Tags: SUMMARIZE

Determine cumulative interest payable on an annuity with varying time periods

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Imagine a fixed monthly amount due to an Organisation for services rendered to various customers.  While an invoice is raised every month by this Organisation, not all pay up the dues on time.  For unpaid dues, the Organisation charges its client interest ranging from 3% to 9% per annum.  The objective is to determine cumulative interest payable by various customers to Organisation X.

The base data looks like this

Client Monthly revenue Int. calculation start date Int. calculation end date Interest rate
Client A 33,967 01-Aug-16 25-Jul-17 9.00%
Client B 123 12-Sep-16 30-Nov-17 4.00%

Given the dataset above, the total interest payable by Client A is Rs. 16,237.20.  The calculation is shown below:

From To Days for which interest should be paid Principal Interest
02-Aug-16 31-Aug-16 328.00 33,967.00 2,745.26
01-Sep-16 30-Sep-16 298.00 33,967.00 2,494.17
01-Oct-16 31-Oct-16 267.00 33,967.00 2,234.71
01-Nov-16 30-Nov-16 237.00 33,967.00 1,983.62
01-Dec-16 31-Dec-16 206.00 33,967.00 1,724.16
01-Jan-17 31-Jan-17 175.00 33,967.00 1,464.70
01-Feb-17 28-Feb-17 147.00 33,967.00 1,230.34
01-Mar-17 31-Mar-17 116.00 33,967.00 970.88
01-Apr-17 30-Apr-17 86.00 33,967.00 719.79
01-May-17 31-May-17 55.00 33,967.00 460.33
01-Jun-17 30-Jun-17 25.00 33,967.00 209.24
01-Jul-17 25-Jul-17 - 33,967.00 -
Total       16,237.20

You may download my solution workbook with from here. I have solved this problem using normal Excel formulas and the PowerPivot.

Data slicing and analysis with the Power Pivot

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Visualise an MS Excel file with two worksheets:

  1. Employee headcount – a multi column dataset with information such as Employee code, Date of Joining, Age, Division, Department and Location.  Each row represents data for one employee.  The number of rows on this worksheet is approximately 700.
  2. Training Data - a multi column dataset with information such as Employee code, Training Date from, Training Date to, Training Program Name, Training Program Category (Internal and External), Training Location and Training Service Provider.  Each row represents one training attended by one employee.  The number of rows on this worksheet is approximately 2,600.

Let’s suppose that the training calendar of this company runs from July to June.  Some questions (only few mentioned for illustration purposes) which a Training Manager may need answers to are:

1)   How may unique employees were trained each year; and
a)   Of the unique employees trained, how many were first time trainees and how many were repeat trainees
i)   Of the first time trainees:
(1)    How many joined this year
(2)    How many joined in past years
ii)  Of the first time trainees:
(1)    How many were trained within the first year of joining
(2)    How many were trained in the second year of joining
(3)    How many were trained in the third year of joining
(4)    How many were trained after three years of joining
iii)  Of the repeat trainees:
(1)    What is the average gap (in days) between trainings
(2)    What is the minimum gap (in days) between trainings
(3)    What is the maximum gap (in days) between trainings

Getting answers to the questions mentioned above would entail writing a lot of lookup related formulas, applying filters, copying and pasting and then creating Pivot Tables.  While the example taken above is that of a training database, you may envision “drilling down to and slicing” any dataset – Marketing, Sales, Purchase etc.

You may watch a short video of my solution here

In these two workbooks, you will be able to see the level to which one can drill down and analyse data using the Power Pivot add-in.  When you open this workbook, please go the first worksheet and make the relevant choice of MS Excel version first so that you start looking at the Analysis from the correct worksheet.

1. Analysing Training data of a company; and
2. Analysing Sales data of a company

You will be able to see the analysis in these workbooks only if you are using one of the following versions of MS Office:

1. Excel 2013 Professional Plus; or
2. Excel 2010 with the Power Pivot add-in installed.  Power Pivot is a free add-in from Microsoft which can be downloaded from here.

Lastly, if you are using the Power Pivot add-in in Excel 2010, you will not be able to see the underlying Data Model or the calculated Field formulas because this workbook has been created in Excel 2013 Professional Plus and unfortunately the Power Pivot model is not backward compatible.  However, all the analysis performed in this workbook can be performed in Excel 2010 as well (with the Power Pivot add-in installed).

Computing penalties by Employee, Group and Labour type using a PowerPivot

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Assume a database of Maximum allowed pay and Actual pay for each employee.  The employees have been further categorized into Groups and labour categories.

The task is to create three Pivot Tables (one each with Employee name, Group and Labour category in the row labels) with the following information in the Value area section:

1. Actual salary
2. Maximum permissible salary
3. OverUnderMax - This is calculated as the difference between Actual Salary and Maximum permissible salary
4. Penalty - Maximum of 0 and OverUnderMax

As can be seen in the Pivot Table worksheets of this workbook, there is a problem with the result of the calculated field formula result in the Grand Total cell.

You may refer to my solution in the PowerPivot worksheets.  Please note that to see my result in the PowerPivot worksheets, you will have to use the PowerPivot add-in for MS Excel 2010/2013.

Count unique values with conditions on large databases

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Given a database of 50,000 rows, counting unique values with conditions using formulas would either adversely effect workbook performance or would not work in the first place at all.

In this workbook, I have shown the technique to count unique values with conditions on a large database

1. Using PowerPivot - Will only work in Excel 2010 and higher versions

2. Using a  normal Pivot Table and SUMPRODUCT() function - Will work for all versions but is not as efficient as the PowerPivot solution.

To count unique values with conditions on small databases, you may refer to the following link

Calculate a unique count with conditions in a Pivot Table

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Assume a three column table arranged as follows: Circle, Date of Fault and ID.  Dates in the date range span one week - November 26, 2012 to December 2, 2012.  A particular equipment can be only one specific Region and the same equipment an go faulty multiple times within one week.  Data for one week is about 8,400 rows.

There are three questions to be answered from this data:

1. The Circle wise, count of ID's which went faulty more than twice between November 26, 2012 and December 2, 2012; and
2. The Circle wise, count of faulty instances more than twice between November 26, 2012 and December 2, 2012; and
3. Determine individual sites for 1 and 2 above

The difference between 1 and 2 above is "If a certain ID goes down 4 times, then for question1, the answer should be 1.  For question2, the answer should be 4."

The first question basically boils down to "Count of unique ID's by Circle which went faulty more than twice."

There are two ways one can go about answering the questions above:

Solution A - For Excel 2010 and higher versions - This solution is for those using the PowerPivot MS Excel add-in for Excel 2010 and higher versions.

Solution B - For all versions of MS Excel - This solution will work in all versions of MS Excel but for those using Excel 2010 and higher versions, the PowerPivot solution would be far more efficient.

The steps for creating a pivot table under Solution B for answering both questions above are:

1.  Count of downtime sites.xlsx is saved in a folder on the desktop;
2. Open the workbook, select the data on the Base_Data sheet (including the first row as the header row – it will be range A1:C8741.  Ensure that the header row has some distinctive formatting such as Bold or some colour) and press Ctrl+F3 > New.  In the Name box, type Dummy and click on OK > Close.
3. To cross check that the name assigned above has indeed been assigned correctly, select the data range once again and in the Name box (left of the formula bar), Dummy should appear.
4. Select range A1:C8741 of the Base_Data sheet again and press Ctrl+T to convert this range into a Table.  Ensure that the “My Table has headers” box is checked.  Save the workbook.
5. Open a new worksheet and go to Data > From Other Sources > From Microsoft Query
6. Under Databases, select Excel files > OK
7. In the Directories dialog box, navigate to the folder on the desktop where the workbook file is saved.  So for me, it is saved under C:\Users\Ashish\Desktop\ and double click on the folder where the workbook is saved.
8. In the left hand side window, select the Count of downtime sites.xlsx file and click on OK
9. With Dummy selected, click on the > symbol to bring over all columns of this named range to the right hand side box 10. Click on Next three times
11. Select the option of View Data or Edit Query in Microsoft Query
12. Click on the SQL button, delete the contents in the white space there and paste the following SQL Query

SELECT ucase(dummy.Circle) AS 'Circle', ucase(dummy.Indus_Site_ID) AS 'Indus_site_ID', Count(dummy.Indus_Site_ID) AS 'fault_frequency'
FROM `C:\Users\Ashish\Desktop\Count of downtime sites.xlsx`.dummy dummy
GROUP BY ucase(dummy.Circle), ucase(dummy.Indus_Site_ID)
HAVING (Count(dummy.Indus_Site_ID)>2)

13. Click on OK and on the message box which appears, click on OK
14. Under File, select the last option – Return Data to Microsoft Excel
15. At this stage, if you wish to get data in a tabular form, then select Table.  If you directly want a pivot table, select the second option button – Pivot Table.  For this example, select Pivot Table and in the cell reference box, select any cell where you would like to the result to appear, say cell A1.  Click on OK
16. A counter will run at the bottom left hand side with the title of Reading Data
17. Drag Circle and ID to the to the Row Labels
18. Drag Fault Frequency to the Value Area twice
19. Right click on any one number in the fault frequency column and under Summarise Value by, select Count
20. Right click on any value in the ID column and under Expand/Collapse, select Collapse Entire Field.