If you are looking for ways to remove uncertainty about the future, you have come to the wrong place.

But, in case you seek certainty about the quality of your business decisions, Dynamic business planning might be just what you need.

Spending two minutes on the video below will give you a good idea what we are talking about.

We believe that good decisions make the best possible use of the data, knowledge, and technology available at the time the decision is made.

We observe that companies have room for improvements in these areas:

1. Data is plentiful. In many cases this causes information overload, instead of supporting decision-making.
2. Deep and diverse knowledge in organisations is often unused in planning.
3. Compared to the abundance of technologies for analysing the past, companies struggle to find tools for studying their possible futures.

Any given company has just one history and it will get only one future. There is a big difference, however: While the past is given, and nothing can be done about it, we influence the future through our decisions.

Dynaplan brings the concept of scenario planning and uncertainty analysis to a new level by allowing companies to “experience” the future consequences of different business decisions under varying conditions.

Although traditional scenario planning is useful, it falls way behind in speed and quality compared to simulation-based scenario planning. It takes only seconds to produce and simulate new high-quality scenarios to test out new ideas or assess the impact of new information.

Dynamic business simulation (see info box below) also unleashes resources that are greatly underutilized in many companies: The knowledge of people gets embedded in the simulation model. The creativity of people comes into play when testing out ways to leverage opportunities and address risks uncovered through the business simulator experience.

When the unexpected happens, data-driven analyses and forecasting can become very unreliable. Dynamic business simulation, on the other hand, relies mainly on structural information – which is significantly more stable than data. The speed and efficiency of dynamic business simulation enables companies to analyse new situations faster and derive adequate measures long before real data eventually becomes available.

Driver-based simulations take managerial accounting to the next level

Increasing demands on planning processes also transform the role of managerial accountants. On the one hand, traditional tasks like data acquisition and process execution are increasingly automated. On the other hand, managerial accountants are more and more expected to be business advisors that look ahead, identify challenges themselves, and discuss solutions with senior management at eye level.

How can the efficiency and effectiveness of traditional planning processes be improved to account for an ever-changing uncertain business environment, and to support managerial accountants in their new role as business advisors?

Methods that extrapolate trends based on statistical analysis, like time series decomposition and exponential smoothing, allow for quick updates of existing plans. However, they only consider historical data, and are unsuitable for including information about future developments and structural shifts. Methods based on artificial intelligence, like learning algorithms and neural networks, can use almost any kind of data to derive self-adapting accurate forecasts. However, these methods require training data and are consequently also struggling with structural shifts. In addition, they tend to be a black box whose output is hard to explain.

Driver-based planning models use quantitative as well as qualitative information to connect major business drivers to financial key performance indicators (KPIs) by means of cause-and-effect chains. The resulting model serves as a communication platform, making it possible to trace financial results back to their business cause. This comprehensible glass box approach enables managerial accountants to derive and discuss promising business measures together with senior management.

The cause-and-effect structure is the basis for the actual data input. Independent of the availability of input data, structural shifts can be explicitly accounted for. In addition, the simulation of multiple scenarios improves the understanding of key dynamics and the sensitivity of results to input changes. Hence, driver-based simulation models are well-equipped to deal with uncertainties. Using them can greatly increase the effectiveness of your planning process.

Driver-based simulation models in managerial accounting focus on improving the financial performance by deriving robust measures that relate to the underlying business model. Consequently, it is less important to explain every detail of the result. Instead, the level of detail is reduced by considering only the essential business drivers and their relation to financial KPIs. Studies have shown that this approach improves the speed and transparency of forecasts without reducing their accuracy. Hence, driver-based simulations can greatly increase the efficiency of your planning process.

How Dynaplan Smia lets you get the most out of driver-based simulation models

Dynaplan Smia contains a powerful simulation engine that can build dynamic driver-based simulation models. Learn below how Smia’s unique features can support planning processes.

1. Smia can consider time delays, feedback loops and non-linearities. This enables you to create a realistic model of your business and directly link it to your financial KPIs.

   

   Smia can consider time delays, feedback loops and non-linearities. This enables you to create a realistic model of your business and directly link it to your financial KPIs.

2. Similar to setting up a web page, you can create your own dashboards in Smia, using your own navigation structure, texts, charts, tables, pictures, and colours.

   

   Similar to setting up a web page, you can create your own dashboards in Smia, using your own navigation structure, texts, charts, tables, pictures, and colours.

3. The model structure uses an overarching measurement unit and metadata concept that allows you to assign the measurement unit and relevant dimensions to each variable. Smia supports you by automatically pointing out unit errors and dimensional inconsistencies between variables. Another advantage is that measurement units and metadata can be adjusted in one place, automatically updating all variables that use them.

4. When you have set up the model and defined the metadata, you can start inputting your planning data. To make this process as efficient as possible, you can import data directly from SAP BW, MS Excel, CSV files, and other sources via Smia’s certified interfaces.

5. The model can then be used to prepare for uncertainties and derive robust plans. Smia’s built-in scenario manager enables you to quickly set up, change, and compare several scenarios. In addition, Smia’s uncertainty wizard supports you in simulating a range or set of variable values. That is, you can easily transform your model into a probabilistic model that runs statistical analyses. In contrast to picking only one specific value per variable, this sort of bandwidth planning is especially useful when future variable values are highly uncertain.

   

   The model can then be used to prepare for uncertainties and derive robust plans. Smia’s built-in scenario manager enables you to quickly set up, change, and compare several scenarios. In addition, Smia’s uncertainty wizard supports you in simulating a range or set of variable values. That is, you can easily transform your model into a probabilistic model that runs statistical analyses. In contrast to picking only one specific value per variable, this sort of bandwidth planning is especially useful when future variable values are highly uncertain.

6. Dynaplan Smia models serve as boardroom-ready communication platforms that enable stakeholders to discuss and align their mental models. Different assumptions and decisions can be simulated in real-time, enhancing not only the quality of discussions between managerial accounts and senior management, but, most importantly, the quality of the derived plan.