Using AI to Solve Complex Global Supply Chain Management Challenges


Companies are starting to apply artificial intelligence across global supply chain management to improve efficiency, speed and decision-making in areas such as supply chain planning, warehouse automation, and logistics.

The SCM World 2016 Future of Supply Chain Survey found that the importance of artificial intelligence has grown rapidly, with 47 percent of supply chain leaders believing the technology is disruptive to global supply chain management strategies. Market-research firm IDC predicts that by 2020, 50 percent of mature supply chains will use AI and advanced analytics for planning, and to eliminate sole reliance on short-term demand forecasts.

AI for Global Supply Chain Management Planning

Supply chain planning and optimization, including demand forecasting, are among the key areas where AI is already beginning to be deployed. Experts say that global supply chains have become so complex, and are affected by so many variables, that AI may be essential to help identify and predict problems and potential solutions.

“Supply chain managers must take into account more data than any person can possibly process,” Nucleus Research analyst Seth Lippincott told EBN. As IDC analyst Simon Ellis (writing on an IBM blog) put it: “Most companies simply do not understand the full depth and breadth of their supply chain risks, and are therefore not prepared to respond efficiently or effectively to the many potential disruptions.” The inherent complexity of global supply chains, along with the dramatically increased volume of data, make it almost impossible to extract all the necessary insights and make informed business decisions. And the volume of data continues to increase, in part due to the trend to connect supply chain management devices to the Internet, according to DHL’s 2016 Logistics Trends Radar report.

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Accordingly, companies are already applying AI-based machine learning to automatically analyze vast amounts of supply-chain management data, identify trends, and generate predictive analytics — the ability to predict problems and outcomes. Lippincott says that the benefits in global supply chain management include reductions in forecasting errors. “Software solutions are beginning to apply machine learning capabilities that can automatically detect errors and make course corrections, while processing real-time data streams,” he says. “With companies collecting mountains of data that can be used to train algorithms to learn where things went wrong, we’re at the tip of the iceberg of how much companies will leverage these capabilities.”

For example, some supply chain management solutions use AI to gather and correlate external data from many sources, including social media, newsfeeds, weather forecasts and historical data.

One major food manufacturer used an AI-based demand forecasting solution to tackle a common problem: meeting customer demand while minimizing inventory. The challenge was complex, involving around 10,000 different products, each subject to variation in demand. By applying predictive analytics, the company was able to more accurately anticipate customer behavior by integrating the impact of promotions and other special offers into its statistical models.9

In a 2016 survey of 1,100 supply chain and manufacturing companies by Deloitte and MHI, only 17 percent of companies were using predictive analytics; but that number is expected to jump to 79 percent over the next three to five years.

Anticipating Orders Before they are Placed

Predictive algorithms may also enable “anticipatory logistics” — the ability to shorten delivery times and improve efficiency by predicting demand before a request or order is even placed, as global logistics provider DHL described in Logistics Trends Radar. For example, global supply chain managers could use AI systems to detect risks in trade shipping lanes and, using shock-detecting sensors, potential damages to cargo; they could then take corrective action and minimize operational delays.

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Supply chain managers who have analyzed their customers’ purchasing behaviors might move goods to distribution centers that are closer to the customer, allowing faster delivery. Within warehouses, machine learning systems may be able to recognize common scenarios and trends, and link these to specific customers and orders; anticipating the content of an order, these systems would then pre-pick-and-pack without first waiting for orders to be placed, according to the DHL report.

Robots and Self-Driving Vehicles

Autonomous vehicles, which rely on AI to sense their surroundings and make decisions, have already made inroads into logistics, although large-scale adoption may be several years away, according to DHL. Self-driving vehicles have been gradually adopted in controlled environments such as warehouses and yards; DHL predicts that warehouses of the future will deploy the next generation of self-driving vehicles, such as autonomous forklifts, carts and pallet movers, which will be able to navigate without the aid of magnetic strips or other guides. The use of goods-delivery drones and other vehicles in public spaces is farther from mainstream adoption, the company notes. Robots are also being used by big online retailers and logistics companies to quickly help pick and stack goods.

The Takeaway

Artificial intelligence is starting to be used in global supply chain management to help companies analyze and act on global supply chains’ vast data. However, while experts consider machine learning and other AI technologies important and disruptive, they note challenges such as the technology skills required, the need to integrate multiple data sources, and regulatory hurdles that may need to be overcome to enable widespread adoption.

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This feature is written by Mike Fadden & originally appeared in American Express.



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