Data Science-Driven Sales Forecasting SolutionData Science-Driven Sales Forecasting Solution

Logistics and Shipping

BUSINESS CHALLENGE

To optimize sales strategy and maximize cargo vessel capacity, the client needed a product that enables intelligent customer outreach and efficient cargo loading.

• Heavy reliance on individual expertise and past sales experiences limits adaptability to market changes and exploration of new customer engagement strategies, hindering effective estimation of future sales and customer engagement.
• Managing multiple Excel spreadsheets for sales data analysis impedes efficient insights extraction and resource management.
• Manual forecasting hampers responsiveness to market fluctuations, resource allocation, and strategic decision-making, hindering effective planning, trend prediction, and growth through accurate sales forecasting.

OUR SOLUTION

Leveraging a Data Lake, Predictive Analytics, and Machine Learning for Optimized Cargo Planning and Sales Forecasting.

• We created a Data lake for getting all the data dumped from the IMOS data since the data changes over time. Introducing the Data lake enabled us to build a robust API that could handle different queries based on the requirements of the sub-application.
• We outlined the various Stages of a Cargo Journey and predicted the SOS (Start of Shipment) sales and Departure Sales for a specific route so that inventory and vessels could be scheduled in advance accordingly as per the estimated demand.
• We combined the data of various elements of the Departure Sales Forecast and applied and trained a model of machine learning to it.

Python | ML Algorithms | Flask

Optimized Cargo Planning and Improved Revenues through a Sales Forecasting Engine.

• We have built a sales forecasting engine that assists the sales team resulting in ships getting filled to their maximum capacity on time. It results in better revenues and profit margins.
• Achieved accuracy of approximately 80 to 85% depending on region or cluster.
• Compared to manual forecasting, the SOS sales forecasting model obtained better results with lesser deviations of ~15%.
• The client can now analyze if further optimization can be done to save on vessels running optimized cargo and make port handling more efficient.