How Data Science applications solves key challenges for the Logistics Industry

11 November 22


The Logistics industry is enormous. It is estimated to reach a value of $15.5 tn by 2023 and 92.1 bn tonnes by 2024. 

Supply chains today undergo high levels of change due to internal and external pressures resulting from rising costs, the prevalence of challenger startups transforming the landscape through technology and automation, and prompting the digital transformation of a seemingly traditional sector. Data Science and Machine Learning is the principal force causing this change. 

Machine Learning has the potential to revolutionize the Logistics and Transport industry by determining the most critical factors for the success of a supply network. 

This is how logistics companies leverage the pile of customer data collected 

Logistic giants such as DHL, UPS, Amazon, and FedEx have realized that investing in data science is wise to stay on top of consumer behaviors. Data science is emerging as the key to unlocking the potential of improving logistics companies to globalize operations. 

We live in a consumer-first world where we want things now. As a result, we constantly strive to improve ourselves and how we work to adapt to market demands. 

Big data presents the opportunity to optimize performance across the board. Logistics companies frequently face challenges in automating and optimizing their services. A 3PL survey reports that “70% of logistics companies agreed big data would be best applied to route optimization.” 

Amazon credits the expansion of its business to investing in big data, which has helped it better understand its operations and customer base. It, says Amazon, has increased its customer base. 

Route optimization assists logistics companies in planning cost-effective strategies to improve operations and transport networks. It also helps to contend with unfavorable conditions such as high fuel costs, poor weather, and staff shortages. 

UPS conducted a study using big data and discovered that it was more cost-effective if its trucks avoided turning left. This caused delays and was potentially more hazardous to oncoming traffic.  

Logistics companies use big data to analyze market intelligence to understand their customers better, tailor their services to exceed customers’ expectations, and improve customer experience. Logistics factors that diminish the customer experience, such as late deliveries and limited global coverage, can be avoided by advanced predictive techniques and real-time processing. 

Three examples of how data science provides a competitive advantage 

Data science is an emerging field. The extensive logistics companies in APAC and worldwide are only starting to recognize its potential to transform global transport networks. Apart from route optimization, data science provides a competitive advantage in other areas, including: 

Enhanced transportation of goods 

It is a big challenge for logistics companies to establish an efficient transport network that moves sensitive goods such as cosmetics and perishables. But a temperature sensor combined with artificial intelligence can determine the best conditions for sensitive goods and adapt to the environment to ensure the goods remain fresh.  

An interconnected network of data 

Logistics leaves a trail of market intelligence, and within it are hidden opportunities for improvements. Data science enables actionable insights to be extracted for a competitive advantage.   

Upgrading warehouse management 

Data science provides innovative methods of warehouse management, enabling logistics companies to cut costs. An analysis of loading, carrying, and delivering procedures can lead to more effective strategies. 

Big data can cut costs and help to implement better supply-chain planning. 

Only a few logistics companies have invested in utilizing big data to extract actionable insights to implement better supply-chain planning. Most companies have not considered data science’s possibilities for their business. Most do not have the resources or time to invest in training their staff.  


According to a 3PL survey, 80% of logistics companies attributed their enhanced supply-chain planning to insights derived from big data. The survey found that machine-learning algorithm forecasts helped supply-chain managers stay on top of warehouse management to the extent that costs got reduced by half. While that is a significant figure, many 3PL companies do not have a structure to create a data science team due to the lack of talent available. 

To unravel more insights about the logistics industry, please contact us.