Market Outlook:

AI is transforming warehousing processes, such as collecting and analyzing information and inventory processes, enabling companies to increase efficiency and boost revenues. The application of data science can help companies better optimize operations, everything from which delivery routes to take, how to better manage fuel and more accurate forecasting of supply and demand.  

Implementing AI and data science to logistics can help companies use quickly delivered insights to adjust as needed along the way, as different variables can be acted on faster.  


Operational Capacity Planning:

Big data technology and AI can be used for operational capacity planning. Demand sensing tools can show market trends in real-time. This means that logistics companies can plan their transportation and storage capacity based on real data and not simulated datasets.

Market Forecasting:

By using predictive analytics, precise market forecasting can be provided to track supply vs. demand, thus decreasing loss due to an oversupply or undersupply of inventory. Such forecasts can also be automated and extended to vendor analysis, e.g., predicting which vendors are the most reliable regarding payment, pickup, delivery, complying with national and international regulations, etc.

Route Optimization:

Big data and analytics tools in the logistics sector use weather data, shipment data, traffic situations, and delivery sequences to help you determine the best possible routes. The shortest route possible is assigned for delivery, saving a huge chunk of money that could have been channeled to fuel and other expenditures.

Strategic Network Planning:

Data science and AI use special software tools that enable them to perform strategic location planning, transport network planning, storage capacity utilization, and structural optimization. The goal behind this software optimization is to reduce costs and achieve a strategically oriented logistics network.

Improved Customer Service:

Big data presents an opportunity for logistics companies to improve the overall experiences of their customers by optimizing operations. Data analytics tools can analyze data collected from surveys and social media, helping companies gain valuable insights into what customers want. This allows them to tailor their services to exceed the expectations of their clients.


Enriching Data Quality

Increased productivity

Reduced operating cost

Improve customer experience

Case Study


Swyft Logistics does not have an analytics dashboard for the operations team to monitor the health of the business in terms of parcel deliveries, cash collected and rider and vendor performance. The reporting was excel-based through a manual effort which led to a lot of time being wasted on redundant tasks.  


Data Pilot has built an ETL pipeline andData Warehouse, fetching data from Swyft’s operational data store in a DWHdesign, based on their reporting needs. The dashboard gets refreshedautomatically with new incoming data. This gives visibility to the Operationsteam so they can take prompt, data-driven decisions.


This will enable real-time visibility of operational efficiency for the business.