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AI in Supply Chain Management & Logistics

Updated: Aug 9, 2022

When it comes to running a business, it’s not enough to just be smart. You need to be good looking. Enterprises working along the supply chain today are heavily dependent on their far-reaching network of suppliers and partners for support, expertise and an efficient supply chain.

To achieve this, they need the right technology to incorporate strategic and sustainable considerations while also managing the various risks in such complex processes.

AI-driven supply chains are the next big thing for Supply Chain. By leveraging AI, smart supply chains can help automate our mundane tasks, and we can use this time to focus on tasks that will enhance our quality and increase efficiency.

Although early supply chain adopters will be adopting AI technology in the next 3-5 years, the majority of companies are being cautious because they don't yet understand how they can benefit from it.

Below article enables you to understand How AI can revolutionize your Supply Chain.

Let's first understand / revise Supply Chain Context

Supply Chain Management

Supply chain management is the process of coordinating, planning and controlling all the elements of a business’s supply chain to provide good customer service, optimize costs and as well as reduce waste.

Physical flows involve the transformation, transportation, and warehousing of goods and materials. They are the most evident and visible pieces of the supply chain. But just as important are information flows.

Information flow is the movement and manipulation of information necessary for supply chain management, but it can also be used to measure and analyze performance..

How can AI be used in the supply chain?

Supply chain management is a complex subject, and it consists of many smaller processes and interactions. Many of these processes are ripe for artificial intelligence-driven automation solutions. The immense opportunities for artificial intelligence-driven supply chain management lay within these processes..

Integrating machine learning (ML) in supply chain management can help automate many mundane tasks and allow the companies to focus on more strategic and impactful business actions.

Below you can find current possibilities and applications of artificial intelligence in the supply chain.

AI opportunities in Supply Chain Management Areas

Enhance Human Workforces

Automate shop floor and warehousing orperations
Streamline manufacturing processes, improving throughput
Spot risk factors and help safer operations

In general, it seems that AI is being positively received by the manufacturing and warehouse industry. It is an intelligent way to improve efficiency, safety, and cost in these areas, while eliminating menial tasks and improving performance.

In other words, AI has the potential to introduce unprecedented levels of speed and accuracy into warehouse procedures. And by taking over many of these tasks at a much faster rate than humans can, it opens the door for efficiency improvements as well.

Supply/Demand Forecasting

Demand-driven manufacturing
Determining profitable pricing
Improved resource planning

AI and predictive analytics algorithms can make the supply chains leaner by forecasting inventory needs, including re-balancing across the network with continuous optimization based on the supply and demand.

This information loop makes it possible to adjust the stocks and supplier planning. Thanks to AI technology, real-time information, planning, and distribution systems can be reconfigured to be proactive without waiting for specific order placement triggers.

Inventory Optimization (Turnover and Wastage)

Keep inventory as low as possible
Introduce self-managing inventory systems
Enable planning ahead and avoid stock-ups or shortages

It can be inspiring to look at the supply chain model and see all the different, independent parties that make the global logistics network click. When you look up closer, though, there are places where you'll see waste and unoptimized processes.

Take food supply chains and the fact that approximately half of the food wastage occurs in the distribution stage. To ensure that all your orders can be filled in without running out of certain items, suppliers, manufacturers, retailers, and wholesalers along your supply chain hold more inventory than they need as a safety margin.

AI can help here by providing prescriptive analytics that considers supply and demand so more accurate planning can take place, waste can be reduced, and costs can be cut.

Quality Control and Smart Maintenance

Improved end-product quality
Reduced cost and increased accuracy of quality assurance
Avoid loss of production with high uptime and availability

In the identical way that detecting delicate developments resource in higher provide chain planning, inspecting particular parameters with AI permits you to predict, count on and stop great issues. A normal instance is that businesses can introduce AI to promote a excessive degree of precision in manufacturing the use of picture analysis.

A visible inspection straight on the manufacturing or meeting line can seize traits that ought to now not be detected in any other case in many processes. The availability of high-res cameras, coupled with effective photograph attention technology, has dramatically reduce the real-time in-line inspection cost.

Additionally to photograph recognition, sensor-based methods used for product excellent inspections convey uniformity and effectivity in fine control.

Augment and Enrich Data

Spot and react to small trends
Optimize processes and build new strategies
Use external data points to get a new angle in planning

The use of artificial intelligence to improve supply chains could benefit from the data sets generated from cameras, IoT sensors, logistics, and transportation systems. Supply chains could benefit from the wide range of data sets created by these systems.

Small details like order frequency, delivery vehicle routes, scheduling trends, and more may be spotted, analysed, and planned for quickly. Artificial intelligence models trained on historical data and external data are excellent at spotting patterns or trends.