How Machine Learning Impacts Logistics and Supply Chain Management

In recent years, supply chain management has changed dramatically and the pandemic is one of the main reasons behind that change. Business owners have suddenly found themselves unprepared for newly formed challenges (i.e. shortage of products or delivery delays) and some of them are still having a hard time adapting the business to a new reality. And even though many businesses managed to recover, there is also a fair number of those that struggle – and the adaption of ML may be the key for them.

Machine learning has proved to be of immense assistance to businesses across various domains and logistics is no exception. Learn how ML transforms modern supply chains and how it helps business owners enhance resilience.

Five key components of a supply chain

In order to better understand how exactly machine learning assists in supply chain management, it’s important to first define the main components of a supply chain. CIO lists five components of any supply chain that are:

• Planning: includes planning not only the needed resources but also defining those metrics that will help monitor the efficiency of a supply chain.
• Sourcing: involves selecting suppliers and establishing management of related processes, such as ordering or authorization of supplier payments.
• Manufacturing: in addition to creating products or services, this stage also includes packaging for shipping and scheduling for delivery.
• Logistics: delivering products or goods, invoicing customers, and receiving payments.
• Returning: management of products that were returned back or that were defective, excessive, or unwanted.

Needless to say, every stage involves dozens of processes and every process impacts the final result and customer satisfaction. However, it’s highly challenging to manually manage all these processes and timely identify any issues or warning indicators.

The main challenges of the logistics industry

Even though every business is unique, there are several common challenges that almost every business within the industry faces. Let’s look at each of them below.

Lack of flexibility

Due to various reasons, many businesses in logistics lack the flexibility which is highly needed these days. Whether anything unexpected happens (i.e. the Ever Given container ship blocking the passage in the Suez Canal in 2021) or whether the customer demand keeps rising exponentially, some business owners are simply not ready for it and their legacy processes do not work for modern conditions.

There may be a disruption in a supply network or tariffs may go unexpectedly high – and businesses should be resilient enough for such challenges.

Organizational immaturity 

It often happens that a business is not mature enough and hence, cannot timely react (or proact) to occurring disruptions. As well, a business may not be scalable enough or may lack financial flexibility. Hence, a business owner needs to perform a constant evaluation of business maturity and its readiness for both forecasted and unexpected changes.

Guesswork in demand forecasting

Predicting customer demand is one of the biggest challenges in logistics since it impacts several other areas, like price formation or warehouse management. Even though the majority of businesses now rely on the data as a base for demand forecasting, there are still businesses out there that also rely on guesswork or do not support their decision-making with data. This may lead to significant financial losses and poor customer experience.

Machine learning in supply chain management

Now that we’ve overviewed the main challenges of the industry, let’s talk about how machine learning makes a difference. Note that you don’t have to implement ML straight away or automate everything. The main idea here is to identify the biggest problem areas and evaluate whether ML can recover the issue and bring you long-term tangible benefits.

Inventory and warehouse management

Inventory management is one of the core processes that impact the company’s revenue. If there are too many or not enough products, the company will most probably lose money – and nobody wants that. 

In a perfect world, a business always knows what products and in what quantities the customers would demand in the nearest future. Thanks to machine learning, data-based forecasting has now become a reality and businesses no longer have to rely solely on their guesswork. Therefore, the biggest benefit of ML for inventory management is data-based forecasting of the future demand for a product or service. Based on these forecasts, a company can precisely stock their inventory and avoid over- or under-stocking.

Other big advantages that machine learning brings to inventory and warehouse management are:

• Detection of package defects (visual damages) by using computer vision;
• Automation of warehouse operations (i.e. product sorting) with the help of robots;
• Automation of manual work (i.e. work with the documents);
• Monitoring storing conditions (i.e. temperature monitoring) and immediate notification in case of an issue.
• Price planning: by analyzing the possible demand, business owners can better adjust their pricing.

Logistics and transportation

Another area of supply chain management where machine learning comes into play is logistics and transportation. The main benefit of ML here is route optimization and route calculation – for that, a machine learning model analyzes traffic, weather, and other conditions that impact the driving time. In this way, ML significantly speeds up the delivery process which leads to a better customer experience. 

As well, machine learning simplifies the process of tracking goods during transportation and also allows monitoring the transportation conditions (which may be highly important in the case of fragile goods). 

Production process

When it comes to product manufacturing, machine learning brings the following advantages:

• Predictive maintenance: with the help of computer vision and equipment monitoring, employees can timely identify if anything needs maintenance and they can prevent equipment from malfunctioning.
• Monitoring the correspondence of products to the required quality level.
• Automation of the production process and faster product delivery.

In general, machine learning helps businesses produce products in a faster and more accurate way, avoiding the majority of common issues like packaging defects or recovering out-of-service equipment.

Customer service

Even though customer service is not considered a component of a supply chain, it can be called a critical part of it since excellent customer experience is the ultimate goal of any business. And with the advancement of technology, customer expectations are now as high as ever so machine learning helps businesses retain their customers and keep afloat from the competition.

Here are a few examples of how ML improves the customer experience in terms of logistics and supply chain management:

• Easy package tracking in real-time;
• Customized notifications on the status of the delivery;
• ML-powered chatbots for 24/7 customer support;
• Monitoring of online customer behavior for a better understanding of buying habits;
• Collecting customer data to use for demand forecasting.

With the help of machine learning, businesses can analyze customer-related data and get a solid understanding of customer preferences, buying habits, and expectations. These insights help predict future demand for the products and this creates a win-win situation both for the customers (whose demands are met) and the company (that knows how to allocate resources).

Tips on making ML work for you

As with any other technology, you can’t just implement machine learning in your processes and wait for a miracle to happen. In order for any technology to benefit your business, first, you need to identify the “problem areas” that this technology can improve and set up certain KPIs to monitor the progress and see how the state of the business changes depending on the use of the technology.

Hence, here is a quick checklist of things to take care of before implementing ML:

• Evaluate your supply chain structure: identify bottlenecks, possible risks, and the overall state of the supply chain, including its security.
• Establish KPIs to monitor and set a certain ROI as a business objective.
• Evaluate whether your business is ready for ML implementation: whether you have all the needed resources, whether it’s scalable enough, and whether you need to assemble a team of professionals to take care of data management and processing.
• Determine how you will store the data and how exactly you will collect it.

One of the biggest issues that occur during the ML implementation is that an organization is simply not ready for it. By that, we mean lack of needed resources (i.e. lack of Data Science talents), lack of understanding of how exactly to apply ML, and lack of experience in working with the data. Therefore, your first step towards improving your supply chain management would be assessing the current state of your organization and identifying what can be improved with ML and what exact benefits it will bring in the long run. Once you have clear answers to these questions, you can proceed with implementing machine learning but make sure to have a robust team of data scientists as data management is nearly impossible if your employees lack the needed skills and knowledge.