Forecasting and Planning are central aspects of Corporate Management and Controlling activities. However, the respective implementation often lacks economic efficiency and effectiveness in equal measure.s-peers specifically meets this situation with a predictive approach which offers corporations an optimal and efficiently automated forecasting solution.
Most forecasting processes are very complex and/or influenced individually. In many corporations, forecasting and planning are generally regarded as “routine jobs” which consume a lot of time – and which prevent valuable employees from performing other important tasks.
However, a change seems to happen increasingly in this respect:
In many cases, our projects and customer meetings have shown that internal processes for forecasting and planning are scrutinized more and more frequently.
Our customers expect predictive models to improve their planning results and the planning processes proper in order to get more meaningful results in a quicker way. Existing processes are to be supported better and to be simplified in technical terms; the workload of planners is to be reduced. Simultaneously, the quality (accuracy and significance) of planning should be increased by identifying cause-effect relationships and by integrating these drivers into the planning models.
s-peers AG meets these requirements very specifically by developing appropriate forecasting and analysis models – especially with regard to Accounting and Controlling.
Experience has shown that predictive support is highly suitable for the financial sector: Here, there are well-established standard systems and processes which deliver a very high data quality. Typically, controllers are also very familiar with the target-oriented analysis of data: Their core competencies have always included planning, coordination and control of corporate goals – as well as the provision of decision-relevant information in accordance with the respective target groups.
The basic principle of our predictive solution approach: The traditional, merely reactive evaluation of historical data is replaced by the proactive and automated recognition of data patterns. On this basis, objective forecasts can then be derived with very high accuracy.
At the same time, predictions can be made quicker, more efficiently and therefore more frequently. Thus, also new requirements and influences can be integrated into questions in an ad-hoc way and/or beyond the regular planning. Among other things, this generates the decisive advantage that changed business developments can be re-evaluated promptly.
The methods developed by us analyze very large data quantities. This increases the quality and therefore the reliability of the forecasts. Based on the more precise and more frequent predictions, the management can intervene in a more specific manner and can initiate all necessary measures – a (pro-)active contribution to the achievement of corporate goals and to the identification of potential problems. Simultaneously, potentials for savings and cost optimizations can be exploited optimally. Predestined for this is e. g. the improvement of inventory levels in the retail sector.
Examples of other application areas with potential for predictive optimization:
- Material and Production Planning
- Human Resources (HR) Planning
- Delivery Times
- Price and Conditions Policy
- Assortment Decisions
However, our predictive approach does not only improve and accelerate your decisions on the basis of quantitative, differentiated insights: By analyzing cause-and-effect chains in a holistic way, it is also possible to take interrelationships and cross-functional dependencies between different corporate areas into better account.
Our approach for Predictive Controlling / Accounting is based on the standard procedure in the field of data analysis: A model is created on so-called training data (e. g. sales 2011–17) and then checked with the test data (e. g. sales 2018). The algorithm with the optimal model on the test dataset is then selected and validated with an independent dataset (e. g. sales Jan.–Jun. 2019). State-of-the-art algorithms are used to create the model. The KPI for the model evaluation is always selected in individual consultation with the respective customer.
Predictive models by s-peers achieve an accuracy of 95–99 %.
Scenario A: The results are calculated directly in the database (HANA) according to the model and are transferred into the SAP Analytics Cloud (SAC) for further planning. The calculated values can be adjusted manually and/or be simulated graphically in a Value Driver Tree (VDT).
Scenario B: The calculated results are imported into the consolidation solution (in this case SAP SEM-BCS) for the consolidation of rolling extrapolation and planning.
Scenario C: The forecast results are calculated directly in the SAP Analytics Cloud (SAC) and can then be used for other purposes (e. g. in SAP BW or HANA).
Predictive models by s-peers are universal: They can be used independently of existing analytical systems and/or system landscapes.
The automation of the respective process generates enormous efficiency potentials and supports the objectification of the forecast: Planning distortions due to individual “gut instincts” or political behavior are reduced maximally by using this methodology.
However, IT-supported and individually created forecasts can be a very good combination: Human experience and impulses (possibly not yet available in digital form) can be combined advantageously with calculated, highly precise forecast models. All in all, this results in a maximally realistic evaluation of the situation – and is therefore a sound basis for decisions. An example: One of our customers demonstrates the potential of this hybrid concept by combining the manual forecasts of his individual sales representatives successfully with IT-based versions in the “Demand Forecasting” area.
According to our experience, automated processes should always be implemented very sensitively in the organization. Because despite all technological possibilities and statistical procedures: The “human factor” is still highly relevant – the individual person validates the models continuously to detect new cause-effect relationships and also acts as the corrective for all irregular and/or disruptive developments.
Therefore it is all the more important to involve employees at an early stage and to generate trust sustainably: Automation should never appear as a kind of “magic”. For example, the actual process can be designed in such a way that when a corridor of permitted future values (defined in advance) is left, countermeasures are initiated which, in turn, lead to interactions by staff members.
An automated solution for forecasting and planning undoubtedly offers a great potential but also requires a certain amount of caution: An approach of this kind always depends on the quality of the respective database and also reaches its limits quickly in the case of structural breaks.
Predictive projects are now desired with increasing frequency. Simultaneously, however, there are often major deficits with regard to the clear definition and articulation of the associated issues. Many times, this results in a quite extensive but hardly goal-oriented occupation with currently “hot” topics like e. g. IoT, sensor technology or machine learning. Such technologies are undoubtedly promising for the future. However, most projects of this kind finally come to nothing because the basic prerequisites in terms of data (basis, quality, usability) and interfaces are lacking largely or completely.
This brings us back to the confidence in such new measures and approaches for Corporate Management. Thus you should initially drop any further or additional unknowns: Ideally, you start in an area which already has a very good database.
This is one of the reasons for our clear recommendation to establish your first added value in the Accounting area: Here, tried and tested standard systems already exist – increasingly in a single-circuit system. Central data storage in an integrated business suite (e. g. SAP S/4 HANA) offers a standardized basis. In addition, time-consuming reconciliations between the Finance and Controlling areas are no longer necessary because fragmentation is no longer required. For all analyses, an integrated and highly performant database is available in real time.
- Less effort for non-valuable activities
- More precise results and enhanced transparency thanks to increased information density
- Clarification of interrelationships and cross-functional dependencies (across a wide range of business areas) through disclosure of cause-effect chains
- Massive increase of response capacity due to automation – calendar orientation increasingly becomes obsolete
- Objective forecasts which give a fact-based view
- Multiple use and reusability of algorithms provided by s-peers
- Wide variety of evaluation options in assured quality
- Early possibility of performance evaluation via simulation
Corporate Management by means of Predictive Controlling / Accounting offers an enormous potential.
The use of scientific, statistical models in forecasting is basically not a new development. This method has long been a well-established standard for specific operational applications – and is also the very basis of the methods and algorithms applied by s-peers.
Success factors for the proper implementation of our predictive approach are:
- Quality and value of the data
- Correct use of the algorithms along with constant validation
- The optimal interaction of concept, data, methods, technologies and processes