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Supply Chain & Retail Solutions user guide

About merchandising solution

Merchandising solution for retail buying, replenishment, pricing, and promotion decisions at SKU and location level, spanning the Rebuy, Replenish, Promotions, and Markdown modules.

The UiPath solution for merchandising helps organizations make and act on the commercial decisions that shape demand and protect margin: what to buy, how much stock to hold and where, and how to price and promote it once it's in the network.

It provides retail buying, merchandising, and pricing teams with the tools they need to make consistent, SKU and location-level decisions at scale, using AI models to reconcile demand signals, supply constraints, and commercial guardrails. Rather than relying on manual analysis and spreadsheets, the solution applies data-driven decision logic to help teams evaluate trade-offs, generate recommendations, and act on them with confidence.

Merchandising is designed for environments where these decisions must be made for thousands of SKUs across stores, channels, and locations, at pace, and coordinated with broader supply chain and commercial objectives. Merchandising is typically used alongside inventory management and commercial pricing to support coordinated planning across supply, demand, and commercial functions.

Scope and responsibilities

The UiPath solution for merchandising is designed to support merchandising decision-making. Execution activities — raising purchase orders, allocating stock to stores, publishing promotions, or updating prices — are typically handled by existing enterprise systems (ERP, POS, ecommerce, WMS), and this solution complements those systems by helping teams decide which merchandising actions to take.

When this solution is a good fit

You should consider using the UiPath solution for merchandising if your organization:

  • Manages thousands of SKUs across stores, channels, and locations, making manual or spreadsheet-based decisioning difficult to sustain
  • Needs to coordinate buying, replenishment, pricing, and promotion decisions with inventory availability and supplier or warehouse constraints
  • Wants to evaluate the impact of merchandising actions — reorder quantities, allocation, markdowns, or promotions — before committing budget or stock
  • Relies on manual analysis or fragmented tools to plan or assess merchandising initiatives
  • Seeks more consistent, data-driven merchandising decisions at scale, particularly for sized or seasonal product ranges

This solution is particularly useful when merchandising actions have downstream effects on inventory levels, pricing outcomes, or operational performance.

Modules

The following modules make up the merchandising solution.

  • Rebuy — Generates optimized reorder recommendations for existing SKUs at the SKU-location level. Calculates net requirements from sales, stock on hand, in-transit inventory, and open purchase orders; applies supplier constraints such as lead times and minimum order quantities/values; auto-drafts purchase orders ready for review and ERP export.
  • Replenish — Generates optimized replenishment and allocation recommendations from a central warehouse or DC to stores, at the SKU-location level. Senses store-level demand and stock-out risk, rations finite DC inventory toward the highest-performing stores when supply is constrained, and applies pack/box/size-run rules to produce pick lists for the WMS or ERP.
  • Promotions — Generates optimized promotion recommendations: which products or categories to promote, which mechanic to use, how deep to discount, and when and where. Models expected uplift, margin impact, and cannibalisation, and applies guardrails (max discount, min margin) before handing off to pricing, ecommerce, or POS systems.
  • Markdown — Generates optimized clearance-pricing recommendations for ageing or excess inventory. Models alternative discount depths against expected demand, margin, and sell-through; recommends SKU-level markdown prices within guardrails (min price, max discount, brand-protection); monitors post-markdown performance.
  • Scope and responsibilities
  • When this solution is a good fit
  • Modules

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