Assortment planning
Fulfill customer needs and increase store sales, profit, inventory turnover with optimal product mix.
Our SOLUTION
We developed three service solution, each covering a certain stage of assortment management decisions, which is based on accurate data analysis and modelling and leads to a higher customer satisfaction and financial results.
1. Macro – level product mix
Allocate store space to different categories depending on customer demand and size. Group stores to category specific clusters. Allocate appropriate space to each category in each cluster.
Define each category cluster assortment structure at sub-category and product attribute (taste, price, size, etc.) levels ensuring that it meets customer needs, comply with store format, and maximize store financial KPIs.
2. Item – level product mix
Choose correct single SKUs to satisfy customer needs, maximize sales and profit, considering assortment structure and available space in category clusters, as well as commercial terms with suppliers. Take listing and de-listing decisions on single items, brands, suppliers. Optimize assortment fulfilling more costumer needs with the same number of SKUs and minimize sales cannibalization. Prepare merchandising requirements at SKU level with facings and shelf inventory.
3. Inventory buying optimization
Optimize inventory in sales area and back rooms. Develop purchase plans which allow to avoid both out-of-stock and overstock based on existing supply chain terms. Simulate and optimize various supply chain scenarios. Estimate adequate safety stock and minimum shelf stock. Start auto ordering.
WHY US
Satisfied customers:
•Segmented stores and prepared category clusters
•Covered more customer needs with the same quantity of items
•Localized assortment
Better financial results:
•Higher sales and profits
•Less capital invested in stock
•Lower out of stock, overstock, markdowns
EFFECTIVE assortment MANAGEMENT:
•Faster data driven decision making
•Transparent purchasing and auto ordering
•Efficient utilization of store and shelf space
Future scenario modelling:
•Increase or decrease merchandising space of SKU, brand, category
•List or de-list SKU, brand, supplier
•Change supplier commercial terms
Sumatus is an advanced analytics company specializing in retail.
We combined our more than 20-year experience and knowledge of retail, FMCG and category management with advanced analytics methods like Data Mining, Machine Learning, Predictive Modelling, Clustering, Simulation & Optimization, and developed algorithms which enable retailers convert data into insights and fact based decisions in assortment management in order to ensure customer satisfaction and profitable growth.