Why Dynamics 365 Insights are a Big Deal – Part II, Financials

This is part two of a four part series on Microsoft Dynamics 365 embedded Insights.

Part I covered the following areas:

1. ERP Systems have been focused on entering transactions, process improvement and controls, database , and rudimentary reporting and drill down
2. Data transformation deals with ways in which data can be used to take advantage of internal and external information and the journey will help answers questions around – what/why/what will happen and what should your company do to both respond, anticipate and optimize changes (top and bottom line results).
3. Microsoft’s Dynamics 365 solutions are embedding visualizations and analytical services in several key areas where accurate forecasting can make a significant impact
4. Part I can be found here: Why Dynamics 365 Insights are a Big Deal – Part I

Enter Microsoft Dynamics 365 Insights.  The first examples are being seen in:

• Sales/Customer Relationship Management/Customer Engagement
• Financial Management
• Workforce Management
• Manufacturing/Supply Chain Management

Today, we’re going to investigate Financial Management Insights with a focus on:

• Customer Payment Performance
• Cash Flow Forecasting

Accurate Cash Flow forecasting can have an enormous impact on day to day operations. There are several moving parts with numerous sub-parts that make forecasting difficult.

Customer Payment is a primary feeder to cash flow and thus, vitally important to Working Capital  Management.  Customer payment forecasting is obviously dependent on customer payment performance (e.g.., % on-time, late, very late).  Accurate Customer Payment performance analysis and management helps to improve financial stability by automating and prioritizing specific alerts and communication to identify and resolve habitually late customer payments. And, until and after improvements are made, accurate forecasting helps by surfacing and addressing unforeseen outcomes and through decisions made because of confidence in the forecast.  See Figures 1, 2 and 3 for a Customer Payment views as well as some of the parameter configurability options. For example, the model below (Figure 1) enables risk tolerance through the following options: Conservative (worst case), Mid Point (expected/average), and Optimistic (Best case) in analyzing outcomes.

Figure 1 – Customer Payment Detail

Notice the performance filter indicated – Very Late – showing those invoices predicted to not be paid anywhere near on time. For the first invoice, it shows probability of being paid on time (27%) versus late and very late (40% and 33%, respectively). You’ll notice the Top Factors (causality) that were used to predict this and other relevant customer information.

Figure 2 – Using the Artificial Intelligence (AI) Builder to Refine

This introduces the no-code AI Builder which allows clients to adjust, refine, and assess changes to the model to optimize forecasting accuracy.

Figure 3 – Cash Position Forecast & Analysis

Notice that it’s easy to change risk using Customer Payment Scenarios – Conservative (low risk) to Mid-Point to Optimistic (higher risk).  Snapshots are used and saved to compare actuals and forecasts over time.  This is again looking more at cash in and cash balance position.

Figure 4 – Cash Flow Analysis (Actual versus Forecasted)

Similar to Figure 2 but now including Cash out

Figure 5 – Cash Flow Forecast (with actuals)

Summary view with the ability to change time periods and durations along with drill down to more detail.  This also includes other views (Cash Position, Snapshots, etc.) as well.

Other Comments:

Using this Microsoft Artificial Intelligence platform helps by simulating, saving, and comparing previous forecasts (actuals versus forecast) easily.  External data is added easily and the platform provides no-code tools to adjust the model and see the accuracy impact immediately to help the Financial Management team refine current and future forecasts models.

Risk tolerance is an important parameter/variable to consider since despite what might be forecast or the probability of something occurring, it can be completely offset by risk tolerance dictating certain financial decisions. This platform accounts for that.

Key Takeaways:

• As stated before, complex and sometimes disjointed processes are more difficult to forecast unless you have a platform (like Microsoft provides) that enables the aggregation of data and the tools to easily forecast, save, adjust and keep iterating to help the system learn, adapt and improve accuracy.
• While the ERP solution will provide much/most of the information, an application like this might require integrating Treasury and Capital Management solution data as well so your platform should support external data integration. Industry and company nuances require forecasting solutions to include simulations, historical comparisons, configuration changes and an iterative approach – in other words, don’t expect the solution to be “out of the box” perfect.
• This is a continuous puzzle with moving and changing parts; this is risk mitigation and six sigma process/forecast accuracy improvement rolled together with a significant quantifiable financial impact if managed well.

BrightPoint InfoTech understands this.  We are in the process of creating a new group that will be dedicated to Advanced Analytics, Artificial Intelligence (AI), Machine Learning and Application Solutions (e.g., IoT solutions).  We will be announcing more in the first quarter of 2022 but we are “all-in.”

For more on Finance Insights and availability, see: Finance insights is now generally available in Dynamics 365 Finance – Microsoft Dynamics 365 Blog

Part 3 of this series will take a closer look at how Insights impact Supply Chain performance and agility.

Images by Microsoft Corporation