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8/4/2019 6.0-Topic 6_ Forecasting
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Operations Management
Topic 6 – Forecasting
UiTM Shah Alam Lecturer: Pn. Noriah Yusoff T1-A16-6C
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What is Forecasting?
Process of predicting afuture event
Underlying basis of all business decisions Production
Inventory
Personnel
Facilities
Hmm…. you
gonna get an A forthis subject
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Short-range forecast Up to 1 year, generally less than 3 months
Purchasing, job scheduling, workforce levels, jobassignments, production levels
Medium-range forecast 3 months to 3 years
Sales and production planning, budgeting
Long-range forecast
3+ years New product planning, facility location, research and
development
Forecasting Time Horizons
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Seven Steps in Forecasting
Determine the use of the forecast
Select the items to be forecasted
Determine the time horizon of the forecast
Select the forecasting model(s)
Gather the data
Make the forecast Validate and implement results
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Types of Forecasts
Economic forecasts
Address business cycle – inflation rate, money
supply, housing starts, etc.
Technological forecasts
Predict rate of technological progress
Impacts development of new products
Demand forecasts Predict sales of existing products and services
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Strategic Importance of
Forecasting
Human Resources – Hiring, training, laying off
workers Capacity – Capacity shortages can result in
undependable delivery, loss of customers,loss of market share
Supply Chain Management – Good supplierrelations and price advantages
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The Realities!
Forecasts are seldom perfect
Most techniques assume an underlying stability in the system
Product family and aggregated forecasts are more accurate than individual product forecasts
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Forecasting Approaches
Used when situation is vague and
little data exist New products
New technology
Involves intuition, experience
e.g., forecasting sales on Internet
Qualitative Methods
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Forecasting Approaches
Used when situation is ‘stable’ and
historical data exist Existing products
Current technology
Involves mathematical techniques
e.g., forecasting sales of color televisions
Quantitative Methods
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Overview of Quantitative
Approaches1. Naive approach
2. Moving averages3. Exponential
smoothing
4. Trend projection5. Linear regression
Time-SeriesModels
AssociativeModel
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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, no
other variables important
Assumes that factors influencing past andpresent will continue influence in future
Time Series Forecasting
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Components of Demand
D e m a
n d f o r p r o d u c t o r
s e r v i c e
| | | |
1 2 3 4
Year
Average demand
over four years
Seasonal peaks
Trendcomponent
Actualdemand
Randomvariation
Figure 4.1
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Persistent, overall upward or
downward pattern
Changes due to population,technology, age, culture, etc.
Typically several years duration
Trend Component
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Regular pattern of up and down
fluctuations
Due to weather, customs, etc. Occurs within a single year
Seasonal Component
Number of Period Length Seasons
Week Day 7Month Week 4-4.5Month Day 28-31Year Quarter 4Year Month 12
Year Week 52
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Repeating up and down movements
Affected by business cycle, political, and
economic factors Multiple years duration
Often causal or
associativerelationships
Cyclical Component
0 5 10 15 20
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Erratic, unsystematic, ‘residual’
fluctuations
Due to random variation or unforeseenevents
Short duration and
nonrepeating
Random Component
M T W T F
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Naive Approach
Assumes demand in nextperiod is the same as
demand in most recent period e.g., If January sales were 68, then
February sales will be 68
Sometimes cost effective and efficient
Can be good starting point
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Moving average
Weighted moving average
Exponential smoothing
Techniques for Averaging
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MA is a series of arithmetic means
Used if little or no trend
Used often for smoothingProvides overall impression of data over
time
Moving Average Method
Moving average =∑ demand in previous n periods
n
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January 10
February 12 March 13 April 16May 19
June 23July 26
Actual 3-Month Month Shed Sales Moving Average
(12 + 13 + 16)/3 = 13 2/3
(13 + 16 + 19)/3 = 16(16 + 19 + 23)/3 = 19 1/3
Moving Average Example
10
12 13
(10 + 12 + 13)/3 = 11 2/3
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Graph of Moving Average
| | | | | | | | | | | |
J F M A M J J A S O N D
S h e d S a l e s
30 –
28 –
26 –
24 –
22 –
20 –
18 –
16 –
14 – 12 –
10 –
Actual
Sales
Moving Average Forecast
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Used when trend is present
Older data usually less important
Weights based on experience andintuition
Weighted Moving Average
Weighted moving average =
∑ (weight for period n ) x (demand in period n )
∑ weights
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January 10 February 12 March 13
April 16May 19June 23July 26
Actual 3-Month Weighted
Month Shed Sales Moving Average
[(3 x 16) + (2 x 13) + (12)]/6 = 141/3 [(3 x 19) + (2 x 16) + (13)]/6 = 17[(3 x 23) + (2 x 19) + (16)]/6 = 201/2
Weighted Moving Average
10 12 13
[(3 x 13) + (2 x 12) + (10)]/6 = 121
/6
Weights Applied Period
3 Last month 2 Two months ago 1 Three months ago
6 Sum of weights
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Moving Average AndWeighted Moving Average
30 –
25 –
20 –
15 –
10 –
5 –
S a l e s d e m a n
d
| | | | | | | | | | | |
J F M A M J J A S O N D
Actual sales
Moving average
Weighted moving average
Figure 4.2
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Increasing n smooths the forecast but
makes it less sensitive to changes Do not forecast trends well
Require extensive historical data
Potential Problems With
Moving Average
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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
Exponential Smoothing
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Exponential Smoothing
New forecast = Last period’s forecast + (Last period’s actual demand
– Last period’s forecast )
F t = F t – 1 + (At – 1 - F t – 1)
where F t = new forecast F t – 1 = previous forecast
= smoothing (or weighting) constant (0 ≤ ≤ 1)
Remember This!!!!!!!!
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Choosing
The objective is to obtain the most accurate forecast no matter the
technique We generally do this by selecting the model that gives us the lowest forecast error
Forecast error = Actual demand - Forecast value
= At - F t
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Common Measures of Error
Mean Absolute Deviation (MAD )
MAD = ∑ |Actual - Forecast| n
Mean Squared Error (MSE )
MSE = ∑ (Forecast Errors )2
n
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Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs Actual demand = 153
Smoothing constant = .20
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Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs Actual demand = 153
Smoothing constant = .20
New forecast = 142 + .2(153 – 142)
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Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs Actual demand = 153
Smoothing constant = .20
New forecast = 142 + .2(153 – 142)
= 142 + 2.2= 144.2 ≈ 144 cars
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Exponential Smoothing Example
2Demand for the last four months was:
Predict demand for July using each of these methods:(A)1) A 3-period moving average2) exponential smoothing with alpha equal to .20 (use naïve to
begin).(B)3) If the naive approach had been used to predict demand for April
through June, what would MAD have been for those months?
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Exponential Smoothing Example 2
Month Demand Forecast
March 6 -
April 8 6May 10 6 + 0.2(8 – 6) = 6.4
June 8 6.4 + 0.2(10 – 6.4) = 7.12
7.12 + 0.2(8 – 7.12) = 7.296
A) 1. (8+10+8)/3 = 8.33 (July Forecast)2. Use naïve to begin
B)
Month March April May JuneDemand 6 8 10 8
Naïve - 6 8 10
Error - +2 +2 -2
MAD 6/3 = 2.0
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Exponential Smoothing with Trend
AdjustmentWhen a trend is present, exponential smoothing must be modified
Forecast including (FIT t ) = trend
Exponentially Exponentially smoothed (F t ) + (T t ) smoothed forecast trend
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Exponential Smoothing with Trend
Adjustment
F t = (At - 1) + (1 - )(F t - 1 + T t - 1)
T t = b(F t - F t - 1) + (1 - b)T t - 1
Step 1: Compute F t Step 2: Compute T t
Step 3: Calculate the forecast FIT t = F t + T t
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Moving Average
Weekly sales of ten-grain bread at the local organic food market are in the
table below. Based on this data, forecast week 9 using a five-week moving
average.
Other Examples
Week 1 2 3 4 5 6 7 8
Sales 415 389 420 382 410 432 405 421
(382+410+432+405+421)/5 = 410.0
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Exponential Smoothing & MAD Jim's department at a local department store has tracked the sales of a productover the last ten weeks. Forecast demand using exponential smoothing withan alpha of 0.4, and an initial forecast of 28.0. Calculate MAD.
Other Examples
Period Demand1 24
2 23
3 26
4 36
5 26
6 30
7 32
8 26
9 25
10 28
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Period Demand Forecast Error Absolute
1 24 28.00
2 23 26.40 -3.40 3.40
3 26 25.04 0.96 0.96
4 36 25.42 10.58 10.58 5 26 29.65 -3.65 3.65
6 30 28.19 1.81 1.81
7 32 28.92 3.08 3.08
8 26 30.15 -4.15 4.15
9 25 28.49 -3.49 3.49 10 28 27.09 0.91 0.91
Total 2.64 32.03
Average 0.29 3.56
Bias MAD
Other Examples – Exponential Smoothing