Description
Sales Forecasting various methods of forecasting and application in sales.
Sales Forecasting
Quantitative Techniques Group 3
Introduction
What?
• Best Guess • Time Horizon • Conditional
Why?
• Coordination • Motivation • Control
When?
• Periodic • Event Based
How is it Done? - Techniques
• Single Regression • Multiple Regression • Econometric Models • Moving Average • Exponential Smoothing • Box Jenkins
Multiple Factors Causal
Time Series
Trend Based
Counting Data Search
• Industrial Survey • Intention to Buy Survey
Judgmental
Unstable
• Executive Jury • Composite Sales Force
Moving Average – Time Series Based Technique
What? Where? Types Key Terms
• Analysis of historical data. • Past Patterns
• FMCG Industry • Textile Industry
• N- period Moving Average • Exponential Moving Average
• Average • Trend • Seasonal Influence
•Cyclical Movement •Random Error
An Example
Period (2009)
Jan
Actual Sales
$100,000
3-month Moving Avg.
6-month Moving Avg.
3- month Weighted Avg.
(Jan + 2* Feb + 3*Mar)/6
Feb
Mar Apr May Jun Jul Aug
$101,300
$102,617 $103,951 $105,302 $106,671 $108,058 $109,463 101305.633 102622.607 103956.7 105308.138 106677.143 $103,306.89 $104,649.87 101741.783 103064.427 104404.264 105761.519 107136.419
Understanding the Trade Off
Decision
Trade Off Practice
• No. of N- Periods
• N Increases – Greater Smoothing, Low Responsiveness • N Decreases – Less Smoothing, More Responsiveness
• Stable Demand – High N
• Unstable Demand – Low N
Multiple Regression – Causal Technique
What? Where?
• One Dependent Variable • More than Two Independent Variable
• Financial Industry • Advertising
Equation
• Y= a+ b X + b X +...+ b X
1 1 2 2 k
k
• Y- Dependent, X1..k –Independent, b1…k – Coefficients, a – Estimated Constant
An Example
Dependent Year
1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Independent Price
9 8 9 8 7 6 6 8 5 5 5 3 4 3 4
Extraneous Price of Substitute
10 14 12 13 11 15 16 17 22 19 20 23 18 24 21
Demand
40 45 50 55 60 70 65 65 75 75 80 100 90 95 85
Income
400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800
Understanding the Trade Off
Advantages
• Identify Causal Linkage • Identify Causes for Fluctuation
Disadvantages
• Large Data Requirement • Tools may ignore Causal Factors
Bass Model – Want to Launch a New Product?
• P(t) = p + q/M (N(t))
o o o o MpqN(t) Total Potential Market Coefficient of innovation Coefficient of imitation Cumulative number of customers who have already adopted
- Frank Bass - 1969
An Explanation
• Market Research
Uses
• Forecast Techniques
• Potential Sales
Needs
Identifies
What’s the Way Forward?
New Techniques
Chaos Theory
• Dynamic Evolution Understanding
Evidence Cisco Systems Quality Bicycle Products General Cable Welch
Expert Systems
• Reproducing Work
Neural Networks
• Interconnected Processing Elements
Genetic Algorithm
• Charles Darwin’s Logic
Success is achieved not by being accurate but by being effective!!
Thank You
It’s nice to be important but it’s important to be nice.
Questions ?
If you want a wise answer, ask a reasonable question -Johann Wolfgang Von Goethe
doc_535974268.pdf
Sales Forecasting various methods of forecasting and application in sales.
Sales Forecasting
Quantitative Techniques Group 3
Introduction
What?
• Best Guess • Time Horizon • Conditional
Why?
• Coordination • Motivation • Control
When?
• Periodic • Event Based
How is it Done? - Techniques
• Single Regression • Multiple Regression • Econometric Models • Moving Average • Exponential Smoothing • Box Jenkins
Multiple Factors Causal
Time Series
Trend Based
Counting Data Search
• Industrial Survey • Intention to Buy Survey
Judgmental
Unstable
• Executive Jury • Composite Sales Force
Moving Average – Time Series Based Technique
What? Where? Types Key Terms
• Analysis of historical data. • Past Patterns
• FMCG Industry • Textile Industry
• N- period Moving Average • Exponential Moving Average
• Average • Trend • Seasonal Influence
•Cyclical Movement •Random Error
An Example
Period (2009)
Jan
Actual Sales
$100,000
3-month Moving Avg.
6-month Moving Avg.
3- month Weighted Avg.
(Jan + 2* Feb + 3*Mar)/6
Feb
Mar Apr May Jun Jul Aug
$101,300
$102,617 $103,951 $105,302 $106,671 $108,058 $109,463 101305.633 102622.607 103956.7 105308.138 106677.143 $103,306.89 $104,649.87 101741.783 103064.427 104404.264 105761.519 107136.419
Understanding the Trade Off
Decision
Trade Off Practice
• No. of N- Periods
• N Increases – Greater Smoothing, Low Responsiveness • N Decreases – Less Smoothing, More Responsiveness
• Stable Demand – High N
• Unstable Demand – Low N
Multiple Regression – Causal Technique
What? Where?
• One Dependent Variable • More than Two Independent Variable
• Financial Industry • Advertising
Equation
• Y= a+ b X + b X +...+ b X
1 1 2 2 k
k
• Y- Dependent, X1..k –Independent, b1…k – Coefficients, a – Estimated Constant
An Example
Dependent Year
1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Independent Price
9 8 9 8 7 6 6 8 5 5 5 3 4 3 4
Extraneous Price of Substitute
10 14 12 13 11 15 16 17 22 19 20 23 18 24 21
Demand
40 45 50 55 60 70 65 65 75 75 80 100 90 95 85
Income
400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800
Understanding the Trade Off
Advantages
• Identify Causal Linkage • Identify Causes for Fluctuation
Disadvantages
• Large Data Requirement • Tools may ignore Causal Factors
Bass Model – Want to Launch a New Product?
• P(t) = p + q/M (N(t))
o o o o MpqN(t) Total Potential Market Coefficient of innovation Coefficient of imitation Cumulative number of customers who have already adopted
- Frank Bass - 1969
An Explanation
• Market Research
Uses
• Forecast Techniques
• Potential Sales
Needs
Identifies
What’s the Way Forward?
New Techniques
Chaos Theory
• Dynamic Evolution Understanding
Evidence Cisco Systems Quality Bicycle Products General Cable Welch
Expert Systems
• Reproducing Work
Neural Networks
• Interconnected Processing Elements
Genetic Algorithm
• Charles Darwin’s Logic
Success is achieved not by being accurate but by being effective!!
Thank You
It’s nice to be important but it’s important to be nice.
Questions ?
If you want a wise answer, ask a reasonable question -Johann Wolfgang Von Goethe
doc_535974268.pdf