Over a decade ago I ran a quantitative tracking study on planning and budgeting for six consecutive years looking at the slow migration away from spreadsheets and the benefits of adopting packaged solutions. Year-on-year progress was woefully slow, but one
thing stood out clearly. Those companies that had adopted packaged applications were not able to forecast any more frequently than those still using spreadsheets nor were their budgeting and reforecasting cycles any shorter. Their finance departments gained
some productivity improvements, for sure, but not without the disappointment of having gone through the long, drawn out process of implementing a new planning and budgeting solution only to find they were only marginally better off. I also believe this experience
has given those selecting a new solution today a different list of priorities in what to look for.
One reason companies struggle to reforecast as frequently as they would like is because finance departments often do not have the skills within the department to build and maintain models themselves. This means that when a model needs changing to keep it
aligned with changes in organisational structure or a revised product grouping, they frequently have to call on scarce internal IT resource or external consultants. That’s not good in any situation as it limits the use of the solution, but it’s crippling for
those organisations considering adopting a driver based approach to planning and budgeting, where rules need to be written and amended.
Having the flexibility to analyse the data across a variety of different dimensions is also vital so that when finance or users want to recut the data in different ways they can easily do it themselves. For instance, they may want to show only those products
that are licensed in rather than manufactured or only contractors rather than permanent employees. In these scenarios, being able to select attributes to give an alternate view instantly is invaluable.
One sure way to safeguard against poor budgeting and planning selections from happening again is to have members of the finance function involved in building proof-of-concept models during the selection process.
Flexible Time Hierarchies
Another reason why enterprise planning and budgeting solutions fail to get as much use within the business as they should is that most are limited to a single time period. So if Finance sets the default to ‘month’ as is usual, anyone else in the business
wanting to work in days or weeks has to resort to using spreadsheets, and then cutting and pasting monthly data back into the enterprise solution. That’s not the case with modern solutions where it is now possible for users to plan on any time period they
want with results automatically consolidated into the default monthly view required by finance.
Rapid Scenario Planning
Unless a planning and budgeting solution is easy to use and they can get results in real time, finance and other users across the business will also resort to spreadsheets and work outside of the chosen planning and budgeting solution for scenario planning
and answering ad-hoc questions.
That means being able to copy and amend existing models to reflect new scenarios and running through numerous iterations to assess the impact of the changes. As mentioned above, many solutions fall at the first hurdle since users need expert skills to restructure
models, while others fall at the second in that they calculate results in batch model and lack the spreadsheet-like immediacy needed for ‘what-if?’ analysis.
Authorised users must be able to continually update, restructure and recalculate very granular models that contain both operational and financial data on the fly. As a result planning becomes a continuous activity rather than just a once or twice a year
Accommodating Big Data
There are two reasons why planning and budgeting models are getting larger. First, there is increasing adoption of driver-based approaches to planning and budgeting, which automatically results in bigger data sets. Second, in the pursuit of greater accuracy,
there is a trend to move away from using averages and aggregates to building very granular models. For instance, staff costs are based on named individuals with their exact salary and benefits package and sales forecasts use volumes and prices at the individual
SKU level, rather than at some higher level of aggregation. Not only does this improve accuracy, it also delivers more actionable insight into variances. However many planning and budgeting solutions lack the power to process models that run to many billions
of data points resulting in a drop off in calculation and response times.
More Granular Reporting
Another need that figures high on buyers’ checklists – and brings many of the issues discussed above together – is having the ability to produce very detailed P&Ls segmented on dimensions and attributes such as geography, division, customer, product or even
individual SKU. Now that’s always been possible using some fairly crude allocations. But modern solutions have the ability to import very granular data when reporting actuals and, combined with the ease of writing driver-based rules for assigning individual
line item expenses, you can now produce results that are far more accurate than simple allocations.
In spite of being sold for ‘planning and budgeting’, too often many solutions are only ever used for financial budgeting and are often ignored by both finance and other line of business managers for the myriad of planning and analysis challenges they encounter
in their daily life. Using the list above as a starting point for screening vendors, you can turn planning and budgeting into a continuous process with a solution that is valued by everyone involved.