Transforming Logistics and Supply Chain with Data Science

Data Science in Logistics

Data Science in Logistics

Data science can detect hidden data to derive powerful business insights

The growing landscape of data is altering businesses across diverse industries. The logistics industry is one domain that is driven gradually by big data. As the industry relies on moving parts that can create bottlenecks at some point in the supply chain, it also produces a large volume of data that needs to be processed accurately. Big data in logistics can help optimize routing, streamline factory functions, and enable transparency to the entire supply chain, benefitting both logistics and shipping companies alike.

Since logistics and supply chain management deals with innumerable variables and uncertainties over data processing, companies are now exploring the abilities of data science.

Harnessing the power of data science effectively enable freight costs reduction through delivery path optimization, dynamic price matching of supply to demand, warehouse optimization, demand forecasting, total delivery times prediction and asset management.


Data Science Approach to Logistics and Supply Chain

Data science analyzes large, complicated data sets at a quicker pace to recognize patterns to improve the demand forecast accuracy. It can provide an organization the access to capitalize on their data to envisage future demand based on the product, geographical region, consumer profiles, their interests and others. 

Meanwhile, McKinsey noted that the full impact of big data in the supply chain is restrained by two major challenges – a dearth of capabilities among supply chain managers and a lack of structured process in companies to explore, analyze and capture big data opportunities in their supply chains.

Data science techniques can improve supplier delivery performance and lower supplier risks while reducing freight costs. These techniques could anticipate horizontal collaboration synergies between multiple shipper networks.


Dawn of a New Age of Supply Chain Management

The global supply chain in recent years has witnessed a major transformation driven by big data. To process tremendous amounts of data, supply chain data science teams use advanced technologies like artificial intelligence, blockchain, and robotics. With the increasing implementation of data science techniques to supply chain management, there is also a surging demand for skilled data scientists in the industry. Indeed, the manufacturing and distribution industries are recognized as hotbeds for hiring information technology professionals, including data scientists, according to the Robert Half Technology (RHT) 2020 Salary Guide.

Data scientists extract meaningful insights from vast volumes of structured and unstructured datasets, helping organizations to meet their competitive goals. They further organize and evaluate the data and convey the findings to business leaders.

Data scientists can keep an apt account of shipments and pieces identification. Combining data science with advanced analytics, IoT sensors, and real-time monitoring, companies can gain end-to-end visibility in their supply chains. The technology makes it easier to identify where a product is in a supply chain at a given moment of time. It is expected that the supply chain of tomorrow will get closer to gleaning the right information at the right time with the right tool to get the actionable insights that will enable an optimal real-time decision with minimal human intervention.