Leveraging Data Science for Enhanced Sales Forecasting

About Customer

The Customer is a prominent Indian dairy manufacturer with a dominant presence in the domestic market and an extensive export network spanning over 20 countries worldwide.

Challenge : Addressing Sales Performance Instability

The Customer faced challenges related to unstable sales performance compared to their set targets, resulting in missed sales objectives. They sought a solution to accurately forecast sales and establish achievable targets across product categories, brands, and stores. Additionally, the Customer aimed to understand the variance between planned and actual sales performance and identify areas for improvement.

Solution

Approach:

Spybrick's data scientists initiated the solution by cleansing the historical sales data, addressing issues such as duplicate records and inconsistent store address entries.

Advanced Analysis:

The data science team conducted a comprehensive analysis of actual versus planned sales across product categories, brands, stores, and regions. To ensure precise sales forecasting, they developed an algorithm to mitigate the impact of promotions and select the most suitable statistical model. Depending on the available sales history, the algorithm automatically opted for one of four models: linear regression, autoregressive integrated moving average (ARIMA), median forecasting, or zero forecasting.

Identification of Improvement Criteria:

During the advanced analysis phase, Spybrick's team identified three key criteria to assess potential areas for enhancing the Customer's sales performance. By establishing benchmark products, stores, and regions, they calculated sales improvement opportunities for other entities relative to these benchmarks, integrating this insight into the forecasting process.

Results: Accurate Forecasting and Improvement Opportunities

Outcome:

The Customer received a precise sales forecast generated through statistical models and algorithms applied to historical sales data, augmented with a growth rate component.

Identified Potential:

Spybrick identified potential sales improvements of up to 15% for the Customer, based on benchmark comparisons of products, stores, and regions. These improvement opportunities were incorporated into the sales forecast provided to the Customer.

Technologies and Tools Utilized

  • Microsoft SQL Server: Utilized as a data warehouse.
  • Microsoft SQL Server Integration Services: Facilitated data integration processes.
  • Microsoft SQL Server Analysis Services (OLAP): Supported online analytical processing for in-depth analysis.
  • Python: Employed for advanced data analysis and modeling.
  • Microsoft Excel: Leveraged for data manipulation and visualization.
  • Microsoft Excel Power Pivot: Enhanced data analysis capabilities within Excel.