Ops management lecture 2 forecasting

Session TwoForecasting

Define and understand forecasting Identify the different types of forecasts Identify and discuss the various time horizons Discuss different approaches to forecasting Determine the steps in the forecasting

process Describe and solve averaging techniques in

forecasting

Learning objectives

Solve simple moving average problems Solve exponential smoothing problems Determine what constitutes a good forecast Compare qualitative and quantitative

forecasting methods.

Learning objectives (cont.)

Forecasting = Preface to planning Attempt to predict future value of a changing

variable Subjective or objective Addresses:

Macro-circumstances Competitiveness Market tendencies Sourcing funds required.

2.1 Introduction

Examples of ForecastsManagement Information Services – could predict new technology, eg. Advances in internet

Human Resources – could answer the question “will there be a need to employ more people in the future

Goods and service design – needs of the customer in the future

Finance – need for capital replacement, cash flows, budgets

Operations – scheduling, inventory planning, labour requirements, and project management

Marketing – prices for new products, promotional plans, competition analysis

Plan the system as a whole (long-range) Plan the use of the system Business forecasting (predict demand) Forecasting is never an exact science Context determines choice of method.

2.2 The uses of forecasts

Assumption that past trends will be present in future

No precise prediction can be made Groups more accurate Longer time horizon is less reliable.

2.3 Features common to all forecasts

Short-range (few weeks – 12 months) More accurate

Medium-range (12 months – 5 years) Long-range (5 years +) Medium and long-range: deal with organisation as

a whole.

2.4 Time horizons for doing forecasts

Time Horizon

Accuracy Frequency Management Level

Method

PROCESS DESIGN

Long term Medium Single Top Qual or Quan

CAPACITY PLANNING

Long term Medium Single Top Qual or Quan

AGGREGATE

PLANNING*

Medium term

High Few Middle Casual or time series

SCHEDULING

Short term Very high Many Lower Time series

INVENTORY M/MENT

Short term Very high Many Lower Time series

2.4 Time horizons for doing forecasts (cont.)

* Capacity planning for medium term 3-18 months

Use simple technique Accuracy Cost effectiveness Meaningful units Timely Reliable Should be in writing.

2.5 Requirements of an accurate forecast

2.6 Forecasting steps

Figure 2.3

Accuracy and cost – trade off between accuracy and cost

Availability of data – large pool of data and relevant

Time span – the longer it is the less accurate it is Nature of the goods and services – life cycle,

seasonal variations Changes in the market - difficult for new products Use or decision factors – method used and subject

should be closely related

2.7 Important situational factors to be considered

Failure to select applicable model Inability to recognise that forecasting must form an

integral part of the business Neglecting to monitor the accuracy of the forecast Failure to involve all of the relevant people Inability to realise that the forecast will be wrong Forecasting of incorrect items is not helpful.

2.8 Reasons for ineffective forecasts

Qualitative – mainly judgments of the parties involved

Quantitative – calculations & statistical techniques Associative forecasting techniques – use of

equations that are descriptive of the variables used. A variable is a factor that will influence the composition of a forecast eg. Price of product, weather etc

2.9 Approaches to forecasting

Categories of forecasting techniques:Associative methods – equations

that describe the variables Judgmental forecasts – rely on

subjective judgment of an individualTime series forecasts – data is

manipulated using mathematical techniques


Qualitative approach: Relied upon when hard data not available Used when forecast required in a hurry Approaches:

Consumer surveys – very expensive, validity questionable

Jury of executive opinion – top level managers, long term forecast

Sales-force opinion – grassroots method, very questionable

Delphi method – respondents outside company Educated guess – personal insight. Highly unreliable Historical analogy – only if a similar product exists


Quantitative approach: Time series data – use of historical data. Assumes

future can be based on history Trends – upward or downward movement Seasonality – mostly regular Cycles – e.g. stock market indicators Irregular variations – e.g. flood. Never include in a

forecast Random variations – no logical explanation

2.9 Approaches to forecasting (cont.)

2.9 Approaches to forecasting (cont.)

MOVING AVERAGE IN EXCEL2.9 Approaches to forecasting

PERIOD DEMAND MOVING AVERAGE

1 100

2 250

3 220

4 210 =AVERAGE(B2:B5)





Quantitative techniques: Averaging techniques – the weighted moving

average Very similar to moving average technique Moving average gives equal weight to all data Weighted moving average gives different weight to

each data


2.9 Weighted Moving AverageMonth Sales ( ‘000) 3 period MAJanuary 240February 250March 230April 220 (0.5 x 230)+ (0.3 x 250) + (0.2 x

240) = 238May 270 (0.5 x 220)+ (0.3 x 230) + (0.2 x

250) = 229June 250 (0.5 x 270)+ (0.3 x 220) + (0.2 x

230) = 247July 255 (0.5 x 250)+ (0.3 x 270) + (0.2 x

220) = 250

Longer MA period the more smoothed. Forecast less sensitive to real fluctuations

MA does not identify any trends in the data. Time lag +/- 2 months

Extensive records of past history must be availableWeight allocated is arbitrary – trial and error needed

2.9 Problems with Moving Average

Quantitative techniques: Averaging techniques – exponential smoothing Well accepted because Calculations to test accuracy are easy Technique easy to understand Accuracy high for amount of effort required Only small amounts of historical data needed Requires fewer calculations to reach the same

answer as other methods


2.9 Exponential Smoothing

Predicted 142000 units period 1 Actual 153000 units period 1 α =0.2

Demand period 2 = 142000+0.2(153000-142000) = 144200units

2.9 Exponential Smoothing

Associative forecasting techniques The simple linear regression metho Most widely used method Try to find a linear relationship between two

variables


Defined forecasting Defined business forecasting Common features Requirements Steps Situational factors Reasons for ineffective forecasts Approaches to forecasting.

Summary

Read pages 37 -66 Operations Management Prepare 1 paragraph discussing the use of

forecasts Prepare 1 paragraph discussing the reasons

for ineffective forecasts.

For Next Week