Download presentation
Presentation is loading. Please wait.
Published byBrianna Harper Modified over 9 years ago
1
DEPARTMENT OF MECHANICAL ENGINEERING VII-SEMESTER PRODUCTION TECHNOLOGY-II 1 CHAPTER NO.4 FORECASTING
2
CHAPTER 1:- SYLLABUSDTEL. Need for forecasting 1 Classification of forecasting. 2 Methods of forecasting. 3 Time series analysis 4 2 Least square Method 5
3
CHAPTER 1:- SYLLABUSDTEL. Moving average method 6 Exponential smoothing method 7 3
4
CHAPTER-1 SPECIFIC OBJECTIVE / COURSE OUTCOMEDTEL To understand the concept & need of forecasting. 1 To know the various methods & analysis relevant to demand forecasting & errors occurred in forecasting. 2 4 The student will be able to:
5
DTEL 5 5 LECTURE 1:- NEED OF FORECASTING Introduction.. “Prediction is very difficult, especially if it's about the future.” Nils Bohr
6
DTEL 6 6 LECTURE 1:- NEED OF FORECASTING What is forecasting?. Forecasting is a tool used for predicting future demand based on past demand information.
7
DTEL 7 7 LECTURE 1:- NEED OF FORECASTING Why is forecasting important?. Demand for products and services is usually uncertain. Forecasting can be used for… Strategic planning (long range planning) Finance and accounting (budgets and cost controls) Marketing (future sales, new products) Production and operations
8
DTEL 8 8 LECTURE 1:- NEED OF FORECASTING What is forecasting all about?. Demand for Mercedes E Class Time JanFebMarAprMayJunJulAug Actual demand (past sales) Predicted demand We try to predict the future by looking back at the past Predicted demand looking back six months
9
DTEL 9 9 LECTURE 1:- NEED OF FORECASTING characteristics of forecasts. Forecasts are always wrong Forecasts are more accurate for groups or families of items Forecasts are more accurate for shorter time periods Every forecast should include an error estimate Forecasts are no substitute for calculated demand.
10
DTEL 10 LECTURE 1:- NEED OF FORECASTING Key issues in forecasting. 1.A forecast is only as good as the information included in the forecast (past data) 2.History is not a perfect predictor of the future (i.e.: there is no such thing as a perfect forecast) REMEMBER: Forecasting is based on the assumption that the past predicts the future! When forecasting, think carefully whether or not the past is strongly related to what you expect to see in the future…
11
DTEL 11 LECTURE 1:- NEED OF FORECASTING Objectives. Give the fundamental rules of forecasting Calculate a forecast using a moving average, weighted moving average, and exponential smoothing Calculate the accuracy of a forecast
12
DTEL 12 THANK YOU LECTURE 1:- TYPES OF PRODUCTIVITY LECTURE 1:- NEED OF FORECASTING
13
DTEL 13 LECTURE 2:- CLASSIFIATION OF FORECASTING Classification. Judgmental technique. Time series analysis. Causal method (Econometric forecasting)
14
DTEL 14 LECTURE 2:- CLASSIFIATION OF FORECASTING Judgmental technique.. Opinion survey method. Executive opinion method. Customer and distributor survey. Marketing trials. Market research. Delphi techniques.
15
DTEL 15 LECTURE 2:- CLASSIFIATION OF FORECASTING Time series analysis.. Trend(T) Cyclical fluctuations(C) Seasonal variations(S) Irregular variations(R)
16
DTEL 16 LECTURE 2:- CLASSIFIATION OF FORECASTING Causal method. Regression and correlation Input-Output analysis. End use analysis.
17
DTEL 17 THANK YOU LECTURE 1:- TYPES OF PRODUCTIVITY LECTURE 2:- CLASSIFIATION OF FORECASTING
18
DTEL 18 LECTURE 3:- METHODS OF FORECASTING methods. Rely on data and analytical techniques. Rely on subjective opinions from one or more experts. Qualitative methodsQuantitative methods
19
DTEL 19 LECTURE 3:- METHODS OF FORECASTING Qualitative forecasting methods. Qualitative Forecasting Models Market Research/ Survey Sales Force Composite Executive Judgment Delphi Method Smoothing
20
DTEL 20 LECTURE 3:- METHODS OF FORECASTING Qualitative forecasting methods. Grass Roots: deriving future demand by asking the person closest to the customer. Market Research: trying to identify customer habits; new product ideas. Panel Consensus: deriving future estimations from the synergy of a panel of experts in the area. Historical Analogy: identifying another similar market. Delphi Method: similar to the panel consensus but with concealed identities.
21
DTEL 21 LECTURE 3:- METHODS OF FORECASTING Quantitative forecasting methods. Quantitative Forecasting Regression Models 2. Moving Average 1. Naive Time Series Models 3. Exponential Smoothing a) simple b) weighted a) level b) trend c) seasonality
22
DTEL 22 LECTURE 3:- METHODS OF FORECASTING Quantitative forecasting methods. Time Series: models that predict future demand based on past history trends Causal Relationship: models that use statistical techniques to establish relationships between various items and demand Simulation: models that can incorporate some randomness and non-linear effects
23
DTEL 23 THANK YOU LECTURE 1:- TYPES OF PRODUCTIVITY LECTURE 3:- METHODS OF FORECASTING
24
DTEL 24 LECTURE 4:- TIME SERIES ANALYSIS Time Series Models: Components. Random Seasonal Trend Composite
25
DTEL 25 LECTURE 4:- TIME SERIES ANALYSIS Product Demand over Time. Year 1 Year 2 Year 3 Year 4 Demand for product or service
26
DTEL 26 LECTURE 4:- TIME SERIES ANALYSIS Product Demand over Time. Year 1 Year 2 Year 3 Year 4 Demand for product or service Actual demand line Seasonal peaks Random variation
27
DTEL 27 LECTURE 4:- TIME SERIES ANALYSIS Time Series Models Forecaster looks for data patterns as Data = historic pattern + random variation Historic pattern to be forecasted: Level (long-term average) – data fluctuates around a constant mean Trend – data exhibits an increasing or decreasing pattern Seasonality – any pattern that regularly repeats itself and is of a constant length Cycle – patterns created by economic fluctuations
28
DTEL 28 LECTURE 4:- TIME SERIES ANALYSIS Time Series Patterns
29
DTEL 29 LECTURE 4:- TIME SERIES ANALYSIS Time Series Models Naïve : The forecast is equal to the actual value observed during the last period – good for level patterns Simple Mean: The average of all available data - good for level patterns Moving Average: The average value over a set time period (e.g.: the last four weeks) Each new forecast drops the oldest data point & adds a new observation More responsive to a trend but still lags behind actual data
30
DTEL 30 THANK YOU LECTURE 1:- TYPES OF PRODUCTIVITY LECTURE 4:- TIME SERIES ANALYSIS
31
DTEL 31 LECTURE 5:- LEAST SQUARE METHOD Linear Regression The goal of LSM is to minimize the sum of squared errors…
32
DTEL 32 LECTURE 5:- LEAST SQUARE METHOD What does that mean? Alcohol Sales Average Monthly Temperature So LSM tries to minimize the distance between the line and the points! ε ε ε
33
DTEL 33 LECTURE 5:- LEAST SQUARE METHOD Linear Regression Then the line is defined by
34
DTEL 34 LECTURE 5:- LEAST SQUARE METHOD Example b = = = 10.54 ∑xy - nxy ∑x 2 - nx 2 3,063 - (7)(4)(98.86) 140 - (7)(4 2 ) a = y - bx = 98.86 - 10.54(4) = 56.70 TimeElectrical Power YearPeriod (x)Demandx 2 xy 2001174174 20022794158 20033809240 200449016360 2005510525525 2005614236852 2007712249854 ∑x = 28∑y = 692∑x 2 = 140∑xy = 3,063 x = 4y = 98.86
35
DTEL 35 LECTURE 5:- LEAST SQUARE METHOD Example b = = = 10.54 ∑xy - nxy ∑x 2 - nx 2 3,063 - (7)(4)(98.86) 140 - (7)(4 2 ) a = y - bx = 98.86 - 10.54(4) = 56.70 TimeElectrical Power YearPeriod (x)Demandx 2 xy 2001174174 20022794158 20033809240 200449016360 2005510525525 2005614236852 2007712249854 ∑x = 28∑y = 692∑x 2 = 140∑xy = 3,063 x = 4y = 98.86 The trend line is y = 56.70 + 10.54x ^
36
DTEL 36 LECTURE 5:- LEAST SQUARE METHOD Example ||||||||| 200120022003200420052006200720082009 160 160 – 150 150 – 140 140 – 130 130 – 120 120 – 110 110 – 100 100 – 90 90 – 80 80 – 70 70 – 60 60 – 50 50 – Year Power demand Trend line, y = 56.70 + 10.54x ^
37
DTEL 37 LECTURE 5:- LEAST SQUARE METHOD Least Squares Requirements We always plot the data to insure a linear relationship We do not predict time periods far beyond the database Deviations around the least squares line are assumed to be random
38
DTEL 38 THANK YOU LECTURE 1:- TYPES OF PRODUCTIVITY LECTURE 5:- LEAST SQUARE METHOD
39
DTEL 39 LECTURE 6:- MOVING AVERAGE METHOD Simple moving average Assumes an average is a good estimator of future behavior Used if little or no trend Used for smoothing F t+1 = Forecast for the upcoming period, t+1 n = Number of periods to be averaged A t = Actual occurrence in period t
40
DTEL 40 LECTURE 6:- MOVING AVERAGE METHODExample You’re manager in Amazon’s electronics department. You want to forecast ipod sales for months 4-6 using a 3-period moving average. Month Sales (000) 14 26 35 4? 5? 6 ?
41
DTEL 41 LECTURE 6:- MOVING AVERAGE METHODExample You’re manager in Amazon’s electronics department. You want to forecast ipod sales for months 4-6 using a 3-period moving average. Month Sales (000) Moving Average (n=3) 14 NA 26 35 4? 5? (4+6+5)/3=5 6 ?
42
DTEL 42 LECTURE 6:- MOVING AVERAGE METHODExample What if ipod sales were actually 3 in month 4 Month Sales (000) Moving Average (n=3) 14 NA 26 35 43 5? 5 6 ? ?
43
DTEL 43 LECTURE 6:- MOVING AVERAGE METHODExample Forecast for Month 5? Month Sales (000) Moving Average (n=3) 14 NA 26 35 43 5? 5 6 ? (6+5+3)/3=4.667
44
DTEL 44 LECTURE 6:- MOVING AVERAGE METHODExample Actual Demand for Month 5 = 7 Month Sales (000) Moving Average (n=3) 14 NA 26 35 43 57 5 6 ? 4.667 ?
45
DTEL 45 LECTURE 6:- MOVING AVERAGE METHODExample Forecast for Month 6? Month Sales (000) Moving Average (n=3) 14 NA 26 35 43 57 5 6 ? 4.667 (5+3+7)/3=5
46
DTEL 46 LECTURE 6:- MOVING AVERAGE METHOD Weighted Moving Average: 3/6, 2/6, 1/6 Gives more emphasis to recent data Weights decrease for older data sum to 1.0 Simple moving average models weight all previous periods equally Simple moving average models weight all previous periods equally
47
DTEL 47 LECTURE 6:- MOVING AVERAGE METHOD Weighted Moving Average: 3/6, 2/6, 1/6 Month Weighted Moving Average 14 NA 26 35 4 31/6 = 5.167 5 6 ? ? ? Sales (000)
48
DTEL 48 LECTURE 6:- MOVING AVERAGE METHOD Weighted Moving Average: 3/6, 2/6, 1/6 MonthSales (000) Weighted Moving Average 14 NA 26 35 43 31/6 = 5.167 57 6 25/6 = 4.167 32/6 = 5.333
49
DTEL 49 THANK YOU LECTURE 1:- TYPES OF PRODUCTIVITY LECTURE 6:- MOVING AVERAGE METHOD
50
DTEL 50 LECTURE 7:- EXPONENTIAL SMOOTHING METHOD Introduction Form of weighted moving average Weights decline exponentially Most recent data weighted most Requires smoothing constant ( ) Ranges from 0 to 1 Subjectively chosen Involves little record keeping of past data
51
DTEL 51 LECTURE 7:- EXPONENTIAL SMOOTHING METHOD Introduction Assumes the most recent observations have the highest predictive value gives more weight to recent time periods F t+1 = F t + (A t - F t ) F t+1 = Forecast value for time t+1 A t = Actual value at time t = Smoothing constant Need initial forecast F t to start. Need initial forecast F t to start.
52
DTEL 52 LECTURE 7:- EXPONENTIAL SMOOTHING METHOD Example F t+1 = F t + (A t - F t ) iAi Given the weekly demand data what are the exponential smoothing forecasts for periods 2-10 using =0.10? Assume F 1 =D 1 Given the weekly demand data what are the exponential smoothing forecasts for periods 2-10 using =0.10? Assume F 1 =D 1
53
DTEL 53 LECTURE 7:- EXPONENTIAL SMOOTHING METHOD Example F t+1 = F t + (A t - F t ) = = F 2 = F 1 + (A 1 –F 1 ) =820+ (820–820) =820 iAiFi
54
DTEL 54 LECTURE 7:- EXPONENTIAL SMOOTHING METHOD Example F t+1 = F t + (A t - F t ) = = F 3 = F 2 + (A 2 –F 2 ) =815.5 iAiFi =820+ (775–820)
55
DTEL 55 LECTURE 7:- EXPONENTIAL SMOOTHING METHOD Example F t+1 = F t + (A t - F t ) This process continues through week 10 = = iAiFi
56
DTEL 56 LECTURE 7:- EXPONENTIAL SMOOTHING METHOD Example F t+1 = F t + (A t - F t ) What if the constant equals 0.6 = = = = iAiFi
57
DTEL 57 LECTURE 7:- EXPONENTIAL SMOOTHING METHOD Example F t+1 = F t + (A t - F t ) What if the constant equals 0.6 = = = = iAiFi
58
DTEL 58 THANK YOU LECTURE 1:- TYPES OF PRODUCTIVITY LECTURE 7:- EXPONENTIAL SMOOTHING METHOD
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.