Business challenge addressed
Deploy a shared solution across the Group’s regions and brands while preserving local specificities
L’OCCITANE engaged OneHive to optimise the replenishment of both its permanent products and promotional campaigns across all points of sale.
We designed a single model, deployed across 10 regions, to manage stock allocation and campaign tracking based on sales signals and forecasts.
Client testimonial
“Anaplan significantly improves the analyses I carry out across our stores. Reports are clear, intuitive, and reliable. The tool delivers a major time saving, freeing up capacity to provide a more tailored level of support to each store.”
Before Anaplan was implemented, store replenishment planning relied on locally maintained Excel files. This approach had major limitations: formula errors, untracked manual changes, unreliable data, and a lack of harmonisation across regions. Business expectations were high: improve product availability, reduce store overstocks, and deliver tangible efficiency gains for teams.
Challenge met. Planners, who previously spent around 60% of their time on manual data processing, saw their roles evolve and can now focus on higher value-added activities.
Thanks to the deployment of Anaplan:
The project also helped secure all the data foundations required for the process, through the development and automation of more than 20 interfaces, executed several times per day.
The scoping phase began with one week of on-site workshops bringing together the project team, decision-makers, and key users from the two pilot regions. These sessions made it possible to review the existing process and identify key issues in order to define the main stages of the target process.
Workshops then continued remotely over an additional three weeks to refine design decisions and address open points. From this phase onward, and throughout the project, monthly meetings were also organised with regions outside the pilot scope to share design progress, collect feedback, and anticipate local specificities.
Involving all subsidiaries from the outset was essential to ensure model consistency and the success of future rollouts.
Regional differences made process standardisation challenging. Ignoring certain local requirements would have jeopardised user adoption. The key challenge was therefore to design a solution that was both modular and adaptable to the maturity and needs of each subsidiary, while preserving the Core Model approach and ease of use.
OneHive teams met this challenge by deploying a shared, flexible, and collaborative tool, supported by an architecture that allows local adaptations without undermining the global model.
Like most operational processes, replenishment relies on inventory, in-transit, and sales data sourced from the ERP and other enterprise systems. The reliability of results therefore depends directly on the quality of data integrated into the solution.
In close collaboration with IT teams, we designed a single Data Hub fed by the ERP or BI systems, supported by robust and automated interfaces. Consistency checks and traceability mechanisms were implemented to secure the data used and surface the right alerts on a daily basis.
We also supported the design of outbound interfaces, which are critical to the process. The deployed solution is directly interfaced with the ERP to automatically create and transmit new distribution orders, while a return flow verifies correct integration. The setup handles large data volumes: around 40 million sales records, 8 million inventory records integrated into the models, and a daily flow of approximately 20,000 orders sent to the ERP.
SOLUTION
Daily monitoring, alerting systems, operational reporting, and ERP integration controls
The teams quickly adopted the solution, which significantly improved their day-to-day work and overall efficiency. Collaboration with stores became smoother. Initially hesitant about stock reductions, store teams observed a better balance between product availability and inventory levels. Tangible results were visible within the first two months.
Key figure
-12%
Stock value
Inventory optimization while maintaining a 99% product availability rate.
Key figure
-50%
Data processing time
Reduced time spent on data processing, allowing teams to focus on analysis and decision-making.
Key figure
50
Daily users
A core model deployed across more than 10 subsidiaries, strengthening collaboration between teams.
Specialized solutions for every link of your value chain
Simultaneously optimize product availability and commercial performance. We model your logistics network: warehouses, hubs, distribution points, in alignment with demand flows. This end-to-end view enables optimal inventory levels, efficient warehouse sizing, lower logistics costs, and consistently high service levels.
Anticipate market fluctuations with precision and reinforce your forecasting capabilities. Our statistical and AI models capture predictive signals while our tools harness your organization’s collective intelligence. The result: a reliable forecast that supports decision-making across the entire company.
Align commercial strategy with operational capabilities through a structured S&OP process. We implement applications that enable collaborative decision-making and effective trade-offs between service levels, resource utilization, and financial performance, ensuring seamless execution of your strategy.
Optimize your industrial performance by balancing capacities, technical constraints, and commercial priorities. Our models help you maximize resource utilization while honoring customer commitments, supply constraints, and your broader industrial strategy.
Maximize sales with the right product, in the right place, at the right time. Our replenishment approach combines predictive analytics with customized business rules to ensure optimal availability at point of sale while respecting operational constraints.
Other Case Studies
Assortment and replenishment
By structuring its retail replenishment on Anaplan, Messika, a luxury jewellery house, secures decision-making and supports its international growth.
Assortment and replenishment
By structuring its retail replenishment on Anaplan, premium fashion brand Jacquemus is reducing overstock and improving product availability in stores.
Global planning
By modelling its global end-to-end Supply Chain in Anaplan, an agri-industrial player scenarios its decisions to improve overall performance, optimise financial impact and strengthen synchronisation between Finance, Operations and Supply Chain.
Discover answers to key questions about our services and approach.
Launching an Anaplan project requires, first and foremost, setting up a dedicated governance structure. This includes identifying key users early on, involving them from the solution design phase and relying on their role throughout testing phases to secure both relevance and adoption.
In parallel, data-related challenges must be anticipated in order to collect the data critical to the process as early as possible. This enables teams to work with concrete use cases during demonstrations and testing sessions. Defining the data architecture and automating interfaces is also essential, particularly for day-to-day operational processes that require fresh, reliable data.
This process was selected as the first Anaplan initiative due to its fast and easily measurable return on investment. It enables inventory optimization by reducing overstock levels and associated holding costs, while maximizing product availability and, in turn, sales. It also improves planners’ day-to-day work by allowing them to focus on higher value-added activities. Finally, the strong adoption of the tool and the success of this project paved the way for launching a second initiative focused on Demand Planning.
Given the business impact of in-store replenishment processes, it is essential to carefully design the deployment strategy to reassure teams. One effective approach is to implement a gradual rollout within the pilot region.
Although the application is configured from the outset for the full store network, the teams chose to progressively activate the transmission of orders from Anaplan to the ERP—starting with two stores in the first week and gradually expanding the scope.
Similarly, during model initialization, a dedicated screen was built to import existing target stock levels from the legacy system and compare them with those calculated by the new model, using alerts to highlight discrepancies.
hroughout the pilot phase, monthly meetings were held with the other regions to present the first dashboards. The objective was to gather feedback as early as possible to ensure the architecture was compatible with local specificities. This approach also helped anticipate future evolutions and build a flexible model capable of adapting to upcoming needs.
Once the solution was deployed in the pilot regions, the other regions were gradually onboarded. The key challenge was to assess how closely their requirements could align with the existing model and, where necessary, introduce targeted adjustments to accommodate local specificities.
Three internal Model Builders were trained (two from the business teams and one from IT), becoming the primary points of contact for application users from the pilot launch onward. They provide first-level support, are able to deliver certain enhancements autonomously, and support new rollouts. For more complex evolutions, they rely on OneHive’s teams.
Functional documentation was also produced to facilitate the onboarding of new team members and to formalize the business rules implemented within the model.
After go-live, a new governance framework was established to manage and prioritize the backlog of user requests. Enhancements are deployed through a structured process, with a strong focus on non-regression testing to ensure model stability over time.
Didn’t find what you were looking for?
Contact usLet’s explore how we can make your project a success.
Connect with our experts
