Gala Groceries is a technology-led grocery store chain based in the USA. They rely heavily on new technologies, such as IoT, to give them a competitive edge over other grocery stores. However, this comes with many challenges to consistently deliver high quality, fresh produce from locally sourced suppliers year-round. The company has approached Cognizant to help them with a supply chain issue related to stocking the items they sell. Groceries are highly perishable items, and the company wants to know how to better stock their products in order to avoid overstocking, which leads to wasted money on excessive storage and waste, and understocking, which risks losing customers.
The main objectives of this project are:
- Analyze the data related to the supply chain of Gala Groceries in order to understand the statistical properties of the dataset and identify trends and patterns.
- Develop a model to accurately predict stock levels of products based on sales data and sensor data on an hourly basis.
- Provide recommendations to the company on how to better stock their products and optimize their supply chain based on the analysis and modeling results.
The data for this project includes sales data and sensor data from the company's warehouses and stores. The sensor data includes measurements of temperature in storage facilities and stock levels within refrigerators and freezers. The data is provided in a structured format (e.g. CSV files) and includes a combination of numerical and categorical variables.
The following steps were taken to complete this project:
- Data exploration: Understanding the statistical properties of the dataset, including the distributions of columns, descriptive statistics, and correlations between variables.
- Data cleaning: Removing missing or incorrect values, transforming variables as necessary, and creating derived features.
- Model development: Selecting an appropriate machine learning algorithm and training a model on the cleaned and transformed data.
- Model evaluation: Testing the model on a holdout dataset and evaluating its performance using relevant evaluation metrics.
- Results and recommendations: Summarizing the findings and providing recommendations to the company on how to optimize their supply chain based on the analysis and modeling results.
The following tools and technologies were used in this project:
Python programming language Pandas and NumPy libraries for data manipulation and analysis Matplotlib and Seaborn libraries for data visualization scikit-learn library for machine learning
Based on the analysis and modeling results, we have identified the following recommendations for Gala Groceries to optimize their supply chain:
Implement a forecasting system to predict demand for products based on past sales data and external factors such as weather and holidays. Use the results of the stock level prediction model to inform ordering decisions and minimize waste. Monitor the temperature in storage facilities and adjust ordering and stocking strategies as needed to ensure the quality and freshness of products. Consider implementing a just-in-time inventory management system to further optimize the supply chain and reduce waste.
In order to fully address the business problem and provide a complete solution to the client, the following actions should be taken:
- Conduct further analysis to validate the findings and recommendations.
- Implement the forecasting system and stock level prediction model in a production environment.
- Monitor the performance of the system and make adjustments as necessary to ensure optimal results.
- Communicate the results and recommendations to the client in a clear and concise manner, including any additional resources or data that may be required to implement the proposed solutions.
This project has demonstrated the potential for using data analysis and machine learning to optimize the supply chain of Gala Groceries. By accurately predicting stock levels and implementing forecasting and inventory management systems, the company can improve the efficiency of their operations and better meet the needs of their customers. We recommend that the client consider implementing the proposed solutions in order to achieve these benefits.