Assortment Intelligence: 8-Wk Implementation

One of the 4 pillars for Revenue Growth Management in organizations is Assortment. Maintaining the right mix of products across retailers, channels and geographies to optimize profitability and service levels, assortment helps in optimizing shelf space, boost customer satisfaction, understand market demand, enable efficient inventory management, improve sales performance. But challenges such as Supply Chain Disruptions, intensifying competition, changes in customer preferences exist, with CPGs also struggling with inadequate data infrastructure, disparate data sources and change management issues: The following questions need to be answered:

1.What is the optimal product mix 2.How to meet customer preferences 3.What are the emerging trends 4.How to respond to competitors 5.How to efficiently manage inventory 6.What are regional store-specific needs 7.Financial impact of the decisions taken

To help enterprises with the above, Sigmoid has developed a suite of solutions, to propose growth opportunities through assortment planning. Our solutions help to eliminate SKU-location whitespaces and enable intelligent order creation. We enable organizations with whitespace identification in order to perform sales history analysis and similar store gap analysis. Our hybrid recommendation models are used for recommending new products for each POS, as well as store-level sales forecasting models, for forecasting the sale of existing products and giving optimized quantity recommendations - with product ranking & strategy selection. We have helped organizations in taking decisions at a strategic level by providing them with a lifecycle view of the movement of different categories, brands, products, assign profitability buckets and accordingly realize investment opportunities.

Some solutions offered under assortment intelligence:

1. B2B Order Recommendation Engine
2. Planogram Assortment Optimization
3. Assortment Lifecycle Intelligence

Benefits ensued:

1. Better inventory management - cost savings opportunities
2. Improvement in Market share
3. Realization of profitability in portfolio-level

The following Azure workloads have been used in developing the above mentioned solution:

1. Azure Data Factory (ADF) for orchestrating data integration pipelines to integrate data from diverse sources
2. Azure Data Lake Storage, serving as the underlying storage layer
3. Microsoft Purview for a Unified Data Governance
4. Azure Machine Learning
5. Integrating and analysing large data sets
6. Power BI for Business Intelligence