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Predictive Analytics Assignment Dissertation: Scottish Distillery Case Study for Cask Inventory Optimization
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| University | Loughborough University (LU) |
| Subject | Supply Chain & Logistics Management |
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, optimizing cask rotation, and reducing costs. By integrating real-time data and advanced visualization 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. Aging 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
– Optimize 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