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/ Case Study · Logistics
A Shipper Forecasts Demand Before It Arrives. Python Time Series and Real-Time Tracking.
/ The engagement
A Data Analytics Solution for Shipment Forecasting.
Brainvire built a data analytics solution that forecasts shipments with precision. Python time series models trained on complete past data predict sales, the DeepSort algorithm makes inventory tracking consistent, Power Query pulls scattered cloud sources into one, and a report authoring library gives each role its own authorized view.
/ About the client
The client needed to forecast sales carefully and precisely while tracking a huge, scattered inventory in real time. Data lived across multiple cloud sources, and multiple roles meant access had to be authorized rather than open.
Brainvire delivered a data analytics solution for shipment forecasting. The team implemented Python time series forecasting on complete past data, used the DeepSort algorithm to make tracking consistent, brought scattered sources together with Power Query, and authorized each role through a report authoring library.
/ The approach
Design, architecture, and features, shipped as one program.
Sales, predicted
The client wanted precise forecasts, so the team fetched complete past data and trained a Python time series model with the right libraries and preprocessing.
Inventory, steadied
Tracking was hard across huge, scattered data, so the DeepSort algorithm reduced features and made the tracking module consistent and reliable.
Data, unified
Multiple sources needed one home, so Power Query imports data scattered across the cloud into a single source for real-time inventory tracking.
Access, authorized
Multiple roles needed authorization, so a report authoring library gives each role its own personalized, authorized view.
/ What stood in the way
Four problems, solved without a maintenance window.
Select a challenge to see how it shaped the build.
Forecasting the sales
The client wanted to forecast sales carefully and precisely. The team fetched the complete past data and implemented time series forecasting in Python, installing the required libraries, running data preprocessing, and training the time series model.
Tracking inventory
Tracking was difficult because the data was huge and scattered. The team used the DeepSort algorithm, which reduced features and improved the tracking module for volatile, consistent tracking.
Importing scattered data
Multiple data sources had to be fetched into one. The team implemented Power Query to import data scattered across the cloud into a single source, enabling real-time inventory tracking in the warehouse.
Role specification
Multiple roles were allocated and needed authorization. The team used a report authoring library to give each role its own personalized authorization.
/ Product screens
The delivered experience.
The interface people actually use.
/01Dashboard
/02Forecast
/03Tracking
/04Inventory
/05Reports/ The results
What changed, in numbers.
/ Next step
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