Model design is where a planning transformation either starts to deliver value or begins to struggle.
It is where planning logic becomes operational, shaping performance, maintainability and, ultimately, whether the business trusts the outputs and the overall usability of the solution.
Many organisations approach model design with a legacy mindset. Existing spreadsheets are translated into a new platform, carrying forward the same structures, assumptions and workarounds. In some instances, the user interface also is presented in the current state, which is a missed opportunity with the planning tool that’s being implemented.
This limits the value that platforms like Anaplan are designed to deliver.
As Principal Solution Architect Upali Wickramasinghe notes, “If you design a planning model around the spreadsheet, you inherit its limitations. If you design it around the business process and the pain-points being addressed and optimised the flow of activities for deliver efficiencies, you create something that can evolve.”
In this article, we outline how the Zooss approach to model design ensures models that:
- Support real-world decision-making
- Balance insight with performance
- Scale as the organisation evolves
- Build trust and drive adoption
- Provide an ease of use interface with design cues to assist in decision making
Design for how the business operates
Effective model design starts with the planning process. This means understanding how decisions are made, how functions interact and how planning cycles actually run.
As explored in our earlier article, Requirements gathering that drives real business impact, effective design begins by focusing on the decisions that need to be improved, not the tools currently used to support them.
In a Connected Planning environment, finance, supply chain and operational teams work from shared data and assumptions. Their decisions are interdependent.
A well-designed model reflects this, enabling alignment across functions rather than reinforcing silos.
“Designing for the business requires stepping away from legacy structures and focusing on workflows, decision points, outcomes and an engaging interface,” Upali explains.
Choosing the right level of detail
One of the most common model design challenges is granularity.
There is often pressure to include as much detail as possible. More data and visualisations can appear to provide better insight. In practice, this can slow performance, increase complexity and make models harder to manage.
For example, in retail businesses, this often shows up in SKU, store and promotion-level planning. Capturing every combination can quickly increase model size and processing time, without improving the quality of decisions being made.
At the same time, overly simplified models can limit usefulness and reduce confidence in outputs.
The right level of detail is determined by the decisions the model needs to support. Granularity should serve a purpose. If it doesn’t improve a decision, it probably doesn’t belong in the model.
This requires clear alignment between business questions, reporting needs and data availability.
Many of the drivers of model complexity sit below the surface. Data structure, hierarchies and integration directly influence the level of detail required, and ultimately its performance and user friendliness.
We explore this in more detail here: The implementation iceberg – Data foundations for forecasting accuracy.
Designing for scalability
Planning environments need to adapt as the organisation evolves.
Connected Planning platforms such as Anaplan make it possible to extend planning across domains, but only if the underlying structure is designed to scale.
Modular design supports this.
Separating inputs, calculations and outputs creates flexibility. It allows extensions without disrupting core logic – and makes integration with other processes more straightforward. We also extend this design language into segregation of duties of each component or functional area to ensure additional redundancy is not applied in each step of the planning process.
This becomes critical when expanding into areas such as workforce planning, risk or sustainability.
A scalable design reduces rework, supports long-term value, and allows for expandability with ease.
In banking and financial services environments, this becomes particularly important where planning models must accommodate product complexity, regulatory requirements and multiple legacy systems.
“In banking, we often see models overloaded with detail because of fragmented source systems. Rationalising that structure and focusing on decision-relevant granularity and presentation can significantly improve performance and usability,” Upali explains.
In practice, well-designed models can reduce planning cycle times and manual effort, freeing teams to focus on analysis rather than data reconciliation.
Clarity enables adoption
Technical performance alone is not enough. Users need to understand how the model works and is easier to use – with necessary guardrails – than a freeform spreadsheet.
Clear naming conventions, logical structure, elegant user interfaces and accessible documentation all contribute to this. When users can follow how a number is derived, they are more likely to trust it.
When they cannot, confidence drops quickly.
Upali highlights this as a recurring issue: “We often see models that are technically sound but difficult to interpret and hard to use for the target audience (i.e. user persona). If users can’t explain the numbers or easily navigate the application, they won’t rely on the data or application.”
Clarity underpins trust and is essential for adoption.
The Zooss approach
At Zooss, model design is grounded in how organisations actually operate.
We design Connected Planning environments in Anaplan that align to real business processes, balance insight with performance, scale as the organisation evolves, and provide clarity, enabling users to trust and adopt them.
This ensures solutions are not only technically sound, but also elegantly presented to promote active usage by the business.
Planning transformation succeeds when it delivers measurable value by improving decision quality, increasing efficiency and strengthening business performance.
Read more of our Planning Transformation in Practice series
- Planning transformation in practice: Part 1 – Making the case for change – why modernise planning now?
- Planning transformation in practice: Part 2 – Requirements gathering that drives real business impact
- Planning transformation in practice: Part 3 – The implementation iceberg: Data foundations for forecasting accuracy
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