Optimising logistics with data science

Logistics, the beating heart of any commercial operation, has changed significantly over the last decades. What used to rest on intuition and experience is being transformed by data science, a discipline that combines statistics, programming and business knowledge, and that is changing how companies run their supply chains.

The problems traditional logistics lives with

Traditional logistics has faced a set of problems that hit cost and efficiency directly:

• Route optimisation: finding the most efficient route to deliver on time and at minimum cost.
• Inventory management: holding the right levels to meet demand without paying for excess storage.
• Demand forecasting: anticipating what customers will need, to avoid both stockouts and surplus.
• Risk management: identifying and mitigating what can disrupt the chain, from natural events to production stoppages.

What data science changes

By analysing large volumes of data from warehouse management systems, IoT sensors and commerce platforms, companies obtain insight that lets them decide with better information and optimise their operations.

• Predictive analysis: applying machine learning to historical data forecasts future demand with more precision, which allows inventory levels and production plans to be adjusted.
• Route optimisation: mathematical optimisation calculates the most efficient routes for a fleet, accounting for traffic, distance and time constraints.
• Risk management: analysis surfaces the patterns that signal risk in the supply chain, which allows preventive action instead of reaction.
• Predictive maintenance: monitoring equipment data anticipates failures and schedules maintenance before the stoppage, not after it.

What it looks like in electronic commerce

Electronic commerce shows the change most clearly. The large operators use machine learning to forecast product demand, optimise delivery routes and personalise recommendations for each customer.

What it returns

• Lower cost: optimised routes, less inventory, better energy efficiency.
• Higher efficiency: automated processes, faster and more precise decisions.
• Better customer experience: deliveries on time and a purchase that fits the customer.
• More agility: the capacity to adapt to changes in the market and in external conditions.

Data science is changing logistics by letting companies decide with more intelligence and less guesswork. Used properly, it improves competitiveness, reduces cost and gives the customer a better service.