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.
Machine learning and AI-based techniques use complex algorithms that can contribute to solving complex cost and delivery problems faced by an increasing number of enterprises today. Deploying the right ML model, enterprises can draw significant insights on how to improve and optimize their supply chain performance. By anticipating anomalies in logistics costs in real-time, AI in machine learning can help companies bring down revenue losses tremendously.
Solutions built on AI, such as the AI-Powered Visual Inspection, allows companies to take photos of cargo to immediately identify any damages and take appropriate corrective action.
Improving and optimizing logistics
From storing to picking to sending and receiving products, AI in the supply chain goes a long way in optimizing each function in logistics management. Consequently, machine learning displays a huge potential in not just bringing down the cost associated with logistics but also improving efficiency manifold.
Identifying and analyzing patterns in track-and-trace data that was captured with IoT-enabled sensors. This finding comes from BCG and establishes that such analysis done using machine learning in supply chain management contributed to $6M in annual savings.
Another important logistics solution based on AI is Intelligent Robotic Sorting. In logistics management and supply chain optimization, this technology can accurately, effectively, and speedily sort letters, parcels, and shipments.
Bringing accuracy to demand forecasting
The supply chain is riddled with uncertainties because of a highly obscure and unstable demand side. Way before the wake of AI, there was absolutely no way to deliver full value out of the supply chain because it was difficult to take into account various consumer attributes related to demand.
Thankfully, AI-enabled demand forecasting has set things right. Having information and insights on the demand side is helping reshape how the supply chain functions. From demand planning to product development, one can be sure about the direction to set the sails towards, with artificial intelligence in supply chain management.
Solutions such as automated sorting, inventory systems that run on drones, etc. propelled by machine learning in the supply chain, have made demand forecasting painless. Amazon’s automated distribution centers are one popular example of AI’s work in demand forecasting.
Enhancing supplier relationships
Selecting the right supplier is one of the biggest concerns for logistics professionals. Your entire company’s reputation rests on the efficiency and professionalism that your supplier showcases.
To reduce the risk that suppliers spell, a number of logistic companies have switched to smart AI-based solutions. AI can analyze a vast amount of supplier-related data to arrive at a credit scoring that can be used to decide the ‘worthiness’ of suppliers. They can get insights like which production centers are the quickest and most efficient in detecting errors, which suppliers have the minimum fraud rate, etc. As a result, the company can take better supplier-related decisions.
Other simple solutions, such as offering weather insights to suppliers, help them improve their transportation planning. As a result of this, the overall relations between the supplier and the logistics professionals improve.
Improving customer experience
The DHL’s research team that works towards measuring the impact of emerging technologies on supply chain performance believes that AI in the supply chain has taken customer experience to a whole new level.
When intelligent systems facilitate clean and smooth operations, eliminating the risk of potential fraud, customer satisfaction rates are bound to go up. To add to this, AI has greatly enabled personalization in the supply chain. Consider, DHL Parcel's cooperation with Amazon as an example in this context. DHL offers a voice-based service to track shipment and parcels using Amazon’s Echo. A customer can simply ask Alexa about the whereabouts of his order, and find out the details.
Wrapping it up
With endless benefits of implementing AI in the supply chain, it is time to move on from managing demand and supplies in obsolete excel sheets. Put your data to use, predict and prevent scenarios where you end up worrying about inventory going unsold and perishing on the shelves.
Join the AI and machine learning bandwagon with BluePi that helps you with on-point demand forecasting, automatic inventory replenishment, inventory re-balancing, etc. The only way forward with your supply chain is to let AI drive it. Let’s help you get started with AI in supply chain management.
Contact us today to transform your organization into an intelligent organization.