The Future of Planning Budgeting and Forecasting

The world of planning, budgeting and forecasting is changing rapidly as new technologies emerge, but the actual pace of change within the finance departments of most organizations is rather more sluggish. The progress companies have made in the year since The Future of Planning, Budgeting and Forecasting 2016 has been incremental, with a little accuracy gained but very little change to the reliance on insight-limiting technologies like spreadsheets.

That said, CFOs and senior finance executives are beginning to recognize the factors that contribute to forecasting excellence, and there is a groundswell of support for change. They’ll even make time to do it, and we all know how precious a CFOs time can be, especially when basic improvements like automation and standardization haven’t yet been implemented.

The survey shows that most PBF functions are still using relatively basic tools, but it also highlights the positive difference more advanced technology like visualization techniques and charting can make to forecasting outcomes. For the early adopters of even more experimental technologies like machine learning and artificial intelligence, there is some benefit to being at the forefront of technological change. But the survey suggests that there is still some way to go before machines take over the planning, budgeting and forecasting function.

In the meantime, senior finance executives who are already delivering a respected, inclusive and strategic PBF service need to focus on becoming more insightful, which means using smart technologies in concert with non-financial data to deliver accurate, timely, long term forecasts that add real value to the business.

Making headway

CFOs are making incremental headway in improving their planning, budgeting and forecasting processes, reforecasting more frequently to improve accuracy. But spreadsheet use remains a substantial drag on process improvements, despite organizations increasingly looking towards new technologies to progress the PBF landscape.

That said, respondents seem open to change, recognizing the importance of financial planning and analysis as a separate discipline, which will help channel resources in that direction. At the moment, a slow and steady approach is enough to remain competitive, but as more companies make increasingly substantial changes to their PBF processes to generate better insight, those that fail to speed up will find they fall behind.

Leading the debate

FSN’s insights gleaned from across the finance function shed light on the changes happening within the planning, budgeting and forecasting function, and identify the processes that make a real difference to outcomes. Senior finance executives are taking heed of these insights and making changes within the finance function. The most important one is the increasing inclusion of non-financial data into forecasting and planning processes. The Future of The Finance Function 2016 identified this as a game-changer, for the finance function as a whole, and for PBF in particular. It is starting to happen now. Companies are looking towards data from functions outside of finance, like customer relationship management systems and other non-financial data sources.

Senior executives are also finally recognizing the importance of automation and standardization as the key to building a strong PBF foundation. Last year it languished near the bottom of CFO’s priority lists, but now it is at the top. With the right foundation, PBF can start to take advantage of the new technology that will improve forecasting outcomes, particularly in the cloud.

There is increasing maturity in the recognition of cloud solution benefits, beyond just cost, towards agility and scalability. With recognition comes implementation, and it is hoped that uptake of these technologies will follow with greater momentum.

Man vs machine

Cloud computing has enabled the growth of machine learning and artificial intelligence solutions, and we see these being embedded into our daily lives, in our cars, personal digital assistants and home appliances. In the workplace, machine learning tools are being used for

  • predictive maintenance,
  • fraud detection,
  • customer personalization
  • and automating finance processes.

In the planning, budgeting and forecasting function, machine learning tools can take data over time, apply parameters to the analysis, and then learn from the outcomes to improve forecasts.

On the face of it, machine learning appears to be a game changer, adding unbiased logic and immeasurable processing power to the forecasting process, but the survey doesn’t show a substantial improvement in forecasting outcomes for organizations that use experimental technologies like these. And the CFOs and senior finance executives who responded to the survey believe there are substantial limitations to the effective of machine forecasts. As the technology matures, and finance functions become more integrated, machine learning will proliferate, but right now it remains the domain of early adopters.

Analytic tools

Many of the cloud solutions for planning, budgeting and forecasting involve advanced analytic tools, from visualization techniques to machine learning. Yet the majority of respondents still use basic spreadsheets, pivot tables and business intelligence tools to mine their data for forecasting insight. But they need to be upgrading their toolbox.

The survey identifies users of cutting edge visualization tools as the most effective forecasters. They are more likely to utilize specialist PBF systems, and have an arsenal of PBF technology they have prioritized for implementation in the next three years to improve their forecasts.

Even experimental organizations that aren’t yet able to harness the full power of machine learning and AI, are still generating better forecasts than the analytic novices.

The survey results are clear, advanced analytics must become the new baseline technology, it is no longer enough on rely on simple spreadsheets and pivot tables when your competitors are several steps ahead.

Insight – the top trump

But technology can’t operate in isolation. Cutting edge tools alone won’t provide the in-depth insight that is needed to properly compete against nimble start-ups. CFOs must ensure their PBF processes are inclusive, drawing input from outside the financial bubble to build a rounded view of the organization. This will engender respect for the PBF outcomes and align them with the strategic direction of the business.

Most importantly though, organizations need to promote an insightful planning, budgeting and forecasting function, by using advanced analytic techniques and tools, coupled with a broad data pool, to reveal unexpected insights and pathways that lead to better business performance.

As FSN stated, today’s finance organizations are looking to:

  • provide in-depth insights;
  • anticipate change and;
  • verify business opportunities before they become apparent to competitors.

But AI and machine learning technologies are still too immature. And spreadsheet-based processes don’t have the necessary functions to fill these advanced needs. While some might argue that spreadsheet-based processes could work for small businesses, they become unmanageable as companies grow.


Click here to access Wolters Kluwers FSN detailed survey report

Moving from best to better and better – Business practice redesign is an untapped opportunity

Under mounting performance pressure, many corporate leaders are looking to business process reengineering to improve performance, and in many ways that makes sense after all, processes give shape to an organization and are often useful for coordinating routine flows across large organizations. The routine work of a company should be done as efficiently as possible, which increasingly means incorporating automation.

But organizations may be missing a much greater opportunity to improve performance.

Here’s the thing: Much of the work of many organizations today—at least the work that typically offers the potential for differentiation—is no longer routine or even predictable. When conditions and requirements shift constantly, processes fail. While process optimization can still certainly help

  • reduce costs
  • and streamline operations,

leaders should consider a different kind of organizational rethinking for significant performance improvement. And in an environment of accelerating technological advances and rapid and unpredictable change, constant performance improvement is a must. Competition can come from anywhere—doing well relative to the competitors on your radar isn’t enough. Many barriers to competition are falling, and many boundaries, between industries and between markets, are blurring.

  • Consumers have more access to information and alternatives than ever, along with a coincident increase in expectations.
  • Workers have more access to information and alternatives—and increased expectations.

At the same time, many employees, in all kinds of environments, face increasing pressure to reach higher levels of individual performance. The useful life of many skills is in decline, creating a constant pressure to learn fast and reskill.

Many companies have struggled to effectively respond to these pressures since long before the Internet of Things and cognitive technologies added new layers of complexity. The average return on assets for US companies has declined for the past several decades, and companies find themselves displaced from market leadership positions more often than they used to. While the price-performance improvement in the digital infrastructure has increased exponentially, most companies are still capturing only a small fraction of the value that ought to be available through the technologies built on this infrastructure. Existing approaches to performance improvement appear to be falling short.

It begs the question: In a world of digital transformation and constant change, what does performance improvement mean? Many companies suffer from at least one of three broad problems that can misdirect their focus:

  1. Thinking of performance improvement too modestly. Leaders often think of performance advances as discrete, one-time jumps from A to B, or even a series of jumps to C and D. The initiatives that typically generate these bumps are similarly construed as pre-defined, one-time changes rather than as unbounded efforts that have the potential to generate more and more improvement. As we discuss in more detail, not only do most companies need to continually improve their performance— those that don’t start accelerating may fall further and further behind and become increasingly marginalized. Accelerating improvement, then, should be a goal of operations, not just one-off initiatives.
  2. Thinking of performance improvement too narrowly, focused only on costs. Process dominated much of performance improvement efforts for the past several decades, focusing largely on the denominator of the financial ratio of revenues to costs. But costs can be cut only so far, and technology-based process efficiencies can be quickly competed away, especially at a time when the changing environment and shifting customer expectations are making many standardized processes quickly obsolete. Further reductions can become harder to achieve and have less impact. The relevant performance might be more about an organization’s ability to create significant new value. Workers across an organization regularly encounter new needs, new tools for meeting needs, and opportunities to identify new ways of delivering more value and impact in multiple dimensions, including helping other parts of the organization generate more value. The potential for value creation isn’t confined to certain roles or functions, and is bounded primarily by an organization’s ability to create new knowledge and creatively address new problems. Focusing on new value creation may be the key to getting on a trajectory of accelerating performance improvement. Doing so would require an organization to move beyond efficiency and standardization and begin focusing on cultivating the behaviors—such as experimentation and reflection to make sense of what has been learned—associated with new value creation.
  3. Thinking of performance improvement at the wrong level. Most organizations manage performance where they measure it—which is to say where they have data: broadly, for the department and organization, and narrowly, for the individual. Both levels can miss where work, especially value-creating work, increasingly gets done: in groups. As a result, organizations can miss the opportunity to shape how work actually gets done. Focusing on performance where it matters most to the organization’s work might be a key to having a significant impact on the performance that matters.

The imperative to act seems simple: Today’s environment seems to offer no reprieve, no stabilization that gives us a chance to catch our breath and say, “OK, now we’ve got it figured out.” The methods and processes that led organizations to great success in the past seem to no longer be working. For sustained performance improvement, companies may need to change their focus and look in new directions.

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Click here to access Deloitte’s detailed study