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November 28, 2018

The four most popular time series cash forecasting methods

CFOs, Treasurers and their teams are increasingly looking at more intelligent and efficient ways to manage business on a day-to-day basis. One of the areas that is constantly in focus for improvement is cash flow forecasting which, though it is a business-critical activity, has historically been time consuming and inaccurate.

Because of the enormous potential for improvement in cash forecasting accuracy and efficiency, CFOs and Treasurers are stepping up investment in new technologies and hiring talent in new disciplines such as data analytics.

While new technologies and increased data analytics capabilities have the potential to transform the way treasury and finance teams forecast cash, the underlying forecasting methods are often still built on traditional statistical models.

Most cash forecasts are created on a future “Time Series” that bares a close resemblance to a historic time series. Time series forecasts are created by capturing patterns in historic data and extrapolating these patterns into the future.  There are a broad range of time series forecasting methods that businesses use today.

Some of the most widely used statistical methods of forecasting are:

Method 1 – Naïve Forecasting

A naïve cash forecast is one that simply uses actual cash flow data for a previous period as the forecast for the upcoming period.

Naïve forecasts, or historical data rollovers, are often used as the starting point for a forecast which is then adjusted for changes in underlying business fundamentals, such as growth or seasonality.

Unadjusted Naïve forecasts are often used for comparison to business cash forecasts which have been created using different techniques, such as direct or indirect forecasting.

Method 2 – Simple Moving Average Forecasting

A simple moving average cash forecast adds cash movements or cash positions, such as net cash inflows of closing cash balances, at points in time for a set period and divides the sum of all numbers by the number of points in time.

The simple moving average method can be useful for forecasting trends. The duration and granularity of the forecast will determine the how many points in time should be used for the forecast. For example, a 30-day simple moving average would be effective for forecasting a trend 30 to 90 days into the future.

Method 3 – Exponential Smoothing

Exponential smoothing can be used to create a cash forecast when the near past is more indicative of the future than the distant past. This method applies decreasing weights to data points over time.

This method of forecasting is particularly useful for creating short-term cash forecasts due the extra weight it applies to the recent past. It is particularly useful and powerful when actual cash flow data is updated on a regular basis.

Method 4 – Box Jenkins

The Box Jenkins method of cash forecasting applies autoregressive moving average (ARMA) and autoregressive integrated moving averages models (ARIMA) to find the best fit for a historic cash flow data set.

These models identify patterns in time (autocorrelations) and are therefore more suitable for longer term forecasting using stable historic data sets.

How to select the appropriate statistical method for your cash forecasts

While deciding which method to use for cash forecasting is, to a certain extent, subject to trial and error, it is always best to consider the business objectives at the outset.

Clarifying the following will provide a valuable baseline for selecting and relying upon a new forecasting method:

  • What the forecast will be used for – the business use
  • The required duration of the forecast
  • The level of detail and granularity required
  • How frequently is will be refreshed
  • How the output will be summarised and communicated

Then, when a clean historic cash flow or balance data set is in place, running experiments to understand which method works best is reasonably straightforward.

Using software to get started

As mentioned at the start of this article, technological software solutions have the capacity to transform the way treasury and finance teams forecast cash.

While the underlying mathematics of the forecasting models remain the same statistical techniques familiar today, it is the surrounding activity and processes that are revolutionised.

By using software to automate cash forecasting processes, much of the administrative burden can be removed. This therefore dramatically decreases the time required to produce cash forecasts while reducing risk and improving accuracy through the decreased potential for human error.

In addition, sophisticated software tools offer advanced data visualisation techniques so that trends in the data can be identified, providing the level of insight that adds real value back to the core business.

About CashAnalytics

CashAnalytics has helped many companies across a broad range of industries to build and maintain best-in-class cash forecasting processes that leverage software to produce the highest quality reporting and analytics outputs.

If you would like to see a demonstration of how software and automation can improve your cash forecasting processes, or would like to see the business case for introducing CashAnalytics to your company, please contact us directly.


We've developed a range of software solutions that help overcome these challenges so that companies can accurately forcast cash and liquidity in the most efficient manner possible.

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November 22, 2018

Monthly cash flow forecast

In the third part of our series discussing how different time horizons are used in cash forecasting we explore all the elements of the monthly cash flow forecast.

Selecting the right time horizon for a forecast depends on the business objectives of the forecast. By exploring the uses, benefits and drawbacks of the monthly cash flow model, you should be able to ascertain whether it is suitable for your business needs.

Our other pieces, which discuss the daily cash flow forecasting process, and the 13 week cash flow model, are useful to read if you are setting up a new cashflow forecasting process.

This post will discuss:

  • Why companies set up a monthly cash flow forecast
  • What a monthly cash forecast looks like
  • The sources of data that feed a monthly cash flow forecast
  • The advantages / disadvantages of a monthly cashflow forecast

Why companies set up a monthly cash flow forecast

The key use of a monthly cash forecast is longer-term planning for strategic and tactical purposes. Unlike a daily or weekly forecasting process, a monthly cash forecast is not focused on the day-to-day management of cash and liquidity but on the longer term.

The business objectives of a monthly cash flow forecast are often management reporting focused. For example, senior management may require a monthly pack which includes a month-end cash forecast so that they can take a view on the health of the company’s liquidity reserves heading into the future.

The drive to set up a monthly cash forecasting process can come from a range of sources. In some cases, there may be a desire for greater granularity than the annual budgeting processes can afford, but without getting down to the level of day-to-day or week-to-week cash management.

This is often the case if senior management have asked to be guided on anticipated cash positions at year-end, half-year, or quarter-end. This can be an area of particular focus if management are seeking guidance on acquisitions activities or aid with the timings of any other significant capital expenditures.

Depending on the circumstances of a company’s debt arrangements, covenant forecasting requirements can also be a catalyst to the setting up of a monthly cash forecasting process.

What does a monthly cash forecast look like?

As you would reasonably expect, the units of time included in a monthly cash forecast are months. However, it is also not unusual to have a hybrid forecast layout which works in combination with an additional time horizon. For example, the forecast view might show weeks in the short term, and then switch to a monthly forecast horizon after 13 weeks.

The forecast itself is composed of top-level line items such as receipts, payments, intercompany flows, investing levels, and capital expenditure. These line items are usually captured at a relatively high level of detail, rather then getting down into the granularity of customers’ or suppliers’ details as you might expect to see on a shorter-term forecast.

Screenshot of a monthly cash flow forecast for H1 2019

Typically, a monthly cashflow forecast extends six to twelve months into the future. However, certain businesses may extend beyond this, for example if they have a multi-year project to oversee. Additionally, many insurance companies require a longer-extending view because their particular planning purposes.

In order to anchor the forecast view to the current business environment, as with most cash forecasting time-horizons, classified actuals are usually included.

Sources of data for a monthly cash forecasting process

Unlike shorter-term forecasting horizons, where the process mostly consists of automated data flows from ERP, TMS, billing, and ledger systems, as well as automated data feeds from bank accounts, a monthly forecasting process is more closely aligned to a planning process.

From a systems and records perspective the sources of data for a monthly cash forecast include, but are not limited to:

  • Budgets
  • Historical data from previous periods
  • Business plans
  • Sales plans
  • Intercompany data flows

However, a key component separating a monthly cash forecasting process from shorter-term time horizons is the level of human involvement. As is the case with any process that is not entirely systems data driven, human input requires facilitation. The teams that are required to report into the process usually include the financial planning & analysis team, sales team, and any other department which would have an impact on the monthly closing cash balances.

As would be expected, the forecast data is usually updated once per month. This encompasses the capturing of classified actuals for that particular month. Additionally, cash flows are generally captured in the base currencies of the underlying subsidiaries so that head office has the full picture and can implement hedging processes if required. As part of the monthly data refresh cycle, the forecast horizon would be extended by one month to replace the month that dropped from forecast to actual.

Advantages of monthly cash flow visibility

The key advantage of the monthly cash forecast is that it provides a level of long-term visibility that aids business planning activities. As corporate treasury departments are increasingly offering strategic input into business planning, the monthly cashflow forecast is a key tool in their arsenal.

Rather than being a burden, the level of human involvement in the process can be a boon because it provides understanding of the longer-term planning activities.

When is a monthly cash flow forecast not suitable?

As referenced at the start of this article, in general, a monthly cash forecast would not be suitable for short-term liquidity planning. For short-term liquidity planning a daily, or a weekly, cash forecast would be more appropriate.

To overcome the short-term limitations of the monthly cash forecast, as mentioned earlier, it is not unusual for a hybrid forecast to be used. This can often be the case for businesses who are seeking to use the forecast to manage liquidity risk. In this instance the hybrid model of weekly for 13 weeks could help to identify medium-term liquidity risk concerns, and a monthly horizon after 13 weeks could help to flag up any longer-term issues.

Choosing the right forecasting time horizon

As mentioned in previous posts, when setting up a new cash forecasting process, it is important to select a time horizon that aligns with the overall business objectives.

As the table below shows, if the business objectives of the cash forecast are more geared towards supporting precise decision making, a shorter-term (or hybrid) forecasting horizon may be more appropriate.

Business Objective

Forecast Horizon

Reporting Date Granularity

Reporting Categories

Frequency of Creation

Short-term liquidity planning

10 business days


High level flows and balances

Twice a week

Interest and debt reduction

13 weeks


Management reporting categories and flows


Covenant and key date visibility

To next significant reporting date (at least)


Management reporting categories and flows

Weekly, then more frequent approaching key date

Liquidity risk management

Six months

Weekly for 13 weeks, then monthly for three months

High level flows and balances


Long term strategy planning

One year


High level flows and balances



What are the benefits of using software?

All cash forecasting processes can be improved with the use of specialist cash flow forecasting software, and there is no better time to introduce software than when you are setting up a new forecasting process or rolling out a new forecasting time horizon.

While shorter-term cash forecasting horizons can benefit from automated upload of system data (as they are much more reliant on data from system feeds), using software in a monthly cash forecasting process greatly eases the administrative burden while enabling much greater cash forecasting accuracy analysis.

About CashAnalytics

CashAnalytics has helped many companies across a broad range of industries to build and maintain best-in-class cash forecasting processes that leverage software to produce the highest quality reporting and analytics outputs.

If you would like to see a demonstration of how software and automation can improve your cash forecasting processes, or would like to see the business case for introducing CashAnalytics to your company, please contact us directly.


October 30, 2018

Forecasting skills: how to be an effective cash forecaster

Cash flow forecasting plays a crucial role in supporting a company’s stability by ensuring the company has enough cash available to cover outgoing payments. The forecast can also underpin a range of key financial decisions, from mergers and acquisitions to investment planning. As such, it’s important to get it right. So, increasingly, attention is being paid to the skills that make an effective forecaster.

In practice, cash forecasting can take up the lion’s share of attention within the treasury or finance team. Indeed, 64% of respondents to AFP’s 2017 Strategic Role of Treasury Survey cited cash management and forecasting as a key area of focus over the next three years.

In order to build a successful forecast – in other words, one that is timely, accurate and adds value to the organisation – treasurers and finance professionals need to have the right skillset. But forecasting isn’t a single skill, so much as a constellation of different skills. Naturally, it’s important to have top-notch technical expertise and the ability to model data effectively. But it’s also about having commercial acumen and being able to communicate effectively with others across the organisation.

Four key skills for effective forecasting

While forecasting skills include many different components, the following four attributes are among the most important:

Business understanding

Treasurers and finance professionals are increasingly conscious of the need for greater commercial acumen. Armed with a clear understanding of the business climate, they understand that they can play an active role in helping their companies increase market share and improve the company’s performance.

Cash flow forecasting is no exception. Different companies will approach forecasting differently, based on their business needs – so it’s essential for forecasters to understand why forecasting matters to the organisation, and which business goals they are looking to achieve. For example, the company’s overriding goal might be to support short-term liquidity planning, or to minimise the need for external funding. Other goals may include supporting mergers and acquisitions or providing more visibility over covenant levels at key reporting dates.

Whatever the objectives, the forecaster needs to have a clear understanding of the bigger picture so they can choose the most appropriate forecasting approach. This means understanding the company’s strategic goals; its sales model and the nature of its customer base, and its strengths and weaknesses compared to its competitors.

Technical knowledge

A cash forecast will typically be based on inputs from a variety of different sources. These can broadly be divided into the information provided by subsidiaries, and information that is sourced directly from a variety of systems, such as the company’s Enterprise Resource Planning (ERP) system, Treasury Management System, bank portals and electronic bank statements.

Where the latter is concerned, an effective forecaster will need to have robust technical knowledge in order to obtain the required data from the relevant systems. Sourcing the right information from the right places is only part of the challenge – forecasters also need to be able to collate the data effectively.  Whether the company is using a dedicated cash forecasting solution or a spreadsheet-based system to aggregate data, it is important to have the necessary interfaces and processes in place to bring all of the information together.

Data management skills

Data is at the heart of the cash forecast, so data management skills are also essential. For one thing, it is essential to base the forecast on high quality data – so it may be necessary to carry out data cleansing to remove any defects.

Large data sets are also likely to come with anomalies – acquisitions, for example, may bring one-off cash flow spikes which will not need to be included in the data set for forecasting purposes. The treatment of other anomalies, such as prepayments for raw materials, will be less clear-cut. An effective forecaster will need to know which anomalies should be removed, and which should remain.

As well as gathering the required data, the forecaster will need robust data modelling skills in order to model future events and scenarios. This might involve taking advantage of applications that can model future activity by extrapolating based on previous information.

Likewise, the forecaster will need analytical skills in order to manage and query complex data sets and extract actionable insights. The more automated the cash forecasting process, the greater the opportunity to analyse data and add more strategic value.


Last but not least, good communication skills are a key component of the forecaster’s skillset. The forecaster will need to communicate effectively with a range of stakeholders throughout the process, from sourcing information from business units to explaining the results of the forecast to the relevant people.

  • Communicating with business units.While some of the input for the forecast will be gathered directly from sources such as bank statements or the TMS, other information may need to be provided by people based in different business units around the world. Securing their buy-in is essential to the success of the forecast – so the relevant people will need to understand why the forecast is important, what the forecast is being used for, and what their role is in the process. They will also need to have a clear understanding of the level of detail they are expected to provide, and the deadlines for when data is due.
  • Securing executive sponsorship.Effective communication can play an important role in securing executive sponsorship when rolling out a new forecasting process – and this, in turn, can build a culture in which the value of cash forecasting is widely understood across the business.
  • Communicating the output.The forecaster will also need strong “soft” skills in order to communicate the output of the forecast to the right people on a timely basis. This means having a clear understanding of the forecast’s intended audience – whether that’s the Board of Directors, shareholders or the CEO – and tailoring the output accordingly.

A winning combination: skills and tools

As outlined above, treasurers and finance managers need to develop a wide range of skills in order to tackle this challenging activity successfully, from technical abilities to a comprehensive understanding of the business environment.

Without a good process, cash flow forecasting can be a repetitive and time-consuming activity, even for the most skilled forecaster. But having access to sophisticated tools can address these issues. Specialist forecasting software can support the finance team by automating the data collection process, freeing up more time for the forecaster to analyse the output and draw out valuable insights.

The ideal scenario is therefore a finance or treasury team which commands the skills needed to source the forecasting data and communicate the results effectively, supported by a sophisticated forecasting software solution. With a combination of the right skills and the right system, the forecaster will be well placed to maximise the strategic value of the forecast.

October 17, 2018

How to benefit from evolving technology

The last decade has seen technology dramatically alter the way we work and the way we live our lives. At its most fundamental, this change is about access to information.

You could stop someone walking down the street in Sydney, ask them the results of that week’s American NFL matches and (providing they were amenable enough) they would simply tap for a few seconds at their smart phone, then list out all of the scores.

You could stop someone cycling along the rugged coast of North West Ireland, ask them to check the price of a stock listed on the Tokyo Stock Exchange and (once they’d located their phone) they’d give you the exact price, as it stood at that moment.

In our working lives, this change has meant that we are “always on”. As soon as the question comes in, no matter where we are, we can reach for the laptop or smartphone and begin working on the answer.

That this omnipresent access to information has changed the way we work is not surprising. The aspect that is, perhaps, a little surprising is how large incumbent companies can struggle to use their market dominance to make the most of technological change.

Disruption as driver

There are numerous examples of market leading companies struggling when a newer technology comes along. To stick with our example of mobile phones, think of how Apple and Android wiped out Blackberry and Nokia. Or, to consider an older, more established company, we can cite Kodak’s struggle to respond to the rise of digital photography, ultimately leading to its filing for bankruptcy in January 2012. (These examples are discussed in greater detail in this article entitled: Why big companies squander good ideas.)

However, these are the well-known examples. They capture attention because they involve the collapse of a previous incumbent. What we can take greater leaning from, are the more local examples that impact us day-to-day.

Evolving technology enables new freedoms

The development of the remote desktop opened up a range of options for employers to enable employees to be more flexible with their working hours and location. Around the world, the number of office workers who work remotely for at least one day per week now stands at 70%.

From the employer’s perspective, the gains aren’t just about being able to offer remote work as a perk, or pull from a larger, more geographically diverse talent pool. The main benefit is the freedom that employees being able to login remotely affords decision making. Senior staff away on business trips, for example, now need only an internet connection to pick up on their work. Therefore, time spent in transit no longer has to be factored in as dead-time.

Business software

Other areas of business software have made transformative impacts as well. Enterprise application software (EAS) has seen major shifts in the way companies store and manipulate vast quantities of data. Many modern large corporates are in fact still jaded from gruelling implementation projects for enterprise software. It is perhaps for this reason that they are reluctant to avail of the benefits that newer innovations can offer.

Robotic Process Automation

Many foresee the next stage of evolution of business software is taking the form of robotic process automation (RPA). In business processes, RPA involves automating highly repetitive tasks and processes (those currently undertaken by humans) in order to increase operational efficiency and reduce the risks of human error. Because of its ability to limit the risks of human error, RPA has been most eagerly engaged by those in risk averse industries and risk averse departments (such as finance and accounting).

Software we couldn’t live without

The business software we give the least credit offers some of the greatest benefit. Think of how arduous managing your day would be without the use of your calendar app, for example. Or how an address is now all the directions that are needed for a meeting in almost any city in the world, thanks to maps and gps.

Precise tools for particular tasks

Perhaps the biggest change in recent years is an increase in the vast range of specialised software tools. Most business problems (especially those that are process related) can now be addressed with software solutions. Unlike the complex and time-consuming enterprise software roll-outs, these particular software solutions are often quick and easy to implement, offering an almost immediate ROI. As more and more corporates recognise and adopt these types of solution, the benefits of early adoption quickly become the table stakes just to keep up.

Always on, or always ready?

Meanwhile, turning back to the way we now find ourselves “always on” to receive queries around the clock, this ceases to be an issue when we find the answers a click away.

If the CFO sends a message querying the year-end numbers, and you can respond with thorough, detailed analysis, confidently and quickly, everybody is happy with the outcome.

In fast-paced environments like corporate finance and corporate treasury, the benefits it can offer mean the introduction of new technology is particularly welcome.

October 08, 2018

Daily cash flow

As part of our continuing series discussing how different time horizons can be used in cash forecasting, this post will explore the daily cash flow forecast in detail.

In this post we will review:

  • The uses of a daily cash flow forecast
  • Why companies set up a daily cash flow forecast
  • How to build a daily cash forecast:

    • The sources of data that feed a daily cash forecast
    • What a daily cash flow forecast looks like
    • The workflows around a daily cash flow process
  • How to decide whether a daily cash forecast is right for your company
  • How software can improve a daily cash forecast

What are the main uses of a daily cash flow forecast?

The main use of a daily cash forecast is short-term liquidity planning. The level of granularity produced as part of a daily cash flow forecasting process means that it can offer vastly improved financial control. This can be very useful for businesses operating on fine margins, or those working to tight working capital cycles.

The level of granularity afforded by a daily cash forecast also enables the kind of detailed guidance often required by shareholders or other investors. In addition, the level of data sourced from finance systems reduces the risk of human error.

As opposed to longer time horizons, a daily cash forecast allows greater levels of cash visibility. This means that, on a look-through basis, daily bank positions can be seen at an entity/business unit/departmental level. Each of these entities/business units can also have their respective cash positions forecasted as part of the overall daily cash forecasting process.

Generally, because of its shorter time-horizon, a daily cash forecast has a high degree of accuracy. It is this combination of increased accuracy and greater detail (when compared with other time horizons), that enables a much more proactive and tactical planning processes for short-term liquidity.

Why companies set up a daily cash flow forecast

The catalyst behind the decision to switch to a daily cash forecasting process can come from internal or external drivers.


  • New credit agreement. A new credit agreement may include a covenant that stipulates that the total net cash position maintained by the group doesn’t go overdrawn.
  • Revolving credit line. Making the switch to a revolver (maintaining an open credit line in return for monthly payments) as a funding mechanism usually results in a greater focus on daily cash planning and forecasting to assist with loan drawdown and repayment decision making.


  • Excessive administration. Longer term forecasts are sometimes heavily reliant on human sources, whereas daily cash forecasts pull most (or all) of their data from system sources. This can result in a notable decrease in the levels of administration involved (particularly when specialist software is used, more on that below).
  • Delays in longer term forecasts. The amount of time taken to produce forecasts with a longer time horizon may be affecting the senior management team’s ability to make timely tactical cash management decisions.
  • Lack of visibility. If head office doesn’t have adequate visibility into all group accounts, a daily cash forecast may be requested to gain insight into all accounts across the group.

How to build a daily cash flow forecast

As is true when setting up any new cash flow forecasting process, it is important to begin by setting business objectives. To ensure that a daily cash forecast is the appropriate time horizon, these business objectives should map to the uses of a daily cash flow forecast outlined at the start of this article.

Sources of data that feed a daily cashflow forecast

The predominant difference between the sources of data used for a daily cashflow forecast versus longer-term time horizons is the amount of system data. Whereas longer-term forecasts (such as a 13-week cash flow forecast) are heavily reliant on human involvement, a daily cash forecast principally sources its data from system sources. Namely these are:

  • ERP systems
  • AP / AR ledgers
  • Bank files (e.g. MT940 or BAI2)
  • Payroll systems
  • Billing systems
  • Subsidiary data sources (e.g. CRM systems)

To expedite the forecasting process, all of these systems can interface directly with specialist forecasting tools (this is discussed in further detail in the section on software below).

What does a daily cash flow forecast look like?

As with all types of cash and liquidity forecast, a daily cashflow forecast presents a high-level consolidated view of all of the data that was fed into the process. Therefore, the forecast is usually composed of (but not limited to): customer receipts, intercompany flows, investing data, and capital expenditure.

Daily cash flow forecast screenshot

The above is a screenshot example of a daily cash forecast, showing forecast and actual figures (000s).

Because those choosing to set up a daily cash forecasting process do so because of the level of detail it provides, the base cash flow (line items) are quite often captured at a granular level. In other words, the individual line items showing customers’ and suppliers’ details displayed on a daily cash forecast would generally go into greater detail than they would on a longer-term forecast. In addition, bank account positions would generally be captured on a per account basis, and aggregated to a consolidated view.

As you would suspect, the main visual difference with a daily cash forecast is that the units of time captured are days. Typically, this view would extend two to four weeks into the future, although it is not uncommon that companies might extend this further. However, if a longer view is required (for example, if it is needed to extend beyond six to eight weeks), a longer-term time horizon might be more appropriate for the business requirements.

Workflows around a daily cash flow forecasting process

Many of the workflows that support a daily cash forecasting process are the same as those that support longer-term forecasting. However, the principal difference with daily cash forecasting is the frequency with which these workflows run.

Generally, a daily cash flow process means that data will be updated on a daily (although sometimes weekly) basis. The cycle is usually controlled by head office, that is to say the underlying entities each align with the requirements set by head office.

For large, multinational companies, this often means that data is updated multiple times in a 24-hour period. Depending on the business, is this is sometimes completed on a “follow the sun” basis, whereby each business unit updates the data during its own working hours.

It is important to note that, for larger companies, global business units are often running on multiple ERP systems with multiple bank partners. This means that the software systems used to support the workflow must be able to cater to this need.

How to decide whether a daily cash forecast is right for your company

Start with the objectives

It is important to consider the objectives of the cash forecast when assessing which time horizon will be best. If considering a daily forecasting process, it is important to confirm that those objectives map to the uses outlined at the beginning of this article.

An outline of model structure examples (taken from our cash flow forecasting setup guide) can be seen below:

Forecasting model structure examples

If a longer time horizon is being considered, you may wish to view our article on the benefits and uses of a 13-week cash forecast.

The benefits of software

All cash forecasting processes can be improved with the use of specialist cash flow forecasting software, however a daily cash forecast in particular benefits from integrating a software solution.

To illustrate, let’s review the elements of a daily cash forecasting process discussed in this article and address the practical ways software improves the process:

Automated system data upload

As mentioned earlier in this article, one of the unique features of the daily cash forecast is that data is sourced principally from systems. The use of software means that the extraction of all of this data can be automated. (This point is examined in further detail in this post on cash forecasting automation.)

Automating this data upload process also means that the frequency of refresh required becomes a non-issue.

Enhanced analytic insight

In addition to the decrease in the administration required at the front end of the process, software can greatly improve the analytical capabilities when reviewing the data produced.

As we discussed in an article on cash forecasting data visualisations, the way software can present data graphically greatly enhances the ability to highlight trends, identify anomalies, and uncover insights that will be missed when simply reading through raw data.

Increased visibility

If the head office finance or treasury team is struggling to gain visibility into the cash positions of the underlying entities, software can enable real-time views of all bank accounts on a consolidated basis (as well as on a granular level, where drilldown is required).

Process workflow management

Automating the workflows that support the daily cash forecasting process can also alleviate much of the administrative burden placed on head office finance and treasury teams. For example, automated reporting workflows mean that scheduled reporting emails to relevant stakeholders can be generated and sent directly from the system as new forecasts are produced.

About CashAnalytics

CashAnalytics has helped many companies across a broad range of industries to build and maintain best-in-class cash forecasting processes that produce the highest quality reporting and analytics outputs.

If you would like to see a demonstration of how software and automation can improve your cash forecasting processes, or would like to see the business case for introducing CashAnalytics to your company, please contact us directly.

September 28, 2018

How to overcome the key challenges of cash forecasting

Cash flow forecasting is a vital process to understand the working capital of a business. However, for many large, multinational organisations, the process can be daunting and the end product can be frustratingly low quality. This needn’t be the case. Below, we review a few of the key challenges of cash forecasting and provide workable solutions for how to overcome them.

While challenges such as forecasting unexpected expenditure, capturing unpredictable flows and modelling external influences are common to most companies, large multi-location companies who operate a centralised process encounter a range of other forecasting challenges. From our experience in helping large organisations automate their cash and liquidity forecasting processes we have broken these challenges into five broad categories:

  • Education
  • Process buy-in and continuous engagement
  • Manual administration
  • Reporting and analysis
  • Other challenges (intercompany etc.)

1) Education

Cash flow forecasting is a process that is heavily reliant on the input of many people across an organisation. These can include cash managers in head office, financial controllers in business units and subsidiaries, and a range of others. The reporting output is therefore dependent on the knowledge of the people contributing to the process. A high-quality output is supported by all personnel accurately inputting the data for each of the cash flows over which they have responsibility.

Time spent educating or training the various personnel involved in the process can help avoid the “rubbish in, rubbish out” problem encountered when there is a knowledge gap.

To improve the knowledge and awareness of the personnel involved in the process, we have seen the following methods make a significant impact:

  • In-depth review – In advance of implementing a forecasting process, a review of a company’s historic cash flows can provide a significant amount of insight into the nature of key cash movements, such as receipts from customers and payments to suppliers. Documenting and understanding these flows can provide a solid basis for making future cash flow projections.
  • Continuous feedback loop – After the update of actual data flows, ideally the process should incorporate a feedback mechanism to inform users how accurate they have been. As further forecast versions are rolled out this feedback loop continuously drives improvements in data quality.

2) Process Buy-in and Continuous Engagement

Getting buy-in from key personnel helps to overcome many of challenges of cash forecasting. When the process relies on contributions from subsidiaries, business units and other head office departments, ensuring continuous engagement with the process can be challenging. From an operational perspective, this problem usually manifests itself in late or missed reporting deadlines and inaccurate forecast information. This, in turn, increases the workload of the head office function managing the process.

Strategies employed to increase meaningful engagement with a forecasting process include:

  • Executive sponsorship – Sponsorship of a cash flow forecasting process by senior management such as the CFO or Finance Director is a critical factor in ensuring the efficient operation of the overall process. Generally, the requirement for a forecasting process will be driven at executive/board level but subsidiaries and process contributors should be made aware of its importance in the eyes of senior management.
  • Provide value to contributors – Business units sometimes view the cash flow forecasting process as a black hole of information. There can sometimes be the perception that a significant amount of time is spent compiling and reporting information, but minimal value is obtained at a business unit level. Finding ways to ensure that all contributors obtain value from the forecasting process has shown to improve its overall effectiveness and efficiency.

3) Manual Administration

A multiple currency cash flow forecasting process involving numerous business units and subsidiaries, managed using spreadsheets, can result in significant amounts of manual administration. This manual workload typically centres on the consolidation of spread sheets containing cash flow information, error checking the final reporting output and troubleshooting problems such as intercompany mismatches.

Automatic data upload

Aside from the administrative workload posed by the process, the amount of manual intervention required to produce a reporting output can compromise the integrity of the output itself due to a lack of process control and audit capabilities.

4) Reporting and Analysis

Current methods employed by companies to manage forecasting processes are heavily focused on administrative tasks and generally offer limited reporting and analysis options.

Improvements in the below areas can have the greatest impact:

  • Actual versus forecast analysis – This type of analysis is seen as a key factor in developing an understanding of cash movements. This, in turn, contributes to the knowledge base of the personnel involved in the process.
  • Forecast versus forecast analysis – Arguably more important than actual versus forecast analysis, forecast versus forecast analysis can give management an early warning signal with regards to expected changes in future cash position.
  • Historical trend versus forecast analysis – Comparing historical trends to projected future cash movements can provide the most intuitive sense-check of the forecast information being reported, while also giving a very quick insight into potential problems.

These types of analysis are greatly aided with the use of various data visualisation techniques, which we discuss in further detail in this post on cash forecasting data visualisations.

5) Other Challenges

A number of other challenges, primarily relating to technical components of a forecasting process, can also provide obstacles in the way of high-quality forecasting. In some cases these challenges are caused by industry or company specific issues. While we don’t have time to review each of these here, please feel free to contact us directly for advice pertaining to your precise circumstances. At a more general level, the two challenges are felt by a wide range of companies:

  • Intercompany reconciliation – Reconciling forecast cash movements between intercompany entities can prove particularly problematic for high volume trading companies, such as those engaged in manufacturing activities. In extreme cases, the problem can render the entire forecasting process meaningless. However, more commonly, it requires significant reconciliation efforts and causes administration headaches.
  • Linking long and short-term forecasts – Most large organisations maintain at least two cash flow forecasts – a short term forecast (three months or less, split weekly) for day to day planning purposes and a long-term forecast (12 months plus, split monthly) for strategic and long-term planning purposes. Depending on their current processes, this can make it extremely difficult to link short and long-term forecasts, which presents numerous administration and reconciliation problems.

Overcoming challenges with software

All of the challenges listed above can be overcome by building a good cash forecasting process, facilitated and managed through the use of specialised cash forecasting software. CashAnalytics has helped a range of companies across many industries to build and maintain best-in-class cash forecasting processes that produce the highest quality reporting and analytics outputs. If you would like to see a demonstration of how automation can overcome these obstacles, please click the “schedule demo” button in the top right corner.

To assist those considering setting up a new process we have written this guide, which outlines how the process can be built and rolled out within six weeks. The guide is based on our experience with a wide variety of clients. However, if you have any questions that relate to your precise circumstances, please do not hesitate to contact us now.

September 20, 2018

Covenant Forecasting

In the approach to the final quarter of the year, all eyes will turn towards forecasting year-end financials. Depending on the company’s corporate debt levels, year-end covenant forecasting might be a particular point of focus.

By the end of Q3, senior management including CFOs, financial controllers, and treasurers will have a will have a good handle on the income and expense side of the business. Of course for cyclical businesses, whose busiest time may fall in the last quarter of the year, such as retailers in the run up to Christmas, this isn’t true. For them the year can be won or lost in the last month. However, for most businesses, forecasting full year profit and loss should be reasonably straightforward coming into the final quarter.

Forecasting balance sheet numbers into year end can be a far more challenging task as they are a snapshot at a point in time. As a result, they are subject to more volatility as the key date approaches. Balance sheet forecasting is an important task in its own right, but becomes doubly important when key components feed external debt covenant calculations.

Covenant forecasting in focus across the board

Banks and other lenders use covenants as a key measure of risk and closely monitor the covenants of the companies they have lent to, on an ongoing basis. Lenders aren’t the only market participants interested in corporate covenants.  Rating agencies such as S&P and Moody’s very closely monitor covenant levels for breaches, which in some cases trigger defaults, but also show deterioration which can lead to credit downgrades for rated businesses.

Due to the impact a covenant breach can have on a company, they are also very closely watched by equity markets and anyone who owns shares in a highly leveraged business. As mentioned, a covenant breach can trigger a default which, in many cases, hands control of a business over to its lenders, wiping out most or all equity. While this is a worst-case scenario, based on the currently elevated levels of corporate debt, a movement upwards in interest rates above expected levels or tightening credit conditions could lead to a sharp increase in covenant breaches and defaults.

Covenant forecasting data sources

CFOs, Controllers and Treasurers will be crystal clear on the covenants and related levels they must comply with. However, accurately forecasting these levels approaching a key reporting date, such as year-end, can be a fraught and challenging process. This is due to two factors:

  1. Gathering all of the data on an ongoing basis to compile the forecast, and
  2. The difficulty in accurately forecasting certain balance sheet numbers, such as a year end cash.

In a large organisation, unless they are using dedicated cash forecasting software, it’s unlikely that the person or team forecasting the future covenants will have all of the required information readily available. For example, looking at two of the most common debt covenants, the leverage ratio (Net Debt/ EBITDA) and the interest covenant ratio (EBITDA/ Interest Expense) we can quickly see the number of inputs that are required and where they need to be sourced from. The process is further complicated by the need to sometimes capture forecast and actual data from different people and teams. The table below outlines the required inputs to these covenants and their sources.


Forecast Source

Actual Source

Total debt





Treasury and accounting



FP&A and accounting


Treasury and FP&A

Treasury and accounting

Of course, this tables ignores the many people who are involved in creating these headline numbers, which in large companies is a lot of people. It also ignores any adjustments that need to be made to the figures before inclusion in the covenant calculation. For example, an adjusted measure of EBITDA is often defined by a bank for use in the calculation, which removes some business activity if it distorts the true underlying measure of cash generation. When these factors are considered, the task of covenant forecasting becomes increasingly challenging.

Preparing for key reporting dates

Often responsibility for forecasting covenants falls to the Treasurer or Financial Controller. Their teams gather and compile this information and guide all necessary parties as the key date approaches. The focus on these numbers and the frequency of refresh required will be like no other time in the year, and therefore preparations will need to be made to ensure the increased demand can be met. In the run-up, they will need to:

  1. Open up lines of communication as early as possible with everyone on whom there is a data dependency.
  2. Clearly communicate to everyone involved in the process the importance of the numbers and what they ultimately feed into – debt covenants for external lenders.
  3. Prepare everyone for more frequent data refreshes as year end approaches. For the couple of weeks before year end, a daily refresh (at minimum) is likely.
  4. Try to build an understanding of other data and process dependencies and use these to manage expectations accordingly.

Ideally, covenant forecasting should be part of weekly or monthly management reporting. If this is the case, the impact of the year end process should be lower and lines of communication already open.

Tips for forecasting the biggest variable – cash

The year end cash number will be the biggest variable in the covenant calculation. In the final month before year-end, earnings and debt numbers should be reasonably under control. Cash, on the other hand, can be hard to pin down until the last moment. This is because there are so many inputs into the overall cash figure.

1: What are cash levels right now?

The best place to start is to confirm the exact cash number as it stands now. A large, multinational organisation will have a vast number of accounts spread across the globe. The only way to be able to achieve a consolidated view of the overall “live” cash figure is with specialised bank and liquidity reporting software. This will enable a view that includes all banking data in one place, pulled automatically from a multitude of sources (including MT940 or BAI2 files).

2: Gain executive sponsorship

Because of the number of business units (subsidiaries, global network companies, other departments, etc.) and number of people that are needed to produce data for accurate covenant forecasting, executive sponsorship is key. If senior management issue a communication to all relevant personnel outlining the reasons they are being asked to contribute data to the process, they are far more likely to produce high-quality data within the stated deadlines.

3: Leverage analytical tools to understand how cash is trending

Historical Cash Trends

Understanding how cash is currently trending can help build a picture of where it is likely to arrive at year-end. This means comparing and measuring movements, rather than just looking at snapshots in time. How cash has trended historically can also help to complete this picture. While past cash movements aren’t necessarily indicative of future positions, if cyclical trends are identified they can help increase the accuracy of year-end forecasts. The most effective way to identify trends in cash movements is with specialised analytics software tools.

About CashAnalytics

As a specialised liquidity forecasting and analytics software, CashAnalytics are dedicated to helping large, multinational organisations better understand the current and future cash and liquidity positions. To see how this would work for your company, contact us now to arrange a demo.

September 06, 2018

The power of clean data: a baseline for good forecasting

Sometimes the simplest tasks can be made problematic by the most obscure hurdles. This is particularly true for data management.

Imagine a Database Administrator is charged with sorting a range of historic texts by estimated publishing date. Let’s imagine these texts have estimated publication dates ranging from the early 18th century to the mid 20th, and that the headline information (including dates) for these texts are all stored in Excel. At first, you might assume this would simply require selecting the column containing the dates, and sorting by time.

However, in Excel the world began on the 1st January 1900 and is due to end on the 31st December 9999. After researching how to overcome this problem, the Database Administrator has installed a handy plugin that captures pre-1900 dates. Then the next hurdle presents itself, the eleven “lost days” between 2-14 September 1752. (Which arose from the Julian to Gregorian calendar switch.) By now the “simple task” has revealed itself to have far more steps than originally estimated.

The value in data

A good data set is a large data set. Large data sets, however, come with increased complexities. As we saw with the example above, even fairly simple data sets can throw up difficulties. The larger the volume of data, the harder these problems are to unpick. The main issue with large data sets, though, is the increased likelihood of anomalies.

In short, the larger the data set becomes, the more difficult it becomes to work with. This catch-22 has led to increased investment in the field of data cleansing. A task that is necessary before any data mining activities, which is where the real value-add is found, can take place.

The data sets that are used in a cash forecasting process are no different. Just like all other forms of data analysis, cash forecasts suffer the same decreased output quality if the data integrity is compromised.

Key cash flow data sources

In the field of cash forecasting, having a clean set of historic data is enormously powerful as it forms a baseline from which accurate forecasts can be made. The data sources that feed into a cash forecasting process can be broadly broken into two categories, sources of actual data, and sources of forecast data.

In most cases the actual cash flow data is sourced from:

  • Enterprise Resource Planning (ERP) / Accounts systems
  • Bank file downloads (BAI2 or MT940 files)

Whereas forecast cash flow data is usually sourced from:

  • ERP systems
  • Treasury Management Systems (TMS)
  • Data modelling software
  • People (individual business units)

Data sources infographic

The extraction of this data can either be automated, through the use of specialised cash flow forecasting tools, or it can be drawn manually. Because of the range of data sources that input into the process, it is important that the data is standardised into a common format before it is used. A common cause of data defects is that incorrect information has been input, possibly due to human error (if the process is manual).

As previously stated, this standardisation step can be an automated part of the process, but it is important that input feeds are mapped carefully, by someone with the appropriate expertise, to ensure the correct fields are captured.

What to watch out for

Ultimately, the goal of cleaning a historical cash flow data set is to create to a representative view of what’s happened in the past, which in turn provides a reliable basis for building a view of the future. Key to this exercise is removing items and once off cash flows that will not be repeated in the future as they will ultimately pollute the modelled forecast output.

Things to watch out for when cleaning a cash flow data source include:

Accounting Journals

Any information exported from an ERP or accounting system could potentially contain accounting journals such as reversals or currency adjustments that will impact the quality of the underlying data that will be used for forecasting. These journals don’t represent underlying business activity and will need to be removed.

Acquisitions & Divestures

Acquisitions are likely to be the biggest cash out flow, or series of cash flows, leaving a business over the course of year. It’s highly unlikely that the amount spent and timing of an acquisition will be repeated in the future and therefore it will be necessary to remove these cash flows before using the data set for forecasting purposes. The same is true for divestures.

Investment Capital Expenditure

Aside from acquisitions, investment capital expenditure, particularly for spend on large once off projects, can be the lumpiest cash out flows over a period of time. Even if capital expenditure levels are expected to remain consistent with previous periods, the amount and timing of this expenditure will likely be very different to previous periods. Which in turn warrants their removal from the data set.

Debt Movements

Debt drawdowns, repayments and refinancing can have a huge impact on total cash movements over a period of time. Typically, these movements aren’t representative of day-to-day business activity and should be removed when using the data set to model future activity.


In most mid to large size companies intercompany cash flows between business units can sometimes equal external cash flows in total volume over a period of time. Of course, intercompany shouldn’t have an impact on net liquidity but when analysing a particular business unit or segment it is important to remove non-trading intercompany movements that are unlikely to be repeated in the future.

General Outliers

In the regular course of business unusually large cash movements, be they payments or receipts, will occur for a number of reasons. The winning of a large new customer account could lead to a once off large cash receipt, for example, or the prepayment for raw materials to secure a discount could lead to large once off payment. The removal of these types of cash movements may require a little more judgement than the previous items but, in some cases, data is improved when they are removed.

Data cleaning – a continuous process

Owing to the anomalies listed above, data sets often undergo a “cleaning” exercise before any in-depth analysis is performed. However, as noted, care must be taken not to conflate data anomalies with data defects. While it goes without saying that removing data defects improves data quality, callously removing all anomalies might mean removing some important signals from the data.

In cash forecasting, data cleansing is a continuous process. The various measures that a large organisation must take, such as those listed above, mean that there are regularly factors that cause sizable distortions to these data sets.

Maintenance made easier

Specialised software simplifies the continuous data cleaning process, along with all other elements of the cash forecasting process. As referenced in the section on data sources above, automating the process expedites data cleaning. Elements such as intercompany cash flows should always net to zero. However, with a manual process this balancing task can be hugely time-consuming and inaccurate. Specialised software, with a dedicated counterparty driven intercompany tool, simplifies this task to a touch-of-a-button exercise.

This simplification enables the person analysing the data to quickly review and amend outputs and therefore take the right anomalies into consideration.

Having the right personnel at the right stage of the process

As we discussed in a recent article on the analytics skills gap, it is important to have people with the right skills at the right stages of the process. While not an absolute requirement, some companies may choose to recruit a Database Administrator (DBA) to ensure that all data is formatted and structured properly as it goes into the model.

In any event, the person charged with managing the database should be familiar working with large data sets and have a good knowledge of database theory and database design. This expertise will help with the identification and rectification of defects in the data, as well as managing the integrity and security of the data as a whole.

Progression through iteration

Once all checks have been made, appropriate actions have been taken, and everybody is confident that the data is of sufficient quality and integrity, it can be loaded into the forecasting model. The focus now switches to the output side of the process, i.e. on the reporting, forecasting, and analytics. Here, quality is improved through a process of careful measurement and adjustment. A gradual process, but one that becomes increasingly valuable to the company as these improvements are made.

Help for Corporate Treasurers

Going back to our hypothetical Database Administrator, struggling to sort historical dates in Excel, their best option is to re-format the database. They’ll need to sort their dates into separate columns; one containing year, another for month, another for day. Then filter with a cascading sort, factoring in all three columns.

For Corporate Treasurers, however, we can be of far greater assistance. We provide dedicated cash flow forecasting software to large, multinational organisations and have extensive experience helping clients across a range of industries.

In addition, to assist those considering updating their old processes, we have written a whitepaper which outlines the steps involved in setting up a cash forecasting process. If you have any questions on this, or would like to see a demo of our software in action, please do not hesitate to contact us.

August 30, 2018

Cash flow forecasting template

A well-designed cash flow forecasting template can dramatically improve the quality of reporting outputs. However, this is not a one-size-fits-all situation. For a template to be well designed it must be tailored to align with the business objectives of the cash forecasting process.

As one of the key stages in setting up a cash flow forecasting process, treasury teams often spend considerable time working out how their forecasting template should be designed. To assist, we have written this post which outlines how a cashflow forecasting template should be designed. In this post we will outline: what a cash forecasting template consists of, key decisions that need to be made when in the design process, as well as providing some useful tips.

What is a cash forecasting template?

A forecasting template (also known as a cash forecasting model) is the reporting structure and associated logic that produces the required forecast output. A forecast template has two dimensions and typically collects two types of cash flow data.

Cash forecasting model templates

The two dimensions of a forecasting template are:

  1. Reporting periods split on a daily, weekly or monthly basis to certain points in the future.
  2. Cashflow classifications which group cash flows typically on a “management reporting” level of detail.

The two types of cash flow data in the template are:

  1. Actual data. In the graphic above this is displayed in the column furthest left.
  2. Forecast data. In the graphic above this is displayed in the columns on the right.

What is the right level of granularity for the cash forecasting template?

The level of granularity chosen for both the reporting periods and cashflow classifications will be determined by the overall objectives of the forecasting process.

For example, if the business objective is short term liquidity planning, the template will need will need a reporting granularity of at least daily. If the reporting periods are any broader than this, for example if reporting periods are presented weekly, short-term liquidity shortfalls may be missed.

These are important considerations as reporting granularity that is too fine can muddy the waters and disguise important trends in the data. Conversely, cashflow classifications or reporting periods that are too broad for the objectives might mean that precise but important signals are missed.

In addition, to support decision making in liquidity planning, the forecast will also need to be as accurate as possible. As forecast accuracy generally decreases the further into the future the forecast stretches, the forecast horizon will need to be relatively short. In our experience forecasts used for short term liquidity planning do not usually exceed a horizon of 10 business days.

How to choose the right forecast horizon

To assist those currently making decisions on which is the appropriate time horizon for their objectives, we are in the process of releasing a short series of articles on the subject. The first reviews the practical uses for the 13-week cash flow forecast.

Model structure examples

The table below outlines some examples of illustrative business objectives. It includes which forecast horizon, reporting date granularity, cashflow classifications, and frequency of creation would be appropriate for each.

Business Objective

Forecast Horizon

Reporting Date Granularity

Cashflow Classifications

Frequency of Creation

Short term liquidity planning

10 business days


High level flows and balances

Twice a week

Interest and debt reduction

13 weeks


Management reporting categories and flows


Covenant and key date visibility

To next significant reporting date (at least)


Management reporting categories and flows

Weekly, more frequent approaching key date

Liquidity risk management

Six months

Weekly for 13 weeks, then monthly for 3 months

High level flows and balances


While the above examples are illustrative, ultimately, the structure of the template chosen should be able produce the range of reporting outputs needed to meet the business objectives.

How much actual data should be included in the cash flow forecasting template?

Capturing actual cash flow and balance data as part of the overall forecast reporting process is also an important consideration.

Similar to the granularity decisions, how much actual data should be included will be decided by the business objectives. Actual cash data allows the forecast model to produce both trend and variance analysis which are very useful pieces of supplementary analysis to the base forecast.

As business objectives help to define the scope of the various different elements of the cash forecasting template, having clearly defined business objectives is the first step in designing a forecasting template.

Setting up a cash forecasting process

This post is an extract from the guide we recently produced which covers all aspects involved in setting up a cash flow forecasting process. Please follow this link to the cashflow forecasting setup guide, which we welcome you to download. The guide discusses: what is involved in setting business objectives, how to set the process up, as well as what comes after go-live.

August 17, 2018

Cash forecasting data visualisations

Data visualisation is an important step in any form of data analysis. Presenting data in a graphical format often helps to highlight trends, identify anomalies, and uncover insights that will be missed when simply reading through raw data. The same is true when analysing the data produced as part of a cash forecasting process.

To illustrate how data visualisation can help with data analysis, this post will review three potential ways to visualise cash forecast data. These three options are;

  • Cash Walk Through Visualisation (shows how cash moves from an opening position to a closing position)
  • Forecast Versus Actual Visualisation (quickly understand forecast variances)
  • Time Series Visualisation (analyse accuracy across multiple versions of a forecast)

However, it is important to note that these examples are not exhaustive, and that many other formats of visualising cash forecasting data are available.

Cash Walk Through Graph

A cash walk through visualisation breaks the journey from opening cash balance to closing cash balance into a series of steps that identify the most significant contributors/detractors to the net cash balance.

In the example below, we can see the three headline cash inflow categories highlighted in green (Customer Receipts, Investing Inflow, Dividend Receipts). The four headline outflow categories are highlighted in red (Supplier Payments, Tax, Payroll, Debt Payments).

Cash walk through visualisation - system view

One of the key benefits of this method of data visualisation is that it highlights the extent to which each category of cash flow affects the cash balance in an easy to understand visual. This means attention can be focused on the element(s) that will have the greatest impact. For example, in the graph above we can quickly see that supplier payments (which total $10million in outgoings) cancel out the positive contributions from customer receipts, investing inflow, and dividend receipts combined.

Forecast vs Actual Variance Visualisation

The graph below visualises a comparison of forecast versus actual data for a variety of reporting entities. It breaks the measure into two categories, percentage variance and amount variance. (Here, the different reporting entities’ variances are transposed to a common currency, US dollars).

Forecast vs actual variance visualisation - system view

One of the key benefits of this method of data visualisation is that it allows material variances to be quickly identified and put into context with other entities’ percentage accuracy. This means that attention can be paid to the variations that have the greatest impact. For example, in the instance above, Brazil had a major deviation between forecast and actual (92%) but this only equated to a variance amount of $1.3million. Whereas in China, where forecast accuracy was better (though still poor as China had an 83% divergence from the actuals), the value of this discrepancy  was $4.0million. This means that an increase in the accuracy of forecasts produced by the Chinese entity (where there is considerable scope for improvement) would have a far greater impact on overall, company-wide forecast accuracy than focusing on the most inaccurate forecasting entity.

Time Series Visualisation

A Time Series Visualisation allows variance analysis to be carried out across multiple forecast versions and it is usually best displayed in a table such as the one below.

Actual vs forecast matrix visualisation graph

Here, the table shows how multiple forecast versions compare to the actual data (in bold):

  • y axis: shows versions of the forecast (when they were produced)
  • x axis: shows the month each forecast is projecting the closing balance of
  • Each data point on the chart corresponds to a forecast or actual (actuals are in bold) closing cash position
  • Taking the Feb-18 forecast version as an example, scanning along the row we can see that this submission captured the actual closing balance for JAN 2018, and forecast closing balances for FEB – JUNE 2018.

The advantage of capturing the data in this format is that it enables easy comparison of multiple forecast versions. This means that trends in the data can be easily identified and, where appropriate, addressed to improve accuracy.

For example, the table above shows that while forecast accuracy broadly improved as the forecast horizon reduced, there remained a tendency to underestimate the actuals. If we review the submission made in Apr-18, we can see that the actual figure captured for the March 2018 closing balance was 154,000. Included in this submission were forecasts for the April 2018 closing balance (which it underestimated by 19,000), a forecast for May 2018 (underestimated by 100,000) and a forecast for June 2018 (underestimated by 134,000).

As there is enough data captured (in the table we can see 36 forecasts and six closing balances) it can by hypothesised that this is not a natural variance but rather a trend that is the result of a fundamental area of the forecast being miscalculated. This might be higher than expected sales volumes, lower overheads, perhaps a recent reduction in business rates was secured by the company but this wasn’t reflected in forecast calculations, for example. In any case, once this trend is identified its underlying cause can be investigated, identified, then corrected. Thereby improving forecast accuracy.

Benefits of data visualisation

As mentioned at the beginning of this post, data visualisation is a necessary part of any data analysis. Presenting the information graphically enables an analyst to quickly spot trends, identify anomalies, and helps to uncover the underlying causes of any flaws in the process. For a treasury team, this means that these insights can then be presented back to the business in a clear and concise format for easy interpretation by senior management. This therefore positions treasury as a strategic department within the business.

Data visualisation software and tools

As with all parts of a cash and liquidity forecasting process, data visualisation options can be greatly improved with the use of specialised software tools. We have helped many companies to automate their cash forecasting processes, to increase forecast accuracy, and set up a new cash forecasting process altogether. If you are looking to improve the impact of cash forecasting in your organisation, contact us to see a demo of our specialised software.

August 10, 2018

CashAnalytics Summary Series: ECB Economic Bulletin August 2018

The CashAnalytics Summary Series takes major Central Banks’ extensive reports (often well over 150 pages), and summarises them into five minute reads.

This release summarises the 5th edition of the ECB Economic Bulletin for 2018, which was released on the 9th August.

The key points covered in the report include:

  • The euro area economy is proceeding along a solid and broad-based growth path.
  • Key ECB interest rates were unchanged and are expected to remain at present levels through the summer of 2019.
  • Net asset purchases are to continue at €30 billion per month until the end of September 2018. After which, subject to incoming data, monthly purchases will reduce to €15 billion until the end of December 2018 and then end.
  • The Governing Council intends to maintain its policy of reinvesting the principal payments from maturing securities for an extended period after the end of the APP.

To read the summary of the ECB Economic Bulletin, please click here.

August 01, 2018

13 Week Cash Flow

When setting up a new cash flow forecasting process, it is vital to select the right time horizon. This article will form part of a series we are producing which discusses how various different time horizons can be used in cash forecasting. This post will explore the 13 week cash flow forecast in detail.

Selecting the right time horizon for a forecast depends very much on the business objectives of the forecast. By exploring the uses, benefits and drawbacks of the 13 week cash flow model, you should be able to ascertain whether it is suitable for your business needs.

Uses of a 13 week cash flow model

We chose to start this series of articles with the 13-week cash flow model because of its broad uses. Corporate senior management, including the CFO, are familiar with 13-week forecasts as they will use them in various forms of quarterly strategy and planning sessions. Similarly, investors often prefer the 13-week horizon as it gives them a reasonable view of the health of the company in which they are invested. The same holds true if a company finds itself in a distressed situation. In this instance a 13-week cash forecast should be able to identify when and how any potential liquidity shortfalls should hit a company. This therefore offers the ability to prepare for, if not rectify, any issues before they occur. A 13-week cash forecast is also often a requirement for bank reporting, or when submitting any lending requests. In short, while a 13-week cash flow forecast isn’t suitable for all requirements, it does have a broad range of applications. We will discuss these in further detail below.

CashAnalytics screeenshot: 13 week cash flow

Weekly cash forecast showing actuals (far left column) and forecast figures (columns to the right).

Advantages of a 13 week cash flow forecast

Striking a balance

The time horizon of the 13-week cashflow model is short enough to support agile, tactical decision making, but also takes a long enough view to drive longer term decisions. The 13-week cash flow forecast also helps strike a balance between accuracy and range. It is a universal truth in forecasting that the accuracy of a forecast degrades the further it extends into the future. 13 weeks provides enough sight to have an impact on strategic decision making, while remaining short-term enough to be able to provide a high degree of accuracy.

Planning for the medium-term without interrupting plans for the longer term

13 weeks of visibility allows a CFO, treasurer, or financial controller to make medium-term cash management plans. This could include debt drawdowns and repayments, short term investments, or other management decisions.

Taking a longer-term view, while 13 weeks is sufficient for medium term and month-to-month cash planning, it won’t overlap with longer-term plans which can extend years into the future. Because of this, there is little reconciliation required between plans. Medium and short-term planning decisions can be assisted by the 13-week forecast, whereas longer term planning remains in the purview of the overall business planning process.

Avoiding the short-term planning gap

Most planning processes in companies produce monthly forecasts and budgets, offering limited short-term visibility. A 13-week cash flow forecasting process is usually broken down into weekly reporting periods, therefore offering four times greater granularity. This means that any short-term planning shortfalls can be addressed with a detailed look-through.

Liquidity risk forecasting

Risk management is a key part of any cash forecasting process. Forecasting with a 13-week horizon should be accurate enough to identify any potential liquidity issues, while still offering enough time to take action to resolve those issues. For example, if a potential liquidity shortfall is identified with 10 weeks’ notice, the treasury team have ample time to prepare by arranging bank funding or reviewing intercompany lending options.

Matching business requirements

By covering a full quarter in scope, a weekly rolling 13-week cash flow forecast will always provide cash balance visibility on the next key reporting date or next quarter end. This is extremely valuable for the business, as senior management will always want reporting on cash balances that coordinate with their own key reporting dates.

Suitable for banks and investors

Depending on the ownership structure, debts, bank relationships, or investment status, a company may be compelled to produce a liquidity forecast with at least 13 weeks duration in place. It’s a key measure of good financial control and, from a bank’s or investor’s perspective, it provides clear visibility of the company’s working capital.

When is a 13-week cash flow forecast not suitable?

Short term liquidity planning

A shorter forecast horizon (perhaps of 10 business days) might be better suited if the business objectives are focused more closely on short-term liquidity planning. A shorter-term forecast could have a daily reporting granularity and therefore be able to report on high-level flows and balances with a high degree of accuracy at a more precise date in the very near term.

Longer term liquidity risk management

While medium term liquidity issues can be identified and actioned with a 13-week forecast horizon, a 6-month forecast horizon (broken into weekly for 13 weeks and then monthly for the following 3 months) might be better at flagging up longer-term liquidity risk issues.

How to choose the right time horizon

Take an “objectives first” approach

As referenced at the beginning of this article, it is important to identify the right time horizon in order to produce the desired forecasts. Considering what the objectives of the forecasting process are, then selecting a time horizon which produces forecasts that meet those needs, is the right approach.

The benefits of software

Using specialised cash flow forecasting software can improve the accuracy and quality, remove the administrative burden, and increase confidence in a company’s cash and liquidity forecasts. For example, with rolling 13 week flow model, the weekly forecasts produced can be analysed at the touch of a button. Quick and easy forecast vs forecast, and forecast vs actual, accuracy measurement enables biases to be identified and adjustments and corrections made as necessary.

Setting up a new process?

If you are considering setting up a new cash forecasting process, we have written a cashflow forecasting setup guide, which we welcome you to download. The guide discusses; the practicalities of designing a forecasting model, what is required in preparation for the project launch date, as well as what comes after go-live.

July 26, 2018

Setting up a cash flow forecasting process

For a head office treasury or finance team, setting up new cash forecasting process or refreshing an existing process can bring many benefits. Being able to accurately forecast a company’s future cash position reduces risk of future liquidity issues and minimises the opportunity cost of holding uninvested cash. Also, having fast access to reliable cash flow data allows the treasury and finance team to consistently contribute to the strategic decisions being made within their organisation.

However, designing and rolling out any new reporting process in a large organisation can be challenging. Especially if adequate preparation is not done in advance. Dealing with multiple peoples in multiple places using multiple systems might seem like a daunting task, but taking a structured approach to the design and roll-out of a new process, as well as carefully managing the communication with all stakeholders, will enable a smooth roll-out and ongoing operation.

CashAnalytics has created an in-depth guide to assist people who want to set-up a cash forecasting process in their company. This guide follows a number of straightforward stages and mirrors the successful roadmap we use with our own clients. This roadmap covers five key areas:

1. Setting business objectives
2. Designing a forecasting model
3. Scoping and planning
4. Process set-up
5. Communication and roll-out

1. Setting Business Objectives

The business objectives are the reason the new cash forecasting process is being put in place. Cash forecasting supports a range of high value business activities, and the business case for putting a process in place can have a number of dimensions.

The headline activities supported by cash forecasting often include:



Working Capital Management

Ensuring the short-term cash and working capital needs of the business are adequately planned and provisioned for.

Debt and Interest Reduction

Gaining the required forecast visibility to confidently and safely use excess cash to reduce debt levels and interest costs.

Covenant and Key Date Visibility

Being able to accurately project expected cash levels on key reporting dates, thereby understanding the impact on covenant levels.

Liquidity Risk Management

Cash forecasting may not instigate a direct action but, in many cases, it is used simply as an early warning signal of future problems.

2. Designing a forecasting model

The design of the forecasting model itself is a critical task. At a basic level, the forecast model outlines the level of reporting detail required and the forecast time horizon.

Often the level of reporting detail in a cash forecast mirrors the level of detail in management reporting packs. This, at the very least, is a useful starting point.

It’s always a good idea to start with the simplest model first. One that provides the base level of required information. This can then be scaled to a more detailed or complex model over time.

3. Scoping and Planning

Once the business objectives have been set and the forecast model designed, the next step is to scope the project and set a plan.

This planning process won’t be much different to planning for any other project, but it remains an important consideration. A planning document should identify:

  • Required timelines and other events that may impact the delivery of the project (e.g. year-end closing)
  • Key stakeholders and their availability.
  • Other dependent systems and data sources.
  • Setting the project team and assigning responsibilities.

4. Process Set-up

The process set-up is a practical step that brings the requirements outlined in the design, scoping and planning stages to life. Whether using a manual tool such as excel or a dedicated cash flow forecasting software solution designed to automate the manual parts of the process, the three steps outlined below will need to be covered.

Step 1: Tool Configuration

Configure the chosen tool so that it mirrors the required reporting structure and maps to the correct master file data (business unit names, etc.)

Step 2: Map to Data Sources

Mapping to other data sources such as ERP systems is a key automation step than needs to be considered, but is often left until after the base reporting process is in place.

Step 3: Testing

The testing phase ensures that the implemented structures produce the required reporting output, and that the data interfaces are working correctly.

5. Communication and roll-out

Clear communication to all stakeholders will be required throughout the project. However, communication is particularly important in and around the time the process is ready to be rolled out to the wider organisation.

It is a factor that is sometimes overlooked but clear and consistent communication with stakeholders, particularly those people contributing forecast information, is key success factor in both the initial roll-out and ongoing operation of the forecasting process.

During the roll-out part of the project, training will need to be provided to everyone involved. This typically takes the form of webinars supported by training documentation. On an ongoing basis, dialogue and communication will centre on feedback, business analysis and the continuous improvement of forecast quality.

To download the full pdf of the Cashflow Forecasting Setup Guide, along with many of our other whitepapers, please follow this link to our resources section.


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