mba ii pmom_unit-1.3 forecasting a
TRANSCRIPT
What is Forecasting
Process of predicting a future event based on historical data
Educated Guessing
Underlying basis of all business decisions Production
Inventory
Personnel
Facilities
Importance of Forecasting in PM &OM
• Departments throughout the organization depend on forecasts to formulate and execute their plans.
• Finance needs forecasts to project cash flows and capital requirements.
• Human resources need forecasts to anticipate hiring needs.
• Production needs forecasts to plan production levels, workforce, material requirements, inventories, etc.
Importance of Forecasting in PM &OM
• Demand is not the only variable of interest to forecasters.
• Manufacturers also forecast worker absenteeism, machine availability, material costs, transportation and production lead times, etc.
• Besides demand, service providers are also interested in forecasts of population, of other demographic variables, of weather, etc.
Nature
FORECAST:• A statement about the future value of a variable of
interest such as demand.• Forecasts affect decisions and activities throughout an
organization– Accounting, finance– Human resources– Marketing– MIS– Operations– Product / service design
Uses of Forecasts
Accounting Cost/profit estimates
Finance Cash flow and funding
Human Resources Hiring/recruiting/training
Marketing Pricing, promotion, strategy
MIS IT/IS systems, services
Operations Schedules, MRP, workloads
Product/service design New products and services
• Assumes causal systempast ==> future
• Forecasts rarely perfect because of randomness
• Forecasts more accurate forgroups vs. individuals
• Forecast accuracy decreases as time horizon increases
I see that you willget an A this semester.
Steps in the Forecasting Process
Step 1 Determine purpose of forecast
Step 2 Establish a time horizon
Step 3 Select a forecasting technique
Step 4 Gather and analyze data
Step 5 Prepare the forecast
Step 6 Monitor the forecast
“The forecast”
Types of Forecasts
• Judgmental - uses subjective inputs
• Time series - uses historical data assuming the future will be like the past
• Associative models - uses explanatory variables to predict the future
Judgmental Forecasts
• Executive opinions
• Sales force opinions
• Consumer surveys
• Outside opinion
• Delphi method
– Opinions of managers and staff
– Achieves a consensus forecast
Time Series Forecasts
• Trend - long-term movement in data
• Seasonality - short-term regular variations in data
• Cycle – wavelike variations of more than one year’s duration
• Irregular variations - caused by unusual circumstances
• Random variations - caused by chance
Types of Forecasts by Time Horizon
• Short-range forecast
– Usually < 3 months• Job scheduling, worker assignments
• Medium-range forecast
– 3 months to 2 years• Sales/production planning
• Long-range forecast
– > 2 years• New product planning
Quantitativemethods
QualitativeMethods
Detailed use ofsystem
Designof system
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 Methods
Qualitative
Forecasting
Models
MarketResearch/Survey
SalesForceComposite
Executive Judgement
DelphiMethod
Smoothing
Qualitative Methods
Briefly, the qualitative methods are:
Executive Judgment: Opinion of a group of high level experts or managers is pooled
Sales Force Composite: Each regional salesperson provides his/her sales estimates. Those forecasts are then reviewed to make sure they are realistic. All regional forecasts are then pooled at the district and national levels to obtain an overall forecast.
Market Research/Survey: Solicits input from customers pertaining to their future purchasing plans. It involves the use of questionnaires, consumer panels and tests of new products and services.
• .
Delphi Method
Delphi Method: As opposed to regular panels where the individuals involved are in direct communication, this method eliminates the effects of group potential dominance of the most vocal members. The group involves individuals from inside as well as outside the organization.
Typically, the procedure consists of the following steps:Each expert in the group makes his/her own forecasts in form of statements
The coordinator collects all group statements and summarizes them The coordinator provides this summary and gives another set of
questions to each group member including feedback as to the input of other experts.
The above steps are repeated until a consensus is reached.
• .
Quantitative Forecasting MethodsQuantitative
Forecasting
RegressionModels
2. MovingAverage
1. Naive
Time SeriesModels
3. ExponentialSmoothing
a) simple
b) weighteda) level
b) trend
c) seasonality
Time Series Models
• Try to predict the future based on past data
– Assume that factors influencing the past will continue to influence the future
Product Demand over Time
Year1
Year2
Year3
Year4
Dem
and
for
prod
uct o
r se
rvic
e
Trend component
Actual demand
line
Seasonal peaks
Random
variation
Now let’s look at some time series approaches to forecasting…Borrowed from Heizer/Render - Principles of Operations Management, 5e, and Operations Management, 7e
Quantitative Forecasting Methods
Quantitative
Models
2. MovingAverage
1. Naive
Time Series
Models
3. ExponentialSmoothing
a) simple
b) weighteda) level
b) trend
c) seasonality
1. Naive Approach
Demand in next period is the same as demand in most recent period
May sales = 48 →
Usually not good
June forecast = 48
Naïve Approach
• Simple to use
• Virtually no cost
• Quick and easy to prepare
• Data analysis is nonexistent
• Easily understandable
• Cannot provide high accuracy
• Can be a standard for accuracy
2a. Simple Moving Average
n
A+...+A +A +A =F 1n-t2-t1-tt
1t
• Assumes an average is a good estimator of future behavior
– Used if little or no trend
– Used for smoothing
Ft+1 = Forecast for the upcoming period, t+1
n = Number of periods to be averaged
A t = Actual occurrence in period t
2a. Simple Moving Average
You’re manager in Amazon’s electronics department.
You want to forecast ipod sales for months 4-6 using a
3-period moving average.
n
A+...+A +A +A =F 1n-t2-t1-tt
1t
MonthSales
(000)
1 4
2 6
3 54 ?5 ?6 ?
2a. Simple Moving Average
MonthSales
(000)Moving Average
(n=3)1 4 NA
2 6 NA
3 5 NA4 ?5 ?
(4+6+5)/3=5
6 ?
n
A+...+A +A +A =F 1n-t2-t1-tt
1t
You’re manager in Amazon’s electronics department.
You want to forecast ipod sales for months 4-6 using a
3-period moving average.
What if ipod sales were actually 3 in month 4
MonthSales
(000)Moving Average
(n=3)1 4 NA
2 6 NA
3 5 NA4 35 ?
5
6 ?
2a. Simple Moving Average
?
Forecast for Month 5?
MonthSales
(000)Moving Average
(n=3)1 4 NA
2 6 NA
3 5 NA4 35 ?
5
6 ?
(6+5+3)/3=4.667
2a. Simple Moving Average
Actual Demand for Month 5 = 7
MonthSales
(000)Moving Average
(n=3)1 4 NA
2 6 NA
3 5 NA4 35 7
5
6 ?
4.667
2a. Simple Moving Average
?
Forecast for Month 6?
MonthSales
(000)Moving Average
(n=3)1 4 NA
2 6 NA
3 5 NA4 35 7
5
6 ?
4.667(5+3+7)/3=5
2a. Simple Moving Average
• Gives more emphasis to recent data
• Weights
– decrease for older data
– sum to 1.0
2b. Weighted Moving Average
1n-tn2-t31-t2t11t Aw+...+Aw+A w+A w=F
Simple movingaverage models
weight all previousperiods equally
2b. Weighted Moving Average: 3/6, 2/6, 1/6
Month Weighted
Moving
Average1 4 NA
2 6 NA
3 5 NA4 31/6 = 5.167
56 ?
??
1n-tn2-t31-t2t11t Aw+...+Aw+A w+A w=F
Sales(000)
2b. Weighted Moving Average: 3/6, 2/6, 1/6
Month Sales(000)
Weighted
Moving
Average1 4 NA
2 6 NA
3 5 NA4 3 31/6 = 5.167
5 76
25/6 = 4.16732/6 = 5.333
1n-tn2-t31-t2t11t Aw+...+Aw+A w+A w=F
3a. Exponential Smoothing
• Assumes the most recent observations have the highest predictive value
– gives more weight to recent time periods
Ft+1 = Ft + a(At - Ft)
et
Ft+1 = Forecast value for time t+1
At = Actual value at time t
a = Smoothing constant
Need initial
forecast Ft
to start.
3a. Exponential Smoothing – Example 1
Week Demand
1 820
2 775
3 680
4 655
5 750
6 802
7 798
8 689
9 775
10
Given the weekly demand data what are the exponentialsmoothing forecasts for periods 2-10 using a=0.10?
Assume F1=D1
Ft+1 = Ft + a(At - Ft)i Ai
Week Demand 0.1 0.6
1 820 820.00 820.00
2 775 820.00 820.00
3 680 815.50 793.00
4 655 801.95 725.20
5 750 787.26 683.08
6 802 783.53 723.23
7 798 785.38 770.49
8 689 786.64 787.00
9 775 776.88 728.20
10 776.69 756.28
Ft+1 = Ft + a(At - Ft)
3a. Exponential Smoothing – Example 1
a =
F2 = F1+ a(A1–F1) =820+.1(820–820)
=820
i Ai Fi
Week Demand 0.1 0.6
1 820 820.00 820.00
2 775 820.00 820.00
3 680 815.50 793.00
4 655 801.95 725.20
5 750 787.26 683.08
6 802 783.53 723.23
7 798 785.38 770.49
8 689 786.64 787.00
9 775 776.88 728.20
10 776.69 756.28
Ft+1 = Ft + a(At - Ft)
3a. Exponential Smoothing – Example 1
a =
F3 = F2+ a(A2–F2) =820+.1(775–820)
=815.5
i Ai Fi
Week Demand 0.1 0.6
1 820 820.00 820.00
2 775 820.00 820.00
3 680 815.50 793.00
4 655 801.95 725.20
5 750 787.26 683.08
6 802 783.53 723.23
7 798 785.38 770.49
8 689 786.64 787.00
9 775 776.88 728.20
10 776.69 756.28
Ft+1 = Ft + a(At - Ft)
This process continues
through week 10
3a. Exponential Smoothing – Example 1
a =
i Ai Fi
Week Demand 0.1 0.6
1 820 820.00 820.00
2 775 820.00 820.00
3 680 815.50 793.00
4 655 801.95 725.20
5 750 787.26 683.08
6 802 783.53 723.23
7 798 785.38 770.49
8 689 786.64 787.00
9 775 776.88 728.20
10 776.69 756.28
Ft+1 = Ft + a(At - Ft)
What if the a constant equals 0.6
3a. Exponential Smoothing – Example 1
a = a =
i Ai Fi
Month Demand 0.3 0.6
January 120 100.00 100.00
February 90 106.00 112.00
March 101 101.20 98.80
April 91 101.14 100.12
May 115 98.10 94.65
June 83 103.17 106.86
July 97.12 92.54
August
September
Ft+1 = Ft + a(At - Ft)
What if the a constant equals 0.6
3a. Exponential Smoothing – Example 2
a = a =
i Ai Fi
Company A, a personal computer producer
purchases generic parts and assembles them to
final product. Even though most of the orders
require customization, they have many common
components. Thus, managers of Company A need
a good forecast of demand so that they can
purchase computer parts accordingly to minimize
inventory cost while meeting acceptable service
level. Demand data for its computers for the past 5
months is given in the following table.
3a. Exponential Smoothing – Example 3
Month Demand 0.3 0.5
January 80 84.00 84.00
February 84 82.80 82.00
March 82 83.16 83.00
April 85 82.81 82.50
May 89 83.47 83.75
June 85.13 86.38
July ?? ??
Ft+1 = Ft + a(At - Ft)
What if the a constant equals 0.5
3a. Exponential Smoothing – Example 3
a = a =
i Ai Fi
• How to choose α– depends on the emphasis you want to place on
the most recent data
• Increasing α makes forecast more sensitive to recent data
3a. Exponential Smoothing
Ft+1 = a At + a(1- a) At - 1 + a(1- a)2At - 2 + ...
Forecast Effects ofSmoothing Constant a
Weights
Prior Period
a
2 periods ago
a(1 - a)
3 periods ago
a(1 - a)2
a=
a= 0.10
a= 0.90
10% 9% 8.1%
90% 9% 0.9%
Ft+1 = Ft + a (At - Ft)
or
w1 w2 w3
• Collect historical data
• Select a model
– Moving average methods• Select n (number of periods)• For weighted moving average: select weights
– Exponential smoothing• Select a
• Selections should produce a good forecast
To Use a Forecasting Method
…but what is a good forecast?
Measures of Forecast Error
b. MSE = Mean Squared Error
n
F-A
=MSE
n
1=t
2
tt
MAD =
A - F
n
t tt=1
n
et
Ideal values =0 (i.e., no forecasting error)
MSE =RMSEc. RMSE = Root Mean Squared Error
a. MAD = Mean Absolute Deviation
MAD Example
Month Sales Forecast1 220 n/a
2 250 255
3 210 205
4 300 320
5 325 315
What is the MAD value given the forecast
values in the table below?
MAD =
A - F
n
t tt=1
n
55
20
10
|At – Ft|
FtAt
= 40
= 404
=10
MSE/RMSE Example
Month Sales Forecast1 220 n/a
2 250 255
3 210 205
4 300 320
5 325 315
What is the MSE value?
55
20
10
|At – Ft|
FtAt
= 5504
=137.5
(At – Ft)2
2525
400
100
= 550
n
F-A
=MSE
n
1=t
2
tt
RMSE = √137.5
=11.73
Measures of Error
t At Ft et |et| et2
Jan 120 100 20 20 400
Feb 90 106 256
Mar 101 102
April 91 101
May 115 98
June 83 103
1. Mean Absolute Deviation (MAD)
n
e
MAD
n
t 1
2a. Mean Squared Error (MSE)
MSE
e
n
t
n
2
1
2b. Root Mean Squared Error (RMSE)
RMSE MSE
-16 16
-1 1
-10
17
-20
10
17
20
1
100
289
400
-10 84 1,446
846
= 14
1,4466
= 241
= SQRT(241)=15.52
An accurate forecasting system will have small MAD, MSE and RMSE; ideally equal to zero. A large error may indicate that
either the forecasting method used or the parameters such as αused in the method are wrong. Note: In the above, n is the number of periods, which is 6 in our example
deviation absoluteMean
)(
=MAD
RSFE=TS
t
tt forecastactual
30
• How can we tell if a forecast has a positive or negative bias?
• TS = Tracking Signal
– Good tracking signal has low values
Forecast Bias
MAD
Quantitative Forecasting Methods
Quantitative
Forecasting
RegressionModels
2. MovingAverage
1. Naive
Time Series
Models
3. ExponentialSmoothing
a) simple
b) weighteda) level
b) trend
c) seasonality
• We looked at using exponential smoothing to forecast demand with only random variations
Exponential Smoothing (continued)
Ft+1 = Ft + a (At - Ft)
Ft+1 = Ft + a At – a Ft
Ft+1 = a At + (1-a) Ft
Exponential Smoothing (continued)
• We looked at using exponential smoothing to forecast demand with only random variations
• What if demand varies due to randomness and trend?
• What if we have trend and seasonality in the data?
Regression Analysis as a Method for ForecastingRegression analysis takes advantage of
the relationship between two variables. Demand is then forecasted based on the knowledge of this relationship and for the given value of the related variable.
Ex: Sale of Tires (Y), Sale of Autos (X) are obviously related
If we analyze the past data of these two variables and establish a relationship between them, we may use that relationship to forecast the sales of tires given the sales of automobiles.
The simplest form of the relationship is, of course, linear, hence it is referred to as a regression line.
Sales of Autos (100,000)
MonthAdvertising Sales X 2 XY
January 3 1 9.00 3.00
February 4 2 16.00 8.00
March 2 1 4.00 2.00
April 5 3 25.00 15.00
May 4 2 16.00 8.00
June 2 1 4.00 2.00
July
TOTAL 20 10 74 38
y = a + b XRegression – Example
22 xnx
yxnxyb xbya
General Guiding Principles for Forecasting
1. Forecasts are more accurate for larger groups of items. 2. Forecasts are more accurate for shorter periods of time.3. Every forecast should include an estimate of error.4. Before applying any forecasting method, the total system should
be understood.5. Before applying any forecasting method, the method should be
tested and evaluated.6. Be aware of people; they can prove you wrong very easily in
forecasting