Treasury Forecasting: Challenges and Solutions for Best Practice

As the annual gtnews Cash Management and Trade Finance
survey

testifies, an increasing focus on accuracy in cash flow forecasting has seen
corporates and their treasuries attempt to upgrade from spreadsheet models
towards system-based solutions. This is coupled a with recognition that major
effort is required in implementing a new system and enhancing the associated
processes.

In order to structure the discussion, the figure below outlines
a suggested path to achieve best practice in cash forecasting:

Figure 1:
Path to Sustainable Best Practice in Forecasting Capability.

SEB Path to Sustainable Best Practice in Forecasting Capability
Source:
SEB.

In the graph the Y-axis shows the degree of intelligence obtained from
the forecasts, based on the knowledge pyramid paradigm. By joining comparable
single data components, data that otherwise does not provide information in
isolation becomes information. Experience in interpreting that information
creates knowledge, which can then be used to make sound financial decisions
based on forecasts. Wisdom includes lessons learned from past mistakes,
discovering earlier ‘unknown unknowns’ and accepting that knowledge is a
commodity with a brief lifespan that requires continuous learning.  

The
X-axis shows the path and the capabilities needed to ascend the knowledge
pyramid. By formulating a forecasting strategy using this model, as well as
developing the three basic organisational forecasting capabilities of
involvement, understanding and culture, it is possible to reach the top of the
pyramid and create sustainable treasury best practice for forecasting
capability. This ascent is described in detail below. 

Formulate strategy
to provide a base for the journey and transform data to information:
The first
step on the path is establishing a forecasting strategy. Companies often do not
formulate the purpose of their forecasting capabilities and its expected
benefit. That makes it more challenging to anchor the process and create
involvement at later stages.

Several considerations should be taken into
account when formulating a forecasting strategy. A solid starting point is to
answer the following questions:

  1. What treasury benefits are expected
    from developing a sustainable best practice forecasting capability?
  2. What
    needs to be forecast in order to realise these expected benefits?
  3. How
    are these to be measured and followed up?

The answers should take into
account subsequent challenges, which generally include balancing the scope and
quality of data, the expected benefits and resource utilisation, as well as
succeeding with a fast implementation to justify investments.

A policy
adopted by many larger companies is to focus on developing a high-quality
process covering the short-term day-to-day perspective. This is on the basis
that:

  • Responsibility lies within treasury’s natural mandate and data is
    mostly available in the systems it has access to, which results in
    straightforward implementations.
  • Significant savings can be realised
    based on efficient short-term liquidity management.

A recent trend has also
seen larger companies begin to adopt longer time horizons in their forecasts,
for instance in foreign exchange (FX) hedging and capital expenditure planning
processes such as forecasting 12-24 months ahead on a rolling monthly basis.
This type of additional forecasting pattern brings value, but the challenges of
implementing and managing the overall forecasting process increase. This is
because data is not readily available in any system, thereby creating a need to
rely on receiving sales and purchasing cash flow forecasts from the business
lines to achieve good data quality.

The complexities of converting data
into information increase in proportion to the number of stakeholders, systems
and organisations involved. So when formulating strategy and starting the
implementation journey, the best advice is ‘keep it simple’. Exaggerated
ambitions and expectations in terms of scope and expected benefits are a common
mistake when creating or updating an organisation’s forecasting process.
Initially, the focus should instead be on formulating a strategy that includes
set targets, a clear road map for increasing the forecasting scope at the
treasury and a structure to facilitate implementation.

Create involvement
to increase data quality and convert data to information:
Once the forecasting
strategy is set, the next step is to involve the organisation by describing it
and outlining how the organisation is to work with the strategy and its expected
benefits. At this stage, it is important to anchor the process and to provide a
platform for dialogue. Stakeholders responsible for the cash forecasts
frequently have detailed information related to input data, which lead to
adjustments in expectations and improvements in the proposed process.

Involvement is essential in order to correctly consolidate data and extract
useful information. It is possible to assist the dialogue by creating and
discussing consolidated pro-forma forecasting reports. In this way, input can be
gathered and the data appropriately consolidated so that decisions can be made
based on the reports once the accuracy of the forecasts is established.

At
the same time, the benefits for involved stakeholders should be highlighted and
customised reports for each unit should be offered. Delivering this type of
added-value to business lines and administrative specialised support functions
creates greater incentives for involvement.

It also facilitates the
creation of future forecasting champions, who are treasury’s key resource for
improving the organisation’s forecasting capability. You will need people’s
active involvement to get better cash oversight. These champions identify
improvement opportunities in the process on an operational and tactical level
for both data quality and work methods. It is recommended that each unit
involved has a designated member responsible for managing the process, with
potential for becoming a champion.  Ideally the unit level representatives
should meet annually going forward, to discuss results and provide input to the
continuing forecasting strategy. 

Create understanding to build knowledge
and make financial decisions:
The next phase in the path is to create
understanding of the information. One way is to work actively with forecast
versus actual deviation analysis on an operational and tactical level. This
makes it possible to make financial decisions based on the forecast and
facilitates implementing key performance indicators (KPIs).

From a
treasury operational perspective, a rule of thumb is that forecast versus actual
deviations should be followed up as frequently as new forecasts are made. By
doing so, learning is accumulated for each cash forecast and involvement is
supported. It also reduces the time taken between merely observing forecasts and
making sound financial decisions based upon them. 

While large companies
frequently succeed in following up forecasts and deviations in the daily work,
more longitudinal analysis of forecast versus actuals are, when conducted at
all, made only on an ad hoc basis. This type of analysis is necessary to gain a
holistic perspective and identify long-term trends and sustainable improvements.

In addition, long-term historic deviation analysis provides an improved
structure for KPIs. Those based on deviations and improvements on forecasting
quality should be set when the process has matured for each respective
forecasting unit. KPIs are also an instrument in the involvement phase – for
example, where the number of improvement suggestions is appropriate and supports
the creation of forecasting champions. Fulfillment of KPI targets should be
rewarded and jointly celebrated to facilitate the creation of a forecasting
culture.

Establish a culture to fine-tune existing processes, increase
scope and discover new knowledge:
The final phase of the path is to establish a
forecasting culture. The forecasting strategy, champions and the creation of
success stories – based on the utility of the forecasts from a financial
decision-making perspective – facilitates greater scope in the cash forecasting
process and provides a basic foundation for a company’s forecasting culture.

Continuous improvements in terms of identifying optimal trade-offs between
realised benefits, resource utilisation, scope and data quality is one element
in establishing a forecasting culture. The other branch consists of the ability
to simultaneously challenge old assumptions and truths to discover new
knowledge. Together, they provide the ability to reach the summit of the
knowledge pyramid and build a sustainable best practice forecasting capability
within your organisation.

Conclusion

Building a best treasury practice
forecasting capability should be viewed from the objective of establishing a way
of working within the company that develops a platform for organisational
learning. The platform facilitates the implementation of continuous
improvements, including the optimal trade-offs of realised benefits, resource
utilisation, scope and data quality.

A forecasting process provides only
limited value when conducted forecasts do not guide financial decision-making.
Increasingly fair financial decisions based on conducted forecasts are enabled
by formulating a strategy; creating involvement in the process; increasing the
understanding of the forecast information; and by developing a forecasting
culture.

The path to building and maintaining a best practice forecasting
capability is continuous for each phase. A key success factor lies in
recognising the effort required and setting an appropriate ambition level that
matches treasury’s capability to drive change in its specific context.

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