Company Name: QuickMart
Project Objective
QuickMart is currently struggling to anticipate future sales trends, which has resulted in overstocking some products and understocking others, leading to revenue loss and missed opportunities. The absence of a data-driven approach to forecasting sales patterns creates challenges in inventory management and strategic planning. To address these issues, the project aims to analyse historical sales data, predict future trends, and present actionable insights through interactive dashboards, enabling informed decision-making and improved operational efficiency.
Requirement
Develop a predictive model that:
- Analyses Historical Data:
- Examine sales records to identify patterns, seasonality, and key influencing factors such as holidays or product launches.
- Forecasts Future Trends:
- Predict sales for the next quarter or specific time periods using time-series or regression-based methods.
- Generate confidence intervals for forecasts to guide risk assessment.
- Visualises Data:
- Create dynamic dashboards that showcase historical trends alongside future predictions.
- Include comparison tools for planned versus actual performance.
Detailed Features
App Features
- Data Analysis:
- Identify key sales drivers, such as holidays or product categories.
- Forecasting Model:
- Use linear regression or time-series methods for predictions.
- Dashboard Visualisation:
- Include charts comparing historical and forecasted sales.
Deliverables
- Phase 1 – Design and Mockups:
- Data analysis documentation and feature selection for the model.
- Wireframes for dashboard visualisations.
- Phase 2 – Prototype:
- Excel-based forecasting tool with visual dashboards.
Technology Suggestion
- Excel: For data analysis and model building.
- Power BI/Tableau: For creating dashboards.
Expected Output
- Phase 1: Data insights, feature documentation, and dashboard wireframes.
- Phase 2: Forecasting tool and interactive dashboards.