Data Analytics



The logistics industry is undergoing a fundamental transformation with the explosion of data and devices, complex regulatory laws, emission concerns, changing industry models affected by the networked economy, infrastructure and talent limitations, and rise of new technology. There is a greater interoperability and collaboration between third-party logistics companies, shippers, manufacturers, suppliers, distributors and retailers facilitating common business processes understanding and standardizing the structure and content of data interchanges. However, reducing costs by driving down excessive inventory, both staged and in-transit, proactively responding to inbound and outbound events and sharing assets has become critical in today’s supply chain environment.

Further, increase in data and devices such as GPS, RFID tags, sensors and scanners is warranting a shift in the business model, fusing multiple data sources and integrating business judgement and complex calculations to provide real-time or near real-time insights for timely decision making. Some typical trends on where this data is leveraged are:

  • Logistics interoperability model
    Facilitates a common understanding of business processes and standardizes the structure and content of data interchanges to enable collaboration between the different actors - 3PL companies, shippers, manufacturers, suppliers, distributors and retailers.
  • Collaborative BI on cloud
    The need for scalability, simplification, visibility, lower cost and a cloud-based solution that generates ROI in a matter of weeks has made collaborative BI on cloud an attractive proposition for logistics providers.
  • Internet of Things analytics and big data
    Advancing technology, growth in mobile and cloud computing, big data analytics and increased adoption of GPS, RFID tags, sensors, scanners has given rise to large amounts of data. This data gives insights on operational efficiency, safety and security, and customer experience for near real-time decision making.
  • Near real-time operational BI
    Near real-time dashboards for improving operational efficiency by increasing transparency, optimizing resource consumption, and improving process quality and performance in depot/ warehouse operations.
  • Environmental intelligence (CO2 reporting )
    As carbon footprints of logistics services receive greater attention, the onus is on providers to take the necessary steps to track and reduce emissions in all related services such as shipping, packing, warehousing etc.
  • Real-time route optimization
    Data on operational constraints, traffic conditions, weather, end user availability and more helps to dynamically revise routes and provide instant driving direction updates to drivers. This helps to optimize the last mile delivery which drives down the product cost.

Infosys point of view

Today logistics providers manage a massive flow of goods while creating vast data sets. For millions of shipments every day, origin and destination, size, weight, content and location are all tracked across global delivery networks – a huge potential for improving operational efficiency and customer experience, and creating useful new business models. As enterprises pursue to pervade their insights across the enterprise, data and analytics platforms built through dated data processing techniques and processes make that realization difficult to achieve. Infosys offerings are designed to help logistics companies rethink, evolve and achieve their vision through a three-pronged strategy:

  • Boundaryless Information: In the logistics industry, supplier/ customers/ logistics enterprises form strategic alliance and generates endless array of data that has become available through the continuing adoption of logistics technologies such as Transport Management System (TMS), Warehouse Management Solutions (WMS), supply chain execution systems and IOT devices. There is a vast amount of data to collect and track within a supply chain, such as operational parameters, key performance indicators on suppliers and carriers and maintenance trends. A boundaryless data platform is the one that integrates data from many internal and external systems, provides the right validations and governance to improve the trustworthiness of the data and makes right data available to business users in a self-service manner for exploratory analysis and insight generation.
  • Pervasive Analytics: Contextualized insights derived from BI/ analytics platforms will pervade across the enterprise and shape the thought process of front line decision makers. The entire analytics lifecycle including the consumption and operationalization of insights must be embraced with an open and adaptive framework to realize the vision of seamless integration and pervasiveness.
  • Progressive Organization: Changing market dynamics coupled with the evolution of the ‘boundaryless’ paradigm for information will propel providers to move away from the traditional BI analytics and governance structures. Organizations will evolve into product focused knowledge tribes that aligns BI/ analytics teams with various business processes, supported by horizontal platform and tools and strengthened with strong data governance tailored to next-gen data sets and associated security and business semantics policies.

Success Stories:

Design and launch of a depot operations performance dashboard solution (Operational BI) in 21 countries.. This tool helps supervisors and service centre managers to understand the team’s performance on important KPIs, on a daily basis, helping promote a strong performance-driven work culture for a leading parcel express company

Improved sales performance through a mobile sales KPI dashboard for the territory sales team (Mobile BI). Revenue, potential, planned visits and opportunity information is made available on iPad to improve the effectiveness of the territory sales team.

Helped get current and meaningful view of parcel network performance and take corrective actions in near-real time to address performance bottlenecks and ensure SLA compliance for a prominent European logistics provider. The big data analytics platform-based solution seamlessly integrates with an organization’s Internet of Things (IoT) ecosystem and consumes machine data - structured, semi structured or unstructured from any connected device, real-time. The pre-built data processing layer processes data in real-time while the rich visualization layer rapidly converts raw information into actionable intelligence and delivers them through device agnostic mobile friendly interactive dashboards.

The platform provides accurate and near real-time parcel status to all B2C and C2C customers. It provides the ability to accurately predict parcel delivery time, enabling the operations team to achieve better Speed of Service (SoS) metric and improve customer satisfaction.

Built single reference point for all customer data, centralized data, and data quality services to improve customer data integrity that can be leveraged for analytics and reporting for a class I freight railroad network Improved efficiency in movement, rating and billing. Offered web-enabled self-service access to a high performance data store, utilized intuitive GUI and development environment to optimize productivity. Optimized communication with each customer, providing the right information at the right time and self-service. This resulted in improved customer experience with better market intelligence, thus enabling more relevant and timely price offers to the highest yielding customers, reduced costs by eliminating point-to-point integrations, reduced redundant customer data stores and manual data quality management efforts.

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