Our Beliefs
With extensive experience across diverse retail contexts, OneHive brings clear convictions and proven practices from the earliest stages of a project.
Replenishment optimisation is not only an algorithmic challenge. It is a strategic exercise embedded in your broader commercial ecosystem. Our experience has helped us identify the fundamentals of an effective replenishment model, from data quality to user adoption.
Our approach blends analytical rigor with operational pragmatism to deliver solutions that genuinely fit your business. From defining target stocks to phased deployment, our methodology transforms replenishment into a durable and measurable competitive advantage.
Results
Tangible Results for Our Clients
Beyond direct impact on sales and inventory, implementing an automated replenishment model secures daily operations, improves collaboration between Supply Chain, Retail Ops and Merchandising and frees teams from manual processing.
Clear outcomes observed across our clients:
- Fewer stockouts and higher availability
- Better cash flow through reduced inventory value
- Faster reaction to unexpected events
- Harmonised stock levels across the network
- Time saved and stronger productivity by eliminating manual data handling
For central or regional distribution optimisation, refer to our dedicated page.
Foundations of a reliable replenishment model
Product and store master data
We secure key prerequisites early in the project:
- Product and store master data management with clear attributes and governance, essential for accurate ERP ordering
- Availability and quality of source data, including sales histories, stock levels and in-transit quantities used for replenishment calculations
Defining target stocks
During detailed design, we model daily or weekly sales and determine optimal target stocks using the most relevant method such as Target Coverage, Demand Driven DRP or Gaussian or Poisson modelling. Based on sales history, seasonality, future forecasts and your network specifics, we define optimal stock levels for each store.
Operational onboarding
We involve operational teams at every project stage to validate business rules and user journeys. Clear, intuitive interfaces such as tables and interactive charts support fast adoption and a smooth transition from legacy tools.
MVP methodology and phased deployment
We recommend an MVP approach: a first functional model delivered in three to four months, followed by iterative enhancements. Phased deployment by pilot stores or categories is preferred, especially in international projects. Dedicated reporting and controlled validations help confirm new target stocks and secure user confidence ahead of go live.
Solutions for your operational challenges
We address your concrete challenges including:
- Omnichannel integration including web strategy, ship from store and pick in store, and wholesale
- Management of launches, phase outs, store openings and closures
- Alignment with related processes such as assortment, upstream distribution and demand planning
- Consideration of operational constraints around shipping, receiving and storage, with dedicated reporting for Retail and Logistics teams
- Stock harmonisation via store to store transfers or warehouse returns using linear optimisation models
- Planner routines and alert mechanisms on availability rate, stockouts, overstocks or demand variability
Technical topics & innovation
Throughout the project, we work closely with Data teams to manage automatic interfacing, integration controls, solution robustness, performance and data volume constraints.
With our partner IRIS by Argon and Co, we offer AI augmented replenishment delivering strong results for store openings, product launches or assortment expansion. To learn more.
