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.