Using artificial intelligence in the supply chain

Leveraging artificial intelligence (AI) for supply chains is an important next step to lower costs, improve productivity and drive growth by helping businesses reduces time-to-market.

There are many opportunities to utilize AI along the chain from buying raw materials/components, converting them into finished products, selling to customers and delivering to end customers. Supply chains, generally, still comprise large amount of repetitive manual tasks and this is where AI can offer the most value.

AI can be used in selling to customers using an AI-driven software platform, warehouses, transport, analysis of data and many other areas. AI allows companies to reallocate time and resources to their core business, and other high value, judgment-based jobs by using AI for low value, high frequency activities.

In an AI-driven selling platform, the chat bots handle many of the sales, customer services and operations tasks traditionally done by humans, for example, interacting with buyers, taking down their orders and passing them on along the supply chain. This way, there is significant reductions in staff costs and also can help to overcome manpower shortage. Moreover, this solution is very applicable to green-field markets where there is explosive growth and multiple languages are required.

In warehouses, distribution and fulfillment centers, AI can be seen in the use of robotics and sensors for conveying, stacking and retrieval systems, order picking, checking on stock level and re-ordering when stock is low. Furthermore, powerful algorithms also allow AI to automatically adapt in real-time to events in the supply chains, for example the arrival of new orders over the Internet for delivery in a few hours, changes in manufacturing schedules, or even a hiccup in the transportation schedule.

Amazon is using robotic shelves in warehouses where robots the size and shape of a footstool carry shelves on top. These robots can glide quickly across the floor to rearrange the shelves in neatly arranged rows or bring them over to human workers, who stack them with new products or retrieve goods for packaging.

Amazon’s robotic shelves also allow more products to be packed into a tighter space. They also make stacking and picking more efficient by automatically bringing empty shelves over to packers or the right products over to pickers. The process is more efficient than having humans walk around, so it also a good example of how automation can be combined with human labor to increase productivity.

Autonomous vehicles and drones, for example, deploy a combination of sensors and algorithms to perform the complex work of driverless navigating. DHL is using autonomous forklifts and other self-driven equipment in warehouse operations. The next step for autonomous vehicles in logistics is to overcome regulatory and security challenges to deploy them on public roads for goods delivery operations. In the US, the use of drones is governed by the Federal Aviation Administration’s regulation known as Part 107 that went into effect on 29 August 2016.

Supply chains are generating a huge amount of data and rather than let them go to waste, AI can help businesses make sense of them so that better decisions can be made. AI is able to quickly analyze and organize this data to enable users to see trends, and gain a better understanding of the many variables in the supply chains. Users are thus able to anticipate future scenarios and plan accordingly for uncertainties.

Driving force of AI

Powerful algorithms are fueling the rise of using AI in supply chains. Algorithms are instructions to the robots, drones, and autonomous vehicles etc. for calculations, data processing and automated reasoning. In a nutshell, algorithms give instructions on what and how to do in order to reach a specified end goal. More advanced algorithms, rather than follow only explicitly programmed instructions, can even go a step further in allowing AI to learn on its own in what is known as machine learning.

Using algorithms that continuously and repeatedly learn from new data, machine learning allows AI to find hidden insights without being explicitly programmed where to look. Machine learning is a method of data analysis that automates analytical model building.

The pioneering technology within machine learning is the neural network, which mimics the pattern recognition abilities of the human brain by processing thousands or even millions of data points. This technology is not just about optimization

Take the example of supply chains. The algorithms are able to engage in forward thinking to predict all the volatility in the industry, come up with solutions for different scenarios and then base on the available data, choose and execute the most efficient solution. Whenever the AI is faced with a new situation, the algorithms are also adept at making real-time adjustment to pre-programmed instructions. Moreover, compare to humans, the speed and decisiveness of making decisions for AI is so much faster, because for one thing, AI is void of emotion and biasness.

As a final testament to the power of AI, consider the following example. In January, two researchers from Carnegie Mellon University developed an AI poker player that beat four world champions and won US$1.77 million in poker chips. This is groundbreaking as it signals the ability to deal with incomplete information and to deal with situations that require bluffing and an opponent that generates misinformation.

AI can process huge amount of possibilities and can outthink humans in terms of unpredictability if the algorithms are programmed correctly.

AI is the future of supply chains. AI strengthens a company’s core business and opens up new opportunities that can even lead to a new business model.

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