Planogram Assortment Optimization: 8-Wk Implementation

Modern category is quite a competitive channel in the American Market to drive revenue and there is a need to make the best use of the available shelf and display space. Category managers face scrutiny for strategic decisions, ensuring fair and justifiable treatment of all brands, while also prioritizing the best path of category growth. Retailers moving to store-specific modulars add further complexity in the competitive landscape. Planograms need to be created keeping the best assortment and configuration in mind, while keeping triple win scenarios in mind to keep all parties satisfied.

The following key questions needs to be answered:

1. Long term vision to be inculcated as assortment and planograms updated once/twice a year
2. Considering evolving needs of category managers on specific constraints, strategies and goals
3. Accessibility to quality data for performing the analytics

Sigmoid’s Assortment Category Excellence Solution aims to optimize store specific assortment categories through data science techniques. We factor in an ML-based solution to automate the creation of retail store planograms. Archetypes can be created for different data availability scenarios and develop corresponding deliverables and KPIs. Several KPIs are taken into consideration such as daily revenue, saleslift, days of supply, sales per inch, new items added, items removed, shelf share, etc. Recommendations are also provided for replacing multi-faced items with better performing ones.

Benefits ensured through Sigmoid's solution:

1. Increase in sales and share of the brand across categories
2. Reduction in inventory costs by identifying the most profitable assortment category
3. Minimizing store labour costs by streamlining assortment planning

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