Determine cumulative interest payable on an annuity with varying time periods


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.

Determine number of learners who have completed different stages of multiple online courses


Here is a sample dataset of learners who have cleared different stages of multiple courses on offer within an Organisation:

Learner Stage completed Course
Bill Stage 1 Public Speaking
Bill Stage 2 Public Speaking
Bill Stage 3 Public Speaking
Susan Stage 1 Effective Communication
Bob Stage 1 Public Speaking
Bob Stage 2 Public Speaking
Sheila Stage 1 Effective Communication
Sheila Stage 2 Effective Communication
Sheila Stage 3 Effective Communication
Frank Stage 1 Effective Communication
Frank Stage 2 Effective Communication
Henry Stage 1 Public Speaking
Henry Stage 2 Public Speaking
Bill Stage 1 Effective Communication
Bill Stage 2 Effective Communication

From this sample dataset, one may want to know how many participants have completed each stage of these multiple courses.  The expected result is shown below:

Row Labels Stage 1 Stage 2 Stage 3
Effective Communication 1 2 1
Public Speaking 2 1
Grand Total 1 3 2

In this workbook, I have shared 2 solutions - one using formulas and the other using the Power Query & PowerPivot.

Convert a text entry into its number equivalent


Consider this simple two column table showing text entries in column A and the corresponding numbers in column.  Assume this data is in range A2:B11 (headings are in A1:B1).

text Value
A 1
B 2
C 3
D 4
E 5
F 6
G 7
H 8
I 9
J 0

The objective is to generate the numeric code for text code of any length entered in a certain cell.  For example, a user will type a certain text code, say ABEJ and the expected result should be 1250.  For JABF, the result should be 0126.  The text entry and text length are both user determined.

With ABEJ, typed in cell D2, enter this array formula in cell E2


This formula can now be copied down for generating the numeric code for all text codes entered in column D.

Summarise data with multiple wildcard OR conditions


Imagine a dataset as shown below

Status Text Number
Active bat 1
Passive erq 2
Passive cat 3
Active enm 4
Active dog 5

Suppose the data is in range A3:C7 (headings are in A2:C2).  The objective is to add numbers where the following conditions are met:

1. Status should be Active; and
2. Text should contain either b or c or d

So the result for the dataset above should be 6 i.e. 1+5.  Sounding like a simple case of summarising based on multiple conditions, one may attempt using the SUMIFS() function here.  However, a single SUMIFS() function will not work because a SUMIFS() can only accept multiple AND conditions (not OR conditions).  The only way to make the SUMIFS() function work is to use 3 of them and then add them up.

So this SUMIFS() function will work


A11:A13 has Active and B11:B13 has *a*,*b*,*c*.

While this looks like a solution, the problem is that there will have to be one SUMIFS() function for each OR condition.  So the more the number of OR conditions, the more the SUMIFS() functions and the lengthier the formula.

In this workbook, I have shared two alternative solution.  One uses the DSUM() function and other uses a combination of MMULT(), TRANSPOSE() and SUMPRODUCT() functions.

Compute potential Sales of a retail outlet


The objective is to assist a Store Manager with computing potential sales across different products and colours.  To start with let's assume two datasets:

1. Customer-Colour dataset - a two column table which lists down the colour preference of each customer; and
2. Colour-Product-Price dataset - a three column table which lists down the multiple products associated with each colour and the associated prices

My objective is to compute the "Potential Sales" if each customer buys one unit of each colour of each product.  While this can be resolved by using formulas (see Formula solution worksheet of the workbook), I'd like to resolve this problem with the help of the Power Query and PowerPivot tools.

The initial challenge will be to establish a relationship between the two tables because one cannot establish Many to Many relationships in a PowerPivot.  The two tables above are a perfect example of Many to Many relationships because each customer likes multiple colours and each colour is associated with multiple products.

You may download my solution workbook from this link.

You may also view a short video of my solution here:

Analyse all possible combinations of cheques received and identify the combination which gives maximum benefit to the Customer


Scenario 1

In a Stock Broking business (Firm), Customers deposit margin money through cheques. Although cheques are received from customers at branches, clearing (realization) of the same normally takes 3-4 days.  On one hand, the Firm wishes to allow trading to the customers against the deposited cheques from the day it receives the cheque, on other hand there is a risk associated with this  i.e cheque may get dishonored and the Firm may incur huge financial losses because of allowing customers to trade in anticipation of the cheque realization. Hence, there is a trade off between customer centricity & Financial Risk management.

The Firm may decide to take a calculated risk in order to retain its customer centric approach (it is a delightful experience for customers if they get trading benefit against the deposited cheque instantly before realization). The Firm may decide to give the benefit of cheque which are below a set threshold amount (say 50,000) instantly and for cheques which are above threshold amount the credit will be given only after realization. Sometimes there are  multiple unrealized cheques deposited by one client but the Firm needs to cap the total benefit of unrealized cheques to 50,000 (partial benefit against any cheque is not possible i.e. either a cheque will be considered in full or not considered) E.g. if a customer deposits 3 cheques of 40,000 each then also the total instant benefit against all unrealized cheques shall be capped at 50,000 - hence only one cheques of 40,000 will be credited to customers ledger before realization.

Now the question here is with respect to selection of cheques to give best results i.e. maximum total below 50,000.  Consider the following example:

Cheque1: 55,000
Cheque2: 44,000
Cheque3: 28,000
Cheque4: 7,000
Cheque5: 6,500
Cheque6: 4,000
Cheque7: 3,000
Cheque8: 500

The Firm wants to identify the "Combination of cheques" which gives the maximum benefit to the Customer subject to a limit of 50,000.  The result should be cheques 3-8.  They add up to 49,000.

Here's another example:

Cheque1: 55,000
Cheque2: 45,000
Cheque3: 25,000
Cheque4: 5,000

The result here should be cheque 2 and 4.  They add up to 50,000.

Here are my thoughts:

1. I have assumed that the total number of cheques received from customer will be capped to 8.  This limit can be relaxed but processing will then take time.

2. Create all possible combinations of cheques which can be credited.  So, since no partial credit is allowed, the cheque can either be accepted in full or be rejected.  Hence the possible values can be 2 i.e. 1 (Accepted) or 0 (Rejected).

3. So if there are 8 cheques received from a certain customer, then there will be 2^8 i.e. 256 possible combinations.  This can also be computed by using the =PERMUTATIONA(2,8) formula.  I'd like to than Saurabh Gupta for sharing the formula to generate all these combinations.

4. After creating all possible combinations, add the cheque amount for each combination.

5. Scan this total column and highlight the one which is the largest value <= the threshold value i.e. 50,000.

You may refer to my solution in the Solution worksheet of this workbook.

Scenario 2

Here's another example.  Say, one has a list of numbers as shown below:


Now one may want to know which combination of numbers adds up to or comes closest to 5115.52 (our lookup value).  If none of the combinations adds up exactly to the lookup value, then return the combination which adds to the largest value less then equal to the lookup value.  In the example above, the second, third and fourth add up to 5114.95.

You may find my solution in this workbook.

Prioritise investment liquidation to minimise Capital Gains


Assume a five column dataset with the Fund Name in column A, Market Value in column B, Gain/Loss in column C, Gain/Loss % in column D [column C/column B] and Type of investment in column E (Short term or Long term).  Since multiple investments can be made in one fund, there can be multiple line items for with the same Fund Name (with different market values).  Let's call this dataset the Investment schedule.

The objective is to determine the following:

a. Minimum total gain realised from sale of one/multiple funds; and
b. Short term gain present in a above; and
c. Total Long term gain realised from sale of one/multiple funds; and
d. If Long term investments fall for liquidating the particular fund, then how much is the gain/loss by forcible liquidating the short term investments.

You may refer to my solution in this workbook.

Consider a Pivot Table Value field column as a criteria for computing another Value Field column


Assume a simple three column dataset showing hours worked by different machine on different dates.  So column A is Date, column B is Machine Name and column C is hours worked.  There are duplicates appearing in column A and B .  Blanks in column C depict machine idle time.

The task is to create a simple three column dataset showing all unique Machine names in the first column, Last day on which the machine worked in the second column and hours worked on the last day in the third column.

This problem can be solved by using formulas (Refer first worksheet of the workbook) but if one has to use a Pivot Table, then there would be a few problems.

1. The Grand Total for the Date Field should be blank because on cannot determine the Last day on which the machine worked across different machine types.  A conventional Pivot Table shows the Maximum of all dates appearing in the Date Field.

2. The Grand Total for the Hours worked Field should be a summation of the total hours worked on last day across all machine types.  A conventional Pivot Table shows the Maximum of all hours worked appearing in the Hours worked Field.

3. The biggest problem of them all is that there is no way to give a criteria as the Last day for that machine for computing another Field in the Pivot Table.  Please refer the file for a better understanding.

This problem can be solved using the PowerPivot.  You may refer to my solution in this workbook.

Ignore errors while adding non contiguous cells of a range


In range E5:E13, assume the entries are


In cell I9, one may want to sum up the figures from cells E5,E7,E11 and E13. If one supplies the individual cell references to the SUM function, then the result will be a #DIV/0! error because the SUM function is intolerant towards errors supplied in its range. The formula which will resolve this problem is:


A11:A14 has E5,E7,E11 and E13.

Compute configuration count using Set Theory and Venn Diagrams


Assume a table which lists attendees for a Company's Annual day function.  In this Table, data for every attendee is shown on a separate row so if an employee attends the function with his/her spouse and three children, then there will be 5 rows for that employee.

The question is to determine the count of the following family configuration:

1. Employees only (those who attended without spouse and children); and
2. Employees, spouse and children (Family); and
3. Employees and spouse (no children); and
4. Employees and children (no spouse)

You may refer to my solution in this workbook.  I have solved this problem using:

1. MS Excel Formulas based on Set Theory and Venn Diagram; and
2. PowerPivot