Supply Chain Planning
Perfume and cosmetics group
Transformation of supply chain planning operations at a major French perfumes and cosmetics group, in partnership with Argon & Co
AI in planning: real-world use cases, Anaplan capabilities and business impact
Insights from Guilhem Delorme, Partner and Head of Iris by Argon & Co, and Hugo Van Straaten, Partner and Founder of OneHive.
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Planning means making trade-offs under constraints, based on data and signals that are sometimes incomplete. AI in planning is becoming increasingly widespread, but the use cases that deliver real operational value are still not always clearly identified. Guilhem Delorme and Hugo Van Straaten share their perspectives on organisational maturity, the capabilities available in Anaplan and the evolving role of planners. Their analysis brings together three dimensions: business decisions, the contribution of AI and the technology architecture that makes it possible.
The four approaches to AI in planning
Not all organisations are progressing at the same pace. Guilhem Delorme identifies four approaches, reflecting different levels of ambition and maturity.
- General-purpose chatbots: A conversational assistant is made available to employees. This approach allows organisations to explore potential use cases, but remains disconnected from actual planning decisions. An assistant that has no knowledge of the company’s master data, business rules or performance indicators can only provide generic answers.
- Business-specific use cases: AI is applied to a specific process, such as demand forecasting, assortment recommendations or detecting signals that could affect volumes. The benefits become measurable because they are linked to a clearly identified decision and a tracked performance indicator.
- Agent Studio: Users configure their own agents and make them available to their teams. This approach broadens the range of use cases, but requires a clear framework, with defined responsibilities, validation of results and control over the data being used.
- Domain-wide transformation: AI is integrated into the processes, roles and decision-making practices of a business function. This approach brings processes, organisational structures and technology choices together within a shared transformation roadmap, following the progressive methodology described in our approach to planning projects.
In all four cases, the starting point remains the same: identifying the decision or action to improve. The next steps are to assess the available data, clarify the user’s role and define how the resulting benefits will be measured.
Anaplan and AI: current and upcoming capabilities
Anaplan‘s AI capabilities fit into this progression, although they are at different stages of maturity. They address different needs, and their relevance depends on the targeted process, data quality and the maturity of the teams involved.
- Machine learning for forecasting: Available for several years. Models use historical data and available signals to support demand forecasting. They enhance analysis without replacing knowledge of commercial and operational events.
- AI assistants embedded in applications: Available today. Users can query their business models directly from the interface, understand variances more quickly and explore assumptions without leaving their working environment.
- CoModeler : Available today. This capability is designed to build applications and models from natural language instructions, without going through traditional modelling processes.
- Agent Studio : Coming soon. This component orchestrates agents, platform-native skills, user instructions and connections to the company’s broader agent ecosystem.
- AI Gateway (MCP) : Being rolled out. This capability makes planning data and models available to the wider enterprise AI ecosystem, helping improve decision-making beyond individual system silos.
« Agent Studio will orchestrate both agents and skills native to the platform, prompts created by users, and connections to a broader environment of agents within the company’s ecosystem. » Hugo Van Straaten
These capabilities expand the ways users can interact with planning models. They do not replace functional design or the robustness of a planning platform, where business rules and the allocation of responsibilities remain decisions for the organisation.
What AI is actually changing in planning projects
The impact is first visible in project delivery. AI can handle document preparation, some project management activities and test script generation. These use cases reduce the time spent on repetitive tasks and free up capacity for scoping and business process design.
AI also simplifies how plans are analysed. It generates alerts, identifies variances and highlights weak signals, allowing planners to adjust their plans accordingly. However, this capability must remain part of a clear decision-making framework: AI-generated results need to be validated, explained and linked to an operational action.
One of the most significant changes concerns the nature of the data being used. Planners work with structured data from enterprise systems. They also need to interpret emails, meeting minutes, commercial information and market signals, which are often valuable but difficult to incorporate into traditional planning models.
« The vision, which is already a reality in some companies today, is to give planners AI tools that bring together both worlds: structured and unstructured data, finally combined in a single tool, readily available to them. » Guilhem Delorme
This combination is transforming the planner’s role. Planners spend less time searching for and consolidating information. Instead, they devote more attention to analysing the causes of variances, comparing scenarios and preparing decisions involving Sales, Finance and Supply Chain. Technology accelerates and enriches processes, while business expertise gives meaning to the results.
Real-world example: a demand forecasting project deployed in four months
An ongoing acquisition, a forecasting methodology that needed replacing and just a few weeks to scope the project. This was the context in which Iris by Argon & Co and OneHive joined forces for a global food manufacturer on a demand forecasting project in Canada.
The sales forecasting application was deployed in four months. The project combined the configuration of the Demand Planning process in Anaplan, statistical and algorithmic modelling, and the integration of specific parameters such as promotional effects.
A project of this kind involves much more than a forecasting engine. Sales history must be integrated, data quality assessed, promotions accounted for, models calibrated and business adjustments organised. The results must then be made available through an application that teams can use every day, with performance monitored after go-live.
The project brought together two complementary areas of expertise. Iris by Argon & Co led the statistical modelling work, from data preprocessing to advanced modelling. OneHive accelerated the platform configuration, allowing more time to be dedicated to business-specific adaptations.
« We were able to measure an improvement in forecast accuracy at go-live that actually exceeded expectations. The results confirmed that performance was in line with what we had tested on historical data. And once planners had made their adjustments, we achieved even better results.» Hugo Van Straaten
This experience reinforces the lessons we have identified regarding the key success factors for APS and EPM projects: clearly defined roles, the right expertise brought in at the right time and a well-controlled functional scope. Model performance and user adoption improve together.
Conclusion: a transformation across three dimensions
AI brings new analytical, forecasting and automation capabilities to planning teams. Its value depends on the business framework in which it operates, the quality of the available data and the robustness of the underlying technology architecture.
The most successful projects address these three dimensions from the scoping stage: the decisions to improve, the relevant AI use cases and the components needed to integrate them into existing processes. This approach helps distinguish quick wins from more far-reaching transformations.
For planners, the challenge is less about delegating decisions to AI than about having access to better-prepared, broader and more actionable information.
Watch the full interview (in French)
Guilhem Delorme and Hugo Van Straaten share their perspectives on AI in planning in this seven-minute joint interview.
behind the article
Meet the experts who contributed their vision, experience, and expertise to this content.
Hugo Van Straaten
Partner & Cofounder
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.
Guilhem Delorme
Partner et Head of Iris by Argon & Co
Avec une expertise construite sur l’intégration de la data et de l’IA dans les opérations, Guilhem met ces technologies au service des métiers de la Supply Chain, la logistique, les achats, la production et le service client. À la tête d’Iris by Argon & Co en France, l’équipe Data & AI for Operations d’Argon & Co, il accompagne des organisations de toutes tailles et de tous niveaux de maturité dans leur transformation, depuis la définition de la stratégie jusqu’au au déploiement de solutions concrètes et innovantes.
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October 9, 2026
AI in planning: real-world use cases, Anaplan capabilities and business impact
AI and planning progress together. The question remains: where does the real value lie?
October 1, 2026
OneHive at Anaplan Connect Madrid 2026
Agentic Enterprise, AI and Connected Planning: join us in Madrid
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Discover answers to key questions about our services and approach.
The starting point is a business decision, not a technology. The first step is to identify a specific decision where quality or speed is currently an issue: a weekly forecast that requires manual adjustments, replenishment decisions made without visibility into network constraints, or an S&OP process where assumptions are not shared. From there, three questions need to be answered. What data supports this decision, and is it reliable? Who validates the result, and against which criteria? How will performance be measured before and after implementation? A few days of scoping are generally enough to determine whether a targeted use case or a broader process redesign is required.
An initial scope can be deployed within a few months when the project is clearly defined and responsibilities are properly allocated. The project delivered in Canada for a food manufacturer was completed in four months, in the context of an acquisition and the replacement of an existing forecasting methodology. This timeline assumes that historical sales data is available, business rules are documented and the statistical modelling and platform teams work in parallel. Delays mainly arise when master data quality issues are discovered during the project.
Rarely. AI capabilities are most often added to an existing platform through predictive models, embedded assistants or connected agents. The main considerations are the data architecture and the quality of the existing planning model. A platform with inconsistent master data or fragmented business rules will limit the benefits of AI, regardless of the technology used. Assessing the existing environment is therefore a useful first step before making any investment decision.
The two address different needs. Machine learning is suited to forecasting and calculation problems involving structured data, such as volumes, seasonality and promotional elasticity. Agentic AI focuses on orchestration, querying models and working with unstructured data such as meeting minutes or market signals. In planning, machine learning remains the more mature technology for forecasting. Agentic AI is increasingly positioned around analysis, decision preparation and the automation of tasks throughout the planning cycle.
Measuring ROI requires a combination of forecast accuracy and operational performance indicators. For forecasting, metrics such as forecast accuracy and Forecast Value Added help quantify the model's actual contribution compared with the previous process. From an operational perspective, the impact can be measured through inventory levels, service levels, time spent consolidating data and the number of manual adjustments. It is important to establish these baseline indicators before the project begins, as reconstructing them afterwards can be difficult.
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Warehouse workforce planning: anticipate demand to improve performance
How can you move from reactive workforce management to predictive warehouse workforce planning?
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Manual, approximate and disconnected planning undermines productivity and weakens warehouse financial performance. Decisions must simultaneously account for order volumes, capacity, productivity, workforce constraints, labour costs and service quality. Yet in many organisations, these processes still rely on scattered files and manual data entry. Teams alternate between costly overstaffing and risky understaffing, while performance gaps are identified too late. Firefighting becomes the default mode of operation.
Labour: the largest cost item and the first lever for competitiveness
70% of warehouse operating costs are labour-related (UPS Supply Chain Solutions). It is the largest cost item and the one where economic performance is determined on a daily basis. At the same time, 73% of warehouse operators struggle to recruit enough staff (Prologis, 2024). Labour shortages and cost pressures are converging.
The global warehousing market is expected to grow by around 37% between 2026 and 2030, reaching $1.73 trillion, driven by the rise of e-commerce and increasingly complex supply chains (Grand View Research). Volumes are increasing. So are expectations around speed and reliability. Managing resources at this level of complexity with scattered Excel files is no longer sustainable.
Better planning is not just about adjusting headcount. It also means assigning the right skills to the right activities at the right time. Taking certifications, versatility and productivity levels into account improves resource utilisation. It makes it possible to increase overall performance without increasing headcount.
Four structural challenges that prevent effective anticipation
Most logistics teams face the same obstacles:
- Multiple files: forecasts, planning and execution are managed in separate spreadsheets that are difficult to synchronise across sites and activities.
- Repeated manual data entry: every manual input slows teams down and increases the risk of operational errors.
- Siloed activities: receiving, order preparation and shipping are managed independently, with no consolidated view of the overall workload across teams.
- No dynamic detection: forecast changes, capacity overload alerts and productivity gaps only become visible after the fact.
The consequence is not simply inefficiency. It is a structural inability to meet customer requirements, or significant additional labour costs: teams react to peaks in activity instead of preparing for them.
Financial, operational and workforce considerations: three under-managed dimensions
This way of working generates costs that go far beyond the use of last-minute temporary labour.
From a financial perspective, capacity decisions are made without forward-looking visibility into their budgetary impact. Cost analysis takes place after the decision, never before it. It becomes impossible to compare the cost of temporary labour or overflow warehousing with the cost of smoothing order volumes. It is equally difficult to simulate the impact of a resource decision on site margins.
From an operational perspective, the lack of a multi-horizon view across days, weeks and months prevents teams from activating the right levers at the right time. Workforce versatility, smoothing inbound flows and securing external labour in advance can only be used effectively if capacity overloads are identified early enough.
From a workforce perspective, real operational constraints are rarely incorporated into the plan. Certifications such as forklift licences, mandatory rest periods, safety rules and cross-functional skills are often absent from Excel files. As a result, the plan does not reflect the capacity that is actually available. Some activities are overstaffed while others are understaffed.
The four levers of predictive workforce planning
Moving from reactive management to structured planning relies on four complementary levers.
- Model forecast workload based on order volumes, seasonality and fluctuations in activity.
- Integrate real operational constraints: absences, contracts, public holidays and job-specific certifications.
- Balance internal resources and temporary labour with a consolidated view of costs and availability, while incorporating HR and contractual constraints.
- Measure planned versus actual performance to improve the accuracy of future planning cycles.
These four levers work together. Taken individually, each delivers only limited impact.
Proof through a real-world example: a European 3PL, 40 sites and 2,000 employees planned with Anaplan
A major European third-party logistics provider was planning 2,000 employees every day across 40 warehouse sites. Receiving, order preparation and shipping were managed in separate files, across five disconnected manual processes. There was no consolidated view of workload across the network.
After deploying Anaplan:
- A single tool covers the entire network through a standardised architecture.
- Workload forecasting is automated based on customer orders.
- Daily planning is managed by activity and job role.
- Resources are managed by team and individual.
- Standard working weeks can be configured and replicated across all sites.
The enhancements identified for the next phases illustrate the progressive approach: direct integration with the WMS to eliminate manual workforce data entry, enhanced forecasting using AI/ML algorithms, and consolidated workforce budget reporting across the network.
WMS and workforce planning: two complementary roles
A WMS orchestrates real-time execution. It records inventory movements, routes orders and monitors activity throughout the day. But it does not calculate forecast workload, plan resources or anticipate capacity overloads. Nor does it provide a multi-horizon view or financial analysis of capacity decisions.
This is precisely where Anaplan comes in as a predictive planning layer, bridging the gap between the WMS and actual resource availability. Connected to the existing WMS, Anaplan retrieves operational data and creates the link between demand forecasts, available capacity and workforce planning. It also makes it possible to model the cost of planned resources by activity and site, and to simulate the financial impact of different decisions before they are made.
The two tools are complementary: one executes, the other anticipates.
The OneHive approach: a phased deployment with no organisational disruption
OneHive supports this type of implementation through a phased approach. A pilot site goes live within 2 to 3 months to validate the model and embed it into the teams’ day-to-day practices.
The solution is then rolled out across the rest of the network using the same standardised architecture, site by site, without disrupting the existing WMS.
We also train internal Model Builders to ensure the long-term sustainability of Anaplan and enable teams to extend the solution independently. The objective is to deliver an operational model quickly, ensure it is actually adopted, and then enhance it over time.
Going further
Anaplan and OneHive hosted a french webinar dedicated to this topic: Warehouses: move beyond firefighting with predictive and connected planning.
Watch the replay to see a demonstration of Anaplan applied to warehouse operations, along with feedback from a European 3PL that deployed the solution across 40 sites.
Watch the replay →
behind the article
Meet the experts who contributed their vision, experience, and expertise to this content.
Simon Sander
Manager
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.
Our latest news
Supply Chain Planning
Perfume and cosmetics group
Transformation of supply chain planning operations at a major French perfumes and cosmetics group, in partnership with Argon & Co
October 9, 2026
AI in planning: real-world use cases, Anaplan capabilities and business impact
AI and planning progress together. The question remains: where does the real value lie?
October 1, 2026
OneHive at Anaplan Connect Madrid 2026
Agentic Enterprise, AI and Connected Planning: join us in Madrid
FAQ
Discover answers to key questions about our services and approach.
A warehouse resource planning tool should make it possible to align forecast volumes, workload and actual available capacity. It should also incorporate operational constraints such as skills, certifications, absences, contracts and workforce versatility.
For a multi-warehouse network, the tool should also make it possible to standardise planning rules while retaining the specific requirements of each site, consolidate resource needs and compare multiple capacity scenarios before making a decision.
A WMS primarily manages the execution of warehouse operations: inventory, movements, order preparation and activity monitoring. A resource planning tool operates upstream to anticipate workload, determine the required capacity and build workforce plans.
The two are complementary. WMS data can feed the planning process, while a platform such as Anaplan makes it possible to compare workload and capacity across multiple time horizons and simulate the operational and financial consequences of resource decisions.
Warehouse capacity should not be calculated solely on the basis of the number of available employees. Planning should also incorporate job roles, skills, certifications such as forklift licences, levels of versatility, availability and contractual constraints.
This approach makes it possible to identify capacity gaps by activity. A warehouse may have sufficient overall headcount while lacking qualified employees for specific operations. Skills-based planning can therefore help anticipate workforce reallocations, training requirements or additional resource needs.
Connecting the two systems makes it possible to use operational WMS data to automatically feed the planning process and reduce manual data entry. Volumes and activity data can then be compared with productivity assumptions and available capacity.
In an architecture combining a WMS and Anaplan, the WMS remains the execution system while Anaplan acts as the planning layer. The objective is not to replace the WMS, but to complement its data with forecasting, scenario modelling and resource management capabilities.
Deployment can begin with a pilot site to validate the workload model, capacity rules, required data and operational use cases. Once this initial architecture has been validated, it can be standardised and progressively rolled out to the other warehouse sites.
With the OneHive approach, a first site can go live within 2 to 3 months. The model can then evolve through WMS integration, forecast automation and consolidated workforce budget reporting across the network.
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Successfully managing an international or multi-entity deployment of planning solutions
International deployment of planning solutions: how can you harmonise processes, accommodate local requirements and establish shared governance?
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An international deployment of planning solutions, or their rollout across multiple entities within the same group, raises three fundamental questions. How can practices developed in different contexts be harmonised? How can teams with varying levels of maturity be brought on board? How can a consolidated view be created that genuinely supports global decision-making? This challenge arises both in multi-country environments and across multiple entities within the same group. Technology alone cannot answer these questions. A clear methodology is required. This article explores the recurring challenges of these projects and the key factors for ensuring a successful deployment of your solutions.
1. Common deployment challenges
Locally designed processes that are difficult to consolidate at scale
A planning process developed for a pilot region addresses specific local constraints: a commercial calendar, an ERP system and particular business rules. When the scope expands to multiple regions, these initial choices create friction. Planning cycles differ across regions, time horizons are not aligned and product classifications vary from one entity to another. Consolidation becomes a manual exercise that is time-consuming and produces unreliable results.
Siloed decisions made without a shared view
Without a shared data framework, each region builds its plan using its own tools and submits data in its own formats. Global planning committees then rely on information consolidated after the fact, which is difficult to compare across regions. The absence of clear governance between local, regional and global levels compounds the problem: responsibilities are unclear, decisions are delayed and the consistency of the global plan suffers.
Global models that overlook local operational realities
A core model designed without involving local teams creates business rules that are poorly suited to their operational constraints. Users work around the tool, maintain parallel spreadsheets and the model fails to reflect what is actually happening on the ground. Conversely, an overly flexible deployment that allows each region to adapt the model freely makes consolidation impossible. The challenge is to strike the right balance between standardisation and adaptability, and establish that balance from the design phase.
These obstacles are not inevitable. Approaching deployment as a connected planning project makes it possible to anticipate and overcome them.
2. Approaching international deployment as a connected planning project
Anticipating organisational risks from the project scoping phase
An international deployment involves organisational risks that must be identified and addressed during project scoping, and more broadly from the initial phase of the project (pilot, MVP, etc.). The first risk is the absence of engaged and available local stakeholders: without them, design workshops fail to capture local requirements, and solution adoption may be compromised. Local team involvement must therefore be structured in advance, with roles explicitly assigned at each level. A clear RACI matrix for each region specifies who is responsible for and who approves each stage of deployment. This is a key management tool: it clarifies roles at every level, helps estimate the actual workload and anticipates the associated costs.
Identifying key design decisions early and incorporating them into the MVP
The design phase must first identify the fundamental choices (technical architecture, calculation methodologies and master data) so they can be incorporated into the MVP from the outset. Local requirements are then addressed through multi-region workshops, rigorous gap analysis and early identification of differences between regions. The objective is not to standardise everything, but to define what absolutely must be standardised: key calculations, shared indicators and common decision-making cycles. Everything else can be configured locally, provided the core model supports it. A connected planning process brings each region into a consistent global planning cycle without overlooking its operational realities.
Using a shared platform as the single environment for building and validating plans
A shared platform is more than a consolidation tool. It is where assumptions are developed, challenged and validated by all stakeholders. It harmonises calculations, master data and interfaces with local ERP systems. Without this common foundation, decisions remain informal and difficult to trace. With it, every planning committee can rely on comparable data, scenarios built using the same rules and a consolidated view available in real time.
3. OneHive’s approach to securing an international deployment
Engaging local teams while building a core model focused on key decisions
We identify local champions from the start of the project. Their role is to maintain alignment between design decisions made at the global level and the operational realities of their region. Throughout the project, these champions participate in governance meetings at key milestones: validating business rules, reviewing application screens and making decisions about local requirements. This approach ensures that local teams do not discover the solution at go-live, but are actively involved in building it from the beginning.
Our approach to the core model is based on a clear principle: a global model should focus on the decisions that matter most, rather than attempting to cover every possible exception. We work with business teams to identify the decisions that have the greatest impact on overall performance: inventory allocation between regions, capacity trade-offs, shortage management and market prioritisation. The model is designed to make these decisions visible, faster and properly documented.
When a local requirement cannot be addressed by the core model, two options are considered. The first is to configure the existing model. The second is to accept a local exception, governed by a decision-making process involving the core team. This framework ensures that exceptions remain controlled and do not undermine overall consistency.
Deploying progressively to strengthen adoption at every stage
We favour a phased deployment, starting with pilot regions. The first region validates the core model under real operating conditions, identifies necessary adjustments and delivers measurable results. These results provide the most tangible evidence for engaging subsequent regions. At every stage, change management is integrated into the project: targeted training, operational documentation and hands-on support after go-live. This phased approach reduces the risks associated with deploying across the entire organisation simultaneously and supports adoption region by region.
Establishing a centre of excellence to sustain long-term value
An international deployment does not end when the last region goes live. To sustain the value created, the organisation must be able to evolve the model, support new users and manage the improvement backlog without systematically relying on an external implementation partner. We structure this knowledge transfer by training internal Model Builders and identifying key business experts who can jointly operate and enhance the platform. This centre of excellence becomes the foundation of post-project governance. It maintains model consistency over time, centralises decisions on future enhancements and supports deployment across any remaining regions.
Relying on inherently flexible platforms: Anaplan and Ganacos
We believe that scalable and inherently flexible platforms are particularly well suited to facilitating project deployments across multiple entities or internationally. This is one of the reasons why we have chosen to implement Anaplan and Ganacos for our clients. These platforms adapt to the complexity and scope of each project without requiring major reconfiguration. Built for collaboration, they connect teams, centralise data and structure decision-making processes at scale. This is precisely what enables them to address the most common risks of an international deployment: fragmented master data, silos between regions and decisions that cannot be traced. Our role is to make the most of these platforms to design a robust model, aligned with your decision-making processes, and deliver value from the first weeks of production.
Conclusion
A successful international deployment or multi-entity rollout involves more than replicating a model across new regions. It requires a structured methodology, clear governance and a platform capable of delivering global consolidation without sacrificing local operational realities.
This is what we build with our clients: a robust core model, teams engaged from the design phase and an organisation capable of maintaining and evolving the model over time. Go-live is not the end of the project. It is the starting point for value creation. This is the ambition we bring to every client engagement.
behind the article
Meet the experts who contributed their vision, experience, and expertise to this content.
Nicolas Boussard
Senior Consultant
A graduate of INSA Lyon, Nicolas supports OneHive’s clients in the design and roll-out of planning solutions. With over five years’ experience, he works on a wide range of supply chain issues, including demand planning, DRP, PDP, S&OP, OTB and restocking. His expertise enables him to support projects in demanding environments, particularly in the retail, fashion, luxury and agri-food sectors.
Our latest news
Supply Chain Planning
Perfume and cosmetics group
Transformation of supply chain planning operations at a major French perfumes and cosmetics group, in partnership with Argon & Co
October 9, 2026
AI in planning: real-world use cases, Anaplan capabilities and business impact
AI and planning progress together. The question remains: where does the real value lie?
October 1, 2026
OneHive at Anaplan Connect Madrid 2026
Agentic Enterprise, AI and Connected Planning: join us in Madrid
FAQ
Discover answers to key questions about our services and approach.
Success depends on three elements: a shared core model, clear governance between global and local teams, and a phased deployment approach. The model must harmonise calculation rules, performance indicators and decision-making processes, while allowing certain local configurations. A pilot site or region makes it possible to validate these choices before rolling them out more broadly.
Involving local teams from the design phase is also essential to identify operational constraints and encourage solution adoption.
A core model must define the elements that should remain consistent across all entities: master data, performance indicators, calculation rules and key decision-making processes. It must also identify which parameters can vary locally, such as certain commercial calendars or operational rules.
Building the model involves workshops bringing together central and local teams, a gap analysis of existing practices and validation of common rules. Exceptions must be managed through dedicated governance to preserve the consistency of the global model.
A phased deployment by country, region or entity generally makes it easier to manage risks. An initial pilot region validates the model's functionality, data interfaces and business use cases under real operating conditions.
Lessons learned during this phase are then used to refine the approach before expanding the rollout. This also facilitates user training and change management. A simultaneous deployment remains possible, but requires greater coordination and offers fewer opportunities to make adjustments between stages.
The first step is to distinguish between rules that must be standardised and those that can remain specific to each entity. Core calculations, shared performance indicators and decision-making cycles must be harmonised to enable reliable consolidation.
Local constraints can be incorporated through configuration when the core model architecture allows it. If an exception requires a more significant adaptation, it must be assessed and approved by the team responsible for the global model. This governance prevents the proliferation of incompatible local versions.
The choice should be based as much on the partner's ability to manage a multi-entity project as on their technical expertise in the platform. A partner must be able to design a scalable core model, coordinate local teams, organise decision-making across different governance levels and support adoption after go-live.
It is also important to assess their phased deployment methodology, their ability to integrate existing systems and their approach to knowledge transfer. At OneHive, these dimensions are built into the project from the scoping phase, with particular attention to governance, local champions and establishing a centre of excellence.
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S&OP, MPS and Production Scheduling: Three Levels of Industrial Planning
Understanding industrial planning processes, from strategy to operational execution.
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In industrial organisations, planning operates at three levels, each with distinct time horizons and decision-making approaches: S&OP, MPS and production scheduling.
- S&OP (Sales & Operations Planning)covers a strategic horizon of up to 18 to 24 months. It aligns commercial, financial and industrial priorities, while managing supplier commitments across the organisation.
- MPS (Master Production Schedule)operates at a more tactical level, typically covering weeks 1 to 13. It schedules production at a more detailed level, manages operational procurement and safeguards inventory levels. Its purpose is to ensure that everything is in place when production needs to begin.
- Production scheduling, managed at plant level, sequences daily production activities while accounting for operational constraints, workforce and material availability, and day-to-day disruptions.
These three levels form a decision-making cascade with increasing granularity: product families at S&OP level, individual SKUs at MPS level and manufacturing orders at the production scheduling level. Strategic decisions flow down towards operations, while real-world adjustments and constraints are communicated back to higher levels. Their shared objective is to maintain the balance between demand and capacity, ensuring that every operational decision remains aligned with the company’s overall strategy.
Yet the relationship between S&OP, MPS and production scheduling is often poorly understood. Unclear boundaries between processes, informal interactions and insufficient feedback from operations can lead to misaligned plans, poorly calibrated inventory levels and limited responsiveness to disruptions. This article explores the roles, scope and interactions of each planning level, along with the key factors that improve their effectiveness and overall industrial performance.
1. S&OP (Sales & Operations Planning)
Definition and scope
S&OP is a strategic and tactical planning process operating over a rolling horizon of 18 to 24 months, although this may vary depending on the typical lead times of the industry concerned. It aligns Sales, Supply Chain, Production and Finance around a consistent, shared action plan.
Planning outputs are generally aggregated by product family, broad product category, manufacturing site, production line or group of production lines. However, this level of aggregation does not mean that calculations cannot be performed at a more detailed level. Where business requirements justify it, greater granularity can improve the accuracy of decision-making.
The stages of the S&OP cycle
S&OP follows a structured cycle consisting of four or five complementary stages.
- Product Review (optional): Sometimes combined with the Demand Review when the same teams are involved. This stage analyses product launches and phase-outs to provide the most up-to-date view of volume ambitions and product lifecycle timelines.
- Demand Review: Analysis and validation of sales forecasts, with input from Sales, Finance, Marketing and Demand Planning. Commercial opportunities and risks are identified, documented and incorporated into several demand scenarios for subsequent evaluation, such as optimistic, pessimistic and consensus scenarios.
- Supply Review: Validation of manufacturing and logistics capacities, identification of constraints and assessment of potential imbalances between demand and capacity. This stage helps identify commercial opportunities that the organisation may be unable to fulfil.
- Pre-S&OP: Assessment of gaps between demand and capacity, scenario modelling and development of proposed solutions. This stage prepares cross-functional alignment ahead of executive decision-making. In sufficiently mature organisations, logistics costs can also be quantified to compare scenarios and potential actions from a margin perspective.
- Executive S&OP : Strategic decisions concerning investments, recruitment, subcontracting and demand allocation across sites or channels. Finance can provide a framework for these decisions by quantifying their implications: revenue, margins, inventory strategy and stockout risks are assessed together to measure the actual impact of each scenario on business performance.
Links with other processes
S&OP receives forecasts from Demand Planning, together with commercial opportunities and risks requiring consideration. Its role extends far beyond simply consolidating data. It validates the production capacity approach and defines how demand should be allocated between manufacturing sites, production lines or manufacturing methods. It also ensures that logistics planning and transport capacity are aligned across the entire planning horizon.
By anticipating future challenges, S&OP helps resolve potential tensions before they become operational crises. This includes workforce decisions, inventory policies, production planning and procurement requirements. It also guides purchasing strategy through improved medium- and long-term visibility, while strengthening supplier collaboration around capacity commitments and forecast volumes. Constraints identified at MPS level are escalated to S&OP when adjustments to the overall planning framework are required.
2. MPS (Master Production Schedule)
Definition and scope
MPS, or Master Production Schedule, is a tactical production planning process typically covering a horizon of approximately weeks 1 to 13. Decisions are made at individual item or SKU level, by week and production line. While S&OP generally follows a monthly cycle, MPS operates on a weekly basis. This higher frequency provides greater responsiveness to short-term changes in demand.
Decision-making approach
MPS uses forecasts from Demand Planning to generate a detailed production plan by SKU and production line. It incorporates S&OP decisions regarding target inventory levels, approved capacity, production advancement or postponement strategies, and customer prioritisation. It also considers available inventory, confirmed orders and work in progress. Operational planning rules are integrated into the process, including batch sizes, production cycles, target inventory coverage and simplified production sequencing constraints.
Links with other processes
MPS receives the capacity framework established by S&OP, along with detailed forecasts from Demand Planning. Its output is a master production schedule that feeds material and component requirements planning. When delays or shortages occur, MPS escalates constraints to S&OP so that the capacity framework can be adjusted.
3. Production Scheduling
Definition and scope
Production scheduling is an operational execution process managed at plant level, whereas S&OP and MPS are generally managed centrally. It operates over a short horizon, ranging from a few days to two or three weeks. Its planning granularity is directly executable: manufacturing orders, workstations, teams and hours. The objective is to sequence operations to optimise resource utilisation and meet committed delivery deadlines.
Decision-making approach
Production scheduling assigns manufacturing orders to available resources, including machines, production lines and teams. It considers changeover times, resource availability and production priorities. It also incorporates the activities required for day-to-day plant operations, including planned maintenance and regulatory constraints. Production scheduling must also respond to disruptions such as equipment breakdowns, staff absences, supplier delays and urgent customer requests. Optimisation focuses on very short-term capacity utilisation and minimising idle time.
Links with other processes
Production scheduling receives information on component availability and production programmes generated by MPS. At this level, executable manufacturing orders are created for shop-floor operations. Deviations between planned and actual production, along with capacity constraints, are reported back to MPS so that the master production schedule can be adjusted.
4. S&OP, MPS and Production Scheduling: interactions and overall alignment
The decision-making cascade
The three processes operate as a cascade. S&OP translates strategic objectives into a production planning framework. MPS converts this framework into production programmes planned by SKU. Production scheduling then organises their day-to-day execution at plant level. At each stage, feedback loops communicate actual operational constraints to higher planning levels, enabling continuous adjustments to the plans.
Key risks to watch
Several risks can undermine overall planning consistency.
- Misalignment between planning levels: Unfeasible plans or poorly calibrated inventory levels can arise when the three processes do not share a common data framework.
- Insufficient operational feedback:Decisions made without visibility into shop-floor constraints can lead to choices that are disconnected from operational realities.
- Excessive rigidity: Planning processes that are too inflexible cannot absorb day-to-day disruptions effectively.
- Disconnected planning tools:When planning systems are not aligned, data becomes fragmented and confidence in the plans deteriorates.
Key drivers of effectiveness
Five factors underpin an effective planning system capable of addressing these challenges.
- Shared visibility:A single, reliable data framework covering inventory, capacity and bills of materials, accessible across all planning levels.
- Appropriate planning frequency: Each process must have its own update cycle, aligned with its decision-making horizon while ensuring that it does not disrupt other planning processes.
- Scenario modelling: The ability to test assumptions before committing to decisions reduces risk and accelerates trade-offs.
- Clear governance: Defining who makes which decisions, at what level and according to which criteria eliminates ambiguity and strengthens confidence in the plans.
- Appropriate planning tools: Planning solutions must match the requirements of each level, from tactical production planning to collaborative S&OP platforms.
5. Connecting Planning processes with Anaplan
The benefits of connected planning
A platform such as Anaplan enables end-to-end consistency across planning levels. Teams can simulate scenarios in real time, track decisions and quickly identify the causes of deviations. Collaboration between Sales, Supply Chain, Production and Finance is supported by a shared data model, eliminating duplicate data entry and inconsistencies between systems. This strengthens decision-making agility: rebuilding scenarios in response to changing demand or manufacturing constraints can take hours rather than weeks.
Anaplan is particularly well suited to S&OP and MPS. Production scheduling, however, requires some clarification. In most cases, detailed scheduling involving complex constraints, such as multi-resource sequencing, changeover times and real-time availability, is better handled by a dedicated Advanced Planning and Scheduling (APS) solution.
Anaplan can support time-series-based scheduling or generate manufacturing orders using simple rules, with daily granularity, straightforward operational constraints and short planning horizons. Beyond this scope, a specialised tool is generally more appropriate.
The OneHive approach
At OneHive, we co-design tailored planning models that reflect the specific requirements of each industrial organisation. Implementation can begin with either S&OP or MPS, depending on the client’s level of maturity, before progressively expanding the scope. Our combined business and technical expertise helps ensure user adoption and the robustness of models in production. Because a planning tool only creates value when people actually use it.
Conclusion
S&OP, MPS and production scheduling are three complementary and interdependent processes. Each operates over a different horizon, at a different level of granularity and with distinct objectives. Overall performance depends less on the sophistication of each individual process than on the consistency of the interactions between them.
Aligning planning horizons, sharing data, structuring feedback loops and clarifying governance are essential conditions for effective industrial planning. A platform such as Anaplan can accelerate this alignment, provided it is supported by strong business expertise and a clear understanding of what each planning level must deliver.
Taking the next step
If you would like to assess your organisation against these principles, identify areas of friction or evaluate the most suitable implementation roadmap with Anaplan, our teams can provide a structured, practical perspective.
behind the article
Meet the experts who contributed their vision, experience, and expertise to this content.
Cecilia Gois
Senior Consultant
A qualified engineer with a dual French-Brazilian degree, Cecilia supports OneHive’s clients in transforming their planning processes and deploying Anaplan solutions. Specialising in supply chain and integrated planning (S&OP / IBP), she is involved throughout the entire project lifecycle, from defining requirements through to go-live, including design, training and user support. Her experience in the luxury and agri-food sectors enables her to combine an understanding of business challenges with technical expertise to lead deployments on an international scale.
behind the article
Supply Chain Planning
Perfume and cosmetics group
Transformation of supply chain planning operations at a major French perfumes and cosmetics group, in partnership with Argon & Co
October 9, 2026
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FAQ
Discover answers to key questions about our services and approach.
S&OP operates over an 18- to 24-month horizon, at an aggregated level covering product families and manufacturing sites, typically following a monthly cycle. Its purpose is to align business functions around a shared plan.
MPS works at individual SKU and weekly level, over a horizon of approximately 1 to 26 weeks, with a weekly planning cycle. It translates S&OP decisions into a detailed production programme.
In some cases, yes. Anaplan can support time-series-based scheduling at half-day granularity, provided operational constraints remain relatively simple.
For more complex requirements, such as multi-resource sequencing and detailed changeover management, a dedicated APS solution is generally more suitable.
This depends on the organisation's level of planning maturity. S&OP is often a natural starting point because it establishes cross-functional alignment and creates a shared view. MPS can follow once the capacity planning framework has been stabilised. Support from experts combining business and technical expertise is essential to secure each stage of implementation.
MPS defines the quantities to be produced by SKU, production line and week, generally over a horizon of several weeks.
Production scheduling operates at a more detailed, operational level. It determines the sequence of manufacturing orders while accounting for available resources, changeover times, production constraints and day-to-day disruptions.
MPS therefore establishes the production programme to be followed, while production scheduling organises its actual execution on the shop floor. Operational deviations are then reported back to MPS so that the necessary adjustments can be made.
Effective alignment relies on a consistent decision-making cascade across the three processes.
S&OP establishes the overall framework by aligning demand, capacity and business objectives. MPS translates these decisions into detailed production programmes. Production scheduling then organises their short-term execution.
To maintain consistency, all three levels must share reliable data, operate with appropriate update frequencies and provide structured feedback on operational constraints. Clear governance and connected planning tools facilitate decision-making and enable plans to be adjusted as conditions change.
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Architecture of a demand planning model in Anaplan: features and granularity
Explore the design and configuration choices behind an effective demand planning model in Anaplan.
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An effective demand planning model starts with sound business design decisions. Historical data quality, calculation granularity, product segmentation and the level of automation all determine forecast reliability. In this article, we explore the key features of a demand planning model in Anaplan, along with the architecture and configuration choices that make it easier for teams to adopt.
1. Classification, product linking and historical data cleansing
The first critical element is the quality of the sales signal. Unprocessed historical data introduces bias into every downstream calculation. This is one of the most underestimated areas of investment during the early stages of a project. We configure product linking features to maintain historical continuity across product code changes, product substitutions and cannibalisation. This approach is essential when product portfolios evolve regularly.
We model plausibility ranges using configurable dynamic thresholds. These identify outliers and trigger automatic corrections. Promotional effects are captured and separated from the baseline sales signal.
We also incorporate the reconstruction of lost sales during stockouts. Unfulfilled sales are estimated to avoid underestimating actual demand. The result is a linked, cleansed historical dataset that can be used directly for statistical forecasting.
In our experience, product lifecycle management and ABC/XYZ segmentation are fundamental pillars of the model. ABC measures each product’s contribution in terms of volume and revenue. XYZ assesses demand volatility using the coefficient of variation in sales. These segments inform data cleansing rules and help prioritise planner alerts. They also guide sales signal modelling, including the choice of statistical algorithms, the use of Machine Learning and the need for manual review. Without well-calibrated segmentation, maintaining the model quickly becomes unmanageable as the product portfolio grows.
2. Statistical forecasting, Machine Learning and Best Fit in Anaplan
The value of a forecasting engine depends primarily on how appropriately it is configured. It is not simply a matter of using more complex models. Statistical models cover most forecasting requirements, typically around 80% of SKUs depending on the portfolio. They account for seasonality and trends through moving averages, exponential smoothing and model families such as ETS and ARIMA.
At OneHive, our demand planning consultants configure Anaplan Forecaster according to demand profiles, product segments and the performance indicators being monitored. They adapt the parameters to the specific characteristics of each portfolio to improve forecast accuracy. A standard implementation of the forecasting engine is therefore not enough to guarantee reliable results.
Machine Learning is used for segments where it can deliver measurable improvements. Its application is not limited to erratic demand patterns. It can also incorporate explanatory variables such as promotions, cannibalisation, calendar effects and specific product characteristics. Depending on the context, a dedicated model can deliver a 3 to 5 percentage point improvement in Forecast Accuracy, subject to confirmation against the results of the relevant project.
We configure the Best Fit functionality at node level, according to client requirements. Anaplan runs several model families in parallel and automatically selects the best-performing candidate. The selection metric can be configured using indicators such as MAPE, WAPE, RMSE or bias. RMSE is our default starting point.
We also use ABC/XYZ segmentation to guide model selection. An A/X product with stable demand should not be treated in the same way as a C/Z product with volatile demand. Demand planning teams retain the ability to manually override the selected model when business circumstances require it.
We continuously measure forecast quality through backtesting. These analyses are based on validation periods defined in advance. Forecast Accuracy dashboards identify deviations and help teams refine model parameters over time.
3. Promotions, events and product lifecycle management
Promotional planning is an area of business design that is often underestimated. Before any configuration begins, the promotional mechanics must be clearly defined: offer type, discount depth, duration, channel, customer and the category of products concerned. A promotion on a fast-moving SKU does not have the same effect as a clearance campaign or a new product launch. When poorly modelled, these activities distort the statistical baseline and forecasts for subsequent campaigns.
At OneHive, we build dedicated modules to separate promotional uplift from the baseline sales signal. Bulk integration of promotional activities relies on configurable rules. The model proposes forecast volumes based on the type of promotion, channel, customer and product category. Execution tracking then makes it possible to compare actual sales against forecasts and measure the performance of each event.
We also feed promotional data back into Machine Learning models. This approach captures halo effects, cannibalisation and promotional uplift. It also accounts for changes in demand before and after a promotional event. Forecast accuracy improves as the promotional history becomes richer.
We manage the product lifecycle directly within the model. Product launches can be configured manually or automatically. Two approaches are possible: applying a volume to a launch curve or using a comparable product to reconstruct historical demand and calculate a baseline forecast. Cannibalisation mechanisms can be configured between SKUs. End-of-life products are managed using dedicated decline curves to progressively reduce planned volumes.
4. Forecast adjustments and scenario planning
The granularity at which the sales signal is managed is one of the most important design decisions. Working at too detailed a level multiplies manual activities without adding value. Working at too aggregated a level hides useful demand signals. We configure top-down and bottom-up approaches according to business needs: some adjustments are made at a detailed level, while others are performed at an aggregated level. Combining both approaches within the same model is essential for an efficient review process.
We configure multiple scenarios, including baseline, optimistic and pessimistic. Each scenario incorporates business assumptions relating to pricing, marketing budgets and supply constraints. Differences between scenarios are displayed in absolute or relative terms, with contributions broken down by segment. The analysis metric is configurable (kg, units, cases, revenue or euros), allowing teams to quickly assess the impact of their assumptions on demand and service levels.
5. Demand review and alignment with S&OP
We structure a shared validation process that connects local teams with a centralised view. Bottom-up forecasts submitted by countries or business units are consolidated and compared with the top-down perspective derived from global objectives. Differences are made visible and provide the basis for consensus decisions during Demand Review meetings. For our clients, this structure significantly reduces the time required to prepare these meetings. On some projects, the time needed to complete the S&OP cycle has been reduced by more than 30%.
Consolidation covers multiple planning horizons, from short-term operational requirements to long-term budgeting. It incorporates the financial perspective, including revenue forecasting and margins, and supports the development of a consistent overall plan, from the Demand Plan to the Supply Plan and ultimately the Financial Plan. Gaps between commercial ambitions and operational realities are identified, measured and translated into decisions and actions.
Conclusion
An effective demand planning application is not built around an algorithm alone. It relies on three foundations: reliable, cleansed historical data; forecasting models configured for each demand profile; and structured management of commercial events and the product lifecycle.
Anaplan provides the technical framework needed to bring these components together in a consistent, auditable application connected to the S&OP process. The value delivered then depends on the quality of the business design, the precision of the configuration and how effectively the process is embedded in teams’ day-to-day practices. A high-performing model is therefore about more than its features. It must also be understood, used and maintained by the teams responsible for managing demand.
In practice, we consistently observe a progressive improvement in forecast accuracy over the 6 to 12 months following implementation. The most successful projects achieve Forecast Accuracy levels above 90% at S&OP granularity, with a bias below 5%. This progress does not simply reflect an immediate improvement in statistical performance. It also demonstrates the gradual adoption of the model by teams and the cumulative benefits of historical data cleansing mechanisms.
Are you building or improving your demand planning model in Anaplan? Get in touch with our experts.
behind the article
Meet the experts who contributed their vision, experience, and expertise to this content.
Etienne Pethelaz
Senior Consultant
A graduate of Arts et Métiers ParisTech with a Master’s degree in Mechanical Engineering, Etienne supports OneHive’s clients in their Supply Chain transformation and planning optimization projects. As a certified Anaplan Model Builder, he specializes in Demand Planning, Supply Planning, S&OP, and inventory optimization, combining functional expertise with technical capabilities. With experience across a wide range of Supply Chain and operations strategy projects, he helps organizations improve their planning processes, enhance operational performance, and implement tailored digital solutions to support business transformation.
Our latest news
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Perfume and cosmetics group
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FAQ
Discover answers to key questions about our services and approach.
Granularity depends on operational requirements. The calculation level is often more detailed than the review level: forecasts may be generated at SKU and point-of-sale level, then consolidated by product family or channel. Anaplan allows these levels to be adjusted dynamically without redesigning the model, making it easier for the planning process to evolve over time.
No. The Best Fit approach allows both model families to be combined. Statistical models perform well with regular and seasonal demand patterns. Machine Learning adds value for erratic demand profiles or products with limited historical data. The best-performing model is automatically selected at node level, with the option to override the selection manually.
Stockouts are identified in historical data and trigger a lost sales reconstruction mechanism. This correction estimates actual unmet demand and prevents the statistical sales signal from being biased downwards during affected periods. This is particularly important for fast-moving SKUs.
The forecast is the statistical prediction or the forecast adjusted by demand planning teams. The consensus is the version collectively validated during the Demand Review, following alignment between bottom-up and top-down perspectives. The plan is the final decision, incorporating supply constraints and financial commitments. Anaplan makes it possible to track and compare all three levels continuously.
Promotions are separated from the sales signal before statistical calculations are performed. A dedicated module models incremental demand according to the type of event, channel and customer. The promotional forecast is then added back on top of the baseline. This separation maintains the reliability of the forecasting model outside promotional periods.
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Moving beyond S&OP in Anaplan: towards integrated business planning (IBP)
Discover how to evolve your Anaplan S&OP by connecting operations, finance, CSR, and supplier collaboration to achieve an Integrated Business Planning approach.
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Linking the operational plan to the P&L, integrating CSR considerations, collaborating with suppliers, and expanding into IBP: the drivers that transform S&OP into a strategic management tool.
A mature S&OP process does not stop at the Supply Review. Its strategic value lies in its ability to translate operational trade-offs into measurable financial outcomes and, when the organisation is ready, to expand into broader planning dimensions such as sustainability, supplier collaboration and Integrated Business Planning (IBP). This article explores the extensions that transform S&OP into a true enterprise-wide planning framework.
Would you like to start by structuring your S&OP architecture? Discover our best practices for modeling in Anaplan.
Aligning operations and finance through a unified planning model
A high-performing S&OP process connects volumes, capacity, costs and profitability. The ability to translate operational decisions into financial outcomes strengthens executive engagement and accelerates decision-making.
Making the business impact of S&OP decisions visible
Our planning models integrate the financial information required to support effective decision-making, including raw material costs, cost of goods sold (COGS), unit margins, logistics costs, service penalties and product mix effects.
Each scenario becomes a complete business trajectory that combines operational feasibility, supply implications and financial performance. This integrated view transforms S&OP into a powerful alignment mechanism between Operations and Finance.
Connecting operational planning to the P&L in Anaplan
Linking operational volumes directly to the P&L allows organisations to immediately assess the financial impact of changes in demand, production capacity or inventory decisions. Rather than reviewing financial consequences after decisions have been made, management gains a forward-looking planning environment that supports informed decision-making.
This continuity breaks down functional silos and significantly shortens decision cycles. S&OP evolves from a Supply Chain planning exercise into a shared decision-making process involving both Operations and Finance.
Advanced S&OP capabilities in Anaplan
Many organisations use S&OP as the foundation for broader planning capabilities. These extensions increase the value of the monthly planning cycle without adding unnecessary complexity.
The most common advanced use cases include:
- Logistics network simulation and network design studies
- Sustainability impact assessment (carbon emissions and material consumption)
- Product portfolio evolution modelling
- Supplier collaboration through shared planning data
Sustainable S&OP: integrating sustainability into operational planning
Moving towards Sustainable S&OP means connecting operational decisions with their environmental impact. Within Anaplan, each Supply Plan scenario can be associated with sustainability indicators such as transportation emissions, production-related carbon footprint, sourcing strategies or supplier sustainability risks.
These metrics are embedded directly into the existing planning process rather than creating additional governance layers. Instead of introducing new meetings, Supply Review discussions simply incorporate an additional decision criterion alongside customer service and cost: environmental impact.
For organisations pursuing decarbonisation objectives, this visibility transforms S&OP into a powerful alignment mechanism between operational performance and sustainability goals.
Supplier collaboration: extending visibility beyond the enterprise
An S&OP process generates a reliable demand signal covering the next 12 to 36 months. That information creates value well beyond internal planning teams. Sharing this visibility with strategic suppliers improves responsiveness, secures critical supply and facilitates collaborative decision-making when capacity constraints arise.
Within Anaplan, we design collaborative workspaces where suppliers access only the planning information relevant to them. They can confirm available capacity, identify potential supply risks and contribute directly to the Supply Review process.
This capability is particularly valuable in industries facing significant supply risks, including food & beverage, manufacturing, luxury and life sciences, where shared visibility has become a key resilience driver.
Extending S&OP towards integrated business planning
Integrated Business Planning (IBP) represents the natural evolution of S&OP for organisations seeking tighter integration between operations, finance and corporate strategy.
While S&OP focuses on tactical operational decisions, IBP extends planning over a longer time horizon by aligning financial objectives, investment decisions and strategic priorities.
Anaplan provides a seamless transition from S&OP to IBP. Supply plans, budgets, rolling forecasts and strategic planning all operate within the same planning environment. Rather than replacing the existing S&OP architecture, organisations progressively enrich it as their planning maturity increases.
Conclusion
An S&OP process built in Anaplan delivers far more than an operational plan. It provides a structured decision-making framework that aligns business functions, translates operational scenarios into measurable financial outcomes and gradually evolves into an Integrated Business Planning process as organisational maturity increases.
OneHive combines deep Supply Chain expertise, advanced Anaplan implementation capabilities and a rigorous delivery methodology to build planning models that remain scalable, maintainable and robust over time.
Contact our experts to build or strengthen your S&OP process.
behind the article
Meet the experts who contributed their vision, experience, and expertise to this content.
Mathis Georgeault
Manager
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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OneHive at Anaplan Connect Madrid 2026
Agentic Enterprise, AI and Connected Planning: join us in Madrid
FAQ
Discover answers to key questions about our services and approach.
Successful S&OP implementations follow an iterative approach. We quickly deploy a first end-to-end planning cycle covering demand, capacity, supply planning and scenario management to validate key business rules and establish planning routines. Subsequent iterations progressively enhance data quality, KPIs, scenario modelling and user experience. This methodology avoids overengineering, accelerates user adoption and ensures a stable planning architecture while enabling internal teams to become progressively autonomous.
S&OP primarily focuses on balancing demand, supply and operational capacity over a tactical planning horizon. Integrated Business Planning (IBP) extends this process by incorporating financial planning and strategic objectives. Within Anaplan, both processes share the same planning architecture, allowing operational scenarios to be connected directly to financial forecasts, investment plans and long-term business objectives.
Connecting S&OP with the P&L means linking operational planning volumes to their financial consequences, including revenue, raw material costs, COGS, logistics costs, margins and service penalties. Every planning scenario can therefore be evaluated from both an operational and financial perspective, enabling faster and better-informed decisions across Supply Chain, Finance and Executive Management.
Environmental indicators can be embedded directly into S&OP scenarios. Carbon emissions related to transportation, manufacturing or sourcing strategies are calculated alongside operational and financial KPIs. This enables planning teams to compare scenarios using three complementary dimensions: customer service, overall cost and environmental impact, without changing the cadence of the existing S&OP process.
Sharing medium-term demand visibility with strategic suppliers strengthens collaboration, improves responsiveness and secures critical supply. Within Anaplan, collaborative planning workspaces allow suppliers to access relevant planning data, confirm available capacity and proactively communicate supply risks. This information feeds directly into the Supply Review process, improving decision quality and overall supply chain resilience.
The transition to an IBP is appropriate once the S&OP process has been stabilized and adopted by the teams. Once demand, capacity, and supply reviews are under control, the organization can gradually integrate financial considerations, capital expenditures, strategic objectives, and CSR metrics. This phased approach allows the scope of decision-making to be expanded without disrupting the existing architecture.
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S&OP in Anaplan: Best practices for architecture and model design
Product portfolio, business events, capacity levers and scenarios: the design principles behind a robust S&OP model in Anaplan.
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A comprehensive S&OP model is built around five core components: Product Portfolio Review, Demand Review, Supply Review, financial impact assessment and Executive Review. Each component must operate independently while remaining seamlessly connected to the others. This article explores the design principles that make an Anaplan S&OP architecture scalable, transparent and truly decision-oriented.
We design mechanisms that enable planners to duplicate a planning scope in a controlled manner, adjust selected assumptions and instantly assess the impact on capacity, customer service, costs and margins. As a result, Executive S&OP meetings become significantly more effective. Decisions are no longer based on intuition, but on structured comparisons supported by quantified scenarios.
It structures every stage of the planning cycle, enables meaningful scenario comparisons and transforms business trade-offs into clear decision signals shared across Supply Chain, Finance and Executive Management. Discover our S&OP expertise.
Extending the planning horizon to improve visibility
One of the main challenges when implementing an S&OP process is the lack of reliable long-term demand signals. If product portfolios never changed, S&OP would simply extend existing operational planning processes. In reality, however, product life cycles make long-term forecasting far more complex. The real value of S&OP lies in its ability to anticipate future business complexity by modelling new product introductions, market expansion plans and product phase-outs before they occur. We recommend integrating these dimensions directly into the planning model while enabling users to simulate the impact of potential changes. In practice, this means allowing planners to adjust product life cycles by delaying launch dates, modelling end-of-life scenarios or revising promotional assumptions. For longer planning horizons, we create placeholder products that carry future demand while inheriting the physical characteristics required by downstream planning processes, such as production rates, weight, pricing or other operational attributes. These assumptions are based either on available business information or on comparable existing products. This provides the reliable foundation required to build meaningful S&OP scenarios.Making Demand Review the foundation of an effective Anaplan S&OP process
Demand Review is the most critical stage of the S&OP cycle. It starts with an initial demand signal generated from existing customer contracts, marketing plans translated into monthly volumes, statistical forecasts based on historical data, or a combination of all three. This demand signal can either be imported from an existing forecasting solution or generated directly within the Anaplan S&OP application. However, Demand Review goes far beyond validating a forecast. Discover our Demand Planning approach. The process must also capture the risks and opportunities that could significantly influence demand, including promotional campaigns, economic downturns, competitive moves, geopolitical events or the loss of major contracts. Ignoring these business events inevitably drives planners back to spreadsheets for ad hoc analysis, weakening the overall planning process.Why We Favour a Risk and Opportunity-Based Approach
- Manually adjusting forecast values simply to reach a target is rarely sustainable. A structured risk and opportunity approach delivers three major benefits. First, it enables planners to simulate price changes, demand increases or decreases, delayed product launches, product phase-outs and other business events that cannot be captured through manual forecast adjustments alone.
- Second, a single business event can be reused across multiple scenarios and planning cycles without recreating its impact each time.
- Finally, every event remains fully documented, monitored and linked to a defined action plan, significantly improving transparency during executive reviews.
Modelling capacity with the right level of granularity
The level at which production resources are modelled has a direct impact on the quality of Supply Review decisions. Two common pitfalls should be avoided.- An excessively detailed model often generates artificial imbalances between interchangeable production lines where aggregated analysis would be sufficient.
- Conversely, an overly aggregated model can hide constraints affecting critical resources.
Transforming Demand into an actionable Supply Plan
Building the Supply Plan relies on a set of business rules, including target inventory coverage, safety stock levels, customer prioritisation, inter-site balancing, resource allocation and pull- or push-based replenishment strategies. In many organisations, the key planning decisions do not concern these allocation parameters themselves. In the food industry, for example, sourcing decisions are often one of the most critical aspects of the Supply Review process. This is why we favour a tailored modelling approach rather than a one-size-fits-all application. For organisations with relatively straightforward supply planning requirements, a lightweight calculation engine is sufficient. More complex organisations benefit from a dedicated simulation cockpit that supports detailed planning and informed decision-making. One of Anaplan’s strengths is its ability to preserve the origin of demand throughout the entire planning process. Demand can remain segmented by sales channel, demand type, customer priority or any other business attribute. This level of traceability makes available planning levers explicit during the Supply Review and allows each decision to be directly linked to a concrete action plan.Making scenarios the core of the S&OP process
Within an Anaplan S&OP process, scenarios are the real driver of decision-making. They allow organisations to evaluate multiple realistic business trajectories while immediately measuring their operational and financial consequences.We design mechanisms that enable planners to duplicate a planning scope in a controlled manner, adjust selected assumptions and instantly assess the impact on capacity, customer service, costs and margins. As a result, Executive S&OP meetings become significantly more effective. Decisions are no longer based on intuition, but on structured comparisons supported by quantified scenarios.
Observed business benefits
Organisations that implement a structured S&OP process in Anaplan typically experience measurable improvements from the very first planning cycles:- Reduced preparation time for S&OP meetings
- Improved forecast accuracy and customer service levels
- More reliable decision-making through scenario comparison
- Centralised planning data and the elimination of parallel spreadsheets
- Greater long-term visibility over capacity, inventory and margins
Conclusion
A well-designed S&OP architecture in Anaplan delivers far more than a planning model.It structures every stage of the planning cycle, enables meaningful scenario comparisons and transforms business trade-offs into clear decision signals shared across Supply Chain, Finance and Executive Management. Discover our S&OP expertise.
behind the article
Meet the experts who contributed their vision, experience, and expertise to this content.
Mathis Georgeault
Manager
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.
Our latest news
Supply Chain Planning
Perfume and cosmetics group
Transformation of supply chain planning operations at a major French perfumes and cosmetics group, in partnership with Argon & Co
October 9, 2026
AI in planning: real-world use cases, Anaplan capabilities and business impact
AI and planning progress together. The question remains: where does the real value lie?
October 1, 2026
OneHive at Anaplan Connect Madrid 2026
Agentic Enterprise, AI and Connected Planning: join us in Madrid
FAQ
Discover answers to key questions about our services and approach.
Implementing an operational S&OP solution typically takes between four and eight months, depending on process complexity, organisational maturity and data availability. This timeframe covers business scoping, architecture design, model development, core integrations, testing and user training. Once the initial solution is live, additional planning capabilities can be introduced through short enhancement cycles without disrupting the existing model.
Anaplan places no technical limit on the number of scenarios that can be created. In practice, however, two or three well-defined scenarios are usually sufficient: a baseline scenario, a constrained scenario (capacity, materials or cost limitations) and an alternative business scenario. Beyond that, decision-making tends to become less focused. The value of S&OP lies in comparing a small number of meaningful options rather than multiplying planning assumptions.
Risks and opportunities are one of the most powerful ways to strengthen the Demand Review process. Rather than manually adjusting forecasts to reach a target, organisations should explicitly model business events that may affect demand, such as product launches, promotional campaigns, competitive changes, regulatory developments or the loss of major customers. This approach improves decision traceability, facilitates scenario building and provides a clear understanding of how each assumption impacts both operational performance and financial outcomes.
The key is to find the appropriate level of granularity. An overly detailed model generates unnecessary alerts, while an overly aggregated model hides critical constraints. Best practice consists of modelling capacity at a level detailed enough to support meaningful simulations, including production calendars, shift patterns, critical resources and Overall Equipment Effectiveness (OEE), while presenting information at a level that supports effective decision-making during Supply Reviews. This approach helps organisations anticipate bottlenecks and evaluate corrective actions before they affect the industrial plan.
Anaplan brings forecasts, capacity planning, scenarios and financial impacts together within a single connected planning environment. Each stage of the S&OP process remains fully integrated, making it easier to compare scenarios, extend planning horizons and evaluate risks and opportunities in real time. This enables Supply Chain, Finance and Executive teams to make faster, more informed decisions based on a shared and transparent view of the business.
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Supply Chain Planning in Life Sciences: when compliance is no longer enough
Why pharmaceutical and medical device companies must rethink end-to-end planning
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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.
An increasingly unstable environment
Demand has become harder to forecast
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.
Urgent flows are weighing on margins
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.
More complex and variable networks
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.
Pressure on inventory and cash flow
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.
Sector-specific constraints remain decisive
Regulation and traceability
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.
Shelf-life management and strategic inventory
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.
Time-to-market and ESG challenges
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.
Why traditional tools are no longer sufficient for Life Sciences Supply Chain Planning
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.
- Slow integration of new partners.
Incorporating a new CMO or an acquired entity may require weeks of configuration, while industrial networks evolve within months. - Limited simulation capabilities. Modeling the impact of a regulatory constraint or comparing multiple inventory strategies remains difficult. Decisions are often made without sufficient quantitative visibility.
- Rigid models unsuited to multi-horizon planning.
Life Sciences planning requires simultaneous management of short-term horizons (batches, shelf-life alerts), mid-term horizons (S&OP) and long-term horizons (industrial capacity planning). Traditional systems struggle to connect these layers coherently. - Insufficient cross-functional coordination.
Supply Chain, Quality, Regulatory, Operations and Finance teams often work in separate systems. The lack of a shared framework creates delays, duplicate entries and disconnected decisions.
These limitations produce concrete consequences: late issue detection, urgent decision-making and an inability to build alternative plans in real time.
What Life Sciences companies expect from a planning platform
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.
- Rapid scenario planning. Simulate the impact of a supplier disruption, embargo or qualification delay within hours.
- Multi-party coordination. Align Supply Chain, Finance, Quality and Regulatory teams around a shared framework without data fragmentation.
- Progressive use-case integration. Start with a focused scope and expand progressively according to organizational maturity.
- Supply and Finance alignment. Connect distribution plans with cash-flow projections, inventory destruction provisions and budget forecasts.
- Flexible integration. Incorporate new sites, partners or logistics flows without redesigning the data model.
The OneHive approach: a practical response to Life Sciences challenges
Anaplan as a planning foundation
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:
- Multi-market S&OP, integrating regulatory constraints and country-specific stock obligations.
- Demand Planning to improve forecast accuracy and responsiveness.
- Distribution Planning to optimize allocations and reduce inventory destruction.
- What-if scenarios, to anticipate the impact of supply disruptions or logistics changes.
- R&D time-to-market management, coordinating clinical, regulatory and industrial milestones within a shared roadmap.
- CMO collaboration, through modules enabling secure sharing of inventory and forecast data.
- Green S&OP and ESG reporting, integrating sustainability criteria into allocation and partner selection decisions.
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.
The OneHive value proposition
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.
- Fast results. Our MVP (Minimum Viable Product) methodology delivers a first operational version within three to six months, then progressively enriches the platform through iterative deployments. Investments are secured from the very first weeks.
- Guaranteed adoption. We involve key users from the design phase and support teams until they achieve full autonomy. Our projects consistently achieve adoption rates above 95%.
- International coverage. Through our strategic alliance with Argon & Co, present across all continents, we support multi-country deployments with the same level of quality and rigor.
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.
Conclusion
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
Meet the experts who contributed their vision, experience, and expertise to this content.
Florian Richetta
Partner
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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AI in planning: real-world use cases, Anaplan capabilities and business impact
AI and planning progress together. The question remains: where does the real value lie?
October 1, 2026
OneHive at Anaplan Connect Madrid 2026
Agentic Enterprise, AI and Connected Planning: join us in Madrid
FAQ
Discover answers to key questions about our services and approach.
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 : how to Improve forecast accuracy and strengthen decision-making?
Three key levers to structure your Demand Planning process and increase forecast reliability
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The challenges of Demand Planning
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:
- The effective integration of data to improve forecast accuracy
- Alignment across finance, production, and commercial teams through a structured collaborative process
- Integration with broader operational and industrial planning processes
Taken individually, these levers have limited impact. Combined, they transform forecasting into a powerful driver of decision-making and performance optimization.
1. Integrating signals to improve forecast accuracy
Enriching forecasts with relevant signals
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:
- Promotional and marketing plans
- Field sales insights (contracts, ongoing negotiations, opportunities)
- Historical sales data
- Product launches and end-of-life events
- Selected external indicators
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 and review levels
Forecast granularity should be defined based on operational needs to ensure relevance, usability, and traceability. Key dimensions typically include:
- Customer: account, segment, or point of sale, aligned with commercial and operational needs
- Geography: country, logistics network, distribution channel, or cluster, reflecting supply constraints and local dynamics
- Product: category, family, or lifecycle stage, capturing demand patterns and business rules
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 methods
Forecasting approaches must be aligned with the product lifecycle to ensure relevance and robustness:
- New products: estimate sales potential using market data, benchmarks, and comparable products
- Mature products: apply automated statistical methods with best-fit selection to maximize accuracy
- End-of-life products: rely on operational signals such as open orders, remaining inventory, and phase-out plans
Even with these foundational approaches, organizations can already achieve satisfactory forecast accuracy across a large share of their portfolio.
The contribution of Machine Learning and AI
Machine Learning and AI enhance forecasting by introducing additional predictive signals and capturing more complex relationships. They enable:
- Cross-product learning by identifying similar demand patterns
- Integration of external data (weather, market trends, macro indicators) and digital signals (web traffic, search trends, social media)
- Hybrid modeling approaches combining statistical and advanced algorithms
- Automated model selection based on performance metrics such as MAE, MAPE, RMSE, and bias
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.
2. Aligning finance, production, and commercial teams
A collaborative planning process
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.
Frequency: an often underestimated lever
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:
- Weekly reviews for tactical adjustments and prioritization
- Monthly cycles for structural decisions and industrial commitment
- Quarterly reviews for long-term strategic alignment
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)
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.
3. Integrating with operational and industrial planning processes
Connecting planning processes
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.
Scenario planning
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.
Enabling integrated planning with Anaplan
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.
Conclusion
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
Meet the experts who contributed their vision, experience, and expertise to this content.
Jules Coron
Manager
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.
Our latest news
Supply Chain Planning
Perfume and cosmetics group
Transformation of supply chain planning operations at a major French perfumes and cosmetics group, in partnership with Argon & Co
October 9, 2026
AI in planning: real-world use cases, Anaplan capabilities and business impact
AI and planning progress together. The question remains: where does the real value lie?
October 1, 2026
OneHive at Anaplan Connect Madrid 2026
Agentic Enterprise, AI and Connected Planning: join us in Madrid
FAQ
Discover answers to key questions about our services and approach.
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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Excel in Supply Chain Planning: What are its limitations?
Why relying too much on Excel hurts your teams' operational performance.
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Excel has become the default planning tool for many Supply Chain teams. Its apparent flexibility and ease of use make it the obvious choice. However, when faced with the realities of modern planning processes (multidimensional, collaborative and highly interconnected) Excel reveals limitations that undermine both reliability and performance.
These limitations are not isolated: they reinforce one another and ultimately affect the entire planning cycle. This article examines these limitations across three main areas, from technical constraints and functional shortcomings to data governance challenges, and explains how Anaplan addresses each of them.
1. Planning limited by technical constraints
Before collaboration or governance even become an issue, Excel is held back by fundamental architectural limitations. This is where the problem begins: a tool designed to manipulate two-dimensional spreadsheets is being used to manage the inherent complexity of Supply Chain planning.
Not designed for multidimensional planning
Supply Chain planning requires organisations to work across multiple dimensions simultaneously, including products, warehouses, geographical regions and planning horizons. Excel handles these dimensions within a two-axis spreadsheet structure. As soon as three or more dimensions need to be combined within the same calculation, the model quickly becomes unmanageable. Planners multiply worksheets, lose visibility into calculations and can no longer guarantee data consistency.
This limitation cannot be overcome through better spreadsheet design or user expertise. It is inherent to Excel’s architecture and only becomes more problematic as data volumes continue to grow.
Calculation times that slow decision-making
As Excel workbooks grow, they rapidly become difficult to manage. Beyond a certain volume of data, waiting times become a significant operational constraint:
- Opening large workbooks: often takes tens of seconds.
- Recalculating formulas: user interaction becomes noticeably slower after every update.
- Running VBA macros: execution times become unpredictable, with no guarantee of successful completion.
In a time-sensitive planning cycle, this level of latency is unacceptable. It forces planners to reduce the number of planning iterations they can perform, ultimately limiting the quality of business decisions. But performance is only part of the problem—Excel’s overall reliability is equally at stake.
Technical reliability that falls short for business-critical processes
An unsaved crash can erase hours of work. VBA macros developed on one workstation may fail on another. Compatibility issues between Microsoft Office versions can introduce silent errors. And because Excel offers no native mechanism for automated collaboration, files continue to circulate via email with little or no traceability.
Across the projects we deliver with our clients, these situations are far from exceptional. They occur repeatedly, regardless of industry or company size. Excel’s technical fragility is not a theoretical risk—it is an operational reality that many Supply Chain teams have gradually accepted simply because they lacked a viable alternative.
These three limitations: restricted multidimensionality, poor calculation performance and technical fragility, form the foundation of Excel’s shortcomings in Supply Chain planning. Yet they represent only the first layer of the problem. Even if these issues could somehow be eliminated, Excel would still remain fundamentally unsuited to collaborative planning.
Comparison: How Anaplan addresses Excel’s technical limitations
2. Excel is not designed as a Supply Chain planning platform
The technical limitations described above have direct consequences: they make collaboration difficult, interfaces cumbersome and scenario planning almost impossible. Excel was designed as an individual productivity tool. Supply Chain planning, by contrast, is fundamentally a collaborative process.
No true real-time collaboration
Demand planners, merchandising planners, supply planners, logistics managers and commercial teams all need to work with consistent, up-to-date data. Excel simply does not support this way of working. Simultaneous edits create conflicts. Naming conventions and file formats vary from one workbook to another. Managing user access by business scope is either non-existent or entirely manual.
As a result, teams work in silos, using different versions of the same data, and spend a significant amount of time reconciling discrepancies instead of focusing on planning. This loss of efficiency is also reflected in the day-to-day user experience.
User interfaces designed for spreadsheets, not planners
Complex Excel models quickly accumulate worksheets containing raw data, intermediate calculations, summary tables and charts. Navigation is not intuitive or guided. A planner responsible for a limited business scope is often exposed to the entire model. Reporting remains largely static and frequently requires exporting data to Power BI before it becomes truly actionable.
This daily friction slows down planning cycles and increases the likelihood of input errors, particularly in poorly secured models. It also limits the ability of planners to test assumptions and compare alternative scenarios efficiently.
Scenario planning and plan consolidation: structural limitations
Scenario planning is a fundamental requirement of any S&OP process. Excel provides no native capability to manage multiple scenarios within the same planning model. The most common approach is to duplicate the entire workbook for every new assumption. Pivot tables do not update automatically across scenarios, and consolidating multiple business units into a single enterprise plan remains a manual, time-consuming and error-prone exercise.
Ultimately, Excel’s functional limitations are far more than simple inconveniences. They reduce the quality of decisions made during S&OP reviews and weaken the consistency of the entire planning process. The same lack of robustness can also be seen in the way data itself is managed.
Comparison: How Anaplan enhances planning capabilities
3. Difficulty integrating and maintaining data: incompatible with a modern planning ecosystem
The collaboration challenges described in the previous section inevitably lead to another issue: data fragmentation. Once it takes hold, it becomes extremely difficult to reverse. This is arguably Excel’s most significant limitation in a Supply Chain environment, as it directly affects the trust organisations can place in the data used to make critical business decisions.
Uncontrolled file proliferation with no governance
Every planning cycle managed in Excel generates new workbooks. They are shared by email, stored in shared folders with inconsistent naming conventions and duplicated across multiple stakeholders. Over time, identifying the authoritative version becomes increasingly difficult. Teams end up working with inconsistent data, and confidence in planning decisions gradually declines.
This lack of governance is not simply the result of poor internal processes that could be resolved through better discipline. It is a structural limitation of Excel as a planning tool, creating countless opportunities for errors to occur.
Multiple sources of error with no built-in detection
Excel is, by design, an open environment. Any user can accidentally overwrite a formula with a manually entered value. An incorrect copy-and-paste operation can break an entire calculation chain. A planner may unintentionally modify another colleague’s planning scope without anyone noticing. Excel provides no native control centre capable of detecting these issues. In many organisations, maintaining the planning model depends on a single expert, creating a significant operational risk.
These risks are far from theoretical. We encounter them regularly in the planning processes implemented by our clients, where they directly impact the quality of operational decisions. And when organisations attempt to address these issues, they often face one final obstacle: the model itself becomes increasingly difficult to evolve.
Maintenance constrained by technical debt
Enhancing a complex Excel model quickly becomes a project in its own right. VBA macros are written in an ageing programming language that is difficult to document, maintain and transfer to new team members. Adding a new planning dimension often requires restructuring the entire model. Knowledge of how the workbook functions is frequently concentrated in the hands of a single specialist, meaning that any departure can halt future development.
This technical debt accumulates planning cycle after planning cycle until the model becomes virtually untouchable—precisely when business requirements are evolving faster than ever. It is the inevitable outcome of using a spreadsheet application designed for data manipulation to support planning processes of far greater complexity.
Comparison: How Anaplan strengthens data governance and model maintenance
Conclusion
The three categories of limitations explored in this article are closely interconnected. Excel’s technical constraints make collaboration more difficult, collaboration challenges lead to fragmented data, and fragmented data ultimately creates a growing maintenance burden that becomes increasingly difficult to manage. It is a vicious cycle that becomes harder to break as organisations grow accustomed to their existing processes and planning models become more complex over time.
Excel remains an effective tool for ad hoc analyses and quick exploratory work. However, when the objective is to build a robust, collaborative and scalable planning process, its structural limitations can no longer be compensated for by the ingenuity of experienced planners.
The real question is no longer whether Excel is sufficient. It is how much these limitations actually cost: time lost reconciling inconsistent data, undetected errors, inefficient planning cycles and strategic decisions based on unreliable information. Anaplan is designed to break this cycle by combining a multidimensional calculation engine, real-time collaboration, built-in data governance and integrated scenario planning. These capabilities provide the foundation on which our clients build planning processes that match the growing complexity of today’s Supply Chains.
behind the article
Meet the experts who contributed their vision, experience, and expertise to this content.
Julian Stum
Consultant
An engineering graduate of CentraleSupélec, specialising in supply chain and operations management, Julian supports OneHive’s clients in the design and implementation of planning solutions on Anaplan. As a certified Anaplan Model Builder, he contributes to the transformation of demand planning, business planning and integrated S&OP/IBP planning processes. His experience in modelling enables him to contribute to defining requirements, building models and implementing collaborative workflows, particularly for forecasting, budgeting and production planning. His work in the luxury, retail and agri-food sectors allows him to combine an understanding of operational challenges with data analysis and technical expertise.
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FAQ
Discover answers to key questions about our services and approach.
Excel was designed to manipulate two-dimensional data on an individual workstation. Supply Chain planning requires organisations to work across multiple dimensions simultaneously—including products, warehouses and planning horizons, while enabling collaboration between multiple stakeholders and managing large volumes of data. These requirements go far beyond what Excel can reliably and efficiently support.
Excel's limitations fall into three main categories. First, technical constraints such as slow calculation times, limited reliability and the inability to handle multidimensional planning models. Second, functional shortcomings, including the lack of real-time collaboration, guided user interfaces and integrated scenario planning. Finally, data governance challenges, such as version proliferation, accidental formula overwrites and increasingly difficult model maintenance caused by technical debt.
For a large planning workbook of 20 to 50 MB, a common size in Supply Chain environments, recalculation after a data update can take anywhere from 30 seconds to several minutes. Opening the workbook itself may also take more than a minute. These delays reduce the number of planning iterations teams can perform during each planning cycle, ultimately affecting the quality of business decisions.
Anaplan was purpose-built for enterprise planning. Its Hyperblock™ calculation engine natively supports multidimensional models, performs in-memory calculations on the server and enables multiple users to work simultaneously within the same planning model. Scenario planning, plan consolidation and role-based access management are built into the platform, eliminating the need for duplicated spreadsheets while improving governance and data reliability.
The transition becomes worthwhile when Excel models are no longer maintainable by a single expert, when S&OP cycles are disrupted by inconsistent data across teams, or when calculation times begin to slow decision-making. It is also a strong indicator that the organisation needs more advanced scenario planning, stronger data governance and complete traceability of planning changes.
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