In this study, we aimed to detect fraudulent activities in the supply chain through the use of neural networks. The study focused on building two machine learning models using the MLPClassifier algorithm from the scikit-learn library and a custom neural network using the Keras library in Python.

A supply chain analytics project to optimise the transportation of perishable raw materials from production facilities in mainland Europe to processing plants in the UK, and thereafter to storage and distribution centres across UK.

Skills: Data analytics, data modelling, Power BI

The supply Chain is the network of production and logistics involved in producing and delivering goods to customers. And Supply Chain Analysis means analyzing various components of a Supply Chain to understand how to improve the effectiveness of the Supply Chain to create more value for customers.

Analyzed supply chain data to identify trends and key factors. Visualized sales, defect rates, lead times, and costs. Used Decision Tree Regressor to find top features impacting product costs and lead times.

A supply chain data Analysis project which analyses the service levels received by the customers of Amana Foods in order to improve them.

Analyzing and optimizing Maersk's supply chain operations through data-driven insights and interactive dashboards to improve inventory management, shipping efficiency, and customer demand fulfillment.

Linear programming (LP) algorithm that optimizes shipping costs

This repository contains the links for different posts on application of open source programming languages in operation research and supply chain management.

This tutorial for implementing a lot of tasks in supply chain management field that you perharps meet and how to produce in R.

A comprehensive project delved into the data analysis of various functions of a Hardware manufacturing company like Finance, Sales, Marketing & Supply Chain which will help to generate insights and make data driven decisions in these departments.