Advanced Analytics

The surge in low-code/no-code platforms represented a significant tech shift, enabling users across various sectors, including logistics and supply chain management, to engage in advanced analytics without extensive coding knowledge. Platforms like Microsoft Power BI and Google Data Studio democratized data analysis, allowing logistics professionals to visualize and monitor key metrics such as inventory levels, shipment tracking, and supplier performance through user-friendly interfaces. These tools facilitate rapid, informed decision making critical to managing complex logistics operations efficiently.

Many startups are at the forefront of integrating low-code/no-code technologies into their logistics solutions. Locus, a logistics API-based platform company based in India, is an example of such innovation, offering advanced supply chain optimization and analytics solutions that utilize AI and machine learning to automate decision making in logistics operations. Its platform enables businesses to plan deliveries more efficiently, predict transportation volumes, and optimize routes without requiring users to have any coding expertise. By integrating these advanced analytics capabilities, Locus helps logistics companies in Asia and beyond reduce costs, improve delivery times, and enhance customer satisfaction through data-driven strategies, showcasing the potential of low-code/no-code platforms to transform logistics and supply chain management on a global scale.

Data mesh represents a novel architectural paradigm designed to address the complexities and scalability challenges of managing data in large, distributed organizations, including those in logistics and supply chains. At its core, data mesh shifts the perspective from centralized data management to a decentralized approach, where data is treated as a product, with domain-specific teams responsible for its quality, governance, and accessibility. This methodology encourages a more collaborative and efficient data ecosystem, enabling various departments - such as procurement, warehousing, and distribution - to manage and share their data autonomously yet cohesively. For supply chain managers, this means having direct access to high-quality, actionable data across different touchpoints of the supply chain, facilitating better decision making and operational efficiency.

The adoption of a data mesh architecture solves several pressing issues in logistics, including data silos, inconsistent data quality, and the cumbersome process of integrating data from diverse sources for analytics. By promoting a self-serve data infrastructure, it enables non-technical users to easily access and analyze data relevant to their specific domain, thus democratizing data analytics across the organization.

International startups like Paris-based Dataiku, as well as Ververica, headquartered in Berlin, are at the forefront of offering data mesh services that are reshaping how logistics and supply chains manage data. Dataiku provides a platform that supports the democratization of data access and analytics, facilitating the creation of self-serve data products that can empower domain experts within logistics to analyze effectively data relevant to their areas. Meanwhile, Ververica offers real-time data processing and analytics capabilities, which may be essential for implementing a data mesh in dynamic logistics environments. Its technologies enable logistics companies to build and manage data pipelines that process data in real time, allowing for immediate insights and responses to supply chain challenges. By incorporating these innovative solutions, logistics and supply chain organizations can harness advanced analytics for more informed decision making, significantly enhancing operational efficiency and resilience.