According to the research carried out by the Carnegie Institute of Technology, 85% of your financial success is due to your personality and ability to communicate.
For the fourth time, senior finance practitioners joined Dubai FP&A Board.
When preparing the implementation of a planning and forecasting system I am often asked if we can just take the existing spreadsheet solution and squeeze this into the new system. Invariably my answer is: Yes, we can do this, but we should not. Why?
Usually, the existing spreadsheet solution will reflect all the limitations arising from the fact that spreadsheets do not have the capabilities you get from a proper database solution. Its use is likely to be limited to a few users. The structure will probably be based on a maximum of two dimensions. Consolidation and change tracking will be difficult, if possible at all except manually, and most likely it will be used for annual budgeting only.
Implementing a new solution to budget, that means to forecast, set targets and allocate resources, is a great opportunity to redefine how we do things and improve what’s needs to be improved.
The first question we should ask ourselves is why we do a budget at all? What is really the purpose of it and how will it help us to succeed?
What we usually call budgeting does, in fact, include three different tasks; setting targets, forecasting performance and allocating resources. The first thing we should do when preparing a new implementation is to think about those different tasks, what they have in common and where they conflict and how this relates to our business. The purpose of setting targets is not the same as the purpose of forecasting. We set targets to motivate a certain behavior and usually, we do this for the longer term. Forecasting and resource allocation, on the other hand, is something we must do more often since it is a part of managing the business and staying agile. Every time our environment, internal or external, changes, we must reforecast and reallocate resources accordingly.
Furthermore, we must not forget the conflicts that typically arise when we try to combine forecasting and target setting. A forecast should be realistic while a target may be, and should be, ambitious. Combining those gives us unrealistic forecasts and unambitious targets. Unrealistic forecasts prevent effective resource allocation. Unambitious targets limit our potential for success.
Once we realize this we understand that we should perhaps not talk about budgeting at all. Instead, we should talk about target setting, forecasting and resource allocation as separate processes. This should be our starting point.
The next step is to determine which of those processes we need. Do we need detailed targets for the business? What value do they add? Or should we rather make do with general high-level targets? If this is the case, do we really need a software application for this?
When it comes to forecasting and resource allocation the first step is to understand the way our business works.
Do we run a stable company in a quiet market or a growing business in a dynamic market? If the latter is the case we probably need rolling forecasts updated every month or every quarter, using a horizon that fits the business cycle. If not, we may even not need any detailed forecasting. The nature of our business should determine the frequency and detail of reforecast and re-allocation of resources as well as the appropriate time horizon.
Once we have a rough picture of the requirements it is time to think about work processes and people. Here there are two key considerations. First, it must be clear who is responsible for the planning process. This will not necessarily be the person responsible for the content of the plan. Second, when deciding on participants we should think first about the value of their input and then distribute the tasks accordingly.
We should distinguish between the tools we use for forecasting and resource allocation on the one hand and performance measurement on the other.
Those are different processes, performed by different people, and they call for different technical capabilities and different user interfaces. A sales manager pulling together his sales forecast has limited need for advanced reporting. He needs a robust planning tool that includes basic historical sales analysis, not necessarily much more. The key is that it gives him the possibility to plan from all the relevant angles at the relevant levels. The finance professional needs a consolidation tool and the CEO needs standard reports for the board, KPI’s and scorecards. In short, we should understand the tasks we are going to perform and what they require.
Now we can move on and start looking at different software solutions based on the specific needs we have identified.
Of course, every company is different and has its own specific needs. I know this well having done implementations for dozens of clients of all shapes and sizes. However, there are always some common requirements that must be fulfilled. Here are some of the most important demands a good planning solution must fulfill:
- It should be flexible enough to allow you to build a model that fits your business.
- It must be best of breed when it comes to the target setting, forecasting and resource allocation processes, not on reporting and analysis.
- It should be easy to use so that you can let the relevant people work on planning without spending too much time. In other words, your solution should not be used only by finance, it should allow you to go beyond finance.
- It must really cut the time you spend on budgeting and consolidation.
- It should be centralized and provide a single version of the truth.
- It must allow you to use reforecasts/rolling forecasts as a management tool.
- It must link seamlessly with your reporting/BI and business process systems.
- It should be easily customizable with minimal effort.
Once we have selected the appropriate solution it comes to the implementation project itself.
Apart from general considerations regarding good project management, I would only like to stress two really important points:
1. Avoid huge implementation projects.
For 90% of companies’ planning models are relatively simple. Implementation projects should therefore not have to be very large. There are two main reasons for huge implementations. One is that the solution being implemented is too complex technically and thus difficult to install. Given the wealth of good, easy to use applications we should have avoided this problem in the selection phase. The second reason is that we are trying to integrate processes that are better done elsewhere. For example, for most company’s interest expenses and depreciation are often best done in spreadsheets for there you have the flexibility to model those the way you like, and more importantly, to change the way you model them. This part of the process is not distributed among many users so permission and process flow management is of no concern. So why then try to integrate this into the budgeting solution? It only adds complexity to the project and limits your flexibility to model the way best fits you.
2. Remember the rule of diminishing returns.
Based on my experience with such projects I have found that you will achieve 80% of the work in 50% of the time. After this, the return on additional work will diminish fast. So, rather than use the 100% make do with the 50% or thereabout. Leave the rest out, unless really necessary.
Summing it up
Implementing a new planning solution helps streamline planning processes and better manage your business. The key to success is to avoid shortcuts, to reduce the concept of budgeting into its actual components - target setting, forecasting and resource allocation - choose the tools that best fit each purpose and avoid overcomplicating the implementation project.
For the purpose of this article:
- Forecasting means both plan, budget, rolling forecast, business cases or other kinds of forecasts,
- Competitive and business intelligence means an understanding of the key forces that apply (or will be applied) to the industry/business the company is in.
The first step in forecasting is to understand where we are today and how we arrived at that point from the past. This is gain through analysis and reporting.
The second step is in projecting those evolutions and deciding what the company will do about it. This involves risk assessment and taking risks through strategical and tactical choices that need implementation.
The third step is putting all those in a consistent set of tools been plan, budget or forecast.
The loop back to analysis and reporting will measure how accurate were the projections and decisions implementation and measure the variances. It will generate the need for adjustment in decisions and forecast.
This clearly involves both “internalities and externalities”, specific to the business.
I will not insist here on the internalities i.e. the traditional flows of information through accounting, order processing, business cases, marketing and production statistics, etc… In most case, this is the easy part.
I will focus on the externalities that are more difficult to identify, capture and project and are part of competitive and business intelligence. They are by nature very much specific to each industry and business situation and then it is difficult to make a checklist that will cover all cases.
First, let say that there are companies who decide (consciously or not) to ignore some or all of those externalities and just implement on an ongoing basis the changes and reactions to the changes. It drives to short-term and high-stress management with little visibility.
In most case, there is some level of competitive and business intelligence that builds in or is at least available. The issue is whether it is sufficient and accurate enough? Is it really used? and then how to improve it.
Markets analysis (i.e. potential customers profiles, their needs and evolution, their financial health), competitors, their products and services and evolution, geographic reach and globalisation and etc. should permit to assess and take decisions linked to your company products and services competitivity. This will drive prices and profitability evolution, potentially, development plans.
How much information do you get? How much do you use in your forecasting activities? Is your forecast consistent with those externalities? How do you get organised to improve your level of information and better use what you have?
These questions are obviously not addressed to FP&A only but need to involve all departments. There are probably no standard responses. This is a collective effort in which FP&A should be a key actor.
Over the general economic situation, few examples:
- If you have a limited number of key customers, do you follow/analyse their own performances and their own markets? Are they just purchasing or partnering with your company? Is this intelligence integrated into your forecast? Are they looking for merger and acquisition or restructuring?
- Volume versus prices evolution in your markets?
- Is there a proper win and loss analysis done on your proposals? What insights does it give on your markets and competitors?
- How currencies or raw materials prices impacts on your business? What level of natural hedging or other protection do you get in your transactions? How do you follow the evolution of those prices and more importantly integrate it into your forecasting and decision making?
- How do your customers use your product and services? Is it evolving? Is it particular to certain situations? Are substitution products appearing? How digitalisation impacts on your portfolio?
- For international companies, do you assess the political and economic situation in the countries you cover?
- Norms and regulations, is there a systematic effort to follow their evolution?
- How are performing your direct and indirect competitors? How does their portfolio compare to yours? Are they launching new products?
A good way to look at it is to review how much you are able to explain actual evolution and variances.
Is it “We forecasted a 2% revenue growth and achieved -1%”
“The market growth was projected to be 1% which we forecasted to overachieved by 1% thanks to xxx actions, the market growth turned to be -3% due to heavy price battle whereas volume was in line for the 1% growth. We achieved -1% thanks to partly avoiding the price battle but our volume market share has decreased. In such context, the actions programmed where only partly implemented some becoming irrelevant”.
Is it “Our new product X achieved only 60% of the target whereas the previous version decrease by 50% in line with forecast. The resulting decrease in revenue is 5%”
“The new product was forecasted to cannibalise the old version on a 1.1 to 1 ratio in terms of revenue resulting on a 2% revenue increase. Our competitor AAA launch a new product version earlier than ours. As a result, in the initial phase, they gained market share. This seems to stop when our new version arrived. Still, it needs to be verified in the coming months and the revenues lost will impact our yearly forecast by 5%”.
Those purposely built and simplistic examples shows what level of information is needed to reach from the first to the second explanation both at the level of the forecast and actuals. It needs both a clear statement and tracking of the scenarios and decisions made at the time of the forecast and a way to keep track of the elements used in those scenarios.
In many companies, this effort is not (sufficiently) structured. Different departments and managers, have or gain part of the information (or just have their gut feeling!), integrate it more or less consciously in their actions or when preparing forecast and actuals variances with their FP&A partners. They generally react to changes rather than plan for the changes to come. When managers or key people move or change position, part of this information is lost or worse moved to competition…
This can be largely improved through a structured process that identifies the different sensitive information needed, organise the data gathering (even if incomplete or uncertain that will be better than none), structure it in consistent scenario(s), decide on the actions necessary to adjust to those scenarios and share it among management and FP&A. These scenarios are then integrated in a consistent manner to the forecasting.
The difference between these two situations may not seem important in particular in the period of relative market and management stability. It generally provides essential in unstable periods where reactiveness and agility are the key factors of success.
Forecasting is not just a statistical projection of internalities.
Forecasting is taking internalities and externalities into consistent scenarios through risks assessment and decision making.
There’s been a steady move towards having more business partner roles in FP&A, similar to other support functions like IT and HR, but what does this all mean for FP&A?
The good news is that our traditional FP&A roles are still necessary, the advance towards business partnering is more simply a reflection that business expectations have shifted. It’s no longer about us explaining the variances, what happened last month or last quarter. That’s a minimum expectation on us.
The core skill set that we have in analyzing data and sensing the commercial implications reflects that we are being called upon to partner with the business more often by providing them with the deeper analysis of what the data actually mean.
However, in some instances these heightened business partnering expectations have meant that FP&A as a group may now lag behind marketing, sales and other customer-facing functions that are investing widely in digital technologies to capture valuable data that are less focused on tangible assets and more directed towards intangibles like customer satisfaction; partner relationships; the quality of business processes and the reputation of their brands. Notice how none of these data-points are financial.
Naturally, some of you may be thinking or asking whether FP&A will get left behind?
As with any shift in expectations, there’s always a chance we can get it wrong, although a key privilege we in FP&A have is that we generally already have, or can more easily gain access to, a wide and inter-connected view across the enterprise. So do I believe we have the potential to still contribute towards our partners’ enhanced expectations and our enterprises’ success? Of course, but to do so will mean that we may have to play an even bigger role, that of the insight-broker.
This role has three parts:
From our FP&A position we broadly understand its business model, data structures and sources, so we’re ideally placed to go hunting down the necessary data and analysis required to contribute valued insights for successful decision making.
Analysis & insights
Even though we may not have a credible claim to provide all the information needed by our partners in this digital age, we do have an enterprise-wide overview and the required skills to work with diverse internal stakeholders to certify that they’re assembling and analyzing data in the most suitable ways to improve performance. Our data-orientation and quantitative skills allow us to engage in qualifying new data sources as well as involve the language of money to better support decision making and ultimately build better business models that survive the present and thrive into the future.
As insight-brokers we can liberate these data for application and benefit elsewhere in the business. We can identify areas that work well and benchmark these against others that could operate more effectively. By brokering and facilitating insights between partners we can contribute our overview and professional objectivity as part of these collaborative conversations, to safeguard and enhance the quality of data-driven decision-making.
I end this post with an observation from outside our FP&A profession that today’s most competitive & sustainable business models are less based on physically installed fixed assets but instead come from platforms of insights that go deeper into our intangibles, founded upon our information, human and organisational capital. The often quoted Ocean Tomo Study estimates that 87% of the S&P500's market value from 2015 was due to intangibles, a 70%pts growth over the last 40 years.
FP&A has already played our part in supporting this shift, to continue playing a relevant role today and into the years ahead we will need to play the bigger role of insight broker so that our organizations and our profession can reach their potential.
So do you think FP&A have a bigger role to play? What tip would you recommend to others that increases our relevance today and into the future?