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Advanced Data Science in Logistics and Supply Chain Management

Technology Overview

Thanks to the vast degree of digitalization and massive deployment of connected devices, unprecedented amounts of data can be captured from various sources along the supply chain. Through advanced statistical modelling and machine learning techniques, this technology capitalizes the value of big data enabling businesses to uncover and predict data-driven forward-looking insights and potential of their supply chains. When companies adopt Big Data as part of their business strategy, the first question to surface is usually what type of value Big Data will drive? - Operational efficiency. Data is used to make better decisions, to optimize resource consumption, and to improve process quality and performance. - Customer experience. Typical aims are to increase customer loyalty, perform precise customer segmentation, and optimize customer service - End-to-end supply chain risk management.Predictive analytics can increase the resiliency of global supply chains. Big data can be used to mitigate risk by detecting, evaluating, and alerting all potential disruptions on key trade lanes (e.g., growing port congestion or high flood risks). - New business models. New business models complement revenue streams from existing products, and to create additional revenue from entirely new (data) products

Technology Features & Specifications

This technology adopts a data-driven solutions framework by applying intelligent analysis, data-driven strategies and solutions to identify key insights and business opportunities. By combining flexibility and transparency, interactive supply chain analytical dashboards with ‘What-If Analysis’ capabilities present vital information of consumer behaviour patterns, inventory performance as well as sales and demand profile which are essential for strategic forward-planning, business decision-making, risk management and efficiency improvement.

Potential Applications

The logistics sector is ideally placed to benefit from the technological and methodological advancements of big data analytics.

Customer Benefits

The aspect of Big Data analytics that currently attracts the most attention is acquisition of customer insight. This technology helps to create targeted customer value through - Customer loyalty management. Data from the distribution network carries significant value for the analysis and management of customer relations. With the application of Big Data techniques, and enriched by public Internet mining, this data can be used to minimize customer attrition and understand customer demand. - Continuous service improvement and product innovation. Sophisticated Big Data techniques such as text mining and semantic analytics allow the automated retrieval of customer sentiment from public internet sites such as social networks and discussion forums. Meticulous review of the entire public Internet brings unbiased customer feedback to the logistics provider. This empowers product and operational managers to design services capable of meeting customer demand.

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