ops management lecture 2 forecasting
TRANSCRIPT
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
2.9 Approaches to forecasting
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
2.9 Approaches to forecasting
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)
5 240 =AVERAGE(B3:B6)
6 255 =AVERAGE(B4:B7)
7 245 =AVERAGE(B5:B8)
8 195 =AVERAGE(B6:B9)
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 Approaches to forecasting
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 Approaches to forecasting
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
2.9 Approaches to forecasting
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.
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