Main decision levers by S&OP stage (non-exhaustive list).

These rules are too often implicit. Yet they sometimes differ from one entity or region to another and are rarely documented. Clarifying them before designing the application avoids ending an S&OP cycle with a vague comment about an action to be taken, without having simulated its impact or precisely defined the appropriate level of action needed to resolve the issue (how many shifts to add, what inventory coverage to target, etc.).

OneHive supports you during this essential scoping phase. Without agreement on the available decision levers, teams may struggle to maintain the monthly cycle consistently. These levers then become the core of the S&OP application, enabling teams to measure their impact, compare scenarios and develop the action plans resulting from each review.

3. Defining the level of granularity needed for effective planning

Granularity directly affects the robustness of the model. An overly detailed level makes the model more fragile and requires greater volumes of higher-quality information. For example, the same data needs to be available for every SKU. An overly aggregated level, on the other hand, can hide critical signals.

The appropriate granularity varies depending on the level at which decisions are made: forecasts are analyzed from a commercial perspective, while capacity is modeled at the level of key resources. In both cases, the level of detail must allow the S&OP manager to work quickly with the available decision levers while retaining the ability to drill down when necessary.

For example, a production line is overloaded during a given month, and some products can be transferred to a secondary line. We would not want the planner to change the production line SKU by SKU to simulate this adjustment. It would take too long, be difficult to review and duplicate the Master Production Scheduling (MPS) exercise that will take place a few weeks before production to determine which SKUs should be assigned to which lines. Instead, it should be possible to simply check whether enough production volume can be transferred to reduce the load, then approve the transfer to clear the alert.

Our approach reconciles these two levels directly within Anaplan. We design applications that support trade-offs at both aggregated and detailed levels, while consistently performing calculations at the detailed SKU and resource level. This keeps interfaces simple, makes the complexity of simulations transparent and highlights alerts at the most critical nodes of the logistics network.

To address the lack of data that naturally comes with planning over longer horizons, we extrapolate missing information from known data to maintain an overall coherent picture.

Further reading: S&OP in Anaplan, architecture and modeling best practices

4. S&OP process stages: structuring a smooth and controlled cycle

To support actionable decisions, an S&OP process needs a stable cycle and a clear rhythm. Each stage has a specific role:

• Product Review:extend the demand planning horizon and review product life cycles.
• Demand Review : build a consensus forecast and demand scenarios.
• Supply Review : compare demand with available resources and make preliminary trade-offs.
• Pre-S&OP : consolidate scenarios and prepare initial recommendations.
• Executive S&OP meeting: make final trade-offs and align decisions with management.

A well-designed Anaplan model automates transitions from one cycle to the next. It records decisions and makes it possible to monitor adherence to the S&OP cycle over time by linking decisions made across successive cycles. This is why decision levers must be designed so that they can be reviewed quickly, even many cycles later.

Further Reading: From S&OP to IBP

5. Avoiding common pitfalls that weaken an S&OP process in Anaplan

Certain pitfalls arise repeatedly in projects. They do not stem directly from the platform, but from the way organizations structure their decisions.

Too much granularity for too few decisions

Working at the SKU and production line level for decisions made at an aggregated level creates unnecessary complexity. The model becomes sensitive to exceptions, difficult to maintain and of limited use during management meetings.

The most effective organizations focus detailed analysis on critical product families and identified risk areas, while maintaining a more aggregated view for issues they know can be resolved during operational planning.

The temptation to work outside the tool

The model cannot handle every exception. Occasional additional analysis may therefore be necessary. However, if certain cases need to be reworked outside the system month after month, the S&OP tool fails to achieve its purpose: serving as a central platform for S&OP decisions and their history. This is why the application should be as comprehensive as possible in its ability to simulate the decisions that may need to be made.

The temptation to move down to execution-level detail

S&OP supports decision-making; it does not reproduce operational activities in detail. When a model incorporates micro-operational constraints, it loses clarity by flagging every minor issue.

We maintain a clear boundary between planning and execution. This is what ensures that an S&OP model remains agile and useful in every cycle.

Conclusion

An effective S&OP process is built before the model itself. Clearly defined trade-offs, appropriate granularity and structured review meetings form the foundation of a sustainable Anaplan architecture. Only then can the S&OP model remain effective and genuinely useful, even after months of use.

Contact our team to scope your S&OP process.