AI in Supply Chain: Use Cases, Benefits, Examples, and Case Studies
Consumers are becoming increasingly demanding. They want fast delivery of goods at low costs. Conventionally, the supply chain has been driven by just-in-time production. But now, it must also adapt to increased demands for flexibility, shorter delivery times, inclusiveness, and standardization. This all can happen if supply chain companies will adopt advanced technology such as AI and ML in their standard operations.
With Artificial Intelligence (AI) playing a significant role, customers can now expect more options, quicker adaptability to changing markets, and altered product mix by businesses.
There are more advantages that need a comprehensive analysis. This article explores the scope of AI in logistics and supply chains and the benefits on offer.
What is the Relevance of AI in the Supply Chain?
A digital supply chain is a complex, interconnected web of business activities, which is automated and managed by several stakeholders. The process begins with sourcing raw materials from suppliers and ends when the product reaches the end customer.
It is a prime example of an environment where artificial intelligence (AI) can help improve efficiency and reduce costs. Businesses can leverage AI to make better decisions about the purchase of materials, inventory storage capacities, production plans, and more.
By understanding the use of AI in the supply chain, the United States Cold Storage, one of the leading cold chain companies, developed an automated appointment scheduler to accurately predict the arrival time of carriers and the time taken to service them while scheduling an appointment.
Here’s a video that talks in detail about the Intelligent Appointment Scheduler.
AI in Logistics and supply chain allows businesses to improve their supply chains by identifying patterns and trends within the data they have collected over time and providing recommendations based on those patterns.
One of the most well-known examples of AI in the supply chain is the use of autonomous forklifts by German logistics giant DHL. They have already experienced increased operational efficiencies and safe working standards in the warehouses.
DHL believes almost 30% of material-handling equipment will have robotic automation capabilities by 2030.
How Can AI and Machine Learning be Applied to Supply Chain?
Digital transformation in supply chains will lead us to a future where robots and automation measures will become the norm in the logistics industry. No more worrying about replenishing stocks just in time or spending resources on manual tasks that you can easily automate.
Here’s how AI and Machine Learning can be applied in different areas throughout the supply chain process:
- Organizations must be aware of how much inventory is always in stock. You could use machine learning algorithms to track the quantity sold, what amount got shipped out, and the amount ordered/received by customers. These algorithms will also be able to predict the demand based on historical trends and seasonal changes.
- There are often discrepancies between ordered and received items when receiving shipments from suppliers or distributors. AI helps analyze the product code numbers on each package and ensure everything matches what’s listed on the invoice before it gets unloaded from the truck into storage or onto shelves for sale.
- AI and machine learning can also increase efficiency and speed up the supply chain processes. This is especially true if you have an automated workflow management system. It will allow you to automate tasks that were previously performed manually, which will result in higher productivity and less human error.
How is AI Used in Supply Chain?
Artificial intelligence has wide-ranging uses in supply chain operations. AI-enabled tools can help businesses improve the efficiency of their existing workforce.
They can also help reduce the impact of labor shortage, especially in events like a pandemic where large-scale human gatherings are not possible. In this context, a digital supply chain also plays a significant role to reduce the number of processes done manually and also helps to detect early problems and fix them before it becomes critical.
The use of AI is applicable throughout the different stages of the supply chain process. It starts right from manufacturing items to shipping them to the customer. For example, IoT-enabled devices help track the status and health of shipments in transit.
Similarly, AI also helps retail businesses understand the behavior patterns of their customers. The related data will help them target their customers with customized offerings. It can directly impact sales growth and increase revenue.
These tangible benefits also have the backing of extensive industry research and findings. For example, according to McKinsey research, early adopters of AI in the supply chain space have found their logistics costs decrease by 15%.
The same study also found that such companies witnessed a 65% better service delivery than their slow-moving competitors. Market growth statistics thus say AI in the supply chain is critical.
Areas where AI and Machine Learning are being Used for Efficient Supply Chain and Operations
AI and Machine Learning have a broad scope in supply chain and operations -
Predictive analytics is an application of AI in the supply chain that is ideal for demand forecasting. Accurate forecasts help maintain optimal inventory levels and reduce holding costs. The insights on offer are an outcome of the historical data and patterns. A well-balanced inventory will ensure you meet customer demand adequately.
The production process in supply chains can be complex, requiring advanced tools and technology. Machine learning algorithms analyze production data to identify areas of opportunities and optimization. They can also identify and deal with disruptions before the latter affect the production process.
AI in the supply chain helps reduce customer service-and-support time by predicting customer behavior with great precision. It can also increase efficiency by automating tasks done manually in the past. For example, let’s say you are running an online business where you sell furniture. You could use AI to track customer orders and predict what items they will order next based on previous purchases.
AI in the supply chain allows warehouse managers to focus on more critical tasks that require human judgment. This application of AI in the supply chain helps improve warehouse management by automating mundane tasks such as inventory planning and forecasting. These can ensure that products get shipped quickly and accurately to customers.
One of the advantages of using AI in supply chain management is predicting demand and supply more accurately. This allows businesses to plan better and avoid under or overstocking products. It also means that companies will be able to better estimate costs and inventory levels, which helps them save money on storage space or labor costs while improving customer service.
What are the Benefits of AI in the Supply Chain?
While there are several benefits of AI in the supply chain, let’s look at the essential ones in detail.
Offers Advanced Data and Insights into Supply Chain
AI benefits from the use of near real-time data in the supply chain. It helps save time and money, reduce waste, and increase efficiency. It also allows businesses to respond intuitively with real-time information about their inventory levels and stock location.
This information enables companies to make fast decisions, so they don’t have to wait till month or quarter-end to find out how much stock they have at each location. When planning the delivery of goods on particular days and times, supply chain scheduling plays an important role. Similarly, insights obtained from supply chain analytics also make it a lot easier to help businesses make good decisions.
Decreases Operational Costs
Artificial intelligence can decrease operational costs by analyzing data and determining which actions are necessary. It helps businesses automate processes that would otherwise require human input. AI in the supply chain allows companies to save money on hiring employees while still achieving their goals.
Reduces Shipping Time
Digital transformation in logistics helps you with everything from determining which routes your trucks should take to how quickly they should travel along those routes and when they should arrive at their destination.
These insights can come from the data generated through supply chain operations. It can also help predict where demand will be highest and ensure that no single location gets overloaded with shipments or runs out of stock for too long.
Enables Safe Warehouse Operations
It is one of the many benefits of AI in the supply chain. It finds wide-ranging uses in supply chain operations to ensure safety. Sensors and cameras allow for a more efficient and safer workflow.
For example, sensors can detect when a forklift or pallet jack has been left unattended, allowing workers to quickly retrieve it if necessary. It prevents accidents from occurring due to lack of supervision. You can also monitor the temperature inside your warehouse to avoid fire incidents in case of high temperatures.
Enables Improved Storage Efficiency
AI in the supply chain gives you a better sense of how much space you need for storage, how long it will take to move items through your supply chain, and what kind of equipment you need to keep things running smoothly.
These systems can also help you understand which products are best suited for different shipping containers and how many boxes each product needs to ship efficiently.
What are the Disadvantages of AI in the Supply Chain?
While there are many advantages to using AI in supply chains, it is essential to note that the technology does have some drawbacks. Here are some Disadvantages:
One of the main concerns about using AI in a supply chain is that the algorithms that run on computers can learn and make mistakes. If an algorithm got trained on bad data, it might not be able to recognize there’s a problem with its analysis.
For example, if an algorithm trained on data from one time period is applied to another time period, it may not work correctly. It is because there are differences between those two periods.
Another disadvantage of AI in the supply chain is that it can’t always account for human error or unpredictability.
This means that even though an algorithm may appear to be working perfectly when tested on historical data, it could fail when applied to real-time situations where humans interact with each other and make decisions based on factors like their moods, emotions, or physical health at any given moment.
Many are discontented about the loss of jobs to automation. AI in the supply chain will automate several manual tasks in the coming years. It will directly impact humans, who businesses may no longer require.
Which Companies are Using AI in Supply Chain?
AI in the supply chain is increasingly becoming popular across global businesses. Let’s look at some examples.
Gramener is a design-led data science company that excels in building custom data and AI applications for supply chain and logistics companies. Gramener has served fortune 500 companies and has customer logistics data applications such as digital twin solutions, computer vision-driven defect detection solutions in manufacturing, warehouse optimization solutions, supply chain visibility solutions, and more.
Symbotic builds and designs AI-powered robots to help businesses automate their workflows. Their solutions further enable enterprises to support their supply chain with advanced solutions. Symbotic’s offerings are primarily towards warehouse automation to reduce costs and improve efficiencies.
Coupa is a well-known company using AI in the supply chain. Coupa’s solutions help businesses manage their transactions holistically. Their offerings are helpful for processes across the supply chain, from procurement to payments. Companies can optimize their supply chains to enable improved decision-making and reduce risks.
The last name in this list of companies using AI in the supply chain is DataArt. It has a range of AI and ML-enabled solutions designed to improve operational efficiencies of businesses. It helps them identify the shopping behavior of customers and better engage with them through customized solutions. They also support the real-time analysis of supply chain data to streamline processes.
Examples of AI in Supply Chain
Here are the different examples and use cases.
AI in the supply chain helps you make accurate forecasts of future demand based on current trends.
Machine learning algorithms allow for a precise analysis of historical data such as sales numbers, consumer trends, and seasonal events to predict changes in consumer demand and ascertain how much inventory to order from suppliers and when to order it.
AI in the supply chain can recognize relationships between different datasets and identify fluctuations in demand.
Route Efficiency and Optimization
Route efficiency is one of the popular examples of AI in the supply chain. When customers receive their orders sooner than expected, they are more likely to give positive reviews and recommend your brand to others.
AI-enabled software can analyze your current routes for inefficiencies and suggest new ones save time and money.
This means you will be able to reduce fuel costs, streamline routes, and get products out faster so that they arrive at their destination sooner with less wear on the vehicles themselves.
We’ve shown below the supplier network for a plant, where each circle represents a supplier. The size of the circle represents the volume of supplies purchased from that supplier. The larger the circle, the bigger the purchases. The color of the circle represents the purchase price. Red indicates a high price, yellow is neutral, and green is low.
This makes it easy to spot which routes are unprofitable, which distant suppliers are not worth traveling to, and which ones to negotiate the most with.
Improved Vehicle Health
AI in the supply chain is helping improve the health and longevity of vehicles by keeping them on the road longer. Fleet managers can keep their cars in top shape by utilizing predictive analytics, ensuring they’re ready to go when needed.
Most importantly, the vehicles will remain available when needed the most. This can help prevent issues like breakdowns or accidents from happening in the first place, which means fewer vehicles will need replacement due to damage or neglect.
How will Artificial Intelligence Change the Future of Supply Chain?
Artificial intelligence is changing the way we work, and the supply chain is no exception. Supply chain management involves planning, forecasting, logistics, production management, etc.
It can also become increasingly challenging to manage when companies grow in size and complexity. The Future of AI in the supply chain presents a scenario where automation will play a prominent role in how companies manage their supply chains.
For example, imagine running a logistics company that ships products from point A to point B. You know from experience that if you don’t order enough products at point A, you will run out before satisfying all your customers.
But if you over-order at point A, some of it will go unsold, costing you money. AI in the supply chain helps with these challenges by providing real-time data analysis, which allows managers to make quick decisions.
Imagine that artificial intelligence has analyzed your data and accurately predicted how much to order at each time interval based on past sales trends.
This means that the AI can forecast whether there will be enough stock left over when it gets delivered! And because it knows exactly how much should be ordered at each interval, you also save yourself from over-ordering items.
AI in Supply Chain Case Studies
At Gramener, we have helped supply chain leaders improve efficiency by up to 30% using our customized AI & ML solutions.
Our data & analytics applications enable logistics companies to boost their operational excellence & build a robust warehousing & distribution network.
Some of our proven industry 4.0 offerings include:
- Supply chain data strategy
- Improve production performance
- Manufacturing defects detection
- Warehouse optimization
- Track supply chain visibility
We helped a German manufacturing company integrate a robust data roadmap aligned with its business objectives by identifying analytics investment opportunities & performing data maturity assessments, saving up to $30 Mn.
We helped develop a digital replica of the drug manufacturing process for a pharmaceutical major, saving up to $6 Mn. This enabled the client to gain more control over the production process & make real-time adjustments using actionable insights obtained through data mining.
United States Cold Storage (USCS), one of the largest cold chain companies in warehousing & logistics, saved $1.2 Million in operations. They installed an Intelligent Appointment Scheduler in 26 warehouses to automate the truck appointment process. The predictive AI solution reduced warehouse dock congestion, reduced turnaround time by 15%, and improved customer service by 86%.
There is a heavy emphasis on supply chain management in present times. Businesses are spending money on improving their supply chains as they grow. AI can help with this process by automating many mundane tasks and leaving room for companies to focus on the bigger picture. As artificial intelligence improves supply chain management, businesses will be able to run safer, more efficient operations and better compete in today’s economy. The future of AI in the supply chain thus looks very encouraging.
Gramener is a leading provider of AI-powered supply chain solutions that revolutionize the way businesses work. Connect with us today to know more about our offerings.