# Introduction – Variance Analysis

Variance analysis refers to a difference between two values. The deviation may favorable or unfavorable as under:

Variance is generally calculated as per below parameters

The time period for measuring deviation can be as under:

• Current Month vs Previous Month
• Current Year vs Previous Year
• First Quarter vs Second Quarter

Variance Reports are made to find out the % increase or decrease for Profit, Revenue, Cost Expenses, Attrition, Manpower Count, Salary, Recruitment Cost etc.

Examples of Variance Analysis charts

# Waterfall Chart – Employee Head Count.

A waterfall chart is generally used to display how the starting position and an ending point which are connected by a series of values (either increases or decreases). By a series of changes from the start value and through all the connecting intermediate increasing/decreasing values we arrive at the end point.

A waterfall chart is also called a bridge chart since the floating in between columns between the start value and the end value form a pseudo bridge.

We display the employee headcount in a monthly basis (increase /decrease) basis the new joinee and resignees in a month.

We start with the opening count of the year. The opening count are those employees who are on active payroll of the company at the start of the assessment year.

For E.g : Assessment year ( 01 Jan 2016 to 31 Dec 2016)

The opening count for 01 Jan 2016 will be the closing count as on 31 Dec 2015.

Then we show the net employee count for each month basis the employee joining or leaving . This is shown till Dec and then we display the closing count for Dec 2016.

In below example the opening count at the start of the year is 500. Throughout the year in each month we have certain number of joinees and resignees. Due to the same we may have an increase or a dercrease in the net employee count . At the end of the year the closing employee count is 509.We can see that in Jan there was a employee count decrease of 2 . So it means number of employees reduced from 500 to 498.

In Feb then there was a increase of 4. So employee count increased from 498 to 502. Continuing the calculations at the end of the year we have a closing count of 509.

Opening Count + Monthly Increase/Decrease) = Closing Count

500 + (-2+4+4+3+2+3-3-1-1-4-1+5) = 509

# Population Chart ( Age Distribution and Gender Count)

Our data consists the age group distribution of employees (bifurcated  across males and females).

The data is as under.

Our resultant chart is as below.