Supply chain data science projects github. You signed out in another tab or window.

Supply chain data science projects github python podcast scheduling blockchain supply-chain planning manufacturing procurement scm logistics hacktoberfest traceability revenue-management purchasing demand-forecasting scheduling-algorithms supply-chain-management operations-management This repository contains the code for an innovative anti-counterfeit product identification system that utilizes the unique capabilities of blockchain technology. A paradigm shift is necessary to pass manual approaches made in Excel to powerful automated models in Python. - Projects/P1_EDA/Project 1 - Data Analytics in Insurance. - bhshivani/Ecommerce-supply-chain-analysis GitHub is where people build software. For consulting or advising on analytics and sustainable supply chain transformation, feel free to contact me via Logigreen Consulting. - Data-Science-Projects/Supply Chain Analysis - Big Data Pipeline in GCP. The study is called "Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios, v1 (1970 – 2080)". Output data: Input data neatly formatted in CSV format. Jan 9, 2022 · To associate your repository with the supply-chain-data-analytics topic, visit your repo's landing page and select "manage topics. As the main data analyst for Just In Time, you will help solve key shipment and inventory management challenges, analyze supply chain inefficiencies, and create insightful dashboards to inform business stakeholders about Excel Analysis Summary for Supply Chain Capstone Project. I am proficient in SQL. The goal is to support data-driven decision-making by providing insights into sales performance, delivery efficiency, key customers, and sales trends. This project provides insights into Applying ARIMA, ADF tests, and VECM techniques, our project enhances medical inventory management through the use of advanced time series analysis, ensuring optimal supply chain performance - mukul Data science applied to Supply Chain . Developed custom chaincodes in Go, configured a Docker-based blockchain network for secure and transparent transactions, and validated the system for efficiency and reliability. Through the use of advanced machine learning algorithms, this project helps farmers make informed decisions on various aspects of agriculture. This supply chain analytics project focuses on the following key areas: Data Preparation: Utilized Power Query to prepare and clean the dataset, including creating custom columns and identifying irregularities. Hello This data was acquired from the Socioeconomic Data And Applications Center (SEDAC) portal. 07029 - chains-project/ethereum-ssc This repository contains the data and files related to the comprehensive analysis conducted on Just In Time's supply chain operations using Power BI. A low capacity and high capacity plant across 5 countries are considered and a linear programming model is built to determine the total lowest costs for satisfying consumer demand across all countries, based on various constraints. ipynb at master · AnirbanDatta87/Projects A Fast Moving Consumer Goods (FMCG) company entered into the instant noodles business two years back. Apr 9, 2021 · In this article, you can find 22 major Data Science for Supply Chain case studies that can be applied to your operations by following detailed tutorials. Data Science Portfolio of Samir SACI focusing on Warehousing, Transportation, Data Visualization and ERP Automation. Focus on all steps of data science (EDA, data processing, model, evaluation, charts) Highlight any trend in data, deep insight, novel steps that you take; Highlight next steps and improvements. From the overall analysis, I have discovered some key insights: 💰 Skincare products are the most profitable, generating Analyzed supply chain data to identify trends and key factors. Include the code that you expect the students to write by the end of the course. 0" 2020. Tools Used: VertexAI, Cloud Storage • Worked on Smart Supply Chain Data Analytics for DataCo using VertexAI, applying suitable Data Science techniques to detect high-risk (fraudulent) product Contribute to DG25924/Supply-Chain-Data-Science development by creating an account on GitHub. View my project here . This is end-to-end project of Supply Chain in Fast-Moving Consumer Goods(FMCG) domain, in which a real business problem is solved and provided useful insights with an interactive dashboard to the s The future of supply chain management starts here. An end-to-end data analytics project featuring supply chain data insights to derive actionable insights that can help optimize inventory management, improve delivery performance, and enhance overall efficiency in the supply chain process - naak-ktr/Data-Analysis-on-DataCo-Supply-Chain A curated list of awesome supply chain blogs, podcasts, standards, projects, and examples. The challenge is to extract meaningful insights and actionable recommendations from the data to optimize supply chain operations and improve profitability. - What You’ll Learn: Predict future The primary objective of this project is to construct a machine learning model that can precisely predict future sales for different store-item combinations based on historical sales data. Updated Data Science project Ideas for Logistics domain; Supply Chain Shipment Price Data Analysis; Supply Chain Shipment Type Prediction; Data Analysis- Supply Chain Optimization; Shipment Duration prediction and Estimating Late Delivery Risk 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. It is designed for analyzing revenue generation, manufacturing expenses, and transportation costs to optimize supply chain I am Asim interested in Business Analytics. Aug 28, 2024 · Explore 12 supply chain projects and datasets to boost your data science skills. In this project, we incorporated the unstructured data and transformed it into facts and figures. com May 15, 2022 · To associate your repository with the supply-chain-data-science topic, visit your repo's landing page and select "manage topics. If you need assistance, please speak with your Curriculum Lead. Built a private blockchain-based supply chain management solution using Hyperledger Fabric. modeling-and-visualization-c2k provides materials to python data-science machine-learning ai tensorflow transportation business-intelligence neural-networks data-analytics inventory-management predictive-modeling logistics operations-research optimization-algorithms supply-chain-management supply-chain-analytics supply-chain-optimization warehouse-optimization logistics-optimization ai-solutions Python: A high-level programming language widely used for machine learning projects. The Supply Chain Network Optimization Model utilizes Linear Programming, also called linear optimization or constraint programming. Sheriff Compliance - Compliance to ICE requests. Data Engineering Project on Supply Chain ETL. It enhances procurement efficiency, mitigate risks while supporting health institutions with real-time insights. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction ARIMA ML Model - Oil and Gas Supply Chain Demand Forecasting with LLM Analysis using AWS Bedrock Foundational Model aws machine-learning artificial-intelligence bedrock lstm-neural-networks demand-forecasting supply-chain-management llm aws-bedrock Supply Chain consulting providing cost effective Procurement solution & sourcing platform which transforms Source to Pay (S2P/ P2P) process in to a Risk free function / Profit center. Create an Excel dashboard for your supply chain capstone project and visualize key insights from your supply chain dataset. You signed in with another tab or window. The project uses data from the Open Food Facts database, which offers detailed, open-source information on numerous food items, including their origins and supply chain details. The Standalone Data Collection Server seamlessly integrates with the open-source Connect Mobile app to bolster agricultural value chains, offering robust product traceability and substantiating payment and sustainability claims Open science data of "The Multibillion Dollar Software Supply Chain of Ethereum", IEEE Computer, 2022 http://arxiv. It is key in effective operation and optimization of retail supply chain. S. Below website just shows data from blockchain docker iot reactjs node-red ethereum sensor blockchain dht11 solidity temperature-sensor ethereum-dapp iot-application supplychain node-red-flow node-red-project fastapi temeperature supplychain-blockchain You signed in with another tab or window. Codebasics roll out monthly resume challenges that exercises the business domain knowledge, tech stack skills like SQL, Excel, PowerBI or Tableau. This data demonstrates how supply chain organizations are understanding the advantages of being able to predict what will happen in the future with a decent degree of certainty. The supply chain dataset comprises 24 columns and 100+ rows, containing information on various aspects of the supply chain, including product details, pricing, availability, sales, manufacturing, shipping, and costs. Cleaning, sorting and filtering data to provide insights on country of origin for further analysis. This report provides a detailed analysis of the purchase orders and deliveries dataset. Dec 12, 2023 · 9 Data Science Projects designed to revolutionize Supply Chain Management, offering insights into essential skills, tools, and outcomes for each project. You signed out in another tab or window. For consulting or advising on analytics and sustainable supply chain transformation, feel free to contact me via Logigreen Consulting . This time no mock up was provided. In this project, features like warehouse age, demand, supply, and demand interaction have been created to better understand the supply chain dynamics. ScienceQtech has worked on fraud detection, market basket, self-driving cars, supply chain, algorithmic early detection of lung cancer, customer sentiment, and the drug discovery field. - SUKANTHEN/Sigmathon-1. Contact Open Supply Hub for pricing. 49. The feature engineering process helps create new variables that bring additional value to demand interpretation. Apps Detection - Suspicious app detection for kids. In the context of supply chains, data science integrates advanced analytics, machine learning, and artificial intelligence to enhance decision-making processes and streamline operations. The data contains information about suppliers, the industry sectors they Welcome to the Data-Analysis-Smart-Supply-Chain-EDA repository! This project focuses on exploratory data analysis (EDA) for a smart supply chain, leveraging data-driven insights to optimize and enhance supply chain management. As the main data analyst for Just In Time, you will help solve key shipment and inventory management challenges, analyze supply chain inefficiencies, and create insightful dashboards to inform business stakeholders about Supply Chain Analytics group project using QGIS and Tableau, along with U. You switched accounts on another tab or window. Open Supply Hub is a website that provides worldwide supply chain data for various industry sectors. In this analysis the dataset used is of a USA lighting manufacturing company. The data pre-processing phase facilitates the formation of the inputs to the models. Dive into how Big Data and AI are transforming supply chain management. Linear programming uses a system of inequalities to define a feasible regional mathematical space, and a 'solver' that traverses that space by adjusting a number of decision variables, efficiently finding the most optimal set of decisions given constraints to DESCRIPTION Problem Statement • Demand Forecast is one of the key tasks in Supply Chain and Retail Domain in general. Contribute to jrcinco/supply-chain-shipment-price-data development by creating an account on GitHub. This my Repository of Data Science, ML and AI Projects. Data science involves the extraction of valuable insights and knowledge from large sets of data. This Tableau-driven BI project aims to analyze a real-world supply chain dataset for Just In Time. The five sub-functions in operations and supply chain are as follow (refer to the image): Demand Planning; Procurement An enthusiastic and results-driven supply chain analyst. The ReadME Project. Usage of data (excerpt from SEDAC website) "SEDAC data and Feb 29, 2024 · Understanding Data Science in Supply Chain. pdf at master · Kelviin28/Data-Science-Projects Understanding the supply chain process data and implementing different algorithms, building a machine learning model that can predict whether a given order gets cancelled or goes to back order. Scikit-learn: A machine learning library for Python, providing simple and efficient tools for data mining and data analysis. The Aim of the challenge is to give data analysts/enthusiasts an opportunity to test themselves on real world business cases and to be able to work right Nov 14, 2024 · data-science excel sensitivity-analysis scenario-analysis business-modeling warehouse-optimization lavazza cost-reduction logistics-optimization supply-chain-efficiency Updated Nov 14, 2024 Contribute to prajwalsable99/RA-Data-Science-and-Supply-Chain-analytics. Replenishment - Retail replenishment code for supply chain management. Pandas & NumPy: Libraries for data manipulation and numerical computations. Data Model Creation: Developed a robust data model to support in-depth analysis and You signed in with another tab or window. \ Please have a look at my personal blog: Personal Website 🕸️ My Personal Blog where you can find articles regarding Data Science for Supply Chain Optimization 🚚, Sustainability 🌳 and Productivity ⌚; 🚚 Youtube Channel: Supply Science about Supply Chain Analytics and Sustainability; ⏲️ Youtube Channel: Productive Data about data solutions to improve your productivity with automation. Exploratory Data Analysis (EDA) The EDA process was conducted to get a better understanding of the dataset and the relationships between the different variables. MediTrack is an AI-driven drug inventory and supply chain tracking system that ensures the best availability of drugs by forecasting demand, automating reorder creation, and monitoring vendor performance. Deterministic and Stochastic Dynamic Programs for optimization of Supply Chain. Please have a look at my personal blog: Personal Website The Optimizing Agricultural Production Machine Learning project is a cutting-edge solution aimed at enhancing crop yield and productivity by leveraging data-driven insights. The global supply chain is a crucial driver of the world economy, enabling the movement of goods and services worldwide and directly impacting economic growth. org/pdf/2202. I love to turn raw data into meaningful insights and helps business owners to grow their business. NET Core Razor Pages implementation of inventory order management. 0 Please read the course design process description and complete these steps in the README. Understanding the supply chain process data and implementing different algorithms, building a machine learning model that can predict whether a given order gets cancelled or goes to back order. - What You’ll Learn: Predict future Built a private blockchain-based supply chain management solution using Hyperledger Fabric. - prs98/Backorders_Supply_Chain_Analysis Optimized Supply Chains and Mitigating Risks: DataCo's Smart Supply Chain Data Analysis using GCP Services and Advanced Data Science Techniques. Input data: The data can be obtained either as a JSON dump or via an API. - vishvinpm/Delhivery-Feature-Engineering -- Built a Power BI dashboard covering different KPIs (on-time delivery (OT) %, in-full delivery (IF) %, and on-time in full (OTIF) %) to track service levels of business at AtliQ Mart, a FMCG manufacturer. For each example, I share the Python source code with dummy data on GitHub so you can try to adapt the model for your projects. Through interactive Tableau dashboards and an executive report, the project seeks to identify supply chain challenges, uncover inefficiencies, and propose strategic business improvements. 'Data Science' is synonymous with scalability, automation and value generation by developing forecasting techniques that allow the company to be better prepared for future demand. This data will be analyzed to identify trends and patterns in supply and demand. - Darsh-8/MediTrack A complete data science AI framework to manage live Supply Chain Rx Inventory ERPs using Graph & Predictive Analytics. Welcome to the Excel Supply Chain Dashboard Project! This project demonstrates the effective use of Excel's Power Query and Pivot Table functions to analyze and visualize complex supply chain data. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. This project aims to optimize the supply chain by predicting weekly sales for various stores using a machine learning approach. From that conversation we had to figure out the requirements needed to solve the supply chain problem. Oct 1, 2024 · More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. supply-chain-analytics supply-chain-data-science. With the annual appraisal cycle around the corner This was a Challenge put up by a famous YouTuber Codebasics and he provided all the datasets. We are a group of 5 students from St. The project will involve collecting data on the inventory levels, usage patterns, and other relevant factors. Analysing the supply chain of ingredients for making an avocado toast in the U. Clair college who studied Data Analytics for Business as a Post Graduate Diploma. Data Science Projects done at Data Trained Education during PG Data Science & ML Course data-science machine-learning exploratory-data-analysis data-visualization feature-engineering red-wine-quality world-happiness-report hr-attrition temperature-prediction baseball-analytics customer-churn-prediction hr-analytics insurance-claim-prediction Senior Supply Chain and Data Science consultant with international experience working on Logistics and Transportation operations. Usually organisations follow tranditional forecasting techniques/algorithms such as Auto Arima, Auto Arima, Sarima, Simple moving average and many Also it leads to an increase in the transparency and helps to build an efficient Root of Trust. The dataset used in this project contains various features related to the supply chain process, including product information, sales, revenue, customer demographics, stock Based on the analysis of the data, we developed a forecasting model that predicts the demand for medical supplies. TensorHouse provides the following resources: A well-documented repository of reference notebooks and demo applications (prototypes). It contains examples of Data Science applications at the fields of Finance, Supply Chain and The project further explores data visualization to uncover hidden patterns and insights, followed by data modeling to predict and improve supply chain performance. The analysis is visualized using Power BI, presenting actionable insights to optimize operations and enhance sales performance. Effectively solving this problem requires knowledge about a wide range of tricks in Data Sciences and good understanding of ensemble techniques. A project aimed at forecasting food demand using historical sales data and external factors. Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. The course covers the application of data science and Python to optimize supply chain processes, with hands-on projects and real-world datasets. Data Science Pro: From Insights to Innovations! Contribute to Tam20910/Data-Science-Projects development by creating an account on GitHub. Deeply passionate about fostering continuous growth and innovation, demonstrating a keen curiosity and unwavering determination. in your software supply-chain. 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. ASP. Supply Chain Maturity model is the implementation of machine learning and data science to analyze the business data. Overview: In the dynamic landscape of modern business, efficient supply chain management is the key to success. 📊 Visualizing Key Insights. This project delves into the intricate world of supply chain management, employing advanced data analysis techniques to optimize operations. Author: Amit Shukla Contact: amit@elishconsulting. Below are the . Each subdirectory contains its own analytical workflow and worked example (or series of worked examples). Each subdirectory corresponds to a part in the blog series: part1 - Getting Started with Neo4j GDS and Bloom; part2 - Creating Informative Metrics and Analyzing Supply Chain Performance This Streamlit dashboard provides a comprehensive analysis of supply chain data, focusing on key metrics such as production volumes, stock levels, order quantities, revenue, manufacturing costs, lead times, shipping costs, transportation routes, risk factors, and sustainability factors Oct 19, 2017 · A curated list of awesome supply chain blogs, podcasts, standards, projects, and examples. 🕸️ My Personal Blog where you can find articles regarding Data Science for Supply Chain Optimization 🚚, Sustainability 🌳 and Productivity ⌚; 🚚 Youtube Channel: Supply Science about Supply Chain Analytics and Sustainability; ⏲️ Youtube Channel: Productive Data about data solutions to improve your productivity with automation. Read, Write and Understand and three different aspects of ERP data, while these ERP systems master write part", Julia languages is the language for data science operations and large datasets numerical computing. A project on using mathematical programming to solve multi-modal transportation cost minimization in goods delivery and supply chain management. As a data analyst, I've developed a set of tools and scripts to gather, clean, analyze, and visualize supply chain data, providing valuable insights into the company's logistics and procurement processes. This repository contains a detailed case study on feature engineering for Delhivery, one of India's leading logistics and supply chain companies. Please have a look at my personal blog: Personal Website Saved searches Use saved searches to filter your results more quickly GitHub is where people build software. Then under each of the sub-functions, we will talk about top analytics use cases to make the business more data-driven. Data Science and Machine Learning challenges are made on Kaggle using Python too. List of projects completed in my journey of Data Science that involves Data Preprocessing, Data Analytics and Machine Learning. Analyzing sentiment surrounding the global supply chain in March 2024 reveals whether it's positive or negative, offering insights into improvements and disruptions. Instead we got a conversation between the stakeholders of the company with their Data Analyst. Algorithm challenges are made on HackerRank using Python. Created tables showing the relationship between customers, salesperson, production line and order line using SQL, then queried statistical information to describe the data. -A-Z-with-Python development by creating an account on GitHub. " Learn more Footer Demand Forecasting is one of the crucial elements of any organisation’s Supply Chain Management (SCM) which helps demand planners to predict the future forecasts. Currently working at Waitrose & Partners in The "Pharma Supply Chain System using Smart Contracts" project aims to create a secure, transparent, and efficient system for managing the pharmaceutical supply chain. It should typically be 2 or 3 In this project, features like warehouse age, demand, supply, and demand interaction have been created to better understand the supply chain dynamics. Apply 5 to 6 machine learning algorithms and evaluate it using Test dataset . The insights provided are intended to support strategic decision-making and enhance overall supply chain performance. - hzjken/multimodal-transportation-optimization neural-network tensorflow supply-chain neural-networks keras-neural-networks keras-tensorflow mlp-classifier supply-chain-analytics supplychainanalytics supply-chain-data-science Resources Readme Senior Supply Chain and Data Science consultant with international experience working on Logistics and Transportation operations. Warehouse, product, vendor, customer, purchase order, sales order, shipment, goods receive and more The project provides a real-world dataset focusing on supply chain analytics. In this blog, we will explore 10 essential data science projects that can transform your supply chain management, along with what you'll learn, the tools you'll need, a brief description of each project, and the recommended skill level to tackle them. These datasets are provided by Analytic Labs Research This project is simulation of pharmaceutical supply chain using iot and blockchain . csv This file contains 250000 rows and 24 attributes. Created to provide a customer interface by using Power BI and PowerApps from which users can launch simulations and determine the parameters of the simulation and data, access results. Welcome to the Supply Chain repository created with Power BI! This project aims to provide an interactive and detailed analysis of key metrics of a supply chain to support strategic decision making. Objectives Analyze the Supply Chain : Understand the complex pathways that each ingredient of avocado toast takes from farm to table. - prs98/Backorders_Supply_Chain_Analysis Dec 12, 2023 · 9 Data Science Projects designed to revolutionize Supply Chain Management, offering insights into essential skills, tools, and outcomes for each project. Reload to refresh your session. The "Just In Time" supply chain data is visualized and analyzed through the interactive Power BI dashboard, enabling users to gain valuable insights into the company's supply chain performance. Currently i am pursuing Masters in Project Management from IIT Kharagpur. Senior Supply Chain and Data Science consultant with international experience working on Logistics and Transportation operations. Supply chain leaders may use this data to address supply chain difficulties, cut costs, and enhance service levels all at the same time. The goal of this challenge is to build a model that predicts the count of bike shared, exclusively based on Demand forecasting is the process of making estimations about future customer demand over a defined period, using historical data and other information. The cut-off valuefor p is 1. Creating a dynamic ADF pipeline to ingest both Full Load and Incremental Load data from SQL Server and then transform these datasets based on medallion architecture using Databricks. python podcast scheduling blockchain supply-chain planning manufacturing procurement scm logistics hacktoberfest traceability revenue-management purchasing demand-forecasting scheduling-algorithms supply-chain-management operations-management The Supply Chain team decided to use a standard approach to measure the service level in which they will measure ‘On-time delivery (OT) %’, ‘In-full delivery (IF) %’, and OnTime in full (OTIF) %’ of the customer orders daily basis against the target service level set for each customer. This project aims to perform comprehensive supply chain analysis for a company using Python. The dashboard allowed AtliQ Mart to identify and address issues with key customers before Predict Crashes - Crash prediction modelling application that leverages multiple data sources; AI Supply chain - Supply chain optimisation system. The Supply Chain Management Accelerator helps inventory managers to analyze their products portfolio, inventory This repository contains example code and demos aligning to the Neo4j Graph Data Science for Supply Chains blog series. The objective of this project is to demonstrate the process of creating, refining, and selecting features that improve the performance of machine learning models for logistics and delivery optimization. This data analytics project focuses on analyzing and visualizing the supply chain data of a fashion and makeup product company. The "Supply Chain Optimization Wizard" project is a cutting-edge initiative to revolutionize traditional supply chain processes through the power of data analysis and machine learning. This will empower retailers to optimize inventory management, strategize promotions, and make data-driven decisions to effectively meet customer demand. Census and City of Detroit data to determine accessibility of polling locations via public transportation (DDOT bus). Business Problem: Drug shortages in stocks GitHub is where people build software. This system leverages smart contracts to address key challenges such as counterfeiting, diversion, theft, and inefficiencies in traditional paper-based systems. . By applying machine learning models, this project helps optimize inventory management and minimize food waste, with visualizations provided in Tableau. md file in your course repository. Understand the core Supply Chain Optimization with Python. Syntetos et al categorizes the demand pattern for forecasting purpose into four smooth, erratic, intermittent and lumpy queries that are based on the cut-off value of p and v, where p is the average inter demand interval(ADI) and the square of coefficient variation(CV2). Based on the insights gained from this project, we offer the following recommendations to the FMCG company: Model Integration: Implement the best-performing machine learning model(s) into your supply chain management system to optimize supply quantities based on historical data and predictive analytics. Supply-chain-dapp is such an implementation of a supply chain management system which uses blockchain to ensure a transparent and secure transfer of product from the manufacturer to the customer via the online e-commerce websites. This project provides a comprehensive analysis of supply chain data, focusing on performance trends, revenue distribution, and areas for improvement. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. Currently pursuing studies in Statistics and Data Science with the aim of becoming a Supply Chain Data Scientist. This repository contains the data analysis of DataCo Global's supply chain dataset performed by our project group called Dream Five. This codebase is built using the DataCo Supply Chain dataset. marketing data-science machine-learning reinforcement-learning ai deep-learning models supply-chain personalization customer-analysis llm Updated Jan 24, 2024 Jupyter Notebook The supply chain dataset presents a comprehensive set of information related to product sales, manufacturing, and logistics. http linked-data supply Tax Inequality - Data project around taxation and inequality in Basel Stadt. ABOUT DATASET Our project dataset is a CSV file called Data-1. This project focuses on leveraging the DataCo Smart Supply Chain dataset from Kaggle to build predictive models that serve critical business functions: forecasting monthly demand, detecting fraudulent orders, and clustering orders to identify common traits in fraud cases. The project provides a real-world dataset focusing on supply chain analytics. " Learn more Footer Nov 21, 2024 · A technical blog focusing on Data Science, Personal Productivity, Automation, Operations Research and Sustainable Supply Chain. SupplyGraph | A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks. There are five different sub-functions that we will discuss under operations and supply chain function. In this study, we aimed to detect fraudulent activities in the supply chain through the use of neural networks. Saved searches Use saved searches to filter your results more quickly Challenge submitted on HackerRank and Kaggle. AIO on GitHub is an open-source project to provide our supply chain science community with forward-thinking supply chain data science tools to address every supply chain's core goal: performance, resilience, and sustainability. The goal of this project is to create a model utilising past data that will establish the ideal weight of the product to be delivered to the warehouse on each occasion. Their higher management has noticed that there is a mismatch in the demand and supply. This repository contains notebooks, datasets, and analysis tools developed during the Supply Chain Analytics A-Z with Python course. We used RandomForestRegressor to model the sales data based on features such as store characteristics, promotions, and external factors. Hackathon solution for HackerEarth Data Science competition "Sigmathon 1. By leveraging the This repository contains worked examples for applying Neo4j Graph Data Science to supply chain, logistics, and transportation problems. Power Bi for data visualization, Excel and Python. Where the demand is high, supply is pretty low and vice-versa which results in a loss in inventory cost and This dashboard serves as a comprehensive tool for supply chain managers, enabling better visibility into inventory levels, supplier performance, and operational risks. Social Assistance - Trending information on social assistance; Computational Social Science - Social data science summer school course. ScienceQtech is a startup that works in the Data Science field. - ayush9892/Supply-Chain-ETL Focus on all steps of data science (EDA, data processing, model, evaluation, charts) Highlight any trend in data, deep insight, novel steps that you take; Highlight next steps and improvements. The dataset used in This project leverages advanced forecasting models and data analytics to deliver actionable insights for demand forecasting and inventory optimization, ultimately improving supply chain operations. Transfer Learning Flight Delay - Using variation encoders in Keras to predict flight delay. K. By utilizing the insights from this dashboard, stakeholders can make informed decisions to optimize supply chain processes and ensure business continuity. 32 and v is 0. The goal of the project is to provide a toolkit for rapid readiness assessment, exploratory data analysis, and prototyping of various modeling approaches for typical enterprise AI/ML/data science projects. mpqvn kjicq hjjryj nahvau vudwb nwycswwj jgjqae jsdj yginu zehex