For decades, Life Sciences supply chains were designed around reliability and compliance. Stable markets, recurring prescriptions and strong margins enabled organizations to plan over long horizons. This model proved resilient. Today, it is showing its limits.
The environment has fundamentally changed: demand volatility, increasing regulatory complexity, fragmented industrial networks and rising ESG requirements. Life Sciences companies are no longer shielded from the instability reshaping global supply chains. This article explores the key planning challenges facing the industry, the limitations of current tools and the practical value of a more structured planning approach.
Life Sciences markets long benefited from structural predictability. That is no longer the case. Product launches now generate difficult-to-anticipate demand peaks, especially for innovative therapies whose adoption curves remain uncertain. Epidemic cycles create sudden disruptions: the Covid-19 pandemic exposed the fragility of historical planning models when faced with major supply shocks. The rapid evolution of oncology and targeted therapies accelerates portfolio turnover and complicates demand planning.
The consequences are immediate: service levels deteriorate precisely where criticality is highest, while inventory levels rise due to insufficiently responsive trade-offs.
Urgency has become a recurring operating mode. Air freight usage has increased across several segments, driven by shorter lead times and supply tensions. Air transport costs on average four to six times more than ocean freight. Balancing high service levels with logistics cost control has become one of the central trade-offs for Supply Chain teams.
Tariffs and geopolitical tensions have reignited discussions around industrial footprints. Several pharmaceutical groups are reassessing sourcing strategies to reduce exposure to geographic concentration risks. Production regionalization, driven by healthcare sovereignty concerns, is reshaping logistics networks: new CMOs, multi-site flows and market-specific supply configurations.
These transformations significantly increase network complexity, while planning tools have not evolved at the same pace.
Life Sciences companies invest heavily in R&D. These long cycles increase pressure on working capital requirements. The dilemma is structural: maintaining sufficient safety stock to guarantee service levels without immobilizing cash already tied up in clinical projections. This is not a question of intent. It is a matter of planning models and tools.
Regulatory requirements define the framework for every Supply Chain decision in Life Sciences. Any change in supplier, process or manufacturing site requires a documented qualification process, sometimes lasting six to eighteen months.
Upstream, teams maintain visibility over subcontractors, critical components and process changes through structured change-control procedures. Downstream, batch traceability, market allocation obligations and recall procedures require precise documentation all the way to the final distribution point.
When multiple industrial partners are involved, coordinating this traceability becomes a major operational challenge.
Life Sciences products have limited shelf lives. Inventory destruction risks are real and costly. Effective planning integrates expiry constraints directly into allocation decisions: which batch, for which market and at what date.
In addition, some markets impose mandatory minimum stock levels equivalent to four to six months of coverage. These requirements are non-negotiable. They must be integrated into distribution plans and aligned with available industrial capacity.
Commercialization timelines directly impact the value of new therapies, especially when patent exclusivity windows are limited. Managing time-to-market requires close coordination between clinical, regulatory, industrial and Supply Chain teams – coordination that fragmented systems struggle to support.
ESG commitments now add another layer of complexity. Emission reduction targets include Scope 3 emissions, meaning the entire value chain, including suppliers. Integrating sustainability criteria into allocation decisions and partner qualification is no longer optional: it is now an operational parameter that planning models must be able to manage.
The question is not whether existing tools worked. In many cases, they worked well. The real question is whether they are still suited to today’s challenges.
Gartner data highlights the scale of the gap: Supply Chain costs represent on average 37.3% of the total cost of patient care, yet only 19% of Life Sciences organizations are redesigning capabilities to better anticipate demand, compared with 23% across all industries.
Even more revealing: fewer than 44% use technology to simulate the impact of different scenarios on business objectives. The transformation potential remains largely untapped.
Traditional ERPs and advanced spreadsheets now face four structural limitations in this context.
These limitations produce concrete consequences: late issue detection, urgent decision-making and an inability to build alternative plans in real time.
The shift is already underway. More and more organizations, particularly in the mid-market segment, are redesigning their application landscapes. The rise of fabless operating models, where manufacturing is outsourced to specialized CMOs, makes multi-party coordination even more critical.
These organizations need a platform capable of integrating new partners quickly, without multi-year IT programs.
Functional expectations are converging around five key requirements.
This is where Anaplan provides a relevant answer. The platform offers the flexibility required to model complex business rules, simulate scenarios quickly and progressively extend coverage to new use cases. It does not require lengthy deployment cycles to create value.
Several critical use cases can be supported in a Life Sciences environment:
The platform’s agentic AI capabilities reinforce these use cases through anomaly detection in plans, prioritization of critical alerts and recommendation engines based on historical data. Diagnostics accelerate and team response times decrease.
Deploying Anaplan in a Life Sciences environment requires far more than technical expertise. It demands a deep understanding of business processes, regulatory constraints and sector-specific decision-making dynamics.
This is precisely what OneHive brings. As a premium consulting and integration firm and strategic Anaplan partner, OneHive brings together more than 50 consultants from leading engineering schools, combining technical expertise with deep business knowledge, including in the Life Sciences sector.
Our approach is built around three concrete commitments.
On highly specific challenges such as S&OP, CMO management, distribution planning, production planning or ESG reporting, our combined business and technical expertise makes the difference between a deployed tool and a tool that is truly adopted.
Life Sciences Supply Chains are entering a new phase. Compliance remains necessary. It is no longer sufficient.
The organizations that will succeed are those capable of implementing integrated planning: shared data, real-time scenario simulation and coordinated decision-making across functions.
At OneHive, we support these transformations with rigor and method. Because the performance of a Life Sciences Supply Chain is not declared, it is built, use case by use case, with the right teams and the right tools.
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behind the article
After 13 years in operational consulting at Argon & Co, Florian joined OneHive to scale our growth and strengthen how we run. He brings deep expertise in optimizing decision-making and operational processes, spanning supply chain planning, merchandise planning, and logistics, and in steering large-scale transformation programs. He has extensive sector experience across Luxury, Apparel/Textile, Dermo-Cosmetics, Life Sciences, and FMCG. Internally, Florian plays a pivotal role in people processes, with a focus on HR development (evaluations, training, recruiting). He is a graduate of Centrale Paris.
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Yes. Anaplan is particularly well suited to Life Sciences environments because it enables organizations to model complex processes while remaining flexible and scalable.
The platform can support use cases such as multi-market S&OP, demand planning, inventory allocation, CMO collaboration, what-if scenario planning and time-to-market management. It also allows companies to progressively integrate new industrial partners, markets or regulatory constraints without redesigning the entire model.
One of the main challenges for Life Sciences organizations is the fragmentation of data and decision-making across sites, industrial partners and internal functions.
A collaborative planning platform centralizes forecasts, capacities, inventory and logistics flows within a shared framework. Supply Chain, Quality, Regulatory and Finance teams can then work from the same view of constraints and priorities, significantly improving responsiveness and decision quality.
In Life Sciences, a planning project is not just about technology. The integrator must understand regulatory constraints, traceability requirements, shelf-life management, supplier qualification processes and the decision-making dynamics specific to the pharmaceutical and medtech industries.
A strong partner must be able to structure the scoping phase, secure architectural choices, involve business teams and build scalable models. This is precisely where OneHive stands out, combining Anaplan expertise with deep operational knowledge of Life Sciences Supply Chain processes, supported by a progressive MVP-based approach that delivers value quickly.
Yes. Many industrial companies still rely on legacy APS systems, inflexible ERPs or Excel models that have become difficult to maintain in order to manage their Supply Chain.
Anaplan centralizes planning within a single collaborative environment that is easier to adapt to changes in the industrial network. The platform becomes particularly relevant when processes grow into multi-site, multi-party environments or are heavily impacted by demand volatility and regulatory constraints.
The timeline depends on the functional scope and the maturity level of the organization. An MVP approach generally allows a first use case to go live within three to six months, with functional coverage then progressively expanded over time.
This approach is especially well suited to Life Sciences environments because it secures business value quickly while reducing the risks associated with long or overly rigid transformation programs.
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Demand Planning, or sales forecasting, is not a standalone analytical exercise. In an environment shaped by demand volatility, channel fragmentation, and increasing industrial constraints, forecasting becomes a critical lever in demand planning and business steering.
It drives alignment across Sales, Supply Chain, Production, and Finance, and directly impacts the quality of decisions made at strategic, tactical, and operational levels.
A robust Demand Planning process is built on three interconnected pillars:
Taken individually, these levers have limited impact. Combined, they transform forecasting into a powerful driver of decision-making and performance optimization.
Raw historical sales data alone is not sufficient to build a reliable forecast. While organizations now have access to increasing volumes of data, they often struggle to leverage it effectively.
Relevant business data must be identified, prioritized, and integrated into a structured Demand Planning process. Typically, this includes:
Sales data forms the foundation of statistical forecasting models, but it must first be cleansed and adjusted through a critical historical correction process to reflect operational reality.
Additional commercial inputs then enrich this base in a controlled manner, incorporating the impact of promotions, events, or supply disruptions. Finally, manual adjustments made by teams should be systematically tracked and justified to ensure transparency and consistency across the forecasting process.
In Anaplan, this approach is reflected through layered forecasting models and explicit governance of contributions. The platform enables collaboration and supports the implementation of a structured and disciplined Demand Planning process.
Forecast granularity should be defined based on operational needs to ensure relevance, usability, and traceability. Key dimensions typically include:
Flexible platforms such as Anaplan make it easier to model these dimensions, adjust granularity, and align calculations with business processes without heavy model redesign.
Forecasting approaches must be aligned with the product lifecycle to ensure relevance and robustness:
Even with these foundational approaches, organizations can already achieve satisfactory forecast accuracy across a large share of their portfolio.
Machine Learning and AI enhance forecasting by introducing additional predictive signals and capturing more complex relationships. They enable:
We support our clients in their Demand Planning processes through Anaplan Forecaster, a native solution that provides a scalable and configurable ML-powered forecasting engine. It integrates client-specific data, signals, and metrics to deliver reliable forecasts tailored to each organization’s context.
Effective Demand Planning relies on strong cross-functional collaboration.
Sales teams contribute insights on product launches, store openings, and commercial initiatives, integrating marketing assumptions and field intelligence.
Forecast consolidation and financial translation ensure alignment with Finance, converting volumes into revenue and margin projections and integrating them into budgeting cycles.
Forecast outputs then feed downstream processes such as distribution planning and production planning, ensuring alignment with logistical and industrial capacities.
Demand Planning frequency is rarely challenged. Many organizations still operate on rigid monthly cycles inherited from traditional S&OP practices.
However, in fast-moving environments, this approach quickly shows its limits. Not all decisions require the same cadence, and effective processes typically combine multiple rhythms:
The objective is not to increase the number of meetings, but to clarify decision-making responsibilities and timing. A well-defined review cadence strengthens governance, reduces unnecessary manual adjustments, and enhances forecast credibility.
Forecast Value Added (FVA) is a performance metric used to assess the effectiveness of the forecasting process.
It evaluates whether each step—baseline data, statistical models, or manual adjustments—improves or degrades forecast accuracy.
By isolating the contribution of each stage, FVA helps identify value-adding activities, eliminate unnecessary interventions, and strengthen the overall reliability of Demand Planning.
Integrating Demand Planning with operational and industrial processes has become a critical priority to ensure consistency in decision-making across the entire value chain. Forecasts directly feed into distribution and inventory planning (DRP), enabling the calculation of requirements across the logistics network.
They also support operational and tactical decision-making by informing inventory allocation, particularly in multi-warehouse or multi-channel environments where supply constraints require fast and objective trade-offs. Forecasts further impact tactical and strategic planning when used to build production plans and support S&OP processes.
By connecting these processes, organizations create a continuous flow of information. Forecasting becomes the entry point of an integrated planning approach, where each decision—from distribution planning to master production scheduling—is based on a shared and continuously updated view of demand.
In a context where volatility has become the norm, the ability to simulate scenarios is a strategic capability. Scenario planning makes it possible to explore multiple potential trajectories: a baseline scenario, a pessimistic scenario, an optimistic scenario, or variations based on commercial, industrial, or even geopolitical assumptions.
These scenarios provide a structured framework to test system resilience, assessing impacts on capacity, inventory, costs, and service levels. They also enable the preparation of alternative action plans, anticipation of risks, and more secure decision-making within S&OP processes. Scenario planning becomes a key tool for dialogue between Sales, Supply, Finance, and Production by making the consequences of decisions visible.
By embedding simulation at the core of the process, Demand Planning becomes a true decision-support tool in uncertain environments.
Anaplan provides an ideal environment to connect all Supply Chain planning processes. The platform creates strong links between Demand Planning, S&OP, Distribution Planning, and Production Planning, based on a unified data model and a collaborative process. This continuity ensures that every decision—from forecasting to inventory allocation or capacity trade-offs—relies on consistent and shared information across the organization.
The strength of Anaplan lies in its ability to accurately model business rules, integrate each organization’s specificities, and simulate different scenarios quickly. Teams can test assumptions, measure their impacts, and converge toward a shared view. By bringing stakeholders together within a single collaborative platform, Anaplan strengthens operational alignment and accelerates decision-making.
A high-performing sales forecasting tool is not necessarily defined by computational complexity. Above all, it must serve as the foundation and formalization of a robust Demand Planning process.
With consistent integration of commercial signals, cross-functional alignment through a collaborative process, and coordination with operational and industrial planning processes, Demand Planning transforms forecasting into a powerful decision-making lever.
At OneHive, we advocate for a pragmatic and disciplined approach: clear processes, transparent models, and ambitions aligned with operational and strategic realities. Because a good forecast is not about predicting the future with certainty. It is about preparing for it with method and clarity, and being able to adapt when needed.
behind the article
Jules supports OneHive’s clients in designing and deploying value-driven planning solutions, leveraging platforms such as Anaplan. He works across the full project lifecycle, from scoping business needs to implementation, ensuring consistency, robustness, and delivery excellence. He combines functional consulting with hands-on execution, with a strong focus on team alignment and user adoption. He is recognized for his expertise in Demand Planning, particularly in structuring forecasting processes and improving demand performance. A graduate of INSA Lyon, Jules is a Supply Chain Manager at OneHive, contributing to the development of expertise and the success of client projects.
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Reliability does not improve by adding layers of calculations. It improves when forecasts are enriched with relevant business signals, supported by the right level of granularity, and embedded in a structured collaborative process across distribution, sales, finance, and production. Clear governance of adjustments helps focus efforts on the most critical items, which is often sufficient to significantly improve forecast quality.
There is no single best forecasting method, as performance depends on context, market dynamics, and internal organization. The most effective approach is to define the right level of granularity aligned with operational use cases and to adapt forecasting methods to the product lifecycle. New products, mature products, and end-of-life items each require different approaches. Statistical best-fit models provide a strong baseline, while Machine Learning and AI increasingly enhance forecasting by integrating additional signals and enabling more advanced data combinations. Statistical best-fit models provide a strong baseline, while Machine Learning and AI increasingly enhance forecasting by integrating additional signals and enabling more advanced data combinations.
No. Integrating all available signals without prioritization often reduces clarity. The most useful signals are those that impact operational decisions or capacity commitments. A mature process distinguishes between informative signals and those that trigger review or decision-making. This prioritization is more important than the sheer volume of data.
The appropriate frequency depends on the type of decision. Tactical adjustments may require weekly reviews, while structural decisions are typically managed monthly. Strategic alignment is often reviewed quarterly. The key is to ensure consistency and clarity in governance rather than multiplying review cycles.
Yes, provided that business needs are clearly structured. Anaplan is particularly well suited to multi-dimensional, collaborative, and iterative environments. Its value lies in its ability to model business rules, simulate scenarios, and provide a shared and consistent version of the forecast. However, as with any tool, its effectiveness depends on the quality of the underlying data.
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Explore our full methodology, concrete examples and best practices:
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behind the article
Marina has been supporting OneHive clients for over five years in the design and deployment of value creating planning solutions. She operates across the full project lifecycle, from requirements framing to go live, ensuring consistency of choices, solution robustness and delivery commitments. With a strong delivery culture, she places project success and business adoption at the heart of her approach. She is recognised for her expertise in downstream Supply Chain topics, particularly Store Replenishment. A graduate of École Centrale de Lille, Marina is certified Anaplan Solution Architect.
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The optimal level depends on local rotation, seasonality, store role and logistics constraints. A robust model combines ABC segmentation with the appropriate calculation method, such as target coverage, DRP or statistical approaches, updated daily to reflect real demand evolution.
The most structuring indicators are availability rate, stockouts and missed opportunities, stock coverage and demand variability. Daily updates enable exception based steering that is both faster and more reliable.
This balance is achieved through differentiated stock targets, automated recalculation of critical parameters and short to mid term stock projections. This combination prevents cumulative deviations and improves commercial performance without increasing costs.
Sales histories must be reprocessed, OMS rules integrated and allocation priorities defined consistently across channels. This ensures accurate reflection of ship from store, pick in store and web sales fulfilled by stores, often invisible in traditional steering.
A well designed model improves availability, reduces tied up inventory, increases responsiveness to market changes and stabilises network level trade offs. The result is better sales capture, fewer stockouts and more predictable steering for Retail and Supply Chain teams.
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Raising awareness among non-specialists about supply chain planning issues is never easy. Trade-offs, capacity constraints and cross-functional impacts are difficult to grasp until they are experienced first-hand.
This is precisely why we designed a business simulation dedicated to Connected Planning.
In October and November, students from Centrale Lille and CentraleSupélec took part in this immersive experience.
The objective was clear: to understand, through practice, the importance of a shared vision across teams and the effects of truly unified decision-making.
Over two to three half-day sessions, participants form small teams that act as mini-companies managing an accelerated year of operations. Each group must plan, decide and arbitrate while dealing with the same types of disruptions real organisations face.
The simulated context is built around a company founded in 2010, characterised by:
The simulation mirrors the key steps of a monthly planning cycle:
In practice, participants quickly realise how these steps collide with familiar challenges: unexpected events, lack of coordination, conflicting objectives, incomplete or poorly shared data.
Throughout the game, the teams must manage several structuring events:
These elements require teams to align strategy, operations and real-world constraints while controlling their impact on margin and revenue.
Each team works within a dedicated Anaplan environment. The goal is not to showcase a solution, but to make tangible:
This environment facilitates scenario comparison and enables teams to quickly measure the consequences of their trade-offs.
As the simulation progresses, several key insights emerge:
The experience reinforces a strong conviction: engagement does not come from the tool, but from understanding other functions’ realities.
Connected Planning takes root when each participant understands how their decisions affect other teams and recognises the constraints shaping their work.
Our business simulations are designed precisely to embed this understanding—well before any operational or technological transformation begins.
To bring this experience to your teams and accelerate adoption, shared understanding and collective performance, contact us.
behind the article
With deep technical expertise in planning solution implementation, Simon combines business insight with a strong command of transformation challenges to guide our clients in optimising their decision making processes. An Anaplan Solution Architect, he excels in designing and delivering bespoke models tailored to each organisation’s specific needs. His methodological rigor and pragmatic approach enable him to operate effectively across the full project lifecycle, from strategic framing to operational deployment. Simon plays an active role in developing OneHive’s technical expertise and ensuring the excellence of our deliveries. He is a graduate of Ecole Centrale Paris.
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A simulation quickly exposes cross functional impacts between Finance, Sales, HR and Supply Chain. It provides a live maturity assessment and highlights improvement levers before committing to a full transformation.
Readiness is reflected in teams’ ability to collaborate, share assumptions and challenge constraints. A simulation like OneHive’s reveals this in realistic conditions and highlights cultural or organisational blockers.
Anaplan provides a flexible, multi business, highly scenario driven environment that connects Finance, Supply Chain, Sales and HR. It enables real time decision testing and unified data, both essential for effective organisational steering.
A simulation accelerates adoption by demonstrating the value of connected ways of working. It reduces change resistance, strengthens cross functional understanding and prepares teams for tools like Anaplan.
Beyond the experience, OneHive helps teams turn insights into operational practices: process diagnostics, model design, role definition, Anaplan architecture and the creation of a realistic transformation roadmap.
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At Supply Chain Event 2025, OneHive brought together Groupe Pochet and Yoplait – Sodiaal to discuss a topic that goes far beyond tools: how can an APS or EPM project genuinely transform an organisation? And more importantly: why do some projects create lasting value, while others lose momentum once the tool is live?
From the opening remarks, the tone was clear. « “We wanted to share what actually makes a project sustainable over time, beyond the technology itself”, explains Hugo Van Straaten, co-founder of OneHive. A way to refocus the debate: it is not the platform that transforms planning, but the method, business understanding and the ability to orchestrate change.
At Pochet, the post-Covid rebound left the Supply Chain under pressure, with bullwhip effects and declining demand visibility. Legacy tools no longer enabled effective coordination across teams and sites. Visibility deteriorated, decision cycles slowed, and transformation became unavoidable to restore consistent control.
Sodiaal, on the other hand, faces a different kind of complexity: ultra-fresh products. Volumes fluctuate sharply, shelf life is extremely short and margins for error are minimal. “Planning is played out hour by hour”, summarises Julien Triquet, Planning IS Project Manager at Sodiaal. Existing systems could no longer keep pace, forcing the organisation to rethink its processes around Anaplan.
Two very different stories… yet the same conclusion: a planning project never starts with a tool. It starts with business reality.
These two journeys show that there is no single “right” way to run an APS or EPM project.
Yoplait experienced both extremes: a highly ambitious, technically rich project in France that required multiple adjustments, and a Canadian project delivered using an MVP approach, leveraging Anaplan standards to achieve go-live in less than four months.
Pochet adopted the opposite but equally effective strategy: progressing step by step, with each phase delivering visible value to sustain internal engagement.
In both cases, the conclusion is clear: project value depends less on scope than on coherence and the ability to maintain a realistic pace.
One theme consistently emerged from all feedback: the quality of the initial scoping phase.
This is where structural assumptions, actual scope, success criteria and key trade-offs are defined, preventing months of unnecessary testing.
“We needed a clear, shared planning framework to avoid drift and restore visibility. Without this foundation, no model can last over time”, explains Axel Rousseau, Supply Chain Transformation Projects Manager at Groupe Pochet.
Conversely, weak scoping often leads to scenario inflation, endless adjustments and extended testing cycles, making the project difficult to manage. Clear scoping protects teams, sets priorities and ensures technology never becomes a source of unnecessary complexity.
All participants agreed on one point: Anaplan calculates, but it does not govern data. Without reliable master data, no modelling effort can deliver robust planning.
The message is unequivocal: data is not a configuration topic, it is an organisational one.
A planning project succeeds when the project team works effectively.
Both organisations highlighted the same essentials: stable governance, a project lead able to bridge business and technology, committed IT teams and an integrator who genuinely understands Supply Chain.
And above all: early user involvement.
“Bringing users in at go-live is already too late” , Axel Rousseau reminds us. Field workshops, regular demos and progressive training build ownership that no tool can replace.
Anaplan delivers its full value when its scope is clearly defined. This clarity allows the platform to focus its power on what truly matters for the business.
If Pochet and Sodiaal chose Anaplan, it was first for its ability to model complex processes and adapt to business specificities. The platform is powerful and flexible, provided its use cases are precisely defined.
“With an open platform, you can do almost anything… as long as you know where you’re going. When ambition is clearly framed, Anaplan becomes an extremely powerful tool” notes Julien Triquet.
This balance between a flexible platform and a rigorous approach lies at the heart of success for both Pochet and Sodiaal. It ensures Anaplan’s power does not turn into unnecessary complexity.
Beyond the tool itself, the Anaplan ecosystem is a decisive asset: specialised integrators, proven models and expertise capable of supporting long-term transformation. OneHive plays a full role in this ecosystem by turning the platform’s potential into robust, user-friendly and sustainable models.
Learn more about our project methodology and our Supply Chain expertise.
The experiences of Pochet and Yoplait highlight a fundamental truth: an APS or EPM project succeeds not because it uses the right tool, but because it strengthens how the organisation decides, collaborates and arbitrates.
Scoping, data governance, project teams, change management and post-project organisation are not side topics. They are the true levers of transformation.
A platform can calculate a plan. A successful transformation permanently changes how the company is run.
behind the article
Axel Rousseau is responsible for Supply Chain transformation projects at Groupe Pochet. He works on the evolution of industrial and planning processes in a demanding manufacturing environment, with a strong focus on structure, coordination and execution. He contributes to large scale transformation initiatives aimed at strengthening operational performance and Supply Chain robustness.
Julien Triquet is a Planning Information Systems Project Manager at Sodiaal. He is involved in the design, coordination and deployment of planning solutions supporting industrial and Supply Chain processes. His role focuses on aligning business requirements with information systems to ensure reliable planning, secure execution and strong user adoption.
With deep planning experience in luxury and fashion, Hugo has lived firsthand the transformation that a collaborative planning platform can drive, integrating Supply Chain, Finance, Merchandising, and Sales on a single model (Anaplan). Combining business insight, technical depth, and a clear consulting mindset, he has led successful deployments for 8+ years. He serves as engagement lead for clients across Retail, Luxury, Food & Beverage, Services, and Industry, while also steering OneHive’s strategic and commercial development. Hugo is a graduate of Centrale Paris.
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Anaplan is particularly well suited to organisations managing complex, collaborative or constantly evolving processes. It is often chosen when Excel models no longer scale, visibility deteriorates or existing tools cannot seamlessly integrate industrial or financial constraints.
The platform stands out for its ability to model business rules precisely, bring multiple functions together around a single plan and evolve rapidly as needs change. Its value is fully realised when scope is clearly defined and the project follows a structured approach.
The choice of integrator is critical, as it directly impacts model quality and user adoption. A strong integrator does more than master Anaplan: they understand business challenges, apply sound design principles, arbitrate between simplicity and performance, and support change management.
This dual business and technical expertise is what transforms the platform into a robust and sustainable model. Experience shows that projects succeed when integrators bring structure, coherence and clarity throughout the transformation.
Timelines depend on project ambition, but Anaplan enables rapid progress. An MVP approach can deliver a first go-live in three to four months by leveraging standard features and clearly defining essential needs. This is how Yoplait Canada achieved deployment in under four months.
Larger transformations require more phased sequencing, as seen at Groupe Pochet, where each delivery generates visible value before moving forward. In all cases, speed depends primarily on scoping: the earlier key decisions are made, the smoother the project progresses.
Most risks do not stem from the platform but from the organisation. Overly broad scope, poorly defined assumptions, late trade-offs or weak data governance can quickly complicate a project. Late user involvement also significantly hinders adoption.
These risks are mitigated by clarifying scope early, defining structural assumptions, setting success criteria and involving operational teams from the outset. The most successful projects are those where business, IT and integrator collaborate closely and continuously.
Anaplan can replace multiple legacy tools when they no longer meet needs: ageing APS solutions, unstable Excel models, siloed systems or tools unable to handle complex scenarios. Many industrial players, including Sodiaal, have migrated from OMP or FuturMaster to Anaplan to gain flexibility, consistency and faster simulation.
The platform can centralise processes such as forecasting, S&OP, master production scheduling, allocation and finance within a single environment, improving coordination and eliminating information gaps. Its ability to integrate complex business rules enables alignment without forcing a complete overhaul of internal processes.
Replacement relevance ultimately depends on two factors: clear scope definition and strong data governance. A focused assessment can quickly determine whether Anaplan should become your core planning platform.
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Our teams remain available to share a more detailed feedback on this project or to explore your advanced planning and constrained optimisation challenges.
behind the article
A graduate of École Centrale Paris, Mathis has supported OneHive’s clients for more than five years in designing and deploying planning solutions. Specialising in highly complex modelling challenges, he acts as an expert across the entire project lifecycle, from scoping through to go-live, ensuring design consistency, solution robustness and compliance with OneHive quality standards.
He is recognised for his expertise in ESG topics, upstream Supply Chain processes (including MPS and procurement) and in modelling challenges specific to the agri-food industry.
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Yoplait (Sodiaal) was recognised for an industrial planning project delivered in a highly constrained ultra fresh environment. The award highlights the solution’s ability to support complex industrial decisions, secure daily execution and embed a constrained optimisation model at the core of planning processes.
The Customer Award recognises outstanding transformation projects delivered on the Anaplan platform. It rewards operational impact, process robustness and the value created for business teams in complex environments.
Ultra fresh planning involves strict constraints related to lead times, capacities, product shelf life and distribution. Decisions must be made quickly and with high accuracy while anticipating short and mid term disruptions. This requires particularly robust tools and processes.
Such an application relies on a mathematical model capable of simultaneously integrating industrial capacities, flows, resources and demand. It generates production and scheduling proposals aligned with operational reality and updated regularly to secure daily decisions.
Advanced industrial planning is especially relevant for companies operating in complex or constrained environments such as food and beverage, pharmaceuticals, consumer goods or process industries. It delivers the greatest value where production trade offs are frequent and critical to overall performance.
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A graduate of École Centrale Paris, Mathis has supported OneHive’s clients for more than five years in designing and deploying planning solutions. Specialising in highly complex modelling challenges, he acts as an expert across the entire project lifecycle, from scoping through to go-live, ensuring design consistency, solution robustness and compliance with OneHive quality standards.
He is recognised for his expertise in ESG topics, upstream Supply Chain processes (including MPS and procurement) and in modelling challenges specific to the agri-food industry.
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HiveWay does not replace existing planning tools. It operates upstream or alongside them as a decision-support module designed to inform strategic trade-offs such as warehouse location, transport mix evolution, hub transformation or ESG trajectories.
Its outputs can then be fed into S&OP or IBP processes to guide short- and mid-term planning. Its role is to bring coherence and clarity to structural decisions.
Key inputs include network structure (sites, capacities, geographies), current flows, unit costs, growth assumptions and emission factors used in ESG reporting. HiveWay adapts to your maturity level: the model can start with relatively simple granularity and be refined over time.
Carbon footprint projection requires modelling the network (sites, flows, distances and transport modes) and applying appropriate emission factors. HiveWay automates these calculations and projects them over multiple years based on growth assumptions, enabling comparison of environmental impacts across network scenarios.
Impact is assessed across three dimensions: costs, CO₂-equivalent emissions and operational capacity. HiveWay simulates each transformation scenario and automatically calculates these indicators, making it easier to identify the most efficient and sustainable option.
This requires comparing multiple network architectures: number of warehouses, locations, sizing and transport mix. HiveWay simulates these configurations, calculates financial and environmental impacts and identifies the most robust configuration based on your objectives. This approach avoids isolated decisions and supports a coherent, long-term network strategy.
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