The Impact of Big Data Analytics on Logistics

Logistics Tech Outlook | Thursday, May 26, 2022

As more businesses resort to automated customer service, Data analytics can help customers contact support promptly and obtain answers to their questions.

FREMONT, CA: The logistics industry has never been more complicated than it is now. The supply chain's global character has created continually shifting dynamics that can substantially impact a company's profitability. The enormous burden placed on logistics due to the COVID-19 outbreak demonstrated this. As a result, data analytics is assisting manufacturers, shippers, and retailers gain visibility into and optimizing their operations to better deal with the unexpected. As a result, data-driven businesses are seeing higher profit margins and higher levels of consumer satisfaction. Let's take a look at eight ways data analytics is helping logistics.

Supply and demand forecasting is more precise

Companies may track and adjust to fluctuations in demand in near real-time using the plethora of internal and third-party data access. Companies can use big data analytics to create more accurate supply and demand forecasts to help with inventory and shipment planning. As a result, they will be able to cut waste while also improving delivery times. 

Management of stock

When a product is out of stock, customers are more likely to change their purchasing habits. Overstocking can sometimes be problematic. Due to expiration, money is squandered on unneeded storage, staff expenditures, and product loss. Companies can utilize data to compute logistics and identify how long it takes things to reach the warehouse after filing a purchase order to avoid these problems.

Warehousing operations

Data analytics can increase warehouse management efficiency in addition to lowering stockouts and overstocks by optimizing inventory management. Companies can analyze stock movement and location and integrate this data with sales data to determine the ideal warehouse location for each SKU and uncover chances for streamlining.

Optimization of routes

Many logistics organizations are looking for ways to optimize routes as part of cost-cutting and environmental programs. The best path for each vehicle at any given time is determined by combining GPS data, road condition data, and weather data with fleet data. Companies can choose the optimum route depending on several parameters such as traffic, trip duration, and fuel efficiency.

Monitoring performance

Human and mechanical resources perform best when properly managed and maintained. Companies can use supply chain data analytics to understand their workforce's performance better, uncover inefficient procedures and workflows, and optimal break timings. Sensors on firm equipment communicate performance and maintenance data automatically, allowing managers to avoid bottlenecks caused by downtime.

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