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Predictive Analytics Based On ML Powered Supply Chain System

Identify inventory-related issues that cause unreliability in the inventory and supply chain management with the help of predictive analytics and ML-based automated services.
Our client from the United States is a proactive supply chain and inventory management service, provider. They focus on increasing the efficiency of procurement, distribution and logistical support on a global scale for delivering products.

They provide high-quality services in reducing the lead time between manufacturers, suppliers, and customers.

Business Overview

SoluLab was tasked to create a supervised ML system that uses predictive analytics. They wanted to overcome the challenges faced in a dynamic environment of supply chain and inventory management.
Eliminating excessive stock levels, optimally using inventory space and productively managing inventory storage were some of their top requirements to be fulfilled.
With Rigel, our client can now help their customers in optimizing inventory management by predicting stock levels. They minimize the availability of idle stock and reduce over or under-stocking with improved forecasting.

The Challenges

Demands in every target market is constantly changing. We had to build a ML-based system that provides our client with the opportunity to improve their inventory replenishment capability.

Developing a system that predicts the time duration of holding an inventory based on the certainty in market demands and customer requirements.

Building a vast supervised learning program that improves the customer delivery time and the cross-docking system with the help of predictive analytics.

Our Approach

Used machine learning technology to accurately predict future market demands which help in shrinking stock levels. We created a supervised learning program with the help of historical data and current data to reduce inventory mismanagement. The machine learning model we built tracks the demand changes, scans the inventory in real-time to provide insightful analytics on re-stocking and product demand.

Prediction by Location

Build location-based modelling that predict inventory levels based on geo-locational requirements.

Forecasting Demand

Gather demographic details, market behaviour patterns, promoted sales, previous sales and current sales

Inventory Optimization

Create significantly high elastic procurement and distribution for all Stock Keeping Units (SKUs).

Price-Change Prediction

Build ML programs that use price and market demand data to create effective inventory strategies.

Collaborative Networks

Develop synergies between all supply chain participants and create improved data interoperability

Analyze Data Sets

Create ML based analysis techniques and advanced simulation for supply chain and inventory management

Project Highlights

Create visually recognizable patterns in visual inspection of inventory maintenance

Gain contextual intelligence on inventory storage, FIFO and LIFO of all SKUs.

Combine data tracked with ML models built to generate automated inventory reports

Results – A journey from Ideas to Success

The proficient experts of SoluLab define the project’s Uses advanced statistical models to forecast demand and supply accurately

Stimulate inventory carrying costs in real-time for smarter inventory management

Technology Stack

Tensorflow

Keras

Microsoft Cognitive Toolkit

SAS Advanced Analytics

Client Testimonial

“We wanted to automate our supply chain operations. The Machine Learning team from SoluLab did an outstanding job in developing one of the best predictive systems for the supply chain management.”

Rene Le Fave

Automation Executive, IPsoft.

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Mizar
Predictive Analytics Based On ML Powered Supply Chain System

Business Overview

The Challenges