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Roaring magnanimously, artificial intelligence has emerged as one of the most widely used technologies in every industry today. Looking at the graph below, it is clear that artificial intelligence has established a strong foothold in supply chain management as well. In fact, AI in the supply chain comes at the third in the charts for driving revenue from the investments made by the company.
A growing number of enterprises today are turning their trust towards machine learning in AI and the reasons are way too many. The number one reason being the ability to maintain efficiency when running global operations.
One cannot even dream of running a supply chain on a global scale if it was to be done manually. Imagine the kind of cost and resources that would be involved. Plus, you would never obtain error-free, smooth operations. There will always be a high risk of human error in the process.
As the volume of data fed into the supply chain grows, the need for more sophisticated solutions surfaces. Companies like DHL use AI and machine learning in the supply chain for predictive network management. This capability analyzes 58 different parameters of internal data to identify the top factors influencing shipment delays. Couldn’t be possible solely with human intelligence!
Using machine learning and AI in supply chain :
Traditionally, most businesses are relied on business intelligence to monitor and manage complex supply chain optimization. However, these systems relied on historical or ‘post-mortem’ data, solely. As such operations could be optimized only after they were completed, monitored, and analyzed for performance gaps. With the advent of machine learning, this ‘lag’ in performance analysis has almost vanished. Companies using AI in the supply chain can now proactively examine transactional data in their processes. Insights derived can be used in real-time to detect performance gaps, errors, etc. Consequently, as a result of the proactiveness that comes in with machine learning in the supply chain, revenue losses can be prevented even before they happen!
Let’s consider an example of the order-to-cash cycle in supply chain management. Usually, a marine shipment from the United States to India takes around 40 days. For some reason, a delay occurs and it takes 70 days instead. This delay can cause a lot of operations to come to a halt and trigger a series of challenges. Without machine learning-enabled analytics it could be difficult to figure out what caused this delay. On the other hand, driving the supply chain on AI could mean predicting such delays well in advance, and taking data-driven informed decisions to manoeuver around them. All in all, you would end up reducing revenue losses that may occur from returned or canceled orders because of shipment delays.
How to improve supply chain with machine learning and artificial intelligence?
Apart from the tremendous increase in data volumes, other trends that have propelled in AI in the supply chain include speed, big data access, as well as algorithmic advancements. Based on these, a number of really valuable use cases for AI in supply chain management can be created. In this post, we’re discussing a few ways in which enterprise companies are seeing success in the supply chain with artificial intelligence.