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FORECASTING
TABLE OF CONTENTS: • What is Forecasting?• Forecasting Time Horizons• Approach of forecasting• Types of Forecasting• Importance of Forecasting• Examples of forecasting
WHAT IS FORECASTINGForecasting is the art and science of predicting future events. It
may involve taking Historical data and projecting them into the future with some sort of mathematical model.
It may be a subjective or intuitive prediction, or it may involve a combination of these (subjective + intuitive ).
FORECASTING TIME HORIZONS• A forecast is usually classified by the future time horizon that it
covers. Time Horizons fall into three categories…
SHORT RANGE FORECASTMEDIUM RANGE FORECAST
LONG RANGE FORECAST
SHORT RANGE FORECASTING►This Forecasting has a time span of up to 1 year but generally less then 3 months. It is used for..●Planning and purchasing ●Job scheduling●Workforce Levels●Job assignments●Production levels
MEDIUM RANGE FORECASTING• ►A medium range , or intermediate ,forecasting generally Spans from 3
months to 3 years. It is useful in ….• Sales Planning.• Production Planning and budgeting.• Cash Budgeting.• Analyzing Various operating plans.
LONG RANGE FORECASTING• ►In Long range Forecasting Time span is 3 years or more. Long
range forecasting are used in….• ●Planning for new products• ●Capital Expenditures• ●Facility location or expansion• ●Research and development
THREE FEATURES ARE USED TO DISTINGUISH MEDIUM AND LONG RANGE FORECASTING FROM SHORT RANGE FORECASTING
• 1) First intermediate and long range forecasting deal with more comprehensive issues and support management decisions regarding to….
• Planning and products• Plants and processes • Implementing some facility decisions• Decisions like management or GM’s decision to open a new manufacturing
plant , can take suppose 5 to 8 years from inspection to completion.
• 2) • Short term forecasting usually employs different methodologies
than longer term forecasting. Mathematical Techniques , such as moving averages , exponential smoothing and trend extrapolation are common short run projections .
• Less quantity methods are useful in predicting such issues as whether a new product , like the optical disk recorder, should be introduced into a company’s product line.
• 3)• Short range forecasting tend to be more accurate than longer
range forecasting .• Factors that influence demand change every day, thus as the time
horizon lengthens, it is likely that forecast accuracy will decreases.• Sales forecasts must be updated regularly to maintain their value
and integrity.• After each sales period , forecasting should be reviewed and
revised.
TYPES OF FORECASTING• Organizations use three main types of forecasts in future
operations, which are….ECONOMIC FORECAST
TECHNOLOGICAL FORECAST
DEMAND FORECAST
ECONOMIC FORECASTSEconomic Forecasts address the followings…..• It address the business cycle by predicting inflation rates • Money supplies• Housing starts and other planning indicators
TECHNOLOGICAL FORECASTS• Technological forecasts are concerned with rates of technological
progress , which can result in the birth of exciting new products , requiring new plants and equipment.
DEMAND FORECASTS• Demand Forecasts are projections of demand for a company’s
product or services.• These Forecasts are also called sales forecasts , drive a company’s
production., capacity , scheduling systems and serve as input to financial ,marketing, and personnel planning.
IMPORTANCE OF DEMAND FORECASTING
• Crucial to supplier, manufacturer or retailer• Business decisions• Planning for future finished goods• accurate demand forecasts lead to efficient operations and high
levels of customer service• Improve quality & effectiveness of product
LEVELS OF DEMAND FORECASTING1) Micro Level- Demand forecasting by individuals business
firm for forecasting the demand for its product.2) Industrial Level- Demand estimate for the product of the
industry3) Macro Level- Aggregate demand forecasting for industrial
output at the national level- it is based on the national income/ aggregate expenditure of the company.
IMPORTANCE OF FORECASTING• Good Forecasts are of critical importance in all aspects of a business:• The forecast is the only estimate of demand until actual demand
becomes known. Forecast of demand therefore drive decisions in many areas . The impact of product forecast on three activities…
HUMAN RESOURCES
CAPACITYSUPPLY CHAIN MANAGMENT
HUMAN RESOURCES• Hiring, training, and laying off workers all depend on anticipated demand.• If the human resources department must hire additional workers without
warning , the amount of training declines and the quality of the workforce suffers.
CAPACITY• When capacity is inadequate , the resulting shortages can mean
undependable delivery, loss of customers, and loss of market share.• This is exactly what happened to Nabisco when it underestimated the
huge demand for its new fat Snack well Devil’s food cookies. Even with production lines working overtime, Nabisco could not keep up with demand, and it lost customers.
• When excess capacity is built ,on the other hand , cost can skyrocket.
SUPPLY CHAIN MANAGEMENT• Good supplier relations and the ensuring price advantages for materials
and parts depend on accurate forecasts.
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Seven Steps in Forecasting (Demands) Determine the use of the forecast Select the items to be forecast Determine the time horizon of the forecast Select the forecasting model(s) Gather the data Make the forecast Validate and implement results
APPROACH OF FORECASTINGQualitative
Quantitative
FORECASTING
FORECASTING DURING THE LIFE CYCLE
Introduction Growth Maturity Decline
Sales
Time
Quantitative models
- Time series analysis- Regression analysis
Qualitative models- Executive judgment- Market research-Survey of sales force-Delphi method
QUALITATIVE FORECASTING APPROACHI. Judgmental approach
• Surveys• Consensus methods• Delphi method
II. Experimental approach• Test marketing• Customer buying database• Customer panels
ADVANTAGES & DISADVANTAGES OF QUALITATIVE FORECASTINGAdvantages :-o Ability to predict changeso Flexibilityo Ambiguity
Disadvantages :-o Accurate forecast is not possibleo Judgmental approacho False/ inadequate information
QUANTITATIVE FORECASTING APPROACH
• Relationship approach• Econometric models• Life cycle models• Input-output models
• Time series approach• Static models • Adaptive models
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QUANTITATIVE FORECASTING MODELS· Time Series Models
Naive ForecastSimple Moving AveragesWeighted Moving AveragesSimple Exponential SmoothingExponential Smoothing with TrendLinear Trend ProjectionTime Series Decomposition
· Associative (Causal) ModelsSimple Linear RegressionMultiple Linear Regression Nonlinear Regression
TIME SERIES PATTERN: STATIONARY• The result of many influences
that act independently so as to yield nonsystematic and non-repeating patterns about some average value.
• Forecasting methods: naive, moving average, exponential smoothing
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TIME SERIES PATTERN: TREND
• It represents a general increase or decrease in a time series over several consecutive periods (some sources present six-seven or more periods).
• Forecasting methods: linear trend projection, exponential smoothing with trend, etc.
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TIME SERIES PATTERN: SEASONAL
• Seasonal Patterns represent patterns that are periodic and recurrent (usually on a quarterly, monthly, or annual basis).
• Forecasting methods: exponential smoothing with trend and seasonality, time series decomposition, etc.
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TIME SERIES PATTERN: CYCLICAL
• The result of economic and business expansions (increasing demand) and contractions (recessions and depressions) and usually repeat every two-five years. Cyclical influences are difficult to forecast because cyclical demands are recurrent but not periodic (they happen in different intervals of time with great variability of demands).
• Forecasting methods: time series decomposition, multiple regression
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Product Demand Charted over 4 Years with Trend and Seasonality
Year1
Year2
Year3
Year4
Seasonal peaks Trend component
Actual demand line
Average demand over four years
Dem
and
for p
rodu
ct o
r ser
vice
Random variation
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Set of evenly spaced numerical data Obtained by observing response variable at
regular time periods Forecast based only on past values
Assumes that factors influencing past and present will continue influence in future
ExampleYear: 19931994199519961997Sales: 78.7 63.5 89.7 93.2 92.1
What is a Time Series?
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NAÏVE APPROACH• Assumes demand in next period is the same as demand in most recent
periode.g., If May sales were 48, then June sales will be around 48
• Sometimes it is effective & cost efficiente.g. when the demand is steady or changes slowly
when inventory cost is low when unmet demand will not lose
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MA is a series of arithmetic means Used if little or no trend, seasonal, and cyclical
patterns Used often for smoothing
Provides overall impression of data over time Equation
MAn
n Demand in Previous Periods
Moving Average Method
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You’re manager of a museum store that sells historical replicas. You want to forecast sales of item (123) for 2000 using a 3-period moving average.
1995 41996 61997 51998 31999 7
© 1995 Corel Corp.
Moving Average Example
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Moving Average SolutionTime Response
Yi
MovingTotal(n=3)
MovingAverage
(n=3)1995 4 NA NA1996 6 NA NA1997 5 NA NA1998 3 4+6+5=15 15/3 = 51999 72000 NA
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Moving Average Solution
Time ResponseYi
MovingTotal(n=3)
MovingAverage
(n=3)1995 4 NA NA1996 6 NA NA1997 5 NA NA1998 3 4+6+5=15 15/3 = 51999 7 6+5+3=14 14/3=4 2/32000 NA
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Moving Average Solution
Time ResponseYi
MovingTotal(n=3)
MovingAverage
(n=3)1995 4 NA NA1996 6 NA NA1997 5 NA NA1998 3 4+6+5=15 15/3=5.01999 7 6+5+3=14 14/3=4.72000 NA 5+3+7=15 15/3=5.0
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95 96 97 98 99 00Year
Sales
2468 Actual
Forecast
Moving Average Graph
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Used when trend is present Older data usually less important
Weights based on intuitionOften lay between 0 & 1, & sum to 1.0
Equation
WMA =Σ(Weight for period n) (Demand in period n)
ΣWeights
Weighted Moving Average Method
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Actual Demand, Moving Average, Weighted Moving Average
0
5
10
15
20
25
30
35
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Sale
s Dem
and
Actual sales
Moving average
Weighted moving average
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Increasing n makes forecast less sensitive to changes Do not forecast trend well due to the delay between actual outcome
and forecast Difficult to trace seasonal and cyclical patterns Require much historical data Weighted MA may perform better
Disadvantages of Moving Average Methods
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Form of weighted moving average Weights decline exponentially Most recent data weighted most
Requires smoothing constant (α) Ranges from 0 to 1 Subjectively chosen
Involves little record keeping of past data
Exponential Smoothing Method
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Ft = Ft-1 + (At-1 - Ft-1) = At-1 + (1 - ) Ft-1
Ft = Forecast value At = Actual value = Smoothing constant
Ft = At - 1 + (1-)At - 2 + (1- )2·At - 3 + (1- )3At - 4 + ... + (1- )t-1·A0Use for computing forecast
Exponential Smoothing Equations
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You’re organizing a Kwanza meeting. You want to forecast attendance for 2000 using exponential smoothing ( = .10). The 1995 (made in 1994) forecast was 175.Actual data:
1995 1801996 1681997 1591998 1751999 190
© 1995 Corel Corp.
Exponential Smoothing Example
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Ft = Ft-1 + ·(At-1 - Ft-1)
Time Actual Forecast, F t
( α = .10)1995 180 175.00 (Given)1996 1681997 1591998 1751999 1902000 NA
175.00 +
Exponential Smoothing Solution
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Exponential Smoothing Solution
Time Actual Forecast, F t
( α = .10)1995 180 175.00 (Given)1996 168 175.00 + .10(1997 1591998 1751999 1902000 NA
Ft = Ft-1 + ·(At-1 - Ft-1)
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Exponential Smoothing Solution
Time Actual Forecast, F t(α = .10)
1995 180 175.00 (Given)1996 168 175.00 + .10(180 -1997 1591998 1751999 1902000 NA
Ft = Ft-1 + ·(At-1 - Ft-1)
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Exponential Smoothing Solution
Time Actual Forecast, F t(α = .10)
1995 180 175.00 (Given)1996 168 175.00 + .10(180 - 175.00)1997 1591998 1751999 1902000 NA
Ft = Ft-1 + ·(At-1 - Ft-1)
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Exponential Smoothing Solution
Time Actual Forecast, F t(α = .10)
1995 180 175.00 (Given)1996 168 175.00 + .10(180 - 175.00) = 175.501997 1591998 1751999 1902000 NA
Ft = Ft-1 + ·(At-1 - Ft-1)
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Exponential Smoothing Solution
Time Actual Forecast, F t
( α = .10)
1995 180 175.00 (Given)1996 168 175.00 + .10(180 - 175.00) = 175.501997 159 175.50 + .10(168 - 175.50) = 174.751998 1751999 1902000 NA
Ft = Ft-1 + ·(At-1 - Ft-1)
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Exponential Smoothing Solution
Time Actual Forecast, F t
( α = .10)
1995 180 175.00 (Given)1996 168 175.00 + .10(180 - 175.00) = 175.501997 159 175.50 + .10(168 - 175.50) = 174.751998 175 174.75 + .10(159 - 174.75) = 173.181999 190 173.18 + .10(175 - 173.18) = 173.362000 NA 173.36 + .10(190 - 173.36) = 175.02
Ft = Ft-1 + ·(At-1 - Ft-1)
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Year
Sales
140150160170180190
93 94 95 96 97 98
Actual
Forecast
Exponential Smoothing Graph
FORECASTING EXAMPLES• Examples from Projects:
• Demand for tellers in a bank;• Traffic flow at a major junction• Pre-poll opinion survey amongst voters• Demand for automobiles or consumer durables• Segmented demand for varying food types in a restaurant• Area demand for frozen foods within a locality
• Example from Retail Industry: American Hospital Supply Corp.• 70,000 items;• 25 stocking locations;• Store 3 years of data (63 million data points);• Update forecasts monthly;• 21 million forecast updates per year.
