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CashAnalytics will be at the ACT Smart Cash Conference on 12 February 2019 and will be presenting a case study titled: How Rubix simplified and improved its cash forecasting with CashAnalytics.

We welcome anyone who is planning on attending the conference to come to see our presentation where we will explore how Rubix, Europe’s largest provider of industrial maintenance products and services, used our software to simplify, automate, and improve cash forecasting while combining two separate multinational businesses.

The presentation will walk through the following areas and allow space for questions for those who seek further information.

1. Background to solution: A review of the pain points / drivers behind Rubix initiating the request for a solution.
2. Specifics of Rubix requirements: A detailed look at the specifics of the requirements Rubix needed from the output in terms of business structure, reporting requirements, data requirements, etc.
3. Implementation project overview: A review of how CashAnalytics was implemented as a solution and what it took to go live.
4. The value of CashAnalytics: We’ll take a view of a day in the life of the Rubix Treasurer to explore practical examples of the way they benefit from using the system.
5. Reporting and Analytics in focus: By focusing in on the core reporting and analytics outputs offered by the system, we’ll show how data can be viewed and accessed in a way that isn’t possible without CashAnalytics.
6. Q&A: We’ll close the presentation with a Q&A for those who would like further information on any particular aspect of the project or would like to know more about CashAnalytics.

In addition to the case study presentation, we will also have an exhibition stand out in the main hall opposite the refreshments area for those who would prefer to ask us any questions either in advance of the presentation or after it.

For those that cannot make it to London, shortly after the conference we will be publishing the case study as an article on our website. If you wish to be emailed a copy when it is published please submit your email address on our contact page. For those that can make it, we look forward to seeing you there!

Increasingly, organisations are discovering that Robotic Process Automation (RPA) can be a cost-effective way to streamline operations, improve data quality and enable better decision-making. Driving the trend is RPA’s ability to improve efficiency and reduce costs by mimicking how humans interact with applications.

In a recent survey, management consulting firm Deloitte found that more than half (53 percent) of global respondents already use RPA. Most report that RPA not only met or exceeded their cost reduction expectations but also delivered non-financial benefits such as improved accuracy, timeliness, flexibility and compliance. Deloitte predicts that if take-up continues at the current level, RPA will achieve near-universal adoption within the next five years.

Who uses RPA?

The growing popularity of RPA means that users can be found across many sectors from financial services to manufacturing, telecoms and retail. Some organisations, who previously outsourced back office functions, now see RPA as an opportunity to bring processes back in-house as it can enhance data risk management and improve compliance with internal controls as well as being cost effective.

While RPA tends to be more common in large organisations, it is equally suited to any business with standardised processes which involve high-volume, repetitive tasks.

How does RPA work?

RPA is software that interacts with an organisation’s existing systems to automate certain tasks.

When applied to standardised processes, RPA can assist and/or replace repetitive human actions. For example, it can log in to an accounts system as if it were human, extract accounts receivable and accounts payable data and automatically feed this information into the cash forecasting system.

Form-filling, reconciling or consolidating data, moving files and folders—any task that can be standardised and is rules-based may be suitable for RPA.

How is RPA implemented?

A frequently-cited RPA advantage is that the organisation’s underlying systems do not need to be replaced. This minimises implementation costs and facilitates rapid roll out. However, because RPA interacts with existing systems, problems may arise if there are changes to the underlying user interface or data. So, as with any project, careful planning and management are required. Workflows must be analysed and standardised before initiating RPA and, once implemented, automated processes must be maintained, updated and kept secure.

What’s the ROI from RPA?

When applied to appropriate tasks, RPA’s potential to reduce costs is significant. EY published an article noting that cost reduction can reach 50—70 percent for some automated accounting and finance activities.

Another advantage is that payback is achievable within a relatively short timeframe. The Deloitte study cited above found that payback was reported at less than 12 months.

Potential time savings can be dramatic. Malaysia-based CIMB group, who implemented RPA in September 2017, say that in one month alone, 9 out of 15 banking processes showed a significant reduction of up to 90 percent in turnaround time. 

How does RPA impact compliance and risk management?

In highly regulated industries such as insurance and banking, a McKinsey study showed that companies are finding that automation is a cheap and fast way of applying superior capability to the problem of compliance. Applications may include assisting with know-your-customer processes, limit management, AML monitoring and investigation, remediation processing, monitoring and reporting.

Other businesses, too, are discovering compliance benefits. Royal Mail’s Director of UK Finance Mike Prince has observed that robotics opens up an avenue toward process improvement because unlike humans, “the robot won’t accept non-compliance or create additional steps … It’s a brilliant way to improve adherence to process.”

How does RPA help finance and treasury teams?

RPA’s ability to speed up processing, improve accuracy and allow tasks to be completed at off-peak times, means that it has many potential applications in finance and treasury where data often has to be copied and shared across different systems. Because manual processes and over-reliance on spreadsheets can be error prone, RPA’s ability to minimise the risk of human error is particularly beneficial.

Accounting teams can use for RPA include performing calculations, stock checks, customer credit checks, and running checks on suppliers against a list of approved suppliers. For treasury teams, collecting invoice data and entering it into the accounts payable system, collecting data from spreadsheets or other sources for posting to the general ledger, and other data transfers between systems can all be completed automatically using RPA.

How does RPA help cash forecasting?

When correctly implemented, RPA can automatically extract actual data from an organisation’s ERP or accounts system and feed it directly into the cash forecasting system. With an automated cash forecasting process, as soon as new details become available, data can be refreshed and presented on an intuitive dashboard offering the ability to drill down to the required degree of granularity or roll-up to broad headline numbers. As more, and better quality data becomes available, decision-making and planning are therefore enhanced.

Similarly, RPA’s ability to extract data from various sources creates opportunities to improve analytics which in turn enhances the treasury of finance team’s forecasting ability.

Other potential RPA uses in treasury include service level agreement processing, performing settlements and sending out deal confirmations.

What’s next for RPA?

Artificial intelligence is the next horizon for automation in the finance function. Already, AI is showing that learning from behaviour patterns and combining RPA with cognitive technologies increases the range of processes that can be automated and reduces the need for human intervention.

About CashAnalytics

As specialised cash flow forecasting software providers, CashAnalytics uses RPA to automate cash forecasting processes for companies across the globe.

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

When a private equity firm carves out a business, a new company is snapped into existence. Whereas previously the new company had been a part of a larger whole, it now needs to learn to stand on its own two feet quickly.

To help manage this process, a Transitional Service Agreement (TSA) is negotiated. For the period stipulated on the TSA the newly carved-out company will have access to the main body company’s infrastructure (such as head office treasury functions, etc.)

Therefore, as the newly carved-out company scrambles to get itself in order, the TSA end date can feel like a cliff edge approaching at speed.

Abundant capital looking to be deployed

In their Q3 2018 Private Equity Deals Insight report, PwC predicted that carve-outs and divestures would drive the majority of upcoming private equity deal activity. According to PwC, this trend will be influenced by two key factors:

1. Large corporations looking to divest non-core or non-performing business units and,
2. Private equity firms seeking to deploy cash reserves which now stand at record levels

A business, carved out from a large corporate parent and finding itself with a new private equity owner, will experience shocks on a number of levels as it adjusts to its new ownership structure.

One of the most profound changes a newly carved out or divested business will experience is that they must now become self-sufficient from a funding and liquidity management perspective.

Negotiating the Transitional Service Agreement

When negotiating the TSA, the Private Equity backers and new company management must focus on obtaining the required support from the previous parent and secure access to the information needed to manage cash in the new entity.

Access to debtor and creditor ledgers

Without the information contained in debtor and creditor ledgers, upcoming bill schedules, and other records of committed cash flows, building a cash forecast becomes nigh on impossible.

Therefore, one of the first items on the TSA needs to secure access to these files, allowing the new company to build their own systems of storing this data that can be updated frequently.

Access to bank accounts and bank account information

Approved personnel will need reporting and payment access for all relevant bank accounts.

While this information might be deemed as sensitive, the new company will not yet have had the opportunity to set up new accounts and have built new processes, therefore access to the old accounts for the duration of the TSA is necessary while the new systems are built.

Previous cash forecasts

The last cash forecasts produced by the “parent” company contain valuable information. The newly carved out company will need access to these with granular breakdowns of corresponding assumptions and drivers, so that they can replicate the model as a starting point.

Where to focus

While the TSA is active, the new company needs to use the access and support it has to build the systems required to stand on its own two feet. Therefore, when the TSA end date hits, the new company is ready to embark on its journey as a stand-alone entity.

From a cash management perspective, to get everything under control there are a few key areas to focus on:

Daily bank balance reporting

By building a solid daily bank balance reporting process, the new company will be able to understand the total cash positions, therefore building a better sense of cash flows and aiding overall cash management.

Implement a rolling 13-week cash forecasting process.

This is the basic requirement that will usually be stipulated by the Private Equity backers of the new company. This should cover:

• A classified view of recent actuals (i.e. last week)
• Visibility over short-term payables and receivables
• Visibility over mid-term cash flows modelled from business forecasts

For more detail, please see our post which outlines how a 13 week cash flow forecast can be built, and advice around its practical uses.

Building bank relationships

It is important to build relationships with the relevant banks to understand the remit of reporting options available. This includes electronic bank statements among others which should be able to be transferred into a system directly via an API.

Getting everything under control

The most efficient way to get all of these moving parts under control quickly is to use specialised cash flow forecasting software. Not only will this enable the process to be built and put into practice quickly, but it will also set the new company up for a best-practice cash forecasting and liquidity reporting process from its inception.

About CashAnalytics

CashAnalytics has helped many newly carved out companies from a range of industries to quickly build new cash forecasting and liquidity management processes.

We understand the strains on corporate finance and treasury teams, and that understanding is what helped us to build our software.

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 are in uncertain times. As Larry Summers discussed in an article in the Financial Times earlier this week, many economists believe a recession in the next two years is more likely than not.

In addition, trade worries continue to trouble markets. Lead by a United States caught in a spiral of rising protectionism, corresponding trade wars are affecting major economies across the globe. Further affecting trade, the United Kingdom is approaching the unchartered territory of Brexit with little apparent planning, preparation, or even a target destination.

Perhaps discouraged by the rocky Brexit process so far, the remaining EU members seem, for now at least, to be more closely embracing EU membership, although they are not totally free of concern. Many are facing widespread protests (such as the gilets jaunes in France) or the rise of the far right (such as the AfD in Germany).

All of this has major impacts on currency and stock market volatility, business and consumer confidence, and therefore corporate decision making.

Increased focus on cash forecasting

This uncertainty can make life difficult for corporate treasurers, financial controllers, and CFOs.

Tariff introductions, rule changes, and conditions after Brexit mean that cross border payments could become a headache (and carry as yet unknown costs).

Tightening of conditions and volatility in markets mean that, from an operational perspective, management of excess liquidity and debt levels becomes increasingly tricky.

This leads to an increased focus on cash and liquidity forecasts. As corporate treasurers know, a good cash forecast can help guide decision making and help to plot a route around the obstacles presented by volatile markets.

However, the factors that are behind the need for improved accuracy are the same factors that have a damping effect on forecast accuracy.

The need to refresh data frequently

In such a volatile environment, the type of new information that can have a transformative impact on cash flows is released regularly. Therefore, the need to be able to quickly and easily refresh forecasts is of vital importance.

Say, for example, that a firm generally runs a 13-week cash forecast. Under usual circumstances they might refresh this forecast weekly (or monthly if it is part of a hybrid forecast which combines different time horizons). In this example, the firm is a U.S. multinational that will be heavily affected by tariffs on steel imports. When the details of the tariffs are announced, the CFO opens a channel to the Treasurer wanting to know how this will impact cash flow.

The Treasurer needs to provide an answer quickly, therefore refreshing the forecast needs to be a quick and easy task.

Hurdles to overcome

If the cash forecasting processes are manual and administratively heavy, a speedy refresh is not usually possible without throwing bodies at the problem. However, this means taking highly qualified personnel away from high value activities and reallocating them to administrative work.

Moreover, pulling in lots of people to help turn the task around with speed, sharply increases the risk of human error, jeopardizing the drive for the increased quality requested at the outset.

To exacerbate these hurdles, in a volatile environment, this data might need to be refreshed several times in quick succession as new information becomes available.

Staying in control of the process

Surrounded by a world of uncertainty, a Treasurer or Financial Controller can find a well-managed cash forecasting process to be a life raft.

The best cash forecasting process in these circumstances, the one that most supports key strategic decision makers, is the one that offers them the most control.

To achieve this degree of control, the process needs to be robust, reliable, and, most of all, quick. To ensure speed while reducing the risk of human error, the administrative burden must be removed from the equation. The only way to do this is to manage the process with dedicated cash flow forecasting software.

How does software aid the process?

To demonstrate how software aids this process, let’s review the example of the U.S. headquartered multinational concerned with the impact of import tariffs. Because of the volatility of the situation, the CFO will most likely be requesting an updated liquidity forecast every time a key piece of information becomes available (i.e. when new suppliers are identified, when retaliatory tariffs are announced that affect the company’s sales, etc.)

With an automated cash forecasting process, as soon as new details become available the data can be refreshed and new expectations included quickly and easily, with the results presented on an intuitive dashboard offering the ability to drill down to the required degree of granularity or roll-up to broad headline numbers.

(More detail on how this is achieved can be found in our post on cash forecasting automation.)

How long does this take to implement?

With the current levels of global geopolitical uncertainty unlikely to stabilise any time soon, now is the time to act.

If the right tools are selected, that is to say software that is specialised for cash forecasting, new processes can be built and rolled out within six weeks, and for less than the cost of a salary.

The lesson, therefore, is that further delaying the deployment of these tools can cause unnecessary difficulty when the solution is so easily within reach.

After all, the journey is always that much easier when you know what lies just over the horizon.

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.

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.

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.

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.

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.

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.

Communication

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.

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.

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.

External

• 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.

Internal

• 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.

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:

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.

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.

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.

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.

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

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.

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 18^th century to the mid 20^th, 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 1^st January 1900 and is due to end on the 31^st 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)

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.

Intercompany

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.