Retail Solutions, Merchandise Planning

Stage one addresses item categorization to align merchandise planning with the customer decision tree, outlining a five-level item hierarchy (division, category, subcategory, class, segment) and the eight-step category management process.

Define category management and category definition; use customer decision tree segmentation from interviews and loyalty data to shape merchandise planning, illustrated by detergent with category, subcategory, class, and segment.

Define the category role to set assortment width and position the retailer among four roles: destination (90 to 95%), routine (75 to 80%), occasional/seasonal/impulse (narrow), and convenience (small space).

Assess the category from four angles—consumer/shopper, retailer, market, and supplier—to uncover nature and behavior, then use the category scorecard to guide merchandise planning and assortment decisions.

Applies seven category strategies to attract, retain, and grow shopper spending, and guides tactics to optimize product assortment while balancing SKUs and inventory for destination categories.

Recaps the item categorization stage of the Cecafa technique, aligning company items to a customer decision tree, defining category roles, targets, and a strategy that drives implementation and review.

Group stores into clusters to centralize demand and supply, reduce granular data errors, and save time while enabling inventory control and effective assortment planning.

Identify the three factors controlling store clustering: target audience, performance, and space; apply a two-layer clustering approach across formats, using sales forecast and division and category levels to refine assortments.

Apply a four-step store clustering process to generate and validate category-level sales forecasts per store. Align forecasts with stakeholders and future plans to prepare clusters for next season.

Cluster stores by expected hair care sales after forecast generation and validation, using iterative variance-based grouping to form well-shaped clusters, guiding assortment planning and inventory control.

Explore the final step of store clustering: perform category-level clustering with own expected sales, compare store clusters across categories, and elevate to division or company level when 80% alignment occurs.

Finalize store clustering by category to define unique fingerprints for each store and tailor assortments within merchandise planning to maximize sales opportunities.

Learn how to build an inventory frame to optimize returns by allocating store budgets and cluster-based days, defining assortment for permanent inventory and activities like new items and promotions.

Collaborate in stage four to build the assortment through structured workshops between planning and category participants, using data-driven targets, market insights, and clustering to optimize inventory and shelf capacity.

Define the assortment for each cluster and set target shelf capacity, starting with core offer items, balancing cannibalization, visibility, and replenishment factors to build a rationalized range.

Apply the Csfa technique of merchandise planning to categorize items, cluster stores, define optimum inventory and shelf space per item per store, and plan for new items with planogram tools.

Define the minimum display quantity (MDK) and replenishment parameters, set target shelf capacity (TSC) per item per store from average monthly sales, and guide pull replenishment for regular sales.

Control the store assortment via a robust item location status update to ensure replenishment and prevent undesired transfers, and perform annual Cecafa technique reviews; consider biannual cycles for dynamic categories.