By Stefan Spiegel, CFO SBB Cargo AG
Today's controller is confronted with two developments: On the one hand, an increasing usage of mobile applications for all employees, a steady growth of IT-supported operational resource planning systems and the hype towards Big Data result in an increasing volume of direct business information. On the other hand, more and more detailed accounting standards and higher requirements for compliance and governance enforce heavier control activities. In order not to miss the step into an effective business partnering and to be able to co-decide with line managers, however, a new form of tools based on IT developments should focus on business decision-making and deliver direct proposals for taking measures to line managers. It therefore requires new "decision-delivering" instruments as well as customized profiles for the future "Performance Manager".
The notion of "business partner" is becoming increasingly common in the discussions on strategic developments of finance departments. The aim is to steer the business together with line managers at equal responsibility. This often entails the idea of taking a "challenging role" across the line functions in order to critically illuminate measures and impacts, thus increasing the quality of the decision-making process. In principle this is not wrong, but a finance department may not believe that this makes already the way to becoming a business partner.
A true partnership means that decisions are developed jointly and doesn’t mean a critical challenge from the side line. Similar to a marriage: Who would like to have a partner who simply relaxes, watches, and at the end, after a lot of work, begins to cast critical remarks. Who would not want to have a partner with whom one can constantly discuss, taking decisions together and who actively participates on the common work-load? Considering a finance department, it means that it is not enough to look at an EBIT or KPI figure and then ask some critical questions, or to notice that these figures should be better compared to a really good company. This not only provokes discomfort and frustration, but also leaves line managers “in the rain” without any effective contribution to what should be done or be
optimized. There is a danger that the finance organization - perhaps even unintentionally - will persist in a pure control function and thus be logically avoided by the business if possible. Or even worse, you are drifting off as a finance department and assume the role of a therapist, who considers the line manager as a patient, who must be told what "correct and good" business steering consists of. Of course this also generates a dependency relationship, but surely not on a partnership level.
As already said, a challenging or control function isn’t always a bad thing. Finance departments have been strongly oriented to such a role over the past decades, including instruments such as financial accounting or the classic instruments for management accounting where there are even specialized trainings - such as the CMA. The fact, that accounting and management accounting systems have become more and more complex and that more and more experts from large audit firms have to be approached directly to solve upcoming questions, increases the risk for a finance department to deviate more and more from effective business needs and to miss the developments in connecting and usage of business data.
To discuss the ideas, we consider SBB Cargo AG, the Rail freight subsidiary of the Swiss Federal Railways SBB, which operates so-called single-wagon load traffic in the Swiss domestic freight market. This means that a customer, if he has a track connection, orders a wagon, loads it and sends it over the network of SBB Cargo to its final destination.
Schematically, this network is constructed as follows:
The classic management accounting follows a standard industry logic:
How much costs the last mile tour, how much the shunting activities at the team station, how much the transport from the team station to the shunting yard and so on. On each of these service components or cost blocks it is determined how many wagons are handled and then the costs are allocated proportionally to the number of wagons on the responsible shipments. If, finally, a customer with all its shipments is evaluated, then one sees, in addition to the sales, the corresponding proportionally allocated costs of the individual service components.
If we look for example at the work of a shunting team on the last mile, the cost elements can be divided into the train path costs, wagon costs, costs of shunting staff and costs of the shunting locomotives. Schematically, it works as follows:
Direct primary postings on the customer order in SAP are the revenues triggered by the order itself and services purchased specifically at a third party.
In SAP this cost allocation scheme requires 3 complicated layers of internal posting processes:
The basis for the allocation process are previously determined standard cost rates.
Thus the classical management accounting provides margin contribution and profitability calculations for each customer and each of his corresponding shipment. However, the following disadvantages are associated with such a system:
Normally there’s a need for translating results by customized and understandable reports.
With modern steering systems the disadvantages of classical management accounting will be solved. The ideas of DdS are based on the following principles:
The following chapters should describe the steps to implement the different building blocks of a DdS system.
Pricing is only about what a customer is willing to pay. Of course, this finding is not new, but is logically linked to the beginning of a decision-delivering steering system.
Let us take a customer who sends x wagons from place A to place B. If this customer decides to transport his goods by rail, then the added value for him is the displacement of his goods (depending on the weight) in a suitable wagon over the distance from A to B. How often and for how long the wagon has to be shunted, does not interest him, on the road this doesn’t happen. The price function (simplified) is thus given by
Price = P (industry, wagon, distance, weight, possibly type of goods, ….)
This means that the price is calculated by default from a factor for the industry, the number and types of wagons used, the distance and the weight.
An important element here is a direct comparison with the prices of the other railway competitors and the road price, where goods are also transported from A to B. The goal of benchmarking must be to maximize the willingness to pay or to know where to beat the market price.
The second element in the customer's willingness to pay is the added value that could be offered in comparison to a road transport or to another competitor. In particular, the following
points can be named:
Such points can directly increase a customer's willingness to pay and shouldn’t be forgotten in the pricing and also in the negotiations with the customer.
The DdS-calculator or optimization tool is intended to optimize both acquisition and resource usage in the single wagon load network of SBB Cargo by calculating a perfect production situation.
The tour of a shunting team is perfect, for example, if the team runs constantly with the maximum possible speed, the shunting locomotive transports the maximum number of wagons, no empty wagons have to be moved around and employees work as much as allowed without generating empty times.
If we switch to the network itself, we mean the transport of the wagons from the team stations to the large shunting yards and vice versa. Here the elaboration of the perfect production is more difficult.
As a first attempt it should be mentioned that in principle the following should apply:
This means that the reloading and stopover of the wagons at the customer site should be as short as possible.
Since it is fundamentally not possible to optimize all points at the same time nor to detect the perfect production state by simply analyzing some figures, it requires intelligent mathematical algorithms and simulations that weigh different states with their costs.
The goal is to develop an optimization algorithm in the direction of optimal production, which delivers proposals, how to change the network. We call such an instrument a “DdScalculator”.
To dive a bit deeper we describe the steps in programming such an algorithm:
Step 1: Definition of “Decision Quants”
In the classical approach, a shunting team (as example we focus on this part of production) is controlled, for example, by means of a KPI such as utilization, which measures the effectively productive time in relation to the total working time. For example, a utilization rate of 70% means that the marshalers have "empties" of 30%. The setting of target sizes, however, becomes more difficult. While looking at a large number of shunting teams there’s a good chance to increase the measured average utilization rate from 70% to perhaps 71 or 72%, in the consideration of a single shunting team the cost blocks always change only in jumps. In fact, the costs of a shunting team can only be changed by the following factors:
Therefore a variable like the utilization - analogous to the cost blocks - always changes only in jumps. Instead of setting a target variable, it is therefore much more efficient to directly decide which target state is to be implemented. Instead of discussions about KPI's, concrete decisions take place.
Which decision quants do exist then for our shunting team?
The shunting team serves actually all loading places 1 – 13 without number 3 using 4 different tours. Antenna 4 with LP’s 14 – 16 is actually not serviced.
The following decision quants exist for the shunting team:
And the following decision quants exist on the customer side:
Step 2: Parameterize the production elements
The individual production elements must now be parameterized. The production elements include the customer shipment, the loading places and the individual defined last mile tours.
As already described above, the customer shipments are displayed via S (C, Wg, LPs, LPr)
where C stands for the customer, Wg for the number of wagons, LPs the sending place and LPr the receiving station.
A loading place, in turn, LP (Wg, TtTS, TtSM). Is described by the following parameters:
LP 6 (20, 120, 20) thus means that the capacity of the loading station 6 comprises a maximum of 20 wagons, that it takes 120 minutes from the loading station to the team station and the shunting maneuver takes 20 minutes.
A tour is characterized by the places it serves:
If you look at the tour 1 and evaluate all customer shipments, which have their departure, respectively their final destination at the loading places 1, 2 or 4, then one finds the number of wagons, which are moved on the tour 1. At the same time, the parameters provide the time needed for the tour.
For further discussions, let us consider the following parameters:
The parameters of the charging points are:
LP 1 (5, 20, 5)
LP 2 (5, 30, 5)
LP 4 (10, 40, 10)
The time for delivery and pickup thus results in 2 * 40 minutes travel time + 2 * 20 minutes shunting activities = 120 minutes or a total of 2 hours
The loading place 6 is frequented by a large customer (customer 6) with 15 cars and the parameters are LP 6 (20, 120, 20)
The time for tour 2 with the loading points 5 and 6 is thus 2 * 120 minutes of travel time and 2 * 25 minutes for shunting activities = 290 minutes or about 5 hours.
Tour 4 includes 14 cars and lasts 3 hours.
Step 3: Definition of production conditions – constraints of the model
The definition of conditions is the key element for the understanding of the cost incidence within the shunting team. Often these conditions are discussed by chance during visits in the
area, but systematically they are rarely adressed.
When the production processes are analyzed, the following rules (simplified) are found:
Step 4: Calculation of production organization without optimization
Taking into account the parameters of step 2 and the conditions of step 3, the following production organization should be in place:
Step 6: Data supply
Data should be fully and automatically accessed out of the operating systems of Cargo and on the other hand out of publicly available external sources.
Step 7: Design of optimization algorithm
The new way and key principle for a controlling department now lies in the steering towards the above mentioned “perfect production”. In order to initiate this, it is proposed to adjust the KPI's following the results of DdS.2. In concrete terms, the KPI's should not measure the actual state but the "distance to the respectively optimal production", and therefore the possible potential for improvement.
The following examples are intended to illustrate the idea:
This means that the KPI logic is turned completely. Instead of a simple measurement of an actual state without an idea, whether it is good, bad or can be changed at all, the future KPIs always point to the optimal solution, which can also be achieved with appropriate management decisions.
The detection of these so-called "distances to the optimum" takes place in two ways:
To ensure that these inputs are not simply ignored, the goal here is to systematically capture these inputs via an app and make them visible directly into the KPI system.
Using the DdS-calculator of chapter 5 the further development of management accounting is to determine the marginal resources and costs for lost or newly acquired shipments, instead of a fair but limitedly useful allocation of existing cost blocks.
For this purpose, the DdS-calculation must determine which cost items could be eliminated after a shipment has been canceled. Of course, the instrument must also be capable to combine several shipments together and to determine the same statement. In turn, the calculation must automatically provide the answer, which additional resources are required for a new shipment. Today, all such work is carried out manually and partly leads to great surprises after customer losses, as – out of management accounting – only a small amount of contribution should be lost, but in real production no resources can be removed.
Instead of a relatively heavy and expensive SAP architecture, the future IT landscape can be dynamized and simplified. It consists of the following elements:
- An intelligent DdS calculator with interfaces to
a) a simple system for financial accounting, with no need for any secondary postings,
b) the relevant operating systems with their basic data
c) app's of area workers for the collection of local conditions and potentials
And thus the future steering instruments are:
The following topics can be addressed with this implementation:
Example: Dynamic calculation of margin contribution
For the delivery and collection of the wagons, the following (simplified) cost blocks are considered:
The goal of Decision-delivering Steering DdS is the mapping of a margin contribution “MC 1”, where the sales of one or more shipments are recorded against those cost blocks which are directly caused by these shipments and which could, therefore, be eliminated when the shipment is omitted.
For example, take the shipments of customer 5:
S (5, 4, LPs, 5) = customer 5, 4 wagons from departure point to LP 5
S (5, 4, 5, LPr) = customer 5, 4 wagons from LP 5 to end station
Remark: LPs and LPr are places outside the antenna’s 1 – 4.
These two shipments will be served on tour 2. If these shipments were to be omitted, then the team of tours 1 and 2 would still have to service customer 6 at LP 6. Only the time for the 10-minute maneuvering would be eliminated, empty time would increase. This means, that neither shunting staff nor shunting locos could be eliminated. In the margin contribution calculation which focuses on directly depending cost blocks, the line items personnel and shunting loco costs remain zero, the MC1 seems pretty positive.
|Shipments||S(5, 4, *, 5) und S(5, 4, 5, *)|
|Shunting loco cost||0|
Or take the shipments of customer 4:
S (4, 3, LPs, 4) = customer 4, 3 wagons from departure point LPs a to the loading place 4
S (4, 3, 4, LPr) = customer 4, 3 wagons from place 4 to destination LPr
These two shipments will be serviced on tour 1. If they were to be dropped, the team of the tours 1 and 2 would still have to serve the loading places 1 and 2. However, the time for the outward route to the loading place 4 and the maneuvering on the ground would be omitted, which would amount to a reduction total of 40 minutes. Nevertheless, as in the first example with customer 5, neither staff nor locos could be eliminated. The MC 1 stays positive:
|Shipments||S(4, 3, *, 4) und S(4, 3, 4, *)|
|Shunting loco cost||0|
Completely different the situation when viewing customer 6 with his shipments S (6, 15, LPs,
6), S (6, 15, 6; LPr)
These shipments are also handled via tour 2. However, if these orders were canceled, the situation would immediately be as follows:
The conclusion is that the customer 6 with his orders binds an entire team of a heavy shunting locomotive plus 2 FTE. All line items in the margin calculation are filled out in this case, MC1 gets negative due to the fact, that many cost blocks could effectively be eliminated when giving up customer 6.
|Shipments||S(6, 15, *, 6) und S(6, 15, 6, *)|
|Shunting loco cost||300|
|MC 1||‚- 100|
Customer 6 becomes thus unprofitable, a renouncement of his orders is worthwhile.
The consideration of customers 5 and 6 together would lead to the same conclusions as customer 6 alone, but the two customers together are almost profitable
S(6, 15, *, 6), S(6, 15, 6, *), S(5, 4, *, 5),
S(5, 4, 5, *)
|Shunting loco cost||300|
|MC 1||‚+ 30|
Let us assume that a larger customer 3 asks if one can drive shipments to LP 3. The parameters are:
LP3 (20, 35, 10) = 20 wagons capacity at the LP 3, travel time 35 minutes, shunting time 10 minutes
S (3, 10, LPs, 3) = 10 wagons in the delivery
S (3, 10, 3, LPr) = 10 wagons in the collection
It is found that customer 3 can be served on tour 1. The duration is extended by 20 minutes, but the first team can still use the tours 1 and 2. As a heavy shunting locomotive is already needed for tour 2, the new number of wagons on tour 1 is no problem
|Shipments||S(3, 10, *, 3), S(3, 10, 3, *)|
|Shunting loco cost||0|
|MC 1||‚+ 300|
The very positive MC1 indicates that the new shipment can easily be run with the available resources, which greatly increases the utilization and the profitability in the system.
Example: Margin Contribution under consideration of the environment
The example above gets even more exciting, if we assume that the tours 3 and 4 no longer exist, that there are only the tours 1 and 2. In this situation, the omission of customer 6 leads to the fact that the remaining team can approach the loading positions 1, 2, 4 and 5 in a single time, but the team basically remains. What is changing is that instead of the heavy shunting locomotive a medium can be used. The margin contribution looks like:
|Shipments||S(6, 15, *, 6) und S(6, 15, 6, *)|
|Shunting loco cost||100|
|MC 1||‚+ 400|
This means that without the tours 3 and 4, it is not at all worthwhile to give up the shipments of customer 6.
Or let’s assume the following situation:
The customer 6 offers the tour 2 team the opportunity to take over the loading and unloading of the wagons and other logistical processes when they bring the wagons and pick them up. The duration of this work is 90 - 100 minutes and would be compensated with 300.
Since the team 1 with the tours 1 and 2 currently has an empty time of 70 minutes, this order can not be accepted at the moment. If, on the basis of this assumption, we examine the
contribution of the shipments of customer 4, we see the following:
If the customer's orders 4 were to be omitted, a time gain of 40 minutes would be obtained for the team 1, which would allow the additional service to be executed for customer 6. The
contribution for the shipments of customer 4 will be negative under these new environmental conditions:
|Shipments||S(4, 3, *, 4) und S(4, 3, 4, *)|
|Shunting loco cost||0|
|Lost customer orders||‚- 300|
|MC 1||‚- 230|
The design and implementation of the DdS require the following essential skills, which differentiate from the typical controller profile:
Basic education such as engineering sciences, mathematics or computer science are very suitable for the development of such skills. Furthermore, such graduate students are first likely to get involved in marketing or in the production of a company, in order to directly get the necessary "front" experience. At the same time, the opportunity to switch flexibly between marketing, performance management and operations also empowers any subsequent career.
Instead of a complex, costly, accounting-like management accounting with limited meaningfulness, a DdS-Calculator is implemented, which optimally combines the operational systems, inputs from front people and the effective cost incidence, and which generates direct optimization and decision-making proposals.
The advantages of such a solution are obvious:
Organisations are finding it increasingly hard to plan or predict future performance due to the fast pace and complexity engendered by today's global, online business environment. And yet, within the vast quantities of data. available to management, there are tell-tale trends and correlations that reveal valuable insights to the direction they should take to maximise results. This revealed knowledge is generally known as 'Business Intelligence' and are discovered through a range of analytic tools typically embedded within business applications for planning and reporting.
For organisations to survive let alone grow, it's imperative that FP&A departments take advantage of the latest developments in business analytics. With them, they will be able to improve the transparency of their operations and subsequent organisational decision-making. Without them, they will be subject to the vagaries of market forces and an uncertain future.
Analytic applications have been around for over 40 years, but their adoption has often been slow and without their true potential being realised. Today, more than ever, FP&A staff are aware of the importance of understanding their organisation's business model and its predictability. They also realise that because of the development of analytic technologies which are available to the whole market, they dare not pause in their quest to increase efficiency and forecast accuracy. But just how mature is the current use of analytics?
In the summer of 2016, the FP&A Trends Group were asked to undertake a survey of users as to the adoption of analytics and the impact it was having on the organisation. The survey was sponsored by prevero, a global provider of FP&A analytics technology. This paper outlines the results.
For the purposes of this paper business analytics is defined as the systems used to support an organisation's planning, reporting and decision-support processes. This includes strategic and tactical planning, budgeting, forecasting, reporting and analysis.
The questions chosen for the survey were designed to reveal the types of tools used by organisations; how effective they are in improving efficiency; and the level of satisfaction there is in supporting the different management processes.
The authors would like to thank the many people who contributed to the survey and to the survey sponsor prevero who helped with the analysis and presentation using their business analytic tool.
2.1 How business analytics are used
The majority of organisation's (88%) use analytics for reporting results in terms of 'what happened'. This isn't surprising given that the first FP&A decision-making tools to appear in the market were in the areas of management reporting. Perhaps what is surprising is that 12% do not appear to use any kind of analytic tool, relying purely on the reports that come out of the financial transaction systems.
The issue with this is that these systems are inward looking and focused on transactions with existing customers. As a consequence, they do not contain sufficient information for management to make effective decisions such as what's going on in the marketplace.
Business analytics can do far more than just report results. From the survey, 72% of organisation's use analytic systems for explaining the reason why results were as reported. This comes by producing insightful analyses that allow users to go beyond the basic financial numbers and to drill down into underlying detail, while allowing their own ad-hoc analyses.
But where business analytics really score is in predicting future results (52%) such as when developing a budget or a forecast; while the advanced use is in prescribing what actions should be taken (40%).
This initial overview would indicate that organisation's are quite mature in their use of analytic systems. But the survey reveals real issues that counter this view.
2.2 Responsiveness and effort
For an analytic system to be of any real use, it must be able to respond quickly to fast-changing market events, and not consume valuable resources in doing so. The evidence would say this isn't happening. From our survey almost 70% of businesses spend more than 2 months on their annual planning process with over 3% spend more than 6 months.
Similarly, less than 21% of organisation's can produce a forecast within 3 days, while 40% take more than 7 days.
Assuming these processes generate accurate plans and forecasts (that is a big assumption if they are based entirely on internal data and subject to 'game playing'), this level of responsiveness means that organisations are unable to react to fast changing events. They are also, with annual planning, trying to 'guess' events 15-18 months out which is totally unrealistic in today's business environment. This is probably the reason why the No.1 quest for organisations (73%) is to reduce the time and effort required to create and monitor plans.
2.3 Use of tools
Our belief is that a major reason for a lack of responsiveness is the tools employed to help with planning and reporting. The survey shows (again unsurprisingly) that less than a third of businesses (28%) are making use of specialised planning tools whilst more than half still rely on spreadsheets (55%) for planning and analytics purposes.
It's been said before in many papers that spreadsheets are great personal productivity tools, but as corporate wide analytical systems they lack the essential capabilities (e.g. multi-dimensional analysis, automated selective viewing based on user role and security, alerts and exception reporting) to properly analyse data as well as control feedback to ongoing plans and forecasts.
2.4 Levels of Satisfaction
Overall, less than 5% of those surveyed were very satisfied with their planning and forecasting process, with almost 49% being very unsatisfied or unsatisfied. However, among the users of Specialised Planning Tools over 73% were very satisfied or satisfied, with just under 27% being unsatisfied or very unsatisfied.
2.5 Levels of Data Integration
Business analytics relies heavily on accurate, timely data. The majority of this data probably resides in existing operational systems such as the general ledger and customer relationship databases, and so the speed and ease as to how this data can be summarised and fed into the business analytic system are crucial to overall success. Only 4% or respondents, on average, enter less than 10% of data manually, while 43% enter more than 50% of their data manually.
2.6 Planning capabilities
For planning purposes, we advocate strongly the use of business drivers so that details can be generated automatically from a few key variables. This requires business systems that can model the different parts of the organisation, which are typically the preserve of specialised planning systems. Our survey shows that 33% do not derive data from business drivers, while only 26% generate more than 25% of their plans.
When it comes to forecasting, 28% are able to automatically generate forecasts from statistical trends. 35% say this capability is very important as it can greatly improve accuracy (or at last challenge assumptions made on predicted numbers) and can save a great deal of time and effort. However, 38% of organisations do not have access to these critical capabilities.
2.7 User access
The final area of note is in the use of communication technologies. We are all used to using multiple platforms when it comes to interacting with others. In electronic form, this includes things like email, web browsers, smart apps, and we use multiple connected devices according to our situation and what we are doing.
In this connected digital world, it's perhaps surprising that only 34% of users are able to directly access data and results via multiple methods e.g. the web, mobile, spreadsheet but without having to copy data. Perhaps this has more to do with perceived security breaches and yet we are quite happy to do online banking without a second thought.
For its 10th meeting, held in mid-March 2016, the London FP&A Board embarked on developing the FP&A Analytics Maturity Model to help organisation's assess their 'next steps' in utilising business analytics. It recognises that there are combinations of capabilities that are essential if business as to achieve success in supporting the organisation. After all, what's the value in having fast, responsive management systems if the data is unreliable or incomplete?
In determining this level of combinations, the following five stages of analytic maturity were identified.
3.1 Basic Stage of Maturity
This first stage is described by organisations having the following characteristics:
An example of organisation at this stage would be a business start-up, where the processes, models, systems and measures are not yet defined. It can also happen within a mature organisation, when it decides to spin off one of its operations.
3.2 Developing Stage of Maturity
This second stage is characterised by the following:
All organisations will pass this stage in the process of developing their FP&A framework. This is a necessary step of development. The desired outcome is for an organisation to progress into the defined and advanced stages of the development process.
Sometimes, an organisation can find themselves stuck in this “developing” state. It happens for reasons of poor management, dysfunctional business culture and inadequate investment in analytics. Such a stall can prevent an organisation from unlocking its full potential and competing in the modern world.
3.3 Defined Stage of Maturity
It is characterised by the following:
This Intermediate state of analytical transformation is characterised by relative stability: companies are able to stay in this stage for many years. The processes are stable, but not “best in class”, they are adequate for the traditional budgeting, planning and forecasting process. However, they are arguably highly inadequate for “new world” planning.
3.4 Advanced Stage of Maturity
'Best in class' modern planning processes reside at this stage, who typically have:
3.5 Leading Stage of Maturity
This final stage is the ultimate goal for which organisations should aim. Their FP&A analytics includes:
We took the data from the survey and mapped individual responses onto the FP&A Analytics Maturity Model (see Appendix II for the criteria used). We then analysed organisations within each maturity stage to see how satisfied they were with their planning and reporting processes. This produced the following results:
The detail behind this table has some interesting findings:
The results from the maturity model were a surprise for us. We took a look at the criteria used and wondered whether we should adjust them so that more organisations were at the ‘Advanced’ and ‘Leading’ stages. Part of the issue we found is that many excel at one discipline, eg annual planning, only to then slip back when it comes to forecasting or in their use of analytic tools such as scenario planning.
In the end we decided to keep the criteria as this combination provides the best set of capabilities whereby organisations can cope with today's unpredictable business world.
So the question to be asked is why organisations find themselves in such a lack of analytic maturity? Some of the reason is found in the answer to our question of ‘What holds an FP&A department back?’. Here, 45% of respondents cited a lack of investment, which is probably not helped by there not being ‘a compelling business case’ (22%).
The business case for analytic maturity needs to be made and communicated to senior management as a matter of urgency.
We also believe that Business Analytic software providers have a role to play by ensuring their systems are not used just to automate existing management reports. Using their expertise, vendors should introduce their customers to modern practices and challenge current assumptions on the measures used to plan and manage the business model.
According to our survey, most organisations plan to extend or improve their use of analytics. The main areas for development over the next 12 months are:
This result shows that despite the considerable investment in analytic technologies over the past decade, organisations are still trying to develop systems that meet their needs. Our experience leads us to believe that organisations should look closely at their management processes when investing in technology, rather than just implementing their old, quite often, failing processes.
In trying to understand this continued level of development, respondents cited the following reasons:
The first and third drivers of change have been the same for many, many years. Reducing time and effort in both planning and reporting allows organisations more time to consider alternative courses of action. To do this, modern analytic systems are essential as well as a critical view of the data used by management to make decisions. Both these factors are well known and yet the key issues that hold organisations back are shown as:
Something is going wrong. It seems that FP&A departments struggle to make the business case for modern systems along with a review of what management require and will bring the whole organisation great benefits. Or, maybe, these benefits have not been worked out or articulated in ways that senior management understand.
Analytical transformation is an ongoing process, which requires FP&A departments to increase their sophistication in the use of analytic tools. Ultimately, the analytical transformation is an integral part of the goal in making FP&A more proactive and more valuable to the business. Senior level sponsorship will be secured that much more easily if FP&A’s proponents can clearly demonstrate what it can provide – this could, for example, be via a series of static benchmarks or alternatively through a dynamic model.
Currently, the majority of organizations are stuck at the Basic and Developing stage, while a few are reaching the advanced stage for the best in class companies. Yet the challenges should not be underestimated – there is always the danger that instead of progressing from one stage of development to the next, the process can lose momentum and move backwards if the energy and proactivity are not maintained. FP&A now routinely uses predictive and prescriptive analytics but few have arrived at the stage where it also employs Big Data analytics.
In suggesting the 'next step' to help increase business analytic maturity we would recommend that using our criteria, map your organisation to see where it 'fits' within the maturity model. Then depending where you are, investigate taking the following actions to move up to the next level of maturity:
a) Review your existing use of business analytic models. Do they cover the key questions for the business, e.g.:
There may be other key questions for your organisations. Find out which areas are weak or non-existent.
b) Take a look at the characteristics of the ‘next’ stage.
c) Gain experience in using a modern BI/Analytics system
d) Discuss internally with colleagues/peers
e) Don’t delay ….
We hope you have found this white paper challenging as well as informative as to where your FP&A department should be heading.
Analytical transformation is an ongoing process. We will definitely see more transformation in the future: system implementations, restructuring of processes, re-defining and simplifications of the models, automation of the routine tasks and applying proactive advanced analytics.
At the leading stage, all planning processes will be fully integrated, allowing for the multidimensional advanced analytical process. This is where FP&A will be able to use big data analytics and fully transform organizational corporate performance management and decision-making process.
The survey was conducted internationally during the period July- August 2016. 268 Responses were received, with almost 93% were in a Finance role, 3% in Commercial roles, 2% in an IT role and 1 or 2 respondents with roles in Customer Services, Sales or Marketing.
Over 40% worked for an organisation with more than 500million USD in turnover, with just over 25% turning over 100-500millions, 23% 5-100million, and 11% of respondents turning over under 5million.
Almost 11% held a C-Level Role, just under 34% were a VP or Director, with 26% Senior Managers, 22% Managers and just over 7% as Executives.
The following survey criteria were used to position each organisation in terms of analytic maturity:
The Physics of Wall Street by James Owen Weatherall is an interesting story about the influence of physics in finance. FP&A practitioners can find material in this book to help them improve their work. For me three areas of the book serve as meaningful reference material.
The first area of the book that serves as meaningful reference material is a chapter dedicated to Benoit Mandelbrot. Even though Benoit Mandelbrot was a mathematician rather than a physicist the book dedicated a chapter to his work studying coastlines and cotton prices which provides meaningful reference material about alternatives to statistical concepts like the Bell curve.
The second area of the book that serves as a meaningful reference is a chapter dedicated to The Prediction Company. The work of this company provides meaningful reference material about the study of patterns that may appear chaotic but at some level contain a pattern that can predict outcomes.
The third area of the book that serves as meaningful reference material is a chapter dedicated to Didier Sornette. Didier Sornette is a physicist who identifies situations that lead to significant effects, positive and negative, on wealth. It is Didier Sornette’s work that is most appealing to me as an FP&A practitioner.
Financial planning is thinking about how an organization earns revenues, incurs expenses, and generates cash flows. The most challenging part of this process is determining how revenues will move. One determination is revenues will move forward through a steady growth rate, e.g. an increase of 10% per year over a 5 year period. What the work of Didier Sornette presents is a way to think about movement. Perhaps the revenue is based on a product that has characteristics of a fad. If a product has these characteristics, the movement will not be at a steady rate. As a result, thinking should be engaged on qualitative elements. Active engagement into the qualitative elements of a product or service can determine whether the financial plan is based on a product or service that may be the next big thing.
Financial analysis is learning about how an organization earn revenues, incurs expenses, and generates cash flows. What the work of Didier Sornette presents is a way to think about risk. People think about risk as the probability of financial loss but people should think about risk as an exposure to danger. During the growth of dot.com companies, telecommunication companies like Lucent had a customer base filled with these companies. After the collapse of these companies, Lucent and others like them face financial challenges. Perhaps learning about the types of customers can lead to learning about the risk that they have. The risk that they have is a financial problem in the form of revenue declines; a financial problem like this is parallel to the work of Didier Sornette in non-financial areas described in the book such as Kevlar and earthquakes.
An important quality of FP&A practitioners is a commitment to continuous improvement. A commitment to continuous improvement is through applying ideas from reference material. The Physics of Wall Street is an example of reference material that can stimulate ideas for FP&A practitioners to make a commitment to continuous improvement.
By Thomas Lundell, Director FP&A at NetApp
For quite some time CFOs and the finance community have been talking about transforming the finance function, becoming better business partners and focusing on the value-add, strategic activities.
At the core of that transformation is FP&A, as activities like business planning, business unit strategy, investment allocation and predictive analytics become important to fulfil finance’s new, expanded position within the company.
But to do that, FP&A itself must transform and change from its traditional flux analysis and flash reports to a much more modern and cutting edge department within the company. When starting to embark on your own FP&A Transformation journey, there are a couple of principles you need to keep in mind.
First, there isn’t a one-size fit all optimal FP&A operating model that all companies in all industries should implement. Personally, I have worked in finance in FMCG, Consumer Durables and Silicon Valley IT. It would not be possible to run the same finance operating model in all those companies at the various life-cycle and maturity stages there were in. The industries and companies were at very different levels of maturity, complexity, growth, and the technological capabilities were very different. So you need to make a proper assessment and decide on a model that is right for your specific company.
Second, you need to articulate the future operating model (see figure 1), what your new FP&A department will look like in a future state and what your key focus areas will be. If you only articulate what FP&A is not going to be doing, (for instance no flash reports, accruals, or expense reclasses) your transformation will be materially slowed down or impossible. Your team may even fear that they are losing their jobs and will actively work to resist your transformation efforts. Transforming FP&A needs to be a positive thing that will allow FP&A to take on an even more strategic role within your company.
Figure 1: FP&A Operating Models
Third, you need to work with a phased and structure approach. Transformation is difficult, time-consuming and not without risk. Your FP&A staff is very busy and working very long hours already. They are buried in basic duties and don’t have the time to undertake major transformations. Furthermore, there is operational risk involved when transforming FP&A. If you do it wrong, you could even inhibit your company’s ability to close the books timely and accurately. So you have to approach transformation in a structured way and use a phased approached to balance the workload of your team and mitigate the operational risk of the company.
Lastly, the optimal operating model evolves continuously over time, which means that finance leadership constantly must evaluate the current position and continuously transform their departments. Technology changes, your industry changes, your growth rate changes, your business complexity changes, and you need to continuously evolve and adjust your operating model to those changes. Transformation is an infinite game, not a finite game. What is cutting edge today, is probably old-fashioned tomorrow.
By Michael Royall, author
Interview with founder of the International FP&A Board Larysa Melnychuk
The global trend for FP&A is surprisingly singular; apparently, we all want to know how to influence and predict our financial future. At the International FP&A Boards, professionals from all over are eagerly sharing and learning about how to get there.
Three-and-a-half years ago Larysa Melnychuk left her corporate job of 18 years working as an FP&A (Financial Planning and Analysis) professional for several different large companies in the UK. At the time, the discipline, involving—roughly speaking—the use of financial data to make fast decisions based on prediction, was rapidly developing.
When Melnychuk left, she remembered a question that had fascinated her before: how do the other companies do it? It was the seed that led to the International FP&A Board, a professional discussion and debate forum for senior finance practitioners. The Board is now well established in 10 cities and 9 countries in Europe, the Middle East and Africa. Soon, it will be expanding to North America and Australia.
Why is the Board growing so fast?
People are realizing that the classical style of management accounting and financial planning and forecasting simply isn’t enough anymore. When I was a practitioner myself, we were always working on tight deadlines. But at some point, you just need to stop and look at your company from the outside—think about how others might be doing it.
That’s the main reason a lot of prominent CFO’s and Finance Directors attend our boards: to understand what’s happening around the globe. The Boards are exclusive to CFO and FD-level practitioners. We share diagnostic content and case studies, analyze the latest trends and discuss best practices. Contributing to this global exchange of knowledge really appeals to a lot of people.
Our Boards offer a non-commercial and vendor-agnostic environment to do so.
And it seems to be working?
The first Boards were organized in London. That’s where we developed the FP&A Analytics Maturity Model, Rolling forecast and the FP&A Business Partnering Models. They all received great feedback in the global FP&A community. Soon people were joining in the London boardroom through Skype or flying in from the Middle East and all over the place.
That’s when we decided to come to them. Our first International Board was held in May of last year in Stockholm. Now we're on three continents and the exposure and support we're getting are fantastic.
In the Benelux, the events are complimentary to our members thanks to the partnerships we’ve developed. One of those partners is Tagetik, a company that currently sponsors many of the international Boards, namely in the Benelux, Switzerland, Germany, UK and Sweden. Tagetik will also sponsor the launch of New York FP&A Board on the 6th of April 2017.
The Association for Financial Professional (AFP) is our educational partner. They provide the first FP&A certification in the world. We’re provided with the beautiful boardrooms we meet in by the Page Group, the global recruitment and consulting company we’re partnering with.
You've only just had a Board in Brussels and Amsterdam, how's FP&A developing in the Benelux?
Each country has its own dynamic. Even when we discuss the same case study, the results are different. During the Benelux meetings, we tried to lay the foundation for changing the FP&A Analytics Maturity Model into an operational model. It was incredibly inspiring.
Thomas Lundell was present at both meetings. He’s the FP&A director of NetApp and held a presentation about the big FP&A transformation his company is going through right now. Thomas was asked questions long after we'd finished. You could see people’s inner child being released, they were so curious.
What's interesting about the NetApp case study is that it shows that there's no single 'right way' for making the transition to a more predictive, real-time FP&A — each organization is unique, even though we broadly face many of the same challenges.
What would you say those challenges are?
We’re definitely living in the Big-Data world already. Still, there is an inability to use this data for forecasting and planning—or to distinguish key business drivers. Huge amounts of time are spent by financial analysts on cleaning data, trying to find the logic, consolidating the result. This means less space for creative, predictive analytics.
At the same time, many organizations still have not-so-consolidated processes, very dispersed and disconnected. In reality, this should be a company-wide initiative, integrated and collaborative with a simple planning process that’s easy to manage.
In terms of technology, from spreadsheets and markers, we're moving towards integrated and collaborative planning platforms, where different kinds of planning processes are harmonized.
Isn't there software to help with that?
Of course, but before you introduce the new FP&A systems you have to understand the architecture of your model. Not enough companies have predictive models that are based on key drivers that allow you to react fast enough to developments. Historically there are a lot of partially data-driven businesses — very complex and very static models — and we still continue to have that today.
We need to harness the ability the power of modern technology. Excel is still dominating the world, and because of its ease and flexibility, everyone likes it. However, it has a lot of shortcomings and limitations. As I always say, it's good to have Excel, but it's not enough.
So what's next for FP&A?
If a driver-based model is implemented in a good system properly, then organizations can have incredible results—including an agile and flexible decision-making process. But don’t forget the people. FP&A business partnering and a good communication flow are essential for those results. The right talent is increasingly hard to find. We need people to crunch incredible amounts of data, yet with the ability to communicate with decision makers.
Eventually, companies should be able to predict in real time in order to react to current changes. Leading organizations are already moving towards this. They’re utilizing best practices and trends for their analytics, as well as their forecasting and decision-making processes.
Copyright, ©2018 fpa-trends.com. All rights reserved.