Turning Data Chaos into Inventory Planning Clarity
Discover how a global brand built a unified multi-seasonal inventory management platform for retail demand forecasting using Databricks.
The Client
A multinational sporting goods company.
The Challenge
The client faced significant limitations with their existing planning approach, resulting in lower data quality and thus reduced growth potential. The current approach to estimating and forecasting future orders is built on extensive manual work through Excel sheets/functions, subsequently impacting business performance.
The client’s planning team encountered several critical business challenges:
- Fragmented Data Sources: Data was scattered across multiple sources, making it difficult to obtain a comprehensive view of inventory consolidation
- Manual, Multi-Source Planning: Planning for current and future seasons required extracting data from multiple sources for each season, consuming valuable analyst time.
- Error-Prone Processes: The manual process increased the risk of errors and inconsistencies, leading to suboptimal inventory positioning.
- Cross-Season Metric Gaps: Due to human oversight, metrics could be missed when information is carried forward from one season to another.
- Limited Forecasting Capability: The lack of a unified data source prevented efficient analysis and forecasting across multiple seasons.
The client needed a solution to simultaneously plan for the current season and at least four future seasons. This capability would help them estimate requirements and make informed retail inventory control decisions about what goods to produce and sell in the market while eliminating error-prone manual processes.
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The Solution
GSPANN’s Information Analytics team developed an automated and unified seasonal inventory management platform that leveraged inventory consolidation techniques to provide a holistic view of current and future demand. The solution addressed the client’s challenges by creating a unified data environment that eliminated the need for multiple extracts and manual processing.
The solution transformed their planning capability through:
- Consolidated data environment: Using Databricks, we created a unified view of all future seasonal data sources, eliminating fragmentation and providing a single source of truth.
- Efficient multi-season access: The new system stores seasonal data in Databricks views using an Amazon Web Services (AWS) S3 path, allowing planners to access all seasonal data in one place.
- Comprehensive planning horizon: a single view that combined current season data with two additional in-season periods and two pre-season periods, enabling accurate multi-season planning.
- Standardized time perspectives: We standardized data at different granularity levels (weekly for in-season, monthly for pre-season), which is presented in a uniform monthly view, maintaining metric accuracy while enabling consistent analysis.
- Reliable inventory metrics: Our practice built specialized logic to correctly calculate end-of-period (EOP) and beginning-of-period (BOP) inventory metrics across all time periods.
- Dynamic planning capability: GSPANN engineers vastly enhanced the client’s future planning abilities by incorporating rolling forecasts (rolling seasons) that update as new data becomes available.
- Automated data currency: We automated the entire process through Airflow scheduling to ensure data remained current without manual intervention.
The new system provided planners with a complete end-to-end picture of their data across multiple seasons, enabling more effective retail inventory control planning and eliminating the need for manual consolidation.
Business Impact
The project delivered significant benefits to the client, including:
- Accelerated Decision-Making: Significantly decreased the time required to analyze seasonal data and understand trends, enabling fast and effective business decisions.
- Enhanced Strategic Planning: Supported the client’s new strategy by providing better visibility into future inventory needs across multiple seasons, improving product positioning.
- Operational Efficiencies: Decreased querying time through consolidated views, lowering overall cloud expenses while eliminating manual data extraction efforts.
- Improved Data Quality: Automating data validation and consolidation processes minimized human error, creating more reliable forecasts and inventory plans.
- Streamlined Workflows: Simplified tracking and monitoring through a single consolidated view, making planners more productive and responsive.
- More Informed Decisions: Provided planners with comprehensive data across multiple seasons, enabling more strategic inventory positioning and reducing stockouts and overstock situations.
- Consistent Performance Metrics: Ensured critical metrics like EOP and BOP were correctly calculated across different periods, creating a reliable foundation for all inventory decisions.
Technologies Used
Related Capabilities
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