Data-Driven Demand Forecasting

Duration

The project was completed over a period of

3

months

with continuous monitoring and adjustments to optimize performance

3

months

with continuous monitoring and adjustments to optimize performance

Client

A fast food restaurant chain

US

based

with multiple locations across the country

US

based

with multiple locations across the country

Tools and Technologies

Challenge

The client’s existing forecasting model relied mainly on historical sales data and lacked the granularity needed to account for external influences like weather conditions and local events. This limitation led to inefficiencies in inventory management, staffing, and overall operations.

Solution

Stellans developed a tailored forecasting model to meet the client’s needs. The solution included:

Data Collection:

We gathered historical weather data and demand patterns from various reliable sources, including government weather services and third-party weather APIs.

Data Processing:

The collected data was cleaned, normalized, and aligned with the client’s sales data to ensure compatibility and accuracy. Advanced data processing techniques were used to handle missing values and outliers.

Model Adjustment:

The forecasting models were adjusted to account for the impact of weather conditions on customer behavior and demand patterns. We also included data on local events and holidays to further refine the models.

Validation:

The enhanced models were rigorously tested and validated using historical data to ensure their accuracy and reliability. We conducted back-testing to compare the performance of the new models against the existing ones.

Weather Data Integration:

We sourced and integrated weather data, including temperature, precipitation, humidity, and other relevant meteorological factors.

Model Enhancement:

We meticulously cleaned the fused dataset to eliminate features negatively impacting model performance. Statistical techniques were used to identify patterns and correlations between weather conditions and customer behavior. Model parameters optimization was also performed.

Results

Better Staffing Decisions:

With more reliable demand forecasts, the client could schedule staff more effectively, reducing labor costs by 10% and enhancing customer experience through improved service times.

Enhanced Customer Satisfaction:

By aligning inventory with customer demand, stockouts were minimized, improving the overall customer experience.

Improved Inventory Management:

More accurate demand forecasts allowed the client to optimize inventory levels, reducing waste by approximately 15%. Additionally, the process was fully automated, enabling establishments to move away from manual restaurant load forecasting and making revenue forecasting more transparent.

External Data-
Driven Demand
Forecasting

External Data-
Driven Demand
Forecasting

Duration

3

3

Client

US

US

Tools and Technologies

Challenge