Use of Machine Learning in Logistics and Supply Chain: Key Use Cases and Challenges

Making sure your logistics is optimized to the highest possible level is a good way to make your business more efficient. This is why companies are turning to machine learning in supply chain as a way to cut costs and operation time. However, this is a complex technology that has its challenges and risks. It’s best to learn it well before you adopt it for your own company.

This is why Amconsoft would like to share our knowledge about using ML to benefit your supply chain. From cutting down the time used for daily operations to gathering unique, high-quality data that informs your decisions, machine learning covers all aspects of the supply chain. Through our work with AI and machine learning, we’ve familiarized ourselves deeply with the tech. 

To keep things balanced, we’ll cover the positives and the risks of this technology and present real-world use cases to show you why ML is worth it. The article will also cover the implementation process, as well as highlight the value of professional vendor help. Without further ado, let’s begin.

Benefits of Implementing Machine Learning in Supply Chain

• Increased efficiency
• Cost reduction
• Improved decision-making

Machine learning doesn’t just enable automation, which greatly speeds up warehouse processes. It can also detect problem areas that you would have considered okay. The deeper insight means more optimization, with the robot systems dedicated to the areas that need help.

Your algorithm will track your shipments and suggest route optimizations, as well. This results in faster deliveries with better load factors. Instead of trucks running empty for nearly a quarter of the way, your fleet can be full at every leg of the journey. Thus, maximizing the beneficial use of your assets is one of the core boons of ML for supply chain management.

Along with automation and more efficient use of your fleet comes a decline in operational costs. For one, you will run fewer trips with shorter, optimized routes, majorly cutting down fuel expenses. Using live analysis, you can adjust your routing on the fly and avoid heavy traffic areas, saving time on each trip.

It’s easier to avoid issues in your supply chain if you can create an accurate forecast and predict potential risks. However, a human-made forecast is unlikely to account for all variables. On the flipside, machine learning in supply chain can track the tiniest fluctuations in the market and your business. With constant updates, it never becomes irrelevant or imprecise.

As a result, you can schedule maintenance, increase the size of your fleet, or adopt new technologies. All of these decisions will be based on solid data and thus minimize risks, making decision-making less uncertain.

Challenges in Adopting ML in Supply Chain

Now that we’ve discussed the advantages, it’s only fair to address the main hurdles of adopting ML. We’ll cover the potential problems and ways to navigate them using Amconsoft’s experience with machine learning.

Data is the fuel that makes machine learning tick, meaning that a lack of data or even low-quality sets create major risks. They prevent proper forecasts and interfere with the system’s learning. Thankfully, you can avoid this by using cloud computing to sync all data from your network and feed it into your algorithms. 

It does require technical know-how to implement properly, but that’s where you can rely on Amconsoft.

This is only a problem regarding the initial setup cost and how intimidating it might look. You’ll be spending money on data harvesting, integrating machine learning techniques in supply chain management, and staff training. However, the long-term return on investment will more than make up for these initial expenses. It’s just a matter of agreeing to the admittedly lofty initial budget.

With all the data you’ll amass, securing it is paramount. As the data is centralized, a single breach could result in the loss of invaluable information. Since supply chains involve both you, the business, and your customers, you’re left responsible for multiple companies. 

Thus, it’s crucial to put extra effort into your cybersecurity. From encrypting data to layering access, there is no such thing as too much security. As you can see, the typical risks of machine learning in logistics and supply chain can be mitigated with appropriate preparation and tech knowledge. Thus, having a trusted partner makes it a much more reliable endeavor.

9 Use Cases for Machine Learning for Supply Chain

Now that you have a balanced picture of the positives and negatives of ML, let’s talk about how you can actually apply it. These use cases cover the variety of machine learning and show how it affects every stage of your supply chain. It’s a comprehensive look at using AI in logistics and how it transforms processes.

Inventory Management

You must be ready to provide the needed products at a moment’s notice to keep up with fluctuating demand. Machine learning can help predict these dips and peaks to ensure that you always have sufficient product levels. It means you regulate your manufacturing levels to avoid overstocking your warehouses or ending up with insufficient stock.

Warehouse Management

Speaking of warehouses, machine learning in supply chain management helps revamp all the mundane processes associated with them. For one, you can pretty much eliminate paperwork and digitize all the bureaucratic aspects. Then, you’re able to automate a lot of processes, such as restocking requests, scanning incoming packages, and assigning jobs.

Logistics and Transportation

One common use case when it comes to logistic purposes is to improve routing. Machine learning can do more than find the optimal route for the moment. It will adapt it based on new factors. Thus, you can continuously benefit from faster, more efficient hauls.

Production

From automating smaller processes to scheduling production starts and halts, ML helps keep your assembly lines running smoothly. This means cost savings, as well as the ability to maintain the desired level of product output.

Chatbots

A perfect solution for customer service, using machine learning to cover simple communication, is already becoming standard. Surveys say that pretty much 90% of customers have interacted with chatbots. Using a large language model to quickly answer customer queries saves the supplier time and lets you get through more requests in the same amount of time.

Security

An important use of ML is conducting risk assessment, as well as creating a smart permissions system. The application can determine who’s accessing the data, what credentials are provided, and whether or not the access should be granted.

Business Process Visibility

Using that same model of layered access, your company can provide all the necessary information to clients and partners. Without revealing any confidential information, you increase the transparency of your processes. Giving your partners vision into your operations establishes trust and strengthens your collaboration.

Demand Prediction

As we’ve mentioned before, ML algorithms can very accurately understand and forecast the market situation. Using these forecasts, you can gauge demand quite precisely and adjust your supply levels accordingly.

Predictive Maintenance

Similar to predicting supply and demand levels, you can set up your ML algorithm to warn you about needed maintenance ahead of time. This way, you avoid downtime that comes with unexpected equipment breakage.

Implementing ML In the Supply Chain

Having covered the use cases of ML, we should now discuss how to implement ML and AI in transportation industry. We’ll cover the process in seven simple steps.

Collecting Data

Before anything else, you need to establish data collection and storage practices. This will help you accumulate enough workable information to feed into your algorithms and refine them. The more you get – the better.

Prepping the Data Sets

The information you collect then has to be randomized and cleaned up for use – removing duplicates, resolving any errors, or simply splitting it based on purpose. The latter means separating the data into training and testing batches. Training is essential; thus, any datasets for it should make up about three-quarters of the total amount.

Choosing a Model

In theory, you can use a pre-made model for your business, as there are many available on the market. However, you can also get the help of a development company like Amconsoft to craft your own model. This will result in the solution being tailored to your business and only using your data – making it more precise and able to accomplish unique tasks.

Training Time

This is where you use the data you’ve amassed to basically “teach” your algorithm to accomplish what you want. The more time you put into it, the better results you will get, so this step is particularly crucial.

Test Your Solution

Using the other set of data, verify how precise your forecasts are, whether the automation is working smoothly, and if there are any obvious things that need fixing. You can and should do testing later on as well, but this is the first point and determines how useful ML will be for you at launch.

Configure It

Changing the parameters of your model allows your engineers to get better, more precise results. While you can do it in the future as well, it helps to refine the setup just a bit before you start using it on your system.

Get Started

Use your algorithm to get an accurate prediction or integrate it with a management app to streamline processes. It’s not the testing stage anymore but you should still be on the lookout for ways to improve it and the results it provides.

Future Trends in Machine Learning and Supply Chain

One exciting trend for AI/ML business solutions is establishing generative models that communicate without human oversight. You could use a virtual chatbot to relay shipment information to a partner, where their own model would receive it and create the respective documentation. All that with no need to involve employees that can focus on other tasks.

Another big trend for 2024 is low-code platforms, which make work with the supply chain more accessible. Plus, it speeds up development time by relying on interconnected APIs to deliver functionality.

Lastly, data quality will come to the forefront. With millions of devices that generate data – computers, IoT sensors, even the vehicles themselves – it gets hard to find the valuable nuggets. The trend will emphasize data management using an iterative approach. Crafting a value-based roadmap is recommended so that you can clearly prioritize certain datasets for use in ML.

Amconsoft’s Experience with ML and Supply Chains

Our team offers AI and ML development to boost prediction quality, provide real-time data collection and analysis, and cut operating costs. We focus on delivering genuine results that impact every aspect of the supply chain inner workings. Our technique also involves support for self-driving vehicles for truly futuristic results.

Using ML, we can analyze your supply chain’s efficiency. This will result in higher load factors, faster dispatching, and more effective routing. By taking advantage of our expertise in warehouse automation, you can completely revitalize your approach to inventory management.

Through our years of work in the market, we’ve developed numerous supply chain solutions. Here is one example that was heavy on automation and improving the internal processes. We revamped the company’s systems, boosting its performance. For example, we doubled the load factor of each vehicle, reduced manual work by 37%, and cut delivery times up to 30%.

We also helped Inn.Logist build a powerful logistics platform, which allowed for real-time order management, data recording, and supported a mobile app for drivers. As a result, the company modernized its operations and could structure work more efficiently.

See the Benefits of Machine Learning in Supply Chain

As you can see, machine learning can be a shot in the arm for logistics. From automating operations to reducing fuel usage, it uses analytics and interconnected systems to achieve more with less. However, to get the complete package while working with ML, it’s key to have a trusted development team.

Having competent engineers who can train your model with high-quality data will inevitably yield better results. Besides, the continuous improvement and testing of your algorithm’s capabilities also requires attention and expertise.

This is why you can turn to Amconsoft for your machine learning development needs. With seven years on the market and countless consultations and projects, we’re deeply familiar with the technology. If you wish to cut your operational expenses, optimize deliveries, or automate parts of your supply chain – get in touch with us.