OM Week-Forecasting 2

7/31/2019 OM Week-Forecasting 2

1/16

Operations ManagementWeek-5

Forecasting Collaborative Planning, Forecasting and Replenishment (CPRF) Demand Patterns and Forecasting Techniques

Design of Forecasting Systems


2/16

Forecasting What is it all about ?

Process of developing the most probable view of whatfuture demand will be, given a set of assumptions about :

Technology

Competitors

Pricing

Marketing

Expenditures

Sales efforts


3/16

Elements of a Good Forecast

Timely

AccurateReliable

Written


4/16

Types of Forecasts

Qualitative Methods

Judgmental- uses subjective inputs

Quantitative Methods

Time series- uses historical data assuming thefuture will be like the past

Associative models / Causal Methods- usesexplanatory variables to predict the future (whenhistorical data are available and the relationshipbetween


5/16

Demand Patterns

The repeated observations of demand in their order ofoccurrence form a pattern known as time series.

Five basic patterns: Horizontal

Trend

Seasonal

Cyclical Random


6/16

Steps in Forecasting

Step 1 Determine purpose of forecast

Step 2 Establish a time horizon

Step 3 Select a forecasting techniqueStep 4 Gather and analyze data

Step 5 Prepare the forecast

Step 6 Monitor the forecast

The forecast


7/16

Designing Forecast Methods

Deciding what to forecast

Level of Aggregation

Units of measurement Choosing the type of technique


8/16

1. Judgment Methods

In some cases, these are the only way to make a forecast. Inothers, these can be used to modify forecasts generatedquantitatively. Four types are common:

1) Salesforce Estimates2) Executive Opinion

3) Market Research

4) Delphi Method


9/16

Are Judgmental Methods always reliable?

"Man will never reach the moon regardless of all future scientific

advances." -- Dr. Lee DeForest, Inventor of TV

"The bomb will never go off. I speak as an expert in explosive." -- AdmiralWilliam Leahy, U.S. Atomic Bomb Project

"I think there is a world market for maybe five computers." -- Thomas Watson,

chairman of IBM, 1943

"640K ought to be enough for anybody." -- Bill Gates, 1981

"This 'telephone' has too many shortcomings to be seriously consideredas a means of communication. The device is inherently of no value to

us." -- Western Union internal memo, 1876

We've all heard predictions about the future.Sure, sometimes "experts" are right on target,but check out what they got wrong


10/16

2. Causal Methods

Linear Regression Analysis

- Establishes relationship between a DEPENDANT variable and one ormore INDEPENDENT variables

-We use our knowledge of the relationship between the two and aboutthe future values of the independent variables to forecast the futurevalues of the dependant variable.

- If there is only one independent variable, it is called as SimpleRegression Analysis (Generally time period)

Y = a + bX a0 1 2 3 4 5 X (Time)

Y


11/16

2. Causal Methods


Problem:

The Renovators construction company repairs/reconstructs old roads inSacramento, U.S. Over time they have found that companys dollar volume ofreconstruction work (Sales) is dependant on the total amount of road constructioncontracts offered by City Council every quarter . Management wants to establish

a mathematical relationship to help predict sales.!!!!!!!!!!!!!!!!!!!!!!!!

Year Quarter Sales(Thousand $)

Contracts(Thousand $)

1 Q1Q2

Q3Q4

810

159

150170

190170

2 Q1Q2Q3

Q4

121312

16

180190200

220


12/16


x= independent variable

y= dependent variablen= number of observationsa= vertical axis interceptb= slope of regression liney = mean value of dep. Variable

Y = values of y that lie on the

trend lineX = values of x that lie on thetrend liner= coefficient of correlationr2 = coefficient of determination

a= (x2y - x xy) / (n x2(x)2)

b= ( n xy - x y) / (n x2(x)2 )

r= (nxy - x y) / [n x2(x)2][ny2 (y)2 ]


13/16

Time Series Methods

Naive Forecast :Forecast for the next period equals thedemand (Dt) for current period

o Simple and low-cost

o If random variation is large, highly useless for planning

Estimating the Average: Simple Moving Average

Weighted Moving Average

Exponential Smoothing

Unlike causal methods, these methods use historical information regarding

ONLY the dependent variable


14/16

Exponential Smoothing

A sophisticated weighted-moving average forecasting methodthat involves very little record keeping of past data.

New Forecast = Last periods forecast + ( Last periods actualdemandLast periods forecast)

Or

Ft + 1 =Ft + (Dt Ft)


15/16

Mean Actual Deviation (MAD)

First measure of the overall forecast error

MAD = I Actual demand - Forecast I---------------------------------------------

n


16/16

Trend Adjusted Exponential Smoothing

Simple exponential smoothing, like moving averagesmethods fail to respond to .??????

At = (Demand this period) + (1-) (Average + Trendestimate last period)

= ( Dt ) + (1-)(At-1 + Tt-1 )

Tt = (Avg. this period Avg. last period) + (1 ) (Trend

estimate last period)= (At - At-1 ) + (1 ) Tt-1

Ft+1 = At + Tt

Documents

OM Week-Forecasting 2