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How Digitization is Benefitting the Supply Chain Management

By CIOReview | Tuesday, December 15, 2020

The supply chain management follows the trend of digitization and globalization so that technologies can be used to analyze massive sets of data.

FREMONT, CA: Supply chain has extended across the borders and delivered various opportunities and challenges. In the upcoming years, there will be more new trends in supply chain management.

Achieving environmental sustainability is putting new pressure on supply chain managers. Sustainable supply chain management practices are incorporated into every link in the supply chain, from equipment use, product design, manufacturing processes, product delivery, and even disposal after its proper use. Some green methods are simple, such as energy-saving warehouse lighting and planning of transmission routes to minimize mileage and fuel consumption. But to build a green supply chain, most companies need a complete overhaul of supply chain practice.

Green supply chains are essential for 2020 as consumers continue to demand green practices and global governments push to add environmental legislation. In addition, green supply chain practices translate into overall savings for businesses when implemented.

Artificial Intelligence (AI) and Machine Learning

Artificial intelligence (AI) and AI machine learning have been a buzzword for several years, but they are just starting to make their way into supply chain management. Essentially, artificial intelligence is made up of algorithms that allow computers to act like humans, that is, to solve more complicated issues. Machine learning can take AI one step further and enable the computer to enhance its algorithms based on additional data collected for better results.

AI can be utilized in supply chain planning to analyze large data sets and analyze supply and demand trends for better procurement. AI can also be used by combining with hardware. While there is still room for improvement in autonomous vehicles, AI can help organizations stay one step ahead. One example is the use of machine learning in the aftermarket industry. In industry, machine sensors can be used to identify patterns and proactively determine when a part will break. Machine learning monitors the hardware and sends a signal that a replacement is needed before the part breaks, allowing the machine to continue operating without unnecessary interruptions.

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