Embracing Digital Transformation in Logistics Industry

Embracing Digital Transformation in Logistics Industry

Dorota Owczarek - December 23, 2022 - updated on April 25, 2023

In logistics, digital transformation is all about using technology to improve the efficiency and effectiveness of the supply chain. This can be done in a number of ways, such as through the use of big data analytics, cloud computing, or the internet of things. By embracing digital transformation, logistics companies can streamline their operations and stay ahead of the curve in this rapidly changing industry.

In this blog post, we’ll discuss what digital transformation means for the logistics industry, and we’ll look at some of the ways companies are using technology to improve their supply chains.

Challenges of Today’s Logistics and Supply Chain Systems

Throughout recent decades, the transportation and logistics sector has immensely grown, and supply chains have gained extra complexity due to intense globalization. We got accustomed to the accessibility of imported goods, nurturing the demand for shipment services and higher customer expectations. The rapid development of eCommerce in recent years has contributed to this phenomenon, driving customer centricity. Instead of walking to the closest store, we would now order online whatever we needed, regardless of its origin, enforcing the total rearrangement of the supply chain.

The pandemic has strengthened these trends and made things even more challenging. With the global supply chains becoming more complex, the already existing issues started coming up all at once, impacting everyone involved, from the shippers and the freight forwarders to the end customers. What are the most pressing issues logistics is facing today?

Real-Time Data Processing

Real-time visibility is an essential foundation of today’s supply chain management. The lack of access to real-time data can lead to delays, errors, and inefficiencies. At the same time, there are massive amounts of logistics data from various sources (GPS devices, RFID tags, sensors, and ERP systems) which need to be processed within the supply chain to maintain that real-time visibility**.** In order to gain valuable insights, logistic companies need sophisticated tools that engage modern, developing technologies.

At the same time, as there is more data to process, companies are looking for an efficient way to maintain the efficiency of their systems without increasing their complexity. That could expose them to more costs.

Tracking Shipments

In modern-day complex supply chains, the shipments pass through many hands before they arrive at their final destination. Traveling around the globe, they are picked by different freight forwarders, often consolidated with other shipments to avoid empty miles. With this level of complexity, it becomes much harder to track them, as it requires coordinated communication and information exchange. It makes it much more difficult to solve the issues of lost or damaged goods.

The inability to track and trace shipments or fleets throughout the supply chains also means missed opportunities for customer engagement. Customers expect to be capable of tracking shipments, and the lack of this opportunity may have a negative impact on their overall experience.

Legacy Software and Integrating New Applications and Technologies Into Existing Systems

Many logistic companies still operate on outdated systems that are often complex and challenging to maintain. That inevitably leads to high IT costs since it requires software engineers to stay engaged all the time. Since older systems are often based on outdated technologies, it gets difficult and costly to find specialists capable of solving their specific problems.

Legacy software is also much more challenging to integrate, and integration with different systems is essential in modern-day logistics, as the participants of the supply chain need to exchange information seamlessly and access data from various sources. Integrating new apps and digital technologies is essential to avoid silos of information and stovepiped processes.

Another issue with legacy software is its lack of flexibility. Modern-day solutions are already designed to cooperate with artificial intelligence solutions, but outdated software often does not have this capability due to its simplified architecture and leverage issues. As a result, companies lose a chance for valuable insights since, despite collecting historical data for years, these programs are not capable of integrating AI to process it.

To introduce artificial intelligence-based solutions, companies that rely on legacy software may be forced to redesign their system, which is often more costly than implementing a brand-new one. Often, legacy software completely bars them from benefiting from machine learning and AI in general, which affects their competitiveness.

Migration to Cloud Services

Cloud environments are the new normal, particularly in industries where constant data processing and exchange is basically a must. Logistics definitely classifies to this category. With the cloud environment, businesses can streamline these processes while keeping their costs low as they don’t need to rely on physical IT infrastructure. However, for many of them, the migration still appears as a risky move due to security concerns. Plus, in order to migrate to the cloud, they need to prepare for a complex process that may require the engagement of external support.

Cloud migration to private cloud vs. public cloud

Logistics sector is changing in front of our eyes, mainly thanks to the revolutionizing impact of e-commerce market development. Companies are reaching out for solutions that help them exchange information faster, keep better track of the shipments, and streamline the bidding process and all the cooperation between the shippers, carriers, and freight forwarders. The main focus? Maintain maximum efficiency, lower costs, and, of course, keep the customer informed and happy with fast and flawless delivery. The concepts and technologies listed below support that!

360 Supply Chain Visibility

In order to achieve maximum visibility, it is essential to find an efficient way to gather supply chain data across different channels such as spreadsheets, ERPs, custom systems, and account payables. They are not structured in the same way, which may lead to leaks.

360 approach solves this problem by integrating all this information into one system that provides real-time insights. It allows companies to control and track the goods and products transported within the supply chain in an advanced way, following their timeline and having access to information provided by different companies participating in the process. This way, everyone stays on the same page and has a clear view of all the supply chain activities and their results.

Real-Time Data

Real-time data is a foundation of 360 supply chain visibility, allowing the companies to see the situation as it is at this very moment. In logistics, everything changes fast, and one single change can affect the supply chain. In order to adjust to them right away, the logistics companies need to source and process real-time data (primarily the information regarding locations, routes, stops, carriers, and the state of the shipment).

Modern big data architecture

Modern big data architecture

Real-time data also allows carriers to estimate the most efficient routes based on current conditions and prevent congestion in the transshipment hubs and ports. Without real-time insights, it is much harder for them to maintain streamlined service and avoid issues that could affect customer experience.

Related case study: Developing a logistics platform offering real-time visibility and integrations with different carriersOne of our clients was seeking to improve the global supply chain optimization productOur challenge? Providing visibility and data transmission for maximum efficiency and control. We supported solution development for end-to-end execution of logistics activities in Supply Chain Management at the PO/SKU level, including PO creation, stock management, suppliers and distributors management, consolidation and load planning, carrier allocation, documentation, and final delivery. Read more about this case study.

IoT-Integrated Supply Chains

The Internet of Things has become an integral aspect of the digital transformation of modern supply chains, supporting companies in various tasks - from sorting and identification to transporting products. The usage of IoT devices prevents supply chain data leaks, as it enables carriers to document the route of a particular shipment.

Instead of filling documents manually, which is time-consuming and error-prone, they can use IoT (often paired with computer vision) to identify relevant data and process it in the system automatically. Having this information in the system, they can track down the issues and damages or even the lost shipment and find it in the inventory with the help of drones.

IoT vehicles are an increasingly common view in ports, streamlining the loading and unloading process and choosing the most efficient routes and configurations with the help of AI.

Automation Services

Automated business processes are essential in modern logistics transformation, streamlining the work of the carriers and freight forwarders and allowing them to focus on more demanding tasks. The majority of repetitive, time-consuming, and error-prone processes can be automated, from shipment identification and tracking, through route planning, timeline management, and warehouse operations, to quoting and bidding. All the members of the supply chain can benefit from the potential of automation, and the only thing they need to do for that purpose is to optimize their software and data processing.

Digital Twins

The usage of digital twins is gaining popularity across industries, particularly when it comes to maritime shipping. Digital twins of ships and ports are a perfect playground, facilitating simulation and testing. Since they are 1:1 digital representations of the actual object/vehicle and its processes, companies use them to test their strategies and innovations and estimate the risk linked to these. You can also create supply chain digital twins that provide you with an overview of the processes within the whole supply chain and enable you to carry out tests and predict the impact of the trends and changes on the market or, for instance, the implementation of emerging technologies.

At the same time, digital twins collect and store historical data, which can later be applied to analytical processes. With the usage of IoT devices, such as sensors and cameras, these digitalized representations can also reflect real-time data, facilitating monitoring tasks. Considering that, they can also be seen as an integral element of the 360 visibility approach.

How Is Technology Used to Enhance Logistics?

Technology can improve aspects of logistics, including the efficiency of processes, delivery time, route planning, shipment tracking, customer experience, communication, infrastructure complexity, etc. Here are some of the essential technological solutions modern logistics businesses use. As a software development partner, we have implemented most of them in solutions for our customers, and now, we’ll briefly sum them up for you.

Cloud Computing

For a long time, clouds used to be considered risky by the majority of logistics companies, but in the current business landscape, there is no escape from it. Mainly due to the amounts of processed data from various sources and connectivity requirements that challenge the traditional IT infrastructure.

Cloud computing cuts the costs of its expansion and maintenance, eliminating the need to invest in expensive hardware. Cloud-based solutions are easier to integrate and deliver visibility that the traditional IT infrastructure will never be capable of providing due to its localized nature. Having an access to the cloud, the companies can integrate numerous data sources (from systems to devices) to enhance the visibility of all the activity within the supply chain.

Big Data Analysis

The pool of data in logistics is enormous, and drawing insights from them require thoughtfully created algorithms capable. With deep learning, logistics companies can make good use of large, diverse, often unstructured datasets, discovering hidden patterns and correlations. Big data analysis can help them identify market trends and adjust to them in advance to maintain a competitive advantage.

They can also reach out for it to find the issues that are often invisible on the surface but have a negative impact on the whole supply chain - for instance, micro delays of a particular carrier that accumulate, causing costs and negative customer experience.

Real-Time Stream Processing and Event-Driven Architecture

In logistics, any issue can escalate very fast, and real-time stream processing prevents that, enabling the companies to take action on data the moment it is generated (reaction time is counted in milliseconds). Cloud computing facilitates that process, as all the data is available in one environment, allowing businesses to react on the spot. Event-driven architecture makes that even easier, as all its structure is subordinated to triggering a fast response. You can find key examples of logistics and transportation businesses applying EDA at scale to support their digital transformation in our recent article.

Example of an event-driven architecture for a logistics company

Example of an event-driven architecture for a logistics company

Related case study: Delivering a dedicated IT system to manage and sell freight deals and plan transportation for LTL shipmentsA logistics company approached us to create a dedicated IT system to handle their core business process - managing and selling logistics deals.Our challenge? The key challenge in the logistics sector is cutting the time of concluding deals to an absolute minimum. The tool has to be very responsive and help in the smart matching of carriers and freight, fleet management, and other logistics operations. The platform helps shipping agents minimize fuel consumption, maximize operational efficiency, and optimize fleet performance by matching multiple loadings on a similar route with a single carrier. Read more about this case study.

Artificial Intelligence and Machine Learning

AI is already revolutionizing logistics and supply chain management, but you have to be ready from data and infrastructure perspective in order to apply these solutions. Machine learning is changing the face of the transport and logistics industry, and we’re proud to contribute to that process! Here are some crucial digital technologies that fall under the artificial intelligence umbrella term.

Benefits of AI in the logistics industry on every step of the supply chain management flow to support digital transformation

Benefits of AI in the logistics industry on every step of the supply chain management flow to support digital transformation

Predictive Analytics

Predictive analytics is a powerful tool for logistics and shipping businesses, allowing them to anticipate and plan for future demand, optimize their operations, and make more informed decisions. By analyzing data, machine learning, and statistical algorithms, businesses can identify patterns and make predictions about future outcomes, which can help them to improve their efficiency and effectiveness.

One of the key applications of predictive analytics in logistics is predictive maintenance. By analyzing data on equipment performance and usage, businesses can predict when equipment is likely to fail and take preventive maintenance measures to avoid costly downtime. This can help businesses to reduce their mechanical and technological maintenance costs and improve their equipment uptime.

Another important application of predictive analytics in logistics is demand forecasting. By analyzing data on past demand and other relevant factors, businesses can make more accurate predictions about future demand and adjust their operations accordingly. This can help businesses to optimize their inventory management, reduce waste, and improve customer satisfaction.

Related case study: Optimizing drug distribution activities to hospital pharmacies networkTo improve current large-scale procurement processes, a pharma company approached us to use applied analytics to stock and distribute drugs among US hospitals.Our challenge? Maximizing savings by streamlining the procurement of medication across the hospital network and their pharmacies. Read more about this case study.

In addition to these applications, predictive analytics can also be used in logistics and shipping to optimize routes, improve forecasting of delivery times, and identify opportunities for cost savings.

From reactive and real-time visibility of the fleet, cargo details, schedules, etc., to predictive and prescriptive modeling for fleet and route optimization.

From reactive and real-time visibility of the fleet, cargo details, schedules, etc., to predictive and prescriptive modeling for fleet and route optimization.

Computer Vision

The most common use case of computer vision in logistics is tracking shipments with scanning devices that can detect the QR code or other kind of signature and link it with the data in the system. Computer vision paired with artificial intelligence can also help with sorting and classifying transported goods based on their size and shape in order to optimize inventory and consolidate shipments in the most efficient way.

Deep Neural Network (DNN) for logistics image analysis to support warehouse automation

Deep Neural Network (DNN) for logistics image analysis to support warehouse automation

Another use case for computer vision is container management in ports and loading/offloading points. Image-capturing devices can analyze the visual features of the shipment and suggest the best configuration that will enable the carriers to fit as much load as possible, and ports - to manage its distribution, preventing congestion. Autonomous vehicles, which are increasingly common support tools in logistics, rely on computer vision, too.


Natural language processing is a perfect back-office optimization tool since it can support the automation of various administrative tasks, like document processing. With the help of NLP, logistics companies can easily find information in the large datasets that supply chains generate and organize it in an efficient manner.

How AI can extract data from any document with intelligent document processing to support supply chain operations

How AI can extract data from any document with intelligent document processing to support supply chain operations

Route Optimization

The ultimate objective of last-mile logistics is that the shipment arrives to the client as fast as possible at the lowest cost. In order to achieve that goal, logistics companies can engage machine learning models that schedule delivery by predicting optimal delivery time.

Route optimization is an essential part of this process. The optimization algorithms identify the best routes based on such variables as the current location of the vehicle and the delivery point, traffic intensity, the probability of accidents and traffic disruptions, topography, and speed limits (which impact gasoline consumption), combining it with the insights drawn from the previous routes. Route optimization algorithms can significantly lower fuel expenses while decarbonizing the logistics traffic, which is becoming an important objective due to the climate change-related regulations.

Scheduling deliveries by predicting an optimal time of delivery for last-mile logistics

Scheduling deliveries by predicting an optimal time of delivery for last-mile logistics

Related case study: Implementing AI model to optimize routes and timelines of deliveriesA company from the logistics sector approached us to create a custom AI model that optimizes routes and the scheduling of deliveries.Our challenge? The key challenge here was to prepare a dedicated AI-based system designed for carriers to optimize delivery time depending on the destination address. Thanks to the model we managed to reduce failed and late delivery rates by 30%. Read our detailed case study of this project.

Dynamic Pricing

With the dynamically changing market, maintaining an optimized pricing strategy becomes one of the biggest challenges for logistics companies. Dynamic pricing enables that, finding optimal price points that maximize their profit margins. In logistics, dynamic pricing algorithms calculate the prices based on the current market conditions (demand, fuel prices, traffic on a particular route, etc.) and adjust it to the customer segment. It can track, for example, whether customers tend to negotiate or they usually book last minute and price is not their priority, and estimate the price based on that.

Dynamic pricing model - how it works

Dynamic pricing model - how it works

Dynamic pricing models in logistics - solid base for automated freight qoute that includes carrier costs, base freight forwarder fees and shipping costs and three key pillars of logistics optizmization and supply / demand forecasting

Dynamic pricing also facilitates the bidding process, allowing the carriers to automate the quoting, which is time-consuming and error-prone in a manual form. With dynamic prices, they can easily avoid empty miles by selling the spots last minute and dynamize the sales during low-demand periods.

Digital Freight Matching with Dynamic Pricing for shippers and carriers

Digital Freight Matching with Dynamic Pricing for shippers and carriers

What Are the Benefits of Digitizing Logistics Operations?

A well-planned digital transformation helps the logistics business maximize its profit and optimized costs, which results from:

  • better communication flow and information exchange - with an integrated, updated system, it becomes effortless, granting maximum visibility throughout the supply chain;
  • fewer data leaks - digital transformation seals the systems, preventing important data from being missed; the hyper-connected business world and digital supply chains help in creating exact digital twin models of core business processes (fleet management, shipment tracking, freight management, warehouse operations)
  • more consistent and efficient business processes - artificial intelligence and digital capabilities help logistics companies find the most optimized ways to plan their routes, handle the shipments and consolidate them; insights from logistics and supply chains data can improve business strategy and help create new business models (e.g., logistics services based on digital technology);
  • higher profit margins - with dynamic pricing the companies can find the best price points that will guarantee continuous sales while keeping the profit high
  • lower carbon print - route optimization and shipment container management with artificial intelligence helps with decarbonizing the logistics business, as the companies can transport more goods with reduced fuel consumption;
  • better customer satisfaction - all these digital capabilities impact the delivery time and reduce the chances of losing or damaging the transported goods.

Supporting Companies in Their Digital Transformation

As a software development company, we have already assisted various companies from the logistics sector in this process, helping them upgrade from legacy software to new digital capabilities and solutions that facilitate integration and cooperation with new technologies such as artificial intelligence and machine learning. If you also are interested in moving ahead with your logistics transformation, reach out to us - we’d love to help.

Our team of AI experts has extensive experience in developing predictive analytics solutions, [natural language processing systems](Natural Language Processing), computer vision applications, dynamic pricing models, and other AI-based software solutions for logistics & supply chains.

About the author

Dorota Owczarek

Dorota Owczarek

AI Product Lead & Design Thinking Facilitator

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With over ten years of professional experience in designing and developing software, Dorota is quick to recognize the best ways to serve users and stakeholders by shaping strategies and ensuring their execution by working closely with engineering and design teams.
She acts as a Product Leader, covering the ongoing AI agile development processes and operationalizing AI throughout the business.

Would you like to discuss AI opportunities in your business?

Let us know and Dorota will arrange a call with our experts.

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Dorota Owczarek
AI Product Lead

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This article is a part of

AI in Logistics
51 articles

AI in Logistics

Artificial Intelligence is becoming an essential element of Logistics and Supply Chain Management, where it offers many benefits to companies willing to adopt emerging technologies. AI can change how companies operate by providing applications that streamline planning, procurement, manufacturing, warehousing, distribution, transportation, and sales.

Follow our article series to find out the applications of AI in logistics and how this tech benefits the whole supply chain operations.

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