Forecasting

We the members of group no . 1 would like to thank our “Production Management And Material Management” Prof . Sadaf Shaikh for giving us such wonderful topic “Forecasting” For presentation and guidance to complete project which helped us a lot gain more knowledge through this project ,

Acknowledgement

Prachi Lad Satya Jaiswal Shruti Kajrolkar Sumit Mehta Vinay Mehta Chiranjivi Palita

Group Members

Introduction Forecasting Methods Forecasting Methodology Pricing policy

Topics we cover

Definition: “The use of historic data to determine the direction of future trends.”

"Prediction is very difficult, especially if it's about the future." By Nils Bohr

Introduction

Forecasting in concerned with future

events, It shows the probability of happening

of future events, It analysis past and present data, It uses statistical tools and

techniques, It uses personal observations,

Features of Forecasting

Forecasting Methods

Forecasting

QualitativeOr

Judgmental

QuantitativeOr

Statistical

Projective Causal

Qualitative Method

Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers, experts.

Delphi method

Market Researc

h

Product Lifecycle Analogy

Expert Judgmen

t

Types

Delphi method

Delphi method employs a panel of experts in arriving at the forecast & proceeds through a series of rounds.

Large number of participation

Focuses on ideas

Anonymity for participant

Identification of priorities

Advantages

Time consuming

Complexity

Enthusiasm of participant Problem to keep statement value-free

Limitations

Staff

(Administering survey)

Decision Makers(Evaluate responses and make decisions)

Respondents(People who can make valuable judgments)

Market Research

A systematic approach to determine external consumer interest in a service or product by creating and testing hypotheses through data-gathering surveys

Characteristics :- Use surveys & interviews to

identify customer preferences

Strength :- good determinant of customer preferences

Weakness :- It can be difficult to develop a good questionnaire

Product Life cycle analogy

the stages shows that products go through from development to withdrawal from the market

Based on the experiences of similar product in the past, one can make decision

Basic Stages

Development Stage Growth stage Maturity stage Decline stage

Product Life Cycles

Sales

Time

Development Introduction Growth Maturity Saturation Decline

Stages of the Expanded Product Life

Cycle 1. Research and development

2. Product introduction3. Development of the market4. Exploitation5. Market maturation6. Market saturation7. Market decline

stages No. of competitors

Market growth (%)

Profit Market size

investment

R & D Unknown 0 0 0 Growing

Product introduction

Few Highest 0 Small High

development

Growing fast

High Low Small High

Exploitation

Moderate growth

Good Growing Modest High

maturity Stable Low High Largest Stable

saturation Stable None Lowering

Stable Decline

decline Reducing Negative High & low

Declining Stopped

Expert judgment

Gather a group of experts together,  Describe overall program in enough detail so experts can provide an

estimate.  Each member of the expert group then does an independent of the

resources needed.  Estimates are gathered anonymously and compared,  If there exists significant divergence among the estimates, the estimates

will be returned to the expert group,

The expert group then discusses the estimates and the divergence and works to resolve differences, and

  The expert group once again submits anonymous, independent estimates

which continues until a stable estimate results.

Genius Forecasting

Trend extrapolations are

typically based upon historical data for the event that is of interest

Trend Extrapolation

2 ways Face-to-face interaction Delphi method

Consensus Method

Historical Analogy Mathematical Analogy

Simulation OR

Analogy forecasting

Cross Impact Analysis

Methodology developed by Theodore Gordon and Olaf Helmer in 1966

To help determine how relationships between events would impact resulting events and reduce uncertainty in the future

First format of the method was a card game titled ‘Future’

Use of Cross Impact Analysis

1960sCentral

Intelligence Agency (CIA)

Mid-1970s

Futurists

By 2006Businesses as well as

intelligence analysts

Futures Forecasting Steps

1st

• consider the number and type of events

2nd

• take the initial probability of each event

3rd

• generate conditional probabilities

4th

• test their initial conditional probabilities

5th

• run the analysis to determine future scenarios

Applications

Researchers can use Cross Impact Analysis for a wide variety of applications- Futurists have already used the methodology

for forecasting events in specific industries, politics, markets, and even entire communities

In intelligence analysis, analysts can use the method to predict events, conditions, or decisions based on a wide variety of variables and conditions at local, national, and international levels

Scenario analysis

Scenarios focus on the joint effect of many factors

When you explore the factors together, you realize that certain combinations could magnify each other’s impact or likelihood

Numerous organizations have applied scenario planning to a broad range of issues, from relatively simple, tactical decisions to the complex process of strategic planning and vision building

Scenario forecasting process

1st

• Decide drivers for change/assumptions

2nd

• Bring drivers together into a viable framework

3rd

• Produce 7-9 initial mini-scenarios

4th

• Reduce to 2-3 scenarios

5th

• Draft the scenarios

6th

• Identify the issues arising

Use of scenario planning by managers

In terms of the overall application by forecasting, they can be divided into three main groups of activities Environmental analysis Scenario planning Corporate strategy

The central part represents the specific techniques- which differentiate the scenario forecasting process from the others in long-range planning

Decision tree

A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility

A decision tree consists of 3 types of nodes: Decision nodes - commonly represented by

squares Chance nodes - represented by circles End nodes - represented by triangles

Influence diagram

A decision tree can be represented more compactly as an influence diagram, focusing attention on the issues and relationships between events

Combining Forecasts

Combination of Delphi and Cross Impact Analysis Usually, analysts consider the average prediction

or scenario as the most likely to occur The two are so closely related, that analysts often

use the two techniques in combination or as part of a larger methodology

Combination of Delphi and scenarios Scenario planning concerns planning based on

the systematic examination of the future by picturing plausible and consistent images

Delphi, in turn, attempts to develop systematically expert opinion consensus concerning future developments and events

The output of the different phases of the Delphi method can be used as input for the scenario method and vice versa

Advantages

A combination makes a realization of the benefits of both tools possible

In practice, usually one of the two tools is considered the dominant methodology and the other one is integrated at some stage

Models of pricing

Cost- plus pricing Creaming or skimming Premium pricing Dynamic pricing

Value to the customers Advanced toolsProduct’s life-time durability

Cost is fact ;

Price is policy;

Value is concept