Predictive Analytics Supply Chain & Logistics Management Dissertation Project Brief | LU

Project Brief

Title:

Managing Cask Inventory with Predictive Analytics – A Study on Scottish Distilleries

1. Introduction & Problem Statement

Scottish distilleries face significant challenges in managing cask inventory due to unpredictable demand, complex production scheduling, and high storage costs. This project proposes leveraging predictive analytics to transform inventory management by accurately forecasting demand, optimising cask rotation, and reducing costs. By integrating real-time data and advanced visualisation tools, the project aims to deliver actionable insights that enhance operational efficiency.

2. Proposed Distillery & Flexibility

While a potential case study location has been explored, I am open to working with a distillery that the advisor believes would be more suitable or supportive for this project. Factors such as accessibility, willingness to share data, and potential for real-time implementation are key considerations.

3. Data Collection (Real-Time Example Data)

The project aims to leverage various data sources, including historical production volumes, cask inventory levels, bottling schedules, and environmental metrics like temperature and humidity that affect maturation.

Sample Data Table:

Date       | Production Volume | Cask Inventory | Avg. Ageing Time (Years)
2023-01-01 | 10,000 liters     | 150 casks      | 3.0
2023-02-01 | 12,000 liters     | 140 casks      | 3.1
2023-03-01 | 11,500 liters     | 145 casks      | 3.0

4. Predictive Model & Analysis

Using Power BI’s forecasting tools, the project will:

• Forecast a 15% increase in demand
• Optimise bottling and cask rotation schedules
• Achieve potential operational savings of 10–12% in storage costs

5. Power BI Dashboard Features

The dashboards will include:

• Demand Forecast Graphs
• Cask Maturation Tracker
• Inventory Heatmap

6. Outcomes & Numerical Impact

• 15% improvement in forecast accuracy
• 20% reduction in excess inventory
• 10–12% decrease in operational costs
• Real-time decision support for inventory planning

7. Conclusion

This project explores the potential of predictive analytics to improve inventory management in Scottish distilleries. With your guidance, I look forward to identifying the most suitable distillery partner and tailoring the project accordingly to ensure practical value and implementation feasibility.