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Logistics 4.0 and AI play

Logistics, in its essence, is the systematic management of resource acquisition, storage, and transportation to their designated destination. This process necessitates coordination with suppliers, effective management of transportation and storage facilities, and the fulfillment of customer requirements. The term, originally employed within a military context, has found widespread application in the business sector, particularly within the manufacturing industry.

The intricate task of resource supply and the global proliferation of supply chains have led to the emergence of specialized professionals known as supply chain logisticians. Fundamentally, logistics represents the efficient orchestration of complex operations.

The field of logistics has undergone significant evolution, transitioning from the era of operations research techniques to the contemporary Logistics 4.0 era. In the early stages, optimization of logistics issues such as transportation and allocation was achieved using mathematical models.

Given below are the industry progression and maturity models over time:


This evolutionary progression has substantially improved efficiency and sustainability within the logistics industry. It has also helped link more closely to the 7Rs of Logistics –

  • Right Product
  • Right Place
  • Right Time
  • Right Customer
  • Right Quantity
  • Right Condition
  • Right Price

Logistics 4.0: Persistent and Emerging Obstacles

Logistics firms are confronted with fresh obstacles from the marketplace, lawmakers, and the general public, while enduring difficulties like a lack of qualified personnel have persisted for a long time. The comprehensive digitization of logistics offers solutions to many of these problems as long as it is implemented across the entire value chain and not limited to specific process stages.

The shift from ‘sales centered around company warehouses’ to ‘a model focused on the consumer’ has decreased storage capacities and heightened focus on aspects like 3D printing and warehouse automation. The shift highlights the importance of smaller production batches and shorter supply routes. Despite these changes, the timeliness of on-demand production must be preserved and made achievable through the digitization of logistics across the entire supply chain, especially for complex products.

In the age of digital connectivity, offers are widely available and easily comparable, leading to fierce competition. This is equally relevant for the logistics industry. The rapid expansion of e-commerce has increased parcel volumes and faster delivery cycles – Amazon effect.

In today’s commercial environment, individual consumers and businesses expect real-time updates on their orders. This includes not just the shipment’s current location but also the status of production at subcontractor facilities, requiring minimal effort from all parties involved.

Global economic interactions have led to complex logistics and lengthy transport routes that require efficient management. However, the level of infrastructure development varies, with some areas needing to catch up to industrialized countries.

The logistics sector faces challenges in attracting new talent and ensuring effective communication with workers, such as warehouse staff or drivers, who may need to be fluent in a local language like Mexican Spanish.

Like all sectors of the economy, the logistics industry should implement suitable measures for environmental and climate protection as part of corporate sustainability measures.

As a result, companies are continuously optimizing their transport logistics, not just for cost efficiency but also to avoid empty runs, find the most efficient routes, and intelligently link different modes of transport.

Surge of AI in the Transportation and Logistics (T&L) sector:

Despite obstacles, the Transportation and Logistics (T&L) industry has experienced a surge in AI adoption, fueled by the global increase in e-commerce, particularly during the COVID-19 pandemic. As global trade recovered, the sector was primed for profound digital transformation.

Based on Gartner research -50% of supply chain organizations will invest in applications that support artificial intelligence and advanced analytics capabilities by 2024, and more than 50% of supply chain organizations will introduce a technology leadership role. The leaders will report directly to the chief supply chain officer by 2025.

However, the journey forward is still fraught with complexities and challenges, including rapid industry mergers, technological progress, regulatory shifts like GDPR, and geopolitical developments like Brexit. The World Trade Organization underscores the significance of customer experiences in the T&L industry, with expedited service becoming the new standard.

The industry is transforming network infrastructure dynamics due to Omni-channel logistics that provide transparent, personalized, efficient, and fast delivery options.

Despite the rise of new entrants, numerous logistics firms are constrained by obsolete IT infrastructures and systems, impeding their capacity to innovate and digitally transform swiftly. These systems were not built to interconnect, resulting in data being broadly dispersed among systems designed to support only specific operations.

The T&L sector faces the challenge of breaking free from the restrictions of outdated systems and extracting new value from intricate networks. The emergence of intelligent infrastructure, powered by new vehicle technology, autonomous trucks, drone ships, and the Internet of Things (IoT), is revolutionizing the movement of cargo and decision-making processes.

AI empowers T&L companies to analyze historical trends, manage inventory, and address fluctuating demands across supply chain operations, alongside complying with necessary Environmental Health and Safety (EHS) standards across the value chain. With AI/ML techniques and seamless operations, costs can also be lowered significantly.

However, legacy IT architectures often need to be improved to innovate and effectively utilize AI.

Industry leaders are harnessing AI to boost win rates and expedite revenue growth. They acknowledge the necessity of comprehensive and high-quality data for successful AI deployment. AI techniques like prescriptive analytics provide real-time coaching to logistics sales and operations professionals, enriching the seller and customer experience.

Way Forward

While demanding, Digital Transformation in the T&L industry is essential for advancement. It necessitates forward-looking leadership, effective communication, talent management, and applicable IT budget planning.

Industry leaders must cultivate an agile digital culture and business model that values innovation and comprehends the importance of data for enhanced decision-making.

As T&L companies persist with their digital transformation initiatives, extracting relevant data for strategic, actionable insights remains a top priority. The industry is competing to exploit advanced analytics for hidden insights. As Tom Peters famously stated, “Leaders win through logistics…you must win through superior logistics”. The quest for superior and more intelligent logistics is underway, prompting questions about organizations’ preparedness.

Leveraging Trigent’s MERIT framework for supply chain modernization, organizations can transform their supply chain operations in a phased manner with CX as a central focus. The framework has been developed based on Trigent’s experience working across the value chain and delivers a visible impact on the brand, rapid ROI, and business value.

Build a future-proof, intelligent, and resilient supply chain with Trigent


  • Avishek Chakraborty

    Avishek anchors the global Demand Generation function for Trigent software. With close to two decades of global work experience, he is an established customer advocate and technology evangelist. While being a certified AI-Consultant and digital strategist, he has raised and grown many new logos from the scratch cutting across Digital Transformation work streams. He likes being a blend of creative and analytical skills. While he has contributed on industry specific white papers and blogs, he has also created AI/ML based statistical models for solving key business challenges.