RPA with Dynamics 365 Finance & Supply Chain

Are you looking for ways to automate some of your routine accounting functions, like reconciling sub ledgers to the general ledger?   Microsoft has recently created some examples using Power Automate Desktop (think of it like Task Recording on steroids). It can not only automate steps through Dynamics 365, but it can also automate Excel and other applications. In one example from Microsoft they automate the AR to GL reconciliation: Automatic running of the AR aging and the customer/ledger reconciliation reports. Power Automate then extracts data from both reports in Excel and compares the balances. Power Automate then posts a Team message letting you know the subledger is in balance. Power Automate saves the reports with the date in the file name, so there is backup. Microsoft Directions & Example   I working with the example, I needed to make a couple of adjustments to the sample flow that was provided but I was able to get it to work. Here is a video of the flow running in

COVID 19 Dashboard – Article 4 – Reporting based on population

I will continue to build on my earlier posts about my COVID19 Dashboard.  In this series, I am writing a few different blog articles describing not only how I built it, but some things I learned in the process.
The current list of planned topics include:
PART 4 - Reporting based on population

In looking at the data, I quickly realized simply looking at total numbers or even daily changes was only giving me a limited view of what was happening.   Were the large number of cases in New York simply because they have more people than most other states, or were they growing at a different rate.  Are New York citizens at a higher risk of acquiring the virus than California citizens.  It became evident that I needed to look at the data based on the population. 

Step 1 – Make sure I had the population data

I needed to make sure I had the population data at the levels I needed it.  The data I began with had the population detail for each US County, but I did not have Global population numbers.  I did some quick research to find the latest country population numbers at https://www.worldometers.info/world-population/population-by-country/.  Since this data does not change very often, I did not link Power BI to the source data.  Instead, I simply used Copy and Paste to create a new table in my data model. 

I did run into a few issues when I brought in this information.  Since I only had Country Name and Population, I was going to build the relationship to my country table on country name.  However, some of the countries were spelled differently or they were showing as a province of another country.  I used the transformation tools of Power Query, to clean the data up as much as possible.  Once the data was clean, I brought may population table into my data model and related it to my country table.

Step 2 – Create the need measures

This was actually a pretty straight forward step.  I identified that I needed four different measures related to population.  I was interested in looking at both total cases and total deaths related to the population, and I wanted to view it two different ways.
  • As a % of the population
  • Per 100k people

The four measures I created were simply division
  • Total Cases / Population Total Case % of Population = DIVIDE([Total Cases],sum('US State Province'[Population]),0)
  • Total Deaths / Population Total Death % of Population = DIVIDE([Total Deaths],sum('US State Province'[Population]),0)
  • (Total Cases / Population) * 100,000 Total Cases per 100k = DIVIDE([Total Cases],sum('US State Province'[Population]),0)*100000
  • (Total Deaths / Population) * 100,000 Total Deaths per 100k = DIVIDE([Total Deaths],sum('US State Province'[Population]),0)*100000

Step 3 – Create my desired visualizations

A simple matrix with conditional formatting on the measures
A scatter chart looking at the relationship between cases and deaths

With these measure and visualizations, I was able to quickly confirm that most states have very similar numbers.  However, there are a few states (New York, New Jersey and Louisiana) that appear to be outliers.

Step 4 – Compare total cases with total cases per 100k

In looking at what I had discovered so far, I decided it would be good to compare the total case numbers with total cases per 100k population.  To do this I decided to rank the states based on total number of cases and total number of cases per 100k.  This required using the RANKX function.
  • Rank total cases = Rankx(ALL('US State Province'[State]),[Total Cases]) 
  • Rank total cases per 100k = Rankx(ALL('US State Province'[State]),[Total Cases per 100k])

Since I am using RANK in the table, I need to make sure it ranks each state against all of the other states, so instead of simply entering the table, I included ALL(‘table’[rank column])

This quickly showed me that not only is New York #1 in total cases, the are #1 in total cases per 100k.  However, California is #6 in total cases, but #32 in cases per 100k

This certainly puts a different perspective on the numbers.

In my next article, I will look at some of the mapping abilities.


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