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Machine learning in company logistics

date: 3 August 2021
reading time: 16 min

Artificial intelligence and machine learning are entering more and more industry sectors and areas of our lives – logistics is no exception here.

Application of machine learning in supply chain management can help enterprises automate a number of actions and focus on more strategic and impactful business activities. By discovering new patterns in the supply chain data, artificial intelligence can help businesses limit the risks and enhance their performance.

As a result, the new knowledge and observations based on machine learning will revolutionise supply chain management in organisations.


Key challenges in the logistics industry

Companies nowadays are faced with a series of new challenges, apart from the growing customer expectations: transport complications, remote work, shortages resulting from unexpected growth in demand, and so on. The pandemic has forced businesses to revise their global supply chain strategies and adapt them to the new reality.

Organisations can facilitate supply chain management by making use of machine learning, which will make them more resilient to any disruptions.


1. Resource planning

Resource planning is a crucial element of supply chain management as it allows companies to cope with and adjust to unforeseen shortages. No company that deals with supply chains would like to stop the production to look for a substitute supplier. Likewise, no company would like to be stuck with excess supplies which block their capital and increase storage costs.

Supply management is largely based on keeping balance in purchase order synchronisation to maintain the operation flow and avoid stocking goods that are no longer necessary or usable.


2. Quality and safety

Due to the growing demand for on-time delivery and the need to keep assembly lines running, quality and safety assurance has become a huge challenge for supply chain companies. Resigning from some quality and safety standards may cause real hazard. Moreover, environmental changes, trade conflicts, and economic pressure connected with supply chains may also bring about problems and risks.


3. Problems resulting from supply shortages

One of the most common problems in logistics is related to supply shortages. Luckily, by implementing ML in the supply chain, you will be able to get a deeper understanding of this problem and its various facets. Demand and supply forecasting algorithms analyse numerous factors to enable early planning and storage. By providing new insights into the various aspects of supply chains, ML solutions make supply management much easier.


4. Inefficient supplier relationship management

Another challenge that logistics companies face is a sudden shortage of supply chain specialists. This can actually make supplier relationship management problematic and ineffective. Thanks to ML, you can get a useful insight into supplier data and make real-time decisions.


Benefits of using machine learning in the supply chain

There are many benefits of machine learning for supply chain management, including:

  • effectiveness – ML systematically accelerates cost reduction and improves quality,
  • product flow optimisation in the supply chain – companies don’t need to store large amounts of supplies,
  • smooth supplier relationship management – thanks to simpler, quicker, and tested administrative procedures,
  • practical conclusions – enabling quick problem solving and constant improvement.


The most important examples of using machine learning in the supply chain

1. Supply chain planning

Supply chain planning is one of the most important elements of supply chain management. Machine learning is a very effective technology when applied to the constant search for the key factors impacting the efficiency of a supply chain. Apart from making better decisions concerning the supply chain, ML technologies minimise human interference.

Fulfilment and supply chain as a whole are both extremely data driven, but at the same time are far behind technologically compared to other industries. In order to adapt to incredibly dynamic supply chains, our customers are demanding cutting edge tech and thus we are demanding cutting edge tech to serve them properly.
Frazer Kinsley
CEO of Hook Logistics


2. Warehouse management

Proper warehouse and supply management is necessary for effective supply chain planning. Storing supplies and maintaining them in good condition is a costly process. Both excess and insufficient supplies may pose a real challenge to a company.

Machine learning helps solve the problem of excessive or insufficient amounts of goods and change warehouse management for better, predicting anomalies even before they occur.

Thanks to access to the latest data about the market, ML tools can predict growth in demand and enable restocking in advance or prevent overstock of goods or important manufacturing components.

Shipping rates, inventory levels, order volume, etc. are especially important to new brands, as these data all impact cash flow severely.
Frazer Kinsley
CEO of Hook Logistics


3. Stock management in a warehouse

Machine learning can be used in warehouses to automate manual work, predict possible problems, and limit paperwork for warehouse employees. Thanks to NLP and OCR technologies, warehouse specialists are able to automatically register parcel delivery and status changes. Machine vision can automate barcode and label reading, which accelerates and facilitates the whole process.

Smart warehouses are fully automated buildings, where most of work is done with the help of autonomous robots or software. Autonomous mobile robots use computer vision to identify routes and move to selected areas of a warehouse, helping to receive, pack, unpack, transport, load, and unload goods.

These robots use object classification, detection, and segmentation to:

  • navigate around the warehouse;
  • find the right object attributes and sizes;
  • avoid obstructions;
  • detect visual damage on a parcel;
  • find free space for a box;
  • monitor the right positioning of a package;
  • avoid collisions with vehicles;
  • check the warehouse inventory and create automatic real-time images.

AMRs reduce the number of warehouse management errors and limit human involvement in the warehouse, which consequently lowers the risk of accidents. This way complex tasks are made simple and all operations become more profitable. In fact, Alibaba and Amazon have transformed their warehouses into productivity utopias thanks to the use of automation.


4. AI in logistics for demand forecasting

Demand forecasting is a branch of predictive analytics used to predict the demand for products and deliveries in the whole supply chain, also in uncontrolled conditions. There are several crucial benefits of accurate demand forecasting in supply chain management, including maintenance cost reduction and inventory optimisation.

By using ML models, companies can benefit from the power of predictive analytics for demand forecasting. ML algorithms are able to quickly analyse large and diverse sets of data, thus improving the accuracy of demand forecasting.

What’s more, ML models are capable of identifying hidden patterns in the historical data concerning demand. ML in the supply chain can also be used to detect particular problems before they even manage to disturb the business in any way. When a company has access to a solid supply chain forecasting system, it can react to new problems and risks even before they occur.


5. Logistics route optimisation

ML offers numerous benefits for supply chain networks: reduction of transport costs, improvement of supply efficiency, and risk minimisation for suppliers are the three crucial advantages. To reduce the cost of shipping and accelerate the process, you can use AI to decide which routes are the best. This is particularly important for big e-commerce companies with plenty of customers. ML can help you optimise routes in real-time.

The technology can be used to track the weather and road conditions and to give recommendations regarding route optimisation and shortening the time of driving. Thanks to this, lorries can be redirected at any moment if a more profitable route is available.

Moreover, ML can help in learning where a given parcel is located in the logistics cycle. It allows tracking product location during transport and provides insight into the conditions of transporting. Special sensors allow monitoring various parameters, including humidity, vibrations, and temperature.


6. Selection of suppliers and supplier relationship management

The choice of reliable suppliers and maintaining good relationships with them may be a challenging task. If you make a wrong choice or a mistake in managing your relations, your company may suffer. In the worst-case scenario, it can even go bankrupt.

However, if you take advantage of ML technologies for supplier relationship management activities (e.g. audits and solvency rating), you will receive reliable forecasts for every interaction with potential or current suppliers. This will help you avoid errors and build mutually beneficial cooperation.


7. Workforce management

Machine learning can play a crucial role in production planning optimisation. Workforce management is a must in every modern organisation. It includes various processes, such as recruitment; employee retention, development, and transfer; performance management; and others.

ML and AI solutions can facilitate production planning and streamline your workforce management strategy. What you get by this is a satisfied team. This is important, as employees who like their organisation and work environment are more productive. With a happy team, your company will be definitely more successful.

I think that what we are seeing is the maturity of the RPA as an industry, where we need to build the next set of tools, in addition to our existing product lines.


8. Autonomous vehicles

Logistics and shipping are the key areas of the delivery process. Goods must be delivered to a customer or contractor. Numerous restrictions are related to this field. For example, drivers have to stop driving after a certain amount of time. They need to have a break or be replaced by another driver.

However, having at least two drivers in every delivery vehicle may be expensive; what’s more, the need to wait for a driver to rest may extend the shipping time considerably.

Autonomous vehicles can become a solution to these problems as they reduce the costs and the time necessary for shipment. Such vehicles are not advanced enough for the time being, but when they get fully launched on the market, they will markedly improve logistics.

FedEx Corp and robotics company Nuro on Tuesday announced a multi-year agreement to test self-driving vehicles in the package delivery company’s network, starting with a pilot program in Houston.


9. Drones used in delivery

Delivery drones are the latest solution to aid companies in delivering products to the least accessible sites. Companies often have difficulty in delivering parcels to places where land transport is either dangerous or fallible, or sometimes even impossible.

Drones have revolutionised the logistics sector, especially for pharmaceutical companies, which deliver products with short expiry dates. Problems with transport often result in product wasting or the need to invest in specialised warehouses, which is rather costly.


10. Marketing and sales departments

AI solutions play an important role in facilitating marketing processes in logistics companies. Email marketing is a great example of that. This time-consuming task can be automated thanks to AI. Marketing specialists can now concentrate on more creative tasks, while AI-based software takes care of all the repeatable tasks.


11. Chatbots – AI automating customer service

Every organisation that deals with logistics knows how much work it takes to offer high-quality customer service. Customers normally expect a company to answer their questions quickly and solve their problems as soon as possible. Delivery processes are complex and rather unpredictable, which is why problems occur from time to time. ML-based chatbots are trained to understand specific keywords and phrases. They are widely used in supply relationship management as well as sales and purchase management, which is why employees are able to focus on value-adding tasks rather than become frustrated with responding to basic questions. Moreover, chatbots are able to analyse customer experience and make conclusions regarding possible improvements. What follows is that nowadays, company can better understand their customers’ needs and react to them promptly.


12. Customer satisfaction improvement

For a company to succeed, its customers must be satisfied. One of the ways to make them feel satisfied is to recommend suitable products at the right time. Recommendation systems based on customers’ preferences are integrated with mobile or web apps so to personalise their experience. By means of sentiment analysis, companies can divide products into successful and unsuccessful ones, based on the reviews and ratings given by the customer. This also helps in improving user experience.

What’s more, customers expect to receive up-to-date information about the delivery status. Thanks to ML, it is possible to predict the delivery time, accounting for all the changing conditions. When the delivery time is predicted more accurately, user experience is enhanced.

The lifeblood of the global economy, consumer behavior, has significantly shifted and will continue to evolve with businesses needing to quickly adapt to new preferences and needs. To address this shift, leading retailers like Pandora rely on innovation to increase their business agility by enabling and scaling sustainable supply chain operations using AI and cloud.
Kareem Yusuf
General Manager, AI Applications and Blockchain, IBM


13. Real-time product pricing

Dynamic pricing is advanced time-based pricing, reacting to changes in demand and supply as well as to the changing prices of competitive or dependent products. Thanks to this approach, companies can offer optimum prices for their goods to attract more buyers. Dynamic pricing software makes use of ML algorithms to analyse customer historical data in real time. This way, organisations can react to changes quickly and adjust pricing to the new circumstances.


14. Product damage detection

Nothing can disappoint your customers more than opening a package with what they’ve just purchased only to find out that the product inside is damaged. This usually ends up in negative reviews and customers leaving your business. Logistics centre normally run manual quality inspections to check containers and packaging for any kind of damage in transit. The development of ML technologies allowed increasing the scope of quality assurance automation in the supply chain life cycle. ML solutions are perfect for visual pattern recognition and there are also numerous potential applications in physical inspection in the whole supply chain network. ML algorithms which quickly detect comparable patterns in many sets of data turn out to be very efficient in quality control automation in logistics centres by isolating parcels with damaged goods.

The benefits of using automated quality control translate into the lower risk of delivering defective products to your customers.


15. Back-office task automation

Contemporary companies can automate many back-office tasks. ML in logistics helps create transport schedules, assign tasks to particular employees, and implement parcel tracking in a warehouse. Robotic process automation (RPA) helps in analysing and generating reports or sending automatic emails to stakeholder.


16. Fraud prevention

ML algorithms are able to both improve the quality of the product and lower the risk of fraud by automating control and audit processes and performing real-time results analytics to detect anomalies or deviations from standard patterns. What’s more, ML algorithms can analyse huge amounts of data to protect your business from fraud. For instance, when it comes to the supply chain, ML helps identify fraudulent transactions, prevent authorisation abuse, accelerate fraud investigations, and automate the processes of counteracting fraud.


Examples of companies using machine learning to improve supply chain management


Amazon

Amazon is one of the most renowned leaders of the e-commerce supply chain, using technologically advanced and innovative ML-based systems, such as automated warehousing and drone deliveries.

Thanks to the reliable supply chain, Amazon has direct control over its main areas, including packing, order processing, delivery, customer service, and reverse logistics, based on ample investments in smart software, transport systems, and warehousing.


Microsoft Corporation

Microsoft’s supply chain is chiefly built on predictive analytics, machine learning, and business analytics.

The tech giant has a huge portfolio of products which generate vast amounts of data that must be centrally integrated for the purposes of predictive analytics and improvement of operational performance.

ML technologies allowed the company to build a smooth and integrated supply chain that enables real-time data capture and analytics. Moreover, Microsoft’s dynamic supply chain uses early warning systems to reduce the risk and respond to queries quickly.


Alphabet Inc.

Alphabet is a famous and innovative tech tycoon, whose operations are based on a flexible supply chain that facilitates cooperation between regions.

Alphabet’s supply chain uses ML, AI, and robotics to become fully automated.


Procter & Gamble

P&G is a leading consumer goods corporation with one of the most complex supply chains and a vast product range. The company skilfully uses ML technologies and advanced analytics for comprehensive product flow management.


Rolls Royce

Rolls Royce, the legendary British car manufacturer, produces autonomous ships, where ML and AI substitute for human crew. In the ships, special algorithms are used to examine their surroundings on the water and properly classify the objects based on the level of hazard they pose to the vessel. In the new system of autonomous ships, such technologies will be used for autonomous navigation, obstacle detection, and communication management. ML algorithms can also be used for the monitoring of engine efficiency, safety, and cargo.


UPS

UPS is a multinational shipping & receiving company, which claims to deliver thousands of parcels daily, with about 100 deliveries done by each UPS driver per working day.

To ensure smooth and timely delivery, UPS makes use of a perfectly optimised navigation system called On-Road Integrated Optimization and Navigation (ORIAN). The system makes sure that UPS drivers use the optimum delivery routes in terms of distance, fuel, and time.

According to UPS, ORION uses highly advanced algorithms to collect and process large amounts of data to optimise routes for the drivers. This way, they can ship and receive parcels in a much more efficient way. The system uses online map data to calculate the shipping distance and time and to find the most cost-effective routes.


How to make ML help in supply chain management?


1. Understanding the supply chain structure

Before implementing ML in the supply chain, you need evaluate its whole structure:

  • Define the critical elements of your business
  • Run a detailed analysis of your supplier network
  • Identify hidden relationships as well as mutual relationship nodes
  • Give a quantitative diagnosis of the relative fragility of the supply chain
  • Find the bottlenecks and risk factors in the supply chain
  • Make tangible comparisons with other companies and industry benchmarks
  • Evaluate the supply chain safety
  • Assess your functional maturity in the context of the process, people, and technologies


2. Planning your objectives and the steps necessary to achieve them

To understand in what way the use of machine learning in the supply chain might be beneficial to your business, you need to go through the discovery stage and calculate the short-term and long-term ROI. It is also important to draw up a detailed plan to define your goals and the requirements that must be met to achieve them. In other words, the business problem at hand must be defined in the categories of machine learning.


3. Ensuring an effective ML engineering process

Successful use of machine learning in the supply chain largely depends on:

  • Creating a multi-functional team of professionals experienced in data science, DevOps, Python, Java, QA, Business Analytics, etc.
  • Starting by describing the business problem
  • Deciding on the adequate success factors
  • Selecting the right technology stack
  • Taking data readiness into account: focusing on data quality and quantity
  • Creating, training, testing, and optimising the model
  • Implementing the model
  • Monitoring the model efficiency


Wrap-up

Improving the supply chain efficiency is a key task for every company. Innovative technologies such as machine learning makes it easier to deal with challenges related to changeability and accurate demand forecasting in global supply chains.

Machine Learning makes it possible to detect patterns in the supply chain data, based on algorithms that quickly identify the factors with the hugest success impact as far as supply networks are concerned, and learning in the process at the same time.

Advanced technologies can also help logistics companies in providing the possibility of product, logistics, warehousing, and final delivery at various levels.

AI in logistics improves the quality of customer service. Thanks to the use of ML-based solutions, your products will be delivered in perfect condition and on time. Additionally, your team will have more time and business information to help the managers make effective decisions and ensure the development of your company.

AI is quickly developing in all sectors and it has already proven to be a useful tool of supply chain management. Without ML, companies like Amazon, Nike, UPS, or Walmart wouldn’t be able to work as quickly as they do. AI guarantees not only efficiency and customer satisfaction but also safety in warehouses thanks to the qualities of autonomous vehicles, which make the workflow exceptionally smooth and error-free.

However, to be able to make full use of AI, companies need to plan their future and begin investing in ML and related technologies right away to be able to enjoy increased profitability, productivity, and availability in the supply chain industry.

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