Ops management lecture 2 forecasting
Upcoming SlideShare
Loading in...5
×
 

Ops management lecture 2 forecasting

on

  • 2,188 views

Ops Management Lecture 2

Ops Management Lecture 2

Statistics

Views

Total Views
2,188
Views on SlideShare
2,188
Embed Views
0

Actions

Likes
1
Downloads
71
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Ops management lecture 2 forecasting Ops management lecture 2 forecasting Presentation Transcript

    • Session Two Forecasting
    • Learning objectives∗ 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 (cont.)∗ Solve simple moving average problems∗ Solve exponential smoothing problems∗ Determine what constitutes a good forecast∗ Compare qualitative and quantitative forecasting methods.
    • 2.1 Introduction∗ 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.
    • Examples of ForecastsManagement Information Services – could predict new technology, eg.Advances in internetHuman Resources – could answer the question “will there be a needto employ more people in the futureGoods and service design – needs of the customer in thefutureFinance – need for capital replacement, cash flows,budgetsOperations – scheduling, inventory planning, labourrequirements, and project managementMarketing – prices for new products, promotionalplans, competition analysis
    • 2.2 The uses of forecasts∗ 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.
    • 2.3 Features common to all 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.
    • 2.4 Time horizons for doing 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 (cont.) Time Horizon Accuracy Frequency Management Method Level PROCESS Long term Medium Single Top Qual or Quan DESIGN CAPACITY Long term Medium Single Top Qual or Quan PLANNING AGGREGATE Medium term High Few Middle Casual or time PLANNING* series SCHEDULING Short term Very high Many Lower Time seriesINVENTORY M/ Short term Very high Many Lower Time series MENT* Capacity planning for medium term 3-18 months
    • 2.5 Requirements of an accurate forecast∗ Use simple technique∗ Accuracy∗ Cost effectiveness∗ Meaningful units∗ Timely∗ Reliable∗ Should be in writing.
    • 2.6 Forecasting stepsFigure 2.3
    • 2.7 Important situational factors to be considered∗ 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
    • 2.8 Reasons for ineffective forecasts ∗ 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.
    • 2.9 Approaches to forecasting∗ 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 individual ∗ Time 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 (cont.) ∗ 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 forecastingMOVING AVERAGE IN EXCEL 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)
    • 2.9 Approaches to forecasting∗ 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
    • 2.9 Problems with Moving Average 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 available Weight allocated is arbitrary – trial and error needed
    • 2.9 Approaches to forecasting∗ 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
    • 2.9 Exponential Smoothing∗ Predicted 142000 units period 1∗ Actual 153000 units period 1∗ α =0.2Demand period 2 = 142000+0.2(153000-142000) = 144200units
    • 2.9 Approaches to forecasting∗ Associative forecasting techniques ∗ The simple linear regression metho ∗ Most widely used method ∗ Try to find a linear relationship between two variables
    • Summary∗ Defined forecasting∗ Defined business forecasting∗ Common features∗ Requirements∗ Steps∗ Situational factors∗ Reasons for ineffective forecasts∗ Approaches to forecasting.
    • For Next Week∗ Read pages 37 -66 Operations Management∗ Prepare 1 paragraph discussing the use of forecasts∗ Prepare 1 paragraph discussing the reasons for ineffective forecasts.