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:

1. The effective integration of data to improve forecast accuracy
2. Alignment across finance, production, and commercial teams through a structured collaborative process
3. 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.

Balancing service level, inventory costs and field responsiveness

Key challenges observed in the field

1. Volatile demand across stores: Sales patterns evolve rapidly and differ significantly by location, making uniform rules ineffective.
2. Balancing stockouts and overstock: Maintaining high availability while reducing tied up inventory remains a delicate trade off.
3. Data volumes exceeding manual steering capacity: Daily updates of stock targets, parameter adjustments and variance tracking quickly exceed human capacity.
4. Lack of consolidated visibility on network needs: Without a reliable end to end view, upstream distribution and supply planning operate out of sync.

OneHive deliverables

• ABC product classification with differentiated stock targets: Targets aligned with product rotation and criticality, enabling more relevant allocation and human focus.
• Advanced calculation algorithms with real time stock target updates: Dynamic adjustments incorporating recent sales, trends and business rules.
• Daily automated order generation with system interfaces: Secure and structured replenishment proposals without reliance on manual handling.
• Alerts on key indicators: Availability rate, stockouts, missed opportunities and stock levels, allowing rapid identification of critical signals.
• Short and mid term stock projections with associated distribution plans: Consolidated visibility to anticipate tensions, trade offs and future needs.
• Upstream demand signals: Clear inputs for production and procurement, improving global anticipation.

Benefits: measurable improvements for teams and the business

1. Improved availability rate: Differentiated, daily updated stock targets deliver more stable and higher availability, with average gains of +5 to +10 points.
2. Reduced tied up inventory: ABC segmentation and automated target adjustments reduce excess volumes, with direct cash flow impact, typically around –15%.
3. Smoother, more responsive processes: Automated order creation and reliable allocation rules significantly reduce manual intervention.
4. Planners focused on analysis, not data entry: Alerts prioritise critical actions, freeing time for higher value decisions, often doubling analysis time.
5. Stronger coordination across the Supply Chain: Reliable demand signals and structured projections enable upstream distribution and supply teams to anticipate more effectively.
6. More harmonised stock across the network: Stock projections and rapid variance detection support better arbitration and reduce imbalances between stores.

When planning becomes a lived experience

Raising awareness among non-specialists about supply chain planning issues is never easy. Trade-offs, capacity constraints and cross-functional impacts are difficult to grasp until they are experienced first-hand.
This is precisely why we designed a business simulation dedicated to Connected Planning.

In October and November, students from Centrale Lille and CentraleSupélec took part in this immersive experience.

The objective was clear: to understand, through practice, the importance of a shared vision across teams and the effects of truly unified decision-making.

A simulation covering 12 to 18 months of activity

Over two to three half-day sessions, participants form small teams that act as mini-companies managing an accelerated year of operations. Each group must plan, decide and arbitrate while dealing with the same types of disruptions real organisations face.

The simulated context is built around a company founded in 2010, characterised by:

• A core business focused on a premium product, subject to strong seasonality and significant exposure to international markets
• A diversification strategy launched in 2020 with two new product lines experiencing rapid growth in the US and European markets
• Positive momentum in 2023, to be consolidated in 2024
• A clear objective: maximise profit

The simulation mirrors the key steps of a monthly planning cycle:

1. Financial and strategic framing
2. Business analysis and forecast updates
3. Operational adjustments
4. Final alignment with an executive committee

In practice, participants quickly realise how these steps collide with familiar challenges: unexpected events, lack of coordination, conflicting objectives, incomplete or poorly shared data.

Disruptions that reflect real-world constraints

Throughout the game, the teams must manage several structuring events:

• A product shortage caused by capacity constraints
• Limited but critical recruitment decisions
• Growth opportunities in the US and European markets
• A marketing campaign launched without sufficient anticipation
• The introduction of a carbon footprint constraint, forcing teams to arbitrate between economic performance and environmental impact

These elements require teams to align strategy, operations and real-world constraints while controlling their impact on margin and revenue.

An Anaplan environment to make decisions tangible

Each team works within a dedicated Anaplan environment. The goal is not to showcase a solution, but to make tangible:

• The link between assumptions and decisions
• The direct impact of local adjustments on overall performance
• The importance of a single, shared data reference
• The need for consistent updates across departments (finance, HR, sales planning, supply chain planning)

This environment facilitates scenario comparison and enables teams to quickly measure the consequences of their trade-offs.

What participants quickly discover

As the simulation progresses, several key insights emerge:

• Team connectivity: no decision can be made in isolation
• Cross-functional impacts: any change in capacity, forecast or workforce immediately affects the entire planning system
• The role of digitalisation and data sharing: shared information streamlines collaboration and reduces misinterpretation
• The value of unified steering: capacity, forecasting, hiring and cash flow must be synchronised to avoid contradictions
• The differentiating power of planning decisions: with similar business potential, teams achieve markedly different margins based solely on the quality of their planning trade-offs

A central takeaway

The experience reinforces a strong conviction: engagement does not come from the tool, but from understanding other functions’ realities.
Connected Planning takes root when each participant understands how their decisions affect other teams and recognises the constraints shaping their work.

Our business simulations are designed precisely to embed this understanding—well before any operational or technological transformation begins.

At Supply Chain Event 2025, OneHive brought together Groupe Pochet and Yoplait – Sodiaal to discuss a topic that goes far beyond tools: how can an APS or EPM project genuinely transform an organisation? And more importantly: why do some projects create lasting value, while others lose momentum once the tool is live?

From the opening remarks, the tone was clear. « “We wanted to share what actually makes a project sustainable over time, beyond the technology itself”, explains Hugo Van Straaten, co-founder of OneHive. A way to refocus the debate: it is not the platform that transforms planning, but the method, business understanding and the ability to orchestrate change.

Two contexts, one common quest for control

At Pochet, the post-Covid rebound left the Supply Chain under pressure, with bullwhip effects and declining demand visibility. Legacy tools no longer enabled effective coordination across teams and sites. Visibility deteriorated, decision cycles slowed, and transformation became unavoidable to restore consistent control.

Sodiaal, on the other hand, faces a different kind of complexity: ultra-fresh products. Volumes fluctuate sharply, shelf life is extremely short and margins for error are minimal. “Planning is played out hour by hour”, summarises Julien Triquet, Planning IS Project Manager at Sodiaal. Existing systems could no longer keep pace, forcing the organisation to rethink its processes around Anaplan.

Two very different stories… yet the same conclusion: a planning project never starts with a tool. It starts with business reality.

Cross lessons: the importance of pace

These two journeys show that there is no single “right” way to run an APS or EPM project.

Yoplait experienced both extremes: a highly ambitious, technically rich project in France that required multiple adjustments, and a Canadian project delivered using an MVP approach, leveraging Anaplan standards to achieve go-live in less than four months.

Pochet adopted the opposite but equally effective strategy: progressing step by step, with each phase delivering visible value to sustain internal engagement.

In both cases, the conclusion is clear: project value depends less on scope than on coherence and the ability to maintain a realistic pace.

Scoping: the most underestimated factor

One theme consistently emerged from all feedback: the quality of the initial scoping phase.

This is where structural assumptions, actual scope, success criteria and key trade-offs are defined, preventing months of unnecessary testing.

“We needed a clear, shared planning framework to avoid drift and restore visibility. Without this foundation, no model can last over time”, explains Axel Rousseau, Supply Chain Transformation Projects Manager at Groupe Pochet.

Conversely, weak scoping often leads to scenario inflation, endless adjustments and extended testing cycles, making the project difficult to manage. Clear scoping protects teams, sets priorities and ensures technology never becomes a source of unnecessary complexity.

Data: an organisational issue, not a platform feature

All participants agreed on one point: Anaplan calculates, but it does not govern data. Without reliable master data, no modelling effort can deliver robust planning.

The message is unequivocal: data is not a configuration topic, it is an organisational one.

Behind the technology: people

A planning project succeeds when the project team works effectively.
Both organisations highlighted the same essentials: stable governance, a project lead able to bridge business and technology, committed IT teams and an integrator who genuinely understands Supply Chain.

And above all: early user involvement.

“Bringing users in at go-live is already too late” , Axel Rousseau reminds us. Field workshops, regular demos and progressive training build ownership that no tool can replace.

Anaplan delivers its full value when its scope is clearly defined. This clarity allows the platform to focus its power on what truly matters for the business.

Anaplan: a platform, but above all an ecosystem

If Pochet and Sodiaal chose Anaplan, it was first for its ability to model complex processes and adapt to business specificities. The platform is powerful and flexible, provided its use cases are precisely defined.

“With an open platform, you can do almost anything… as long as you know where you’re going. When ambition is clearly framed, Anaplan becomes an extremely powerful tool” notes Julien Triquet.

This balance between a flexible platform and a rigorous approach lies at the heart of success for both Pochet and Sodiaal. It ensures Anaplan’s power does not turn into unnecessary complexity.

Beyond the tool itself, the Anaplan ecosystem is a decisive asset: specialised integrators, proven models and expertise capable of supporting long-term transformation. OneHive plays a full role in this ecosystem by turning the platform’s potential into robust, user-friendly and sustainable models.

Learn more about our project methodology and our Supply Chain expertise.

Conclusion: transforming planning means transforming decision-making

The experiences of Pochet and Yoplait highlight a fundamental truth: an APS or EPM project succeeds not because it uses the right tool, but because it strengthens how the organisation decides, collaborates and arbitrates.

Scoping, data governance, project teams, change management and post-project organisation are not side topics. They are the true levers of transformation.

A platform can calculate a plan. A successful transformation permanently changes how the company is run.

An award recognising an ambitious project

An application built to steer an ultra fresh environment

Key application capabilities

• Twice daily updates of data and production and distribution proposals
• Weekly transmission of the production plan to the ERP
• Daily management of inter site transfers
• Automated generation of an optimised production schedule

Tangible benefits for teams

• Improved visibility on mid and long term constraints such as shutdowns, maintenance and demand peaks
• Significant time savings, enabling teams to focus on high value decisions
• Preventive monitoring of alerts across the full product portfolio

A reflection of our conviction

Today, Supply Chain leaders must integrate into their decision-making a growing number of factors that directly affect operations:

• evolving environmental regulations,
• energy constraints,
• raw material dependency,
• pressure on transport costs
• increasing expectations around transparency.

Embedding ESG into planning: an operational necessity before a strategic one

1. ESG planning must become a structured routine, either integrated into existing processes or managed through a dedicated cadence
2. Decision-making must reflect the full range of impacts (costs, emissions, risks and dependencies) not just a financial view
3. Tools must offer tangible improvement paths, relying on simple, actionable levers and reliable projections

HiveWay: a tool to project, compare and arbitrate logistics pathways

Modelling the current network

3, 5 and 10-year simulations

Decision support

A three-step methodology to secure deployment

1. Modelling the existing network: data integration, constraint configuration and validation of assumptions
2. Simulation and trajectory selection: comparative scenario analysis and arbitration of transformation initiatives
3. Monitoring key indicators: steering emissions, costs and the effective implementation of selected actions

Concrete applications for a sustainable Supply Chain

• Carbon footprint assessment and ESG reporting: consolidation and projection of emissions based on existing data
• Dynamic order allocation: optimisation of assignments to reduce costs and kilometres travelled
• Minimising unsold inventory: integrating unsold probability into planning to limit waste
  
• Warehouse sizing: defining capacity and resource needs based on growth outlooks
• Logistics network optimisation: simulating different network architectures (number of sites, locations, capacities) to identify the most resilient organisation