paper 6: management information system module 17
TRANSCRIPT
Principal Investigator
Co-Principal Investigator
Paper Coordinator
Content Writer
Prof. S P Bansal
Vice Chancellor
Maharaja Agrasen University, Baddi
Prof Yoginder Verma
Pro–Vice Chancellor
Central University of Himachal Pradesh. Kangra. H.P.
Prof. Manu Sood
Chairman, Department of Computer Science,
H.P University, Summer Hill, Shimla.
.
Paper 6: Management Information System
Module 17: Sensitivity Analysis
Ms.Vinodini Kapoor
Asst. Prof, Northern India Institute of Fashion Technology
Mohali, Punjab
Items Description of Module
Subject Name Management
Paper Name Management Information System
Module Title Sensitivity Analysis
Module Id Module No 17
Pre- Requisites Basic knowledge of the use and importance of analytical modeling techniques
Objectives To understand the role of sensitivity analysis in decision making.
Keywords Dependent variable, independent variable, what-if analysis, sensitivity, decision
support systems.
QUADRANT-I
Module- 17 Sensitivity Analysis
1. Learning Outcome
2. Introduction
3. Characteristics and benefits of sensitivity analysis.
4. Steps involved in sensitivity analysis.
5. Importance of sensitivity analysis to support decision making.
6. Industry applications of sensitivity analysis.
6.1 Advantages and disadvantages of sensitivity analysis.
7. Summary
1. Learning Outcome:
After completing this module the students will be able to:
Understand the basic concept of sensitivity analysis (SA).
Understand the characteristics and benefits of sensitivity analysis.
List the various steps involved in the SA process.
Understand the importance of sensitivity analysis to support decision making.
Analyze various industry applications of sensitivity analysis.
2. Introduction
After venturing into the food business about five years ago, you observe that in the past two years, sales
have been quite dormant. A number of new eateries have opened nearby, leading to lower footfall in your
restaurant. You hire a consultant to look into different aspects that can affect sales positively. He helps
you draft a roadmap of activities for next twelve months. Best is to understand the situation using
sensitivity analysis. You work out changes on the decoration, seating, lighting, menu and promotional
offers. However, each change should be measured against its cost and the impact on sales. Eventually,
changes in the menu and seating plan seem most viable in terms of costing. From a self service mode, if
catering is introduced, it enhances customer experience and can possibly increase sales by 20%. To sum
up, it is sensitivity analysis that measures each option with respect to increase in revenue without a
substantial increase in the cost structure.
Exhibit 1: Concept of Sensitivity Analysis
Image Source: https://cdn.lynda.com/video/432881-148-635784416819949347_338x600_thumb.jpg
A sensitivity analysis is an estimation of what happens if variables are changed. The larger emphasis is on
the overall impact by change in one variable. To change one particular aspect of your business, how will
it affect the other attributes? To simplify, by changing one aspect of the restaurant format in the above
example, how will it impact sales or optimize operations thereby leading to higher revenues?
This can also be understood from the statement
in exhibit1. Keeping other variables constant,
an increase in sales price decreases sales
volume which is needed to attain a target
income.
Exhibit 2: How Change in One variable affects business?
Source: https://www.gljpc.com/sites/default/files/Risk%20%26%20Sensitivity%20Analysis%20-
%20Economic%20Sensitivities_0.jpg
Sensitivity analysis helps managers make powerful analysis into everyday problems that affect business.
It is important to note, that it does not give a solution to a problem. But, it provides the means to
understand the problem better. Technically, “Sensitivity analysis is a method that states how the different
values of an independent variable impact the dependent variable under certain constraints. This technique
is used with boundaries or limiting factors that depend on certain variables, such as, the effect of change
in interest rates on home loans or bond prices”.
Exhibit 3: Uncertainty and Sensitivity Analysis
Image Source: https://upload.wikimedia.org/wikipedia/en/thumb/0/01/Sensitivity_scheme.jpg/500px-Sensitivity_scheme.jpg
As shown in exhibit 3, simulation model may encounter uncertainty and errors from various data sources.
Some of these could include – errors present in data or mathematical models, errors due to parameter
estimation or resolution levels.
3. Characteristics and benefits of Sensitivity Analysis
This concept analyses the possible uncertainty with regard to decision making. It considers each probable
factor and calculates the required change to reverse the original decision. In other words, it takes into
consideration, the ‘what – if’ question. Exhibit 4 highlights the same underlying concept which is the
chief characteristic of sensitivity analysis. Sensitivity analysis would help determine the extent of this
uncertainty.
3. Characteristics of Sensitivity Analysis
Exhibit4: Concept of Sensitivity Analysis
Image Source: http://study.com/academy/lesson/sensitivity-analysis-definition-uses-importance.html
It is the influence that one parameter (the independent variable) has on the value of another (the
dependent variable), both of which may be either continuous or discrete. Exhibit 5 represents a screen
shot of parameter estimation functionality in the MATLAB –Mathwork software package while Exhibit 6
shows the optimization functionality.
Exhibit 5: Sensitivity analysis for parameter estimation
Image Source: https://www.mathworks.com/help/sldo/ug/pe_import_from_sa.png
Exhibit 6: Response and parameter estimation using sensitivity analysis
Image Source: https://www.mathworks.com/help/sldo/ug/sa_optimize_button.png
This statistical technique looks into how particular inputs and parameters change outputs. One
input is changed at a time to understand the corresponding affect on output. It does not
necessarily mean that inputs are interrelated.
Sensitivity analysis is a limiting case of ‘what-if analysis’ that involves iterative changes to a
single variable at a time. Usually in a business related scenario, managers repeat changes to a
variable and observe the effects on others variables.
Exhibit 7 below highlights a scenario where impact each of the variables such as development
time, development cost, product cost and performance are monitored in regard to total project
cost.
Exhibit7: Sensitivity analysis to understand project tradeoffs
Image Source:
http://slideplayer.com/slide/3556110/12/images/58/Step+3:+Use+Sensitivity+Analysis+to+Understand+Project+Trade-
offs.jpg
Managers use this technique to interpret the variables that need to be monitored while making
decisions. (E.g. at what certain point, the rate of interest of a home loan renders the project
unfeasible).
Benefits of sensitivity analysis
Sensitivity analysis is a tool that is largely used by the managers at senior levels of the
organization. The likely outcome is referred as a what-if analysis. It is used to test the effect of
critical and non critical variables on the overall profitability of a company. It helps to focus the
concentration of senior managers and strategic decision makers to ensure that business decisions
are in line with the vision and mission statements of the firm.
Exhibit 8: When to carry sensitivity analysis
Image Source: http://www.dot.state.mn.us/planning/program/images/bcfig4.png
Capital budgeting is a very critical application of sensitivity analysis. It helps to get an idea of the
relationship between different attributes such a sales, liquidity, profitability etc
Sensitivity analysis also measures the extent of change in variables and approximation of the
bottom line of the cash flow and profitability of a project.
It helps to create a rough draft of the project before actually committing resources. This helps the
decision makers in the long run to get a better estimate of how the project would turn out.
It helps to organize the information in a more structured and organized manner. This facilitates
better decision making as critical information influencing decisions is highlighted. This concept is
understood better from Exhibit 8 where the need for sensitivity analysis is highlighted w.r.t
availability of data.
Exhibit9: Using the sensitivity
analysis software
Source: http://ars.els-
cdn.com/content/image/1-s2.0-
S0022169415001249-gr1.jpg
It is easy to feed values in a sensitivity analysis software package that can assess values and
perform calculations faster. The graphic user interface one such case of a software package is
shown in Exhibit 9. This shows how sensitivity analysis and uncertainty analysis should be
performed in cohesion. While an uncertainty analysis determines the variability of results,
sensitivity on the other hand determines the inputs to be varied for change in output as shown in
Exhibit 10.
Sensitivity analysis helps the management to lay higher emphasis to quality control and leaves an
impact on determining the success or failure of a project.
Exhibit10: Uncertainty and Sensitivity Analysis
Image Source: http://jasss.soc.surrey.ac.uk/18/4/4/fig_sens_flow.png
4. Steps involved in Sensitivity Analysis
The first and foremost step in this method includes the identification of the dependent variable. This
further helps to predict the independent variables that impact the dependent variable.
Further, the following steps are to be kept in mind.
Determination of the target function and choosing best estimates to arrive at a decision.
Analyzing the variables one by one to determine how much the original estimate can change.
Allocating a distribution function and creating a matrix highlighting inputs.
Assessing the model and calculating the target function distribution.
Choosing a technique for evaluating the impact or comparative weight of every input element on
the target function.
Situation: A company named ‘The Great Wall Beatle’ is located on the hilly terrains of the country Zhongua.
It primarily constructs tunnels for the leading road developers. The company intends to participate in a bid
submission that intends to develop the country's longest tunnel on the expressway. In this 20 Km long tunnel
project the company has decided to receive $1 from every vehicle that crosses the tunnel for the next 100
years. The company's Chief Manager – Strategies came up with a net present value of $1,218 million for the
project. The assumption being that cash flows are received at the end of the year. The manager uses the
concept of weighted average cost of capital with a value of 11%. The daily vehicular throughput is assumed
to be 1,000,000. Every day expenses are estimated at 3% of total revenue. The initial cost is $2 billion. The
idea is to determine how sensitive net present value is to each input.
Solution
We first calculate net present value (NPV) assuming weighted average cost of capital (WACC) is 12.1%
instead of 11%. There are 1,000,000 vehicles, operating expenses are 3% and an initial cost is estimated at
$2,000 million. This gives us a net present value of $926 million. This is calculated using the formula
The percentage change in output is -24.01% which is obtained from (($926 million - $1,218 million) ÷ $1,218
million. The change in input is 10% ((12.1% − 11%) ÷ 11%). Hence, it can be stated that the percentage
change in output per 1% change in input is -2.4
The next data set can be used to determine the sensitivity estimates for daily traffic, daily operating expenses
and initial costs are 2.64, -0.08 and -1.64.
The calculations suggest how sensitive output is to each input. But, a negative sign would imply that output
decreases with an increase in that input (such as discount rate).
Hence, it can be stated that the net present is most sensitive to the estimate of daily traffic and least sensitive
to the estimate of daily operating expenses. Keeping this in mind, the company should try to estimate the daily
traffic with as much accuracy as possible. Source: http://xplaind.com/167040/sensitivity-analysis
5. Importance of Sensitivity Analysis to support decision making
Exhibit11: DSS – An analytical modeling support system.
Image Source: https://farm9.staticflickr.com/8609/16537975560_abb68a9ba3_o.jpg
Decision support system works on the principle of analytical modeling. The different ways in which a
DSS supports information systems are listed in Exhibit 11. Users explore alternatives without pre-
specified information. There are several basic types of analytical modeling activities as shown in Fig1.
Fig1. Analytical Modeling Techniques
A decision maker may require some idea of how sensitive an alternative choice might be to the changes in
one or more of those values. The analyst has to find the range of feasibility around which choice of the
alternative remains the same. Successful decision making requires a sequence of steps, the first being to
carefully define the problem.
Sensitivity analysis analyzes the problem intricately and answers a number of “what if” questions.
In what-if analysis, a decision maker:
Shall introduce a change in variables and study the relationship among them.
Observes the effect on other variables.
A model based decision support system helps:
To test the optimum function and highlight the critical values, the break even and threshold values.
The threshold values explain whether change in a given variable will change the optimal decision.
Identify sensitive variables and optimal solutions.
To support decision making with respect to the present situation.
Comparison of values between situations involving different levels of decision making.
What-If AnalysisSensitivity Analysis
Goal-Seeking Analysis
Optimization Analysis
Knowledge Discovery &
Analysis
The analysis helps to make assessments in a project in case the estimates turn out to be unreliable. This
helps business analysts to analyze the results better before any further investment is made. This implies
the identification of critical values of a project. E.g., project feasibility study, risk assessment.
6. Industry applications of sensitivity analysis
Measurement of sensitivity – The following steps are followed to conduct a sensitivity analysis.
Fundamentally, we keep the output at the base value of the input for which we intend to measure the
sensitivity. Meanwhile, rest of the inputs in the model is kept constant.
In an actual scenario, with the net present value at W1 we intend to measure the sensitivity at the
discount rate. For this, the other inputs like cash flow, growth and tax rate, depreciation are
constant.
The value of output at a new value of the input (say W2) is obtained while keeping other inputs
constant.
The percentage change in input and the output is calculated. Sensitivity is then obtained by
dividing the percentage change in output by the percentage change in input.
The next step is to test the sensitivity for another input while keeping the rest of inputs constant. This
process is carried till we get the critical values for each input. Higher the sensitivity figure, more
sensitive is the output to any change in the input and vice versa.
Exhibit12: Applications of Sensitivity Analysis Image Source: https://image.slidesharecdn.com/b39d55cf-3461-
4a41-acb9-406b79fa5c3b-141230031048-conversion-
gate01/95/sensitivity-analysis-3-638.jpg?cb=1419930691
Applications of Sensitivity Analysis
In a situation where you are taking your family for a
holiday, you may consider attributes such as the
distance involved, the budget, convenience and then
decide to fly instead of driving. However, last minute weather changes or sudden increase in fuel prices
could make you rethink about you plan. In this case, the use of sensitivity analysis helps understand the
situation better. Exhibit 12 highlights applications areas of this concept.
Exhibit13: Risk Analysis Techniques
Image Source: http://www.milestoneintl.com/images/analysis-risk.jpg
Exhibit 13 highlights a number of risk analysis techniques. Sensitivity analysis is one such method of
estimating quantitative risk.
Businesses decisions involve risk in lieu of a higher return or profit. The goal of the management
is profit maximization and cost minimization. They strive to minimize the level of risk involved.
Sensitivity analysis helps them in risk assessment.
This concept helps managers to analyze what values lead to higher profits. The repercussion of
undertaking any last minute change in project plan can be assessed. It helps in a cause effect
analysis of any system.
It helps to remove redundancy of data in a data acquisition system by filtering unsolicited data.
This system converts analog data into its digital equivalent which is represented in binary form
using combination of 0 & 1.
A sensitivity model helps in price determination as shown in Exhibit 14, estimation of required
expenditure on advertising, volume of production. Software packages make it easier to input
values and obtain results. The inbuilt functionality of MS Excel, Lotus 1-2-3 and MATLAB are
such packages that offer these functionalities.
Sensitivity analysis finds numerous applications in areas of finance such as capital budgeting. It
can help determine the discount rate, growth rate, internal rate of return etc.
This technique is of business utility since it highlights the dependency of output value on every
input variable. It reveals the extent to which variables can be altered to achieve the desired
outcome.
Exhibit14: Price Sensitivity Analysis
Image Source: http://rockresearch.com/wp-content/uploads/2016/04/Van-Westendorp.png
A technique which is reverse of sensitivity model is known as backward sensitivity analysis. This
is also termed as goal-seek. This method sets a target value to be achieved. Other variables are
changed time and again till the final outcome is achieved.
For example, to increase the level of production by say 40 percent, the software assigns the target
value to the production level. Eventually, the required changes are made to other factors, such as
the amount of material, men, machinery to obtain the target production level.
6.1 Advantages and disadvantages of Sensitivity Analysis
There are various advantages to the concept of sensitivity. Few of them can be understood below:
1. It makes the identification of variables easy for the decision maker.
2. It helps to identify the weak areas of a project. It helps to align business processes in line with the
corporate goals and mission of the organization.
3. It helps to remove redundancy and focus on attributes that need attention by highlighting the relevant
variables.
4. The availability of software packages makes computation accurate and easier.
At the same time, there are several disadvantages listed below:
1. At certain times, the results may not be very clear which makes the analysis more complex.
2. It may be unable to highlight the interrelationships between certain variable that may affect the final
result. In other words, the assumption that changes to variables can be made independently may not be
correct in each case.
3. Simulation models can enable us to change more than one variable at a time. But the probability of
such a change cannot be highlighted, although it can state the extent to which these variables can be
changed.
4. Also, there is lack of probabilistic measure of the exposure to risk. Although one among the several
outcomes may be achieved, the analysis cannot ascertain the likelihood.
7. Summary
Sensitivity analysis is an analysis method that is used to identify how much variations in the input values
for a given variable will impact the results for a mathematical model. Sensitivity analysis is useful in
various fields such as business analysis, finance, market analysis, engineering, physics and chemistry. In a
business context, sensitivity analysis can be used to improve decisions made based on certain calculations
or modeling. At the organizational level, companies use a number of computing software packages to
carry out sensitivity analysis. A company uses this technique to identify the appropriate data and sees
underlying assumptions regarding investment and return on investment (ROI), or to optimize allocation of
assets and resources. Sensitivity analysis is commonly used for risk estimation. It helps to calculate the
degree of change in variables and assumptions that reflect the criteria to determine the cash flow and
profitability. The idea of carrying out risk assessment before the start of a project is to give managers a
broad view of what critical aspects should be looked at. However, it is important to note that sensitivity
analysis does not give a complete solution to any problem. It enables a better analysis and interpretation
which helps to take business decisions better. It forms an integral part of the decision support systems in
context of management information systems.