Most treasurers accept cash forecasts are a vital tool to ensure their organisation is less exposed to cash ‘surprises’, especially in the current uncertain economic climate. And since they are continually being asked to ‘do more, with less’, it’s never a bad time to re-examine existing processes:
- How can I challenge whether the current framework is fit for purpose? (Clue: if there’s a common theme of “well, we’ve always done it like this”, it’s time for a review)
- How do I identify a more optimal framework?
Assessing the Forecast Framework
An assessment should consider the following dimensions, for each distinct forecast cycle:
- Objectives – the overarching business goals supported by the forecast
- KPIs and Targets – how the effectiveness of the forecast in supporting the objectives is measured
- Forecast Outputs – the key parameters defining what forecast data is prepared and when
- Control Framework – the processes and controls to ensure the forecast functions as intended
- Process Efficiency – how smoothly and effectively the process runs
The following sections provide some key questions you should ask for each dimension, to determine if your overall framework is internally consistent, and fit for purpose.
- Have the business goals (that the forecast is intended to support) been specifically identified and documented? This provides clarity and avoids potentially different interpretations of how the forecast should be used
- Are the documented goals valuable, relevant, and aligned to the broader organisational strategy?
- Have they been approved by senior management? This can increase buy-in and process support
- Have they been communicated to the relevant staff? Increasing the awareness of why the forecast is needed and how it will be used, can improve accuracy though better engagement
- Is there an understanding of how accurate the forecast needs to be, to achieve the desired business objectives, and is this realistic, based on past experience?
KPIs and Targets
- Have forecast KPIs been defined and documented?
- Do they measure actual business outcomes, and are they relevant to the agreed business objectives? For example, cash management efficiency could be measured by idle cash levels
- Do the KPIs also cover control-related metrics (e.g. submission timeliness)? These support process compliance
- Have targets been set for each KPI?
- Have individual accountabilities been assigned for each target, and embedded in performance incentives?
- Have all KPIs, related targets and accountabilities been clearly communicated, understood and accepted?
- Is the definition of cash (e.g. cash book, bank balance or net debt) clearly stated? This helps to prevent misunderstanding
- Do the key parameters (e.g. horizon, currency etc.) align to the objectives? For example, optimising daily liquidity implies a forecast with time buckets by day, and reforecast daily
- Are the forecast outputs defined? Clarity in this area can improve the quality of data inputs
- Is the granularity/materiality of the forecast outputs set at an appropriate level? Too little may not provide enough insight, too much may result in unnecessary effort.
- Is there a reference manual, including objectives, definitions, processes and timelines, and responsibilities?
- Has a forecast methodology been defined? Discipline and consistency improve accuracy and understanding of variances
- Is appropriate stress-testing or scenario analysis performed?
- Are variances robustly monitored and investigated? This helps to identify areas of focus for improvement
- Have the required skills/expertise to support the process been defined, and is this applied in practice?
- Is there segregation between data owners/preparers, reviewers, and forecast approvers?
- Is performance against KPI targets (both objective and control-related) regularly reported?
- Are the targets generally met? If not, this could imply systemic process failure, or unrealistic expectations
- Is there designated responsibility for ongoing review and improvement of the process?
This dimension requires a degree of judgement, but questions to consider include:
- Is there a high level of automation to support forecast preparation, consolidation and reporting?
- Is a high proportion of forecast data available from existing systems?
- Is the elapsed cycle time for forecast preparation, review and reporting in line with other, similar MI?
- Is the process footprint (number of people involved, FTEs of effort, process iterations) in proportion?
- Is there a low number of data/process errors?
- Has any cost/benefit analysis been performed in relation to the process?
The results of the assessment can be summarised using ‘RAG’ (red/amber/green) flags to provide a quick visual indicator of how well your forecast framework matches up (See Diagram 1 above). If there isn’t a high proportion of green, it is definitely time to consider an overhaul of the framework design. A proposed ‘best fit’ approach is outlined below:
Designing a ‘Best Fit’ Framework
While cash forecasting is a recognised discipline, it’s often still as much ‘art’ as ‘science’. This is because each organisation has a unique combination of the fundamental factors that will affect the shape of its forecast processes – including strategic drivers, management risk appetite and cultural values, organisational/functional model, geographical footprint, technology environment, and cash management and financing structures. This is true even of organisations within the same industry.
It is therefore pointless to seek a universally applicable ‘best practice’ design – it is the relevant drivers within the organisation which should be used to design a framework that is ‘best fit’ for its specific circumstances. Implementing a framework from another organisation, without understanding the context for its design decisions, will likely result in sub-optimal processes, and potential gaps in ability to support business objectives.
Unsurprisingly, the same assessment dimensions can be used to frame the design – and if your proposed framework can confidently respond ‘yes’ to all the above questions, then it is likely to be ‘best fit’ for your organisation. However, it’s important to recognise the explicit interdependence between the dimensions, and ideally the design process should be conducted sequentially, to ensure the whole framework is internally consistent and aligned (see Diagram 2).
There are also some additional design issues to consider, as outlined below.
- Identifying Objectives
- Since this is the starting point and key underpinning element for the whole design, it is essential that sufficient effort is applied to this area – in practice, however, this is often the weakest link in the framework chain
- It can be helpful to define objectives by horizon (e.g. operational, tactical, strategic), and rank by priority (e.g. necessity, efficiency, opportunity)
- Setting KPIs and Targets
- Objective-related KPIs should reflect business outcomes
- While forecast accuracy is important, it is a control-related KPI. The forecast is not an end in itself, and accuracy is only an enabler – it is effectively irrelevant unless it generates positive outcomes through informed business decisions and execution
- Defining Outputs
- Redesign is an opportunity to consider if existing processes can be combined (e.g. separate longer-term cash and FX exposure forecasts), or outputs upgraded to meet additional objectives (e.g. trade finance exposures). This removes duplication of effort, and avoids potentially inconsistent MI
- Try to avoid overkill – it is tempting to aim high with design requirements, but in reality the existing infrastructure may not be able to support overly onerous requirements. Even where it can, the potential cost/effort may outweigh the benefits. Careful thought at this point will reduce potential design iteration (see below)
Only after KPIs and outputs have been defined, is it time to think about how the business infrastructure can support them, so that the resulting process design is consistent with the overall forecast framework.
Dealing with Reality
This structural approach is undeniably logical – but it’s not a perfect world. As noted above, the existing infrastructure is often unable to provide exactly the required outputs. For example, common technology challenges include:
- Fragmented systems landscape, requiring multiple data definitions/conversions, and manual intervention
- Unreliable data, due to underlying process issues (e.g. errors/delays in invoice posting)
- Actuals data is not available in same format as forecast, making variance analysis more difficult/manual
- Forecast horizon extends beyond available system data, requiring manual estimation/modelling of future flows
As a result, the design process may have to be iterative. This can be managed by an ‘art of the possible’ approach – judging what can realistically be done to meet required outputs, and assessing the resulting gaps against the ability to achieve the defined objectives. If the gaps are significant, pragmatic debate is needed to balance the criticality of the objectives vs. enhancing the infrastructure, with all the related issues around cost and change management. In practice, this usually results in compromise (e.g. lower accuracy/output granularity, but still meeting the objectives at an acceptable level) – however where the need to meet objectives cannot be lowered, a more formal cost/benefit analysis is required.
The Human Consideration
Finally, a forecast framework is not just about processes and parameters – there’s almost always a critical human dimension, and without genuine engagement, the forecast is unlikely to run smoothly and effectively. Elements to consider include:
- Communication – explain in advance what you are trying to achieve and why, what you expect and when
- Motivation – visible management sponsorship, supported by clear targets and incentives, to promote buy-in
- Consultation – encourage broad participation in the design process, to leverage business knowledge and prevent avoidable mistakes
- Change management – people often mistrust change, and so revert back to previous behaviours at the first sign of trouble – don’t underestimate the inertial impact of legacy processes
- Support – provide escalation and change control procedures, trouble-shooting guides, and feedback avenues
- Compliance – ensure there are adequate process controls and documentation, to remove ambiguity.
A robust and effective cash forecast is a vital tool for any business – so make sure you build it on the right foundations.
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