for enterprise resource planning (ERP), manufacturing execution systems (MES) and warehouse management systems (WMS) have been imbued with AI and analytics capabilities.

Using AI, companies can make smart predictions about future needs for raw materials, work-in-progress components and demand for final products. AI also helps manufacturers to execute on the vision of the smart factory. As Capgemini reports, it enables them to achieve a closed-loop system and, ultimately, self-optimizing operations, “in which the factory constantly adapts to demand, supply variations and process deviations.” This is another case where a smart manufacturing execution system can transform production.

Measuring the Benefits

As an enabling technology, AI also provides the foundation for autonomous planning, predicated on internal as well as external data from suppliers, customers, demographics and broader economic indicators. In addition, predictive maintenance analyzes the historical performance data of machines to forecast when one is likely to fail, thereby limiting time out of service and identifying the root cause of the problem.

“By combining predictive AI with smart human oversight and decision making,” Here Technologies points out, “cognitive-era technologies can transform the supply chain and day-to-day distribution operations.”

According to McKinsey, 61% of executives report decreased costs and 53% report increased revenues as a direct result of introducing AI into their supply chains. Lower inventory-carrying costs, inventory reductions of up to 75%, and lower transportation and labor costs are among the biggest areas of savings. In today’s consumer-driven sales environment, AI also helps ensure that the right products get to the right place at the right time — the modern-day “Holy Grail” for manufacturers, distributors and retailers alike.

For example, Proctor & Gamble uses an AI-enabled digital platform to automate its supply-chain planning, a process that once focused on matching demand with supply over a 30-day period. In today’s dynamic supply-chain environment, P&G’s planners utilize machine learning (a subfield of AI) algorithms that automatically adjust demand plans for new product launches, changes in stocking strategies, or seasonal shifts. Along with being more productive and accurate, the manufacturer has also freed up its planners to focus on more important tasks.

A Silent Partner

Much like a silent business partner works in the background, supporting the organization without personal recognition, AI is a stealthy technology partner that does its job without much prompting. In fact, many companies might not even realize they’re already using AI to forecast product demand, run their transportation activities, or manage customer communications. That’s because technology vendors have been steadily blending more AI into their solutions over the last few years — a trend that will continue in 2021 and beyond.

For now, just 12% of supply-chain professionals say their organizations are deploying AI as a supply-chain management tool. Yet the market as a whole continues to grow, and is expected to reach $10.1 billion by 2025. The global pandemic may push more of these companies to explore their options, at a time when more technology vendors are adding AI to their supply-chain management platforms.

With AI, companies can optimize the wealth of data that exists within their operations, and use that knowledge to work smarter, better and faster. This in turn allows them to future-proof their supply chains, improve responsiveness, minimize risk, deliver a better customer experience and increase visibility across the end-to-end supply chain. Such big wins will help companies around the world to build more resilient, agile supply chains that thrive in any business conditions.

Doug Mefford is chief product development officer at Generix.

Technology Data Management (Big Data/IoT/Blockchain) Supply Chain Planning & Optimization Supply Chain Visibility Artificial Intelligence

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