One of the most challenging aspects of a business is the supply chain, and since there is a significant possibility for error, proper coordination and analytics are crucial. This is why supply chain management nowadays considers data analytics very seriously.
Did you know the Supply Chain Big Data Analytics Market is likely to grow at a CAGR of nearly 17.31% from 2023 to 2028?
By supporting data-driven and analytical decisions and actions, data science and analytics offer data intelligence to organizations. The supply chain can operate more efficiently and provide better services by leveraging machine learning and visualization tools and software. In general, the application of data science in the supply chain enables management and experts to gain a comprehensive understanding of operational efficiency and make prompt decisions. Due to the growing popularity of data science in the SCM domain, it is crucial for data scientists to understand the best and most efficient ways to use data science for SCM business challenges.
Here are five innovative and useful SCM project ideas that are worth exploring for all data scientists-
This is one of the most beginner-friendly supply chain management projects. The primary goal of this project is to use machine learning techniques to leverage external data from Google Trends for predicting retail sales. Use the public datasets for Breakfast at the Frat dataset and Olist Brazilian e-commerce. Use prediction models like the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, the Facebook Prophet tool (FBProphet), the Extreme Gradient Boosting method (XGBoost), and a recurrent neural network with long short-term memory (LSTM).
Source Code: Retail Sales Forecasting
One of the most significant uses of data analytics is supply chain optimization, which identifies the right balance of manufacturing and distribution centers to meet consumer demands. Create a simple workflow for a robust supply chain network using Python and Monte Carlo simulation. There will be several phases in this project, including analyzing consumer demand across various market regions, assessing supplier capacities, estimating fixed and variable budgets, etc.
This project uses neural networks to identify fraudulent activity in the supply chain. You will create two machine learning models using the Python-based Keras framework and the MLPClassifier algorithm from the scikit-learn package. Use the DataCo Supply Chain dataset for model testing and training. You will also use other Python libraries, such as Matplotlib for plotting, Seaborn for statistical data visualization, Pandas for data processing, and NumPy for numerical computing.
Source Code: Supply Chain Fraud Detection
This is one of the most unique and interesting SCM project ideas worth exploring. The goal of this project is to optimize the loading rate and lower the cost per ton of transportation through the visualization and costing of a logistics business transportation strategy. You will extract and process unstructured transportation records to develop your optimization model. Employ Python visualization packages to gain insight into the existing route and truck loading rate. You will create a model to simulate various routing options and evaluate their impact on average cost per ton.
Walking between locations while on a picking route might take up 60% to 70% of an operator's working time in a distribution center. Data science techniques can be used to minimize walking distances and boost warehouse operators' productivity. Use Python's Pandas and Numpy packages to create an orders batch for this project. Use Google OR's pathfinding algorithms for picking route designs and Python's SciPy module to do spatial clustering of the picking location.
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