Powerful Dynamics GP Field Service Reporting with Power BI

On August 23, 2017, Microsoft announced the release of the Power BI solution template for Microsoft Dynamics 365 for Field Service. The template includes a set of reports designed for field service professionals. The solution template offers a very fast guided experience to create compelling reports on an extensible, scalable, and secure architecture and can be customized as needed. This means that instead of spending your time on plumbing, you can instead spend it on extending and customizing the solution template to meet your organization’s needs.

In this post, we will create the same report functionality inside of Power BI using Dynamics GP Field Service’s service call information. We first need to get the information from the following Field Service tables:

  • SVC00100 – Technician Master
  • SVC00200 – Service Call Master
  • SVC00201 – Service Call Analysis Codes
  • SVC00203 – Service Call Line Detail
  • SVC00901 – Cause Codes Validation
  • SVC00906 – Warranty Validation Codes
  • SVC00907 – Problem Codes Validation
  • SVC00908 – Repair Codes Validation
  • SY03900 – Record Notes Master

Almost all the tables are a straight import of the data except for the Service Call Analysis Code. This table contains Equipment Item Number, Problem Code, Repair Code and three note indexes with the Service Techs’ notes related to the fix. For this data, I created the following view:

                    ALTER View [dbo].[ServiceCallEquipment]


                    SELECT [SRVRECTYPE]









                    ,coalesce(a.TXTFIELD, ”) as [Note_1]

                    ,coalesce(b.TXTFIELD, ”) as [Note_2]

                    ,coalesce(c.TXTFIELD, ”) as [Note_3]

                    FROM [dbo].[SVC00201]

                    LEFT OUTER JOIN dbo.SY03900 a on SVC00201.Note_Index_1 = a.NOTEINDX

                    LEFT OUTER JOIN dbo.SY03900 b on SVC00201.Note_Index_2 = b.NOTEINDX

Now that we have determined our Service Call data points and imported the data into Power BI, we need to create our table relationships. The final table relationship model should look like this:

Power BI Service Call Table Relationships

With our relationships established, we now need to create our measures to be used in our reports. Here are the DAX measures I’ll be creating:

  • Completed Service Calls

            Completed Service Calls = CALCULATE(COUNTROWS(ServiceCalls),FILTER(ServiceCalls,ServiceCalls[SRVSTAT]=”70C”))

  • Open Service Calls

            Open Service Calls = CALCULATE(COUNTROWS(ServiceCalls),FILTER(ServiceCalls,ServiceCalls[SRVSTAT]<>”70C”),ServiceCalls[SRVSTAT]<>”OOC”)

  • Total Completion Time (in Minutes)

            Total Completion Time = SUMX(FILTER(ServiceCalls, ServiceCalls[SRVSTAT]=”70C”),[Completion Time])

  • Avg Completion Time per Order (in Minutes)

            Avg Completion Time per Order = DIVIDE( [Total Completion Time],[Completed Service Calls],0)

  • Equip Service Call Count

            Equip Service Call Count = COUNTROWS(ServiceCallEquipment)

Analyzing Open Service Calls

Now it’s time to create our reports. The first report we are going to create is for our Service Calls or Open Work Orders. For this report, we will use the following charts:

  • Active Work Order – Using a Card visual and Open Service Calls measure.
  • Work Orders by Type – Using a Donut Chart and Open Service Calls measure and Service Type.
  • Work Orders by Status – Using a Donut Chart and Open Service Calls measure and Service Status.
  • Open Work Orders by Resource – Using a Stacked Bar Chart and Open Service Calls measure in the Values section and Service Tech and Customer Name in the Axis section of the chart.
  • The final visual on the report is a matrix to see our open work order details associated with each chart above when click on them to cross filter.

Here is what the Open Work Orders report looks like:

Power BI Service Call Open Orders

Analyzing Completed Work Orders

Time to analyze our completed service call/work orders. For this report, we will use the following charts:

  • Completed Work Order – Using a Card visual and Completed Service Calls measure.
  • Avg Completion Time (in Mins) – Using a Card visual and Completed Service Calls measure.
  • Avg Completion Time per Order by Service Type – Using a Stacked Column Chart and Completed Service Calls measure and Service Type.
  • Avg Completion Time per Order by Service Tech – Using a Stacked Column Chart and Completed Service Calls measure and Service Tech ID.
  • The final visual on the report is a matrix to see our open work order details associated with each chart above when click on them to cross filter.

Here is what the Completed Work Orders report looks like:

Power BI Completed Orders

Analyzing Equipment Repairs

Time to analyze our completed service call/work orders. For this report, we will use the following charts:

  • Equipment Service Call count by Problem Code – Using a Stacked Bar Chart and Equip Service Call Count measure in the values section and Problem Code and Customer Name Axis section.
  • Equipment Service Call count by Repair Code – Using a Stacked Bar Chart and Equip Service Call Count measure in the values section and Repair Code and Customer Name Axis section.
  • The final visual on the report is a matrix to see our open work order details associated with each chart above when click on them to cross filter.

Here is what the Completed Work Orders report looks like:

Power BI Equipment Repair analysis


Stay tuned for the future Power BI developments and an overview of their functionality and for help with building reports like this.


Enhancing our Inventory Reorder Point Analysis in Power BI

In my original Inventory Reorder Analysis report, I’m using some the technics discussed by Belinda Allen in her Inventory Item Reorder Dashboard. I converted it to Power BI and added a little extra by also analyzing SOP product sales data and computing “Inventory Logic” based on whether the “Inventory Sold in Time Period” columns total is three times the current “Qty Available” producing a report that looked like the screen shot below.

Original Inventory Re-Order Analysis

Enhancing the Inventory Reorder Analysis

A good Inventory Reorder analysis includes the recalculations to forecast future demand and safety stock find your reorder point. For the sales quantity forecasts for my lead time demand, I’m going to use the DAX time series discussed here by Fountain Analytics. After following time series instructions my sales forecast results looked like this.

Sales Quantity Usage Forecast

With the Sales forecast quantity usage in place, I then needed to calculate our Safety Stock based on the below formula.

Safety Stock Formula

Using the data from the Procurement Analysis and the Sales Analysis reports, I calculated the four data points with the below DAX formulas.

Max Days Consumption =


VALUES ( Dates[Date] ),


VALUES( ‘Products'[ItemNumber] ),

‘Sales Measures'[Units Sold]


Max Days Inventory Lead Time =


VALUES ( Dates[Date] ),


VALUES( ‘Products'[ItemNumber] ),

[Lead Time]


Average Days Consumption =


VALUES ( Dates[Date] ),


VALUES( ‘Products'[ItemNumber] ),

‘Sales Measures'[Units Sold]


Average Days Inventory Lead Time =


VALUES ( Dates[Date] ),


VALUES( ‘Products'[ItemNumber] ),

[Lead Time]


You put it all together to your Reorder Point – (Lead time demand) + (safety stock) = Reorder Point. The end result is the updated Inventory Reorder Analysis report shown below.

Update Inventory Re-Order Point Analysis

Updated Template

You can download the updated template here. In the template, I also updated the Inventory Reorder Point report using the New Matrix functionality.

Stay tuned for the future developments of the reports and an overview of their functionality and for help with building reports like this.

Power BI, R and Marketing Campaign Analysis

For the second post in a row, I’m going to talk about casino performance analysis so I’m feeling a little like Marty McFly from Back to the Future. One of the biggest struggles we had was with our direct mail offers to our players’ club members. A proper direct mail offer campaign would be setup with a control group so we could perform analysis against the control group, but try to tell that to the A tier player that didn’t get an offer or a different amount then the other A tier players.

Without the control group, we would calculate the difference between the average coin in before and after the direct mail offer was received to judge our incremental revenue and evaluate whether the campaign was successful. This method was very simplistic because it assumed all differences between performance before and after the interaction are due strictly to that direct mail offer. Google set out to help digital marketers with this problem of no control group in 2014 and we can use their Causal Impact R package to analyze our casino’s direct mail offers as well.

Putting it All to Use

Using the same daily Coin in (gross sales) data from the last blog on “Use Power BI and R to Quickly Identify Business Insights”, we need to complete the following steps:

  1. Install the following R packages
    1. install.packages(“devtools”)
    2. install.packages(“BoomSpikeSlab”)
    3. install.packages(“dtw”)
    4. install.packages(“chron”)
    5. devtools::install_github(“google/CausalImpact”)
  2. In Power BI Desktop, select the “R” in the Visualizations section and then drag the Denomination, Date and Revenue fields from the Coin In tables into the Values section to build our dataset for are R code.

Power BI Desktop

3. Add the following code in the R Script editor



productsM <- melt(dataset, id=1:2)

productsC <- dcast(productsM, Date ~ Denomination + variable, sum)

colnames(productsC) <- make.names(colnames(productsC))

products <- zoo(productsC[,2:ncol(productsC)],as.Date(productsC[,1]))

pre.period <- as.Date(c(“2008-01-01”, “2008-10-03”))

post.period <- as.Date(c(“2008-10-04”, “2008-11-15”))

impact <- CausalImpact(products, pre.period, post.period, model.args=list(nseasons=7))



4. Click the run button to execute the R script

Power BI R Script

Understanding the Results

The Casual Impact R package reduces the below three graphs

Original: Solid, black line is observed data before the intervention and dotted, blue line is the predicted coin in values for what would have occurred without the intervention

Pointwise: The net difference between the observed and predicted response on the original scale, or the difference between the solid, black line and the dotted, blue line on the original graph.

Cumulative: Dotted, blue line is the individual causal effects added up in time, day after day.

For all three graphs, the light blue shaded area represents the results in a 95% confidence level. The farther that the graph extends past the beginning of the intervention, the less certain of the causal effect; hence, the larger the shaded area.Power BI R visualization


Hope this helps you with your marketing campaign analysis in your organization.

Use Power BI and R to Quickly Identify Business Insights

In statistics, the correlation coefficient or Pearson’s correlation is a measure the strength and direction of association that exists between two continuous variables. The value of correlation, r, is always between +1 and –1. To interpret its value, see which of the following values your correlation r is closest to:

  • Exactly 1. A perfect downhill (negative) linear relationship
  • 0.70. A strong downhill (negative) linear relationship
  • 0.50. A moderate downhill (negative) relationship
  • 0.30. A weak downhill (negative) linear relationship
  • 0. No linear relationship
  • +0.30. A weak uphill (positive) linear relationship
  • +0.50. A moderate uphill (positive) relationship
  • +0.70. A strong uphill (positive) linear relationship
  • Exactly +1. A perfect uphill (positive) linear relationship

Don’t make the mistake of thinking that a correlation of –1 is a bad thing, indicating no relationship. Just the opposite is true! A correlation of –1 means the data are lined up in a perfect straight line, the strongest negative linear relationship you can get. The “–” (minus) sign just happens to indicate a negative relationship, a downhill line. Most statisticians like to see correlations beyond at least +0.5 or –0.5 before drawing any conclusions. Additional information on Pearson’s correlation can be found here.

Putting it All to Use

This is all great knowledge, but how can we apply this in a business environment. When I worked for a casino, we kept a daily record of Coin In (gross sales), High Temp, Low Temp and Fuel price in an Excel spreadsheet and tried to find a correlation between these data points. Try find the correlation with this data.

Excel Casino data

With Power BI and R, we can make this association all that much faster and with slicer for interactivity to drill into the data points. I first downloaded the R Correlation Plot custom visual located here and then Excel spreadsheet from above imported into Power BI. I was then able easily identify the correlations in a matter of minutes.

Positive correlations are identified with increasingly dark blue circles, negative correlation is red. The greater the bigger and darker the circle. Selecting “Nov” from the calendar slicer, you will notice that the intersection between “Low Temp” has a positive correlation (blue circle), so the lower the temperature the less you will realize in coin in for the that month.

Power BI and R Correlation Plot

Selecting the “Jul” from the calendar slicer, and notice that the correlation is different.

In this case, the correlation is zero between temperature and coin in.

Power BI and R Correlation Plot image 2


Hope this helps you quickly identify business insights in your organization.

Supercharge Your Dynamics GP Dashboards & Reports with Power BI

During my last blog post, I walked you through creating a financial dashboard using Power BI with Dynamics GP data. I have created additional analysis and measures now.


If you look closely at the bottom left, you will see two additional reports for Sales Summary and Customer Analysis.

Sales Summary

The data used from Dynamics GP for this report are the Sales Order Processing (SOP) tables, Inventory, (IV) and Customer (RM) tables. The Sales Summary contains the same report filters, Year, Month and Company as the Financial Summary report. The top left visual shows the total sales by product class ranked in descending order. The bottom left visual shows the total sales by customer class/sales channel. The top two visuals in the middle and right show the total sales and transactions based on the filters selected with a seven-day moving average behind them. The bottom right visual shows a comparative between cumulative sales and cumulative costs.


Customer Analysis

The Customer Analysis report allows you to dig into Dynamics GP sales data by Customer, Year, Month and Company. By selecting a customer on the left you get to see three years of comparative sales, sales detail, last sales date and amount, total sales, sales last year, totals profits and percent sales growth for that customer.


Future Dashboard developments

In the coming months, I plan additional Power BI reports associated with my Dynamics GP data. These reports will include:

  • Vendor Analysis
  • Inventory Analysis
  • Field Service – Contract and Service Call Analysis

Stay tuned for the future developments of the reports and an overview of their functionality.

WOW! A Dynamics GP multi-company Financial Dashboard – Part Deux

Back in October of 2015, I posted a blog about how to modify Jared Hall’s awesome Excel Financial Dashboard that you can download here. The problem with his solutions is that most Dynamics GP environments are multi-company setups making these dashboards not very functional without navigating and opening multiple Excel spreadsheets to view each company’s dashboard. So, I took it upon myself to modify the one provided by Jared Hall to work in a multi-company Dynamics GP setup, but that was so 2015!

This is 2017, so let’s create our Financial Dashboard in Power BI. The best part about using Power BI over Excel is that we can make is available through the Power BI service and via the iOS and Android Apps. Making it a much better option for mobility and availability.

The Parts to the Solution

For my solution, I choose to use the following tools:

  • A Small DataMart
  • Power BI Designer
  • Power BI Service

The DataMart

I chose to implement a DataMart to simplify the importing of the tables into the Power BI Designer. I created the following tables to implement my Financial Dashboard:

  • DimAccount
  • DimHeader – to summarize the report layout
  • DimDate
  • DimCompany
  • FactFinance – General Ledger data from the GL20000 and GL30000 tables of each company

Dashboard Design

I imported the above tables into Power BI Designer rather than using the DirectQuery mode. Using the Import option will allow for the full functionality of DAX and more importantly the Time Intelligence functions of DAX. Your table relationships should look like the screen shot below after you import the data.


The DAX Measures

The Profit & Loss Statement layout is handled by several DAX measures, the DimHeader table and the Sign and Report Sign columns within the Account table. I created the following DAX measures in the order listed below:

  1. Header Order
  2. HeaderCalcType
  3. Dollars
  4. Dollars with Sign
  5. Dollars with Report Sign
  6. Running Dollars with Sign
  7. Current Period
  8. Cumulative Sales (Selected)

The code for the DAX measures can be downloaded here from the Finance Dashboard template.

Visualize This 

An easy to use modern multi-company cloud-based or mobile app Finance Dashboard that shows your critical data, so you can spot trends, share insights, and make smart decisions.



I’m loving the Power BI Service and all the modern cloud-based analytics that it can bring to your dashboards and reports.

Power BI Visuals in On Premise SQL Server Reporting Services

Yesterday, January 17, 2017, Microsoft released the Power BI reports in SQL Server Reporting Services (SSRS) technical preview. You can read all about it here. I downloaded the technical preview and installed it on my system and deployed a Power BI Visual to my SSRS web site. Here are the steps to get it installed on your system.

· Download the SSRS Technical Preview and Power BI Designer for the preview


· The Technical Preview has the following system requirements:

    • Your own VM or server (not one in production use)
    • Windows Server 2012 or later (or Windows 8 or later)
    • .NET Framework 4.5.2 or later
    • SQL Server Database Engine (2008 or later), to store the report server database
    • SQL Server Analysis Services (2012 SP1 CU4 or later), to store your data models

· I first installed the SQLServerReportingServices.exe on my system and accepted the licensing terms and agreement and then selected the “Install” button.


· After the installation completed I needed to configure the technical preview of SSRS by selecting the “Configure Report Server” button.


· I provided my SQL Server name to Connect the Reporting Services Configuration Manager.


**NOTE – While the configuration manager let, me configure the SSRS technical preview web site with my original VM’s computer name of ‘BARRYCROWEL38F0’ I could not publish by Power BI report with that computer name. I shortened the name to 7 characters and removed the numbers from the name to get the Power BI Designer Technical Preview to function with the SSRS web site.

· Design a Power BI visual with the Power BI Designer Technical Preview. In my visual, I connected to an SQL Server Analysis Services (SSAS) database. To publish you report to SSRS, select the ‘Save As’ menu option.

Power BI Technical Preview SSRS

· Following the screen prompts and supply the Reporting Server.

Power BI Technical Preview SSRS

· Then enter the report name.

Power BI technical preview SSRS

The final product is a Power BI visual with slicer functionality in an on premise SSRS web site.

**NOTE – the current SSRS technical preview does not work with custom visuals or R visuals.

Power BI On Premise


I’m loving the first on premise technical preview and can’t wait for what next.