AI's rise has been shockingly fast. Its practical use has transformed from science fiction to a routine but critical – and sometimes completely unnoticed – tool in our daily lives.  We speak to our phones and they understand and react.  We receive recommendations for products, activities, and TV shows with an accuracy that borders on unnerving.  

Whether you realize it or not, you enjoy an overall higher quality of life thanks to the ways that functional, production-ready AI tools have been deployed to industries all around you. It's also important to note that AI is one of the rare technologies growing at an exponential rate (as opposed to linear), meaning that next month could see improvements in the field that rival the last 10 months combined.  

One key industry that AI has the potential to impact is supply chain and manufacturing.  AI can improve many different parts of the process and then link them together, creating a coordinated flow of demand, production, and delivery.  But how exactly does AI drive supply chain and manufacturing processes?  And are these improvements enough to truly transform the industry in the near future?  Let's break down the key areas on which AI can have the most impact in the industry:  customer demand, factory operations, and supply shipments.  

Predicting Customer Demand

In order to effectively supply raw materials, manufacture products, and then deliver those products, you must be able to predict customer demand far enough in advance that you can create the products in time to deliver them quickly, but without stockpiling excess inventory.  This has been a significant challenge faced by companies throughout history.  

The best way to predict customer demand is by using historical data.  However, as the ecosystem of global supply gets more and more complex, customers have easy access to brands anywhere in the world, meaning competition is truly unlimited.  The fluctuation of prices, product reviews, and even social media trends can completely disrupt the accuracy of past data.  

This is where AI can come into play, using a combination of advanced algorithms and data to predict customer demand.  Data brokers are providing more and more customer information all the time, meaning that companies with robust AI models can ingest information from various sources and tease out the elements that affect customer demand.  

Seeing if a customer purchased the same product elsewhere or is indicating that they plan to purchase again soon, or seeing customer search terms that match your product, can all help to improve the accuracy of customer demand. This information can then be used to optimize the stocking levels of each product and to automate the ordering process.

Streamlining Factory Operations

Once customer demand is accurately predicted and the raw materials reach the factory, companies must turn this material into a sellable product as quickly as possible.  This is where improvements across the factory, collectively called "Factory 4.0", come into play.  

Among these elements are the use of automation technologies such as robotic process automation (RPA) and industrial robots. Companies can use robotic arms to automate the picking and packing process, often one of the most time-consuming and labor-intensive tasks. These robotic arms can work at a much faster pace than human workers and can be programmed to work 24/7, reducing the need for manual labor and freeing up workers to focus on more strategic tasks. This not only reduces labor costs and increases efficiency, but also helps to improve safety by reducing the risk of workplace accidents. 

Factories can improve their overall operations by collecting data at each step and using simulation to model potential actions and their outcomes. At the core of this data collection and processing is the digital thread: a digital representation of a product's lifecycle, providing a seamless flow of data that allows a complete and transparent view of its manufacturing and maintenance. This then feeds the many different AI models used to optimize the factory. 

As individual processes are improved and streamlined, the rate of benefit is linear. However, as these processes are then connected to one another, the benefits to the overall factory and supply chain start to become exponential. This is game changing for any company, especially those in highly competitive industries.

Optimizing Equipment Maintenance

One example of these benefits is the use of predictive models to effectively monitor production equipment. These models are trained through data collected by Internet of Things (IoT) devices at each stage, and the use of digital twins. 

Each piece of equipment needs to have a variety of sensors installed that can best monitor its variation in performance. With a baseline set of sensor data, an AI model can trigger alerts showing that a failure may be coming soon, and prescribe the maintenance or repair needed to prevent it. This is known as predictive maintenance, foreseeing and tackling equipment issues before they occur.

With the help of AI, factory managers can thus calculate the most efficient maintenance schedule, measuring the flow of product through the factory and comparing it to the probability of the equipment experiencing failure.  The goal is to be able to predict any issues far enough ahead that the model can coordinate with the manufacturing flow and find a time to conduct the maintenance with minimal disruption. 

This results in equipment being maintained at a "just in time" rate, without risking production halts.  The outcome?  Studies show that factories can reduce maintenance costs by up to 10%. 

Capturing Shipping Uncertainty

Once the product enters the supply chain for shipping, especially on a global scale, the most delicate part of the process comes into play.  Transferring a product from one location to another involves many unpredictable elements that can wreak havoc on an optimization plan.  

Carmit Glik, CEO of digital freight forwarder Ship4wd, explains: "Ensuring smooth logistics is all about monitoring your environment closely and responding quickly to any changes or disruptions. It's not a question of if these will take place, but when, where, and how best to meet the challenges presented and minimize their impact. The more accurately you predict these events, the more you can prepare, allowing yourself to adapt painlessly and to make sure that the movement of your products continues, come what may. In effective supply chain management, knowledge really is power."

AI can provide us with this knowledge, by taking into account more and more of these changeable elements. Things like weather have long been taken into account, but expanding AI models also have the ability to scrape the web for stories around labor strikes, road closures, military conflicts, and other elements that are likely to interrupt a smooth delivery.

Modern AI models can find this data, compare the potential effects to a shipping route, and estimate what variation might result. The technology can also make recommendations on the most efficient routes and the best times to schedule deliveries, accurately predicting interruptions and taking action to prevent them. 

Granted, with a collection of information this broad, these models take time to develop. However, improvements in AI are driving us closer and closer to this being a viable option. The subsequent reduction in delays will greatly benefit customer satisfaction.

The Road Ahead

Consumers will always need to purchase products, and so we will always be on a quest to improve customer demand prediction, factory efficiency, and shipping optimization.  Though the technology is still relatively new, AI's track record has shown that our ability to incorporate new data, models, and use cases is what will allow us to succeed in these areas.

Some of these developments remain far off, however, the future will see our global supply chain transform in ways we can only dream of now. As AI continues to advance, we move further along this path by the day.