Forecast Stevenson
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Transcript of Forecast Stevenson
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Fundamentals of
Operations ManagementBUS 3 140
Forecasting
Feb 5, 2008
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Forecasting
A statement about the future value of a variable of interest
Future Sales
Weather
Stock Prices
Other Short term and Long term estimates
Several Methods Quantitative
History and Patterns
Leading Indicators / Associations (Housing Starts &Furniture)
Qualitative Judgment
Consensus
Used for making informed Decisions and taking Actions based on those decisions
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Forecasting
Forecasts make a MAJOR IMPACT (Positive or Negative) on:
Revenue
Market Share
Cost
Inventory
Profit
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Features Common to all Forecasts
Generally assumes that what drove past performance and
behavior will drive future performance and behavior
Credit Rating
Insurance Rates
Other
More accurate for groups vs. individuals
Accuracy decreases as time horizon increases
Forecasts WILL be wrong the goal is to predict as closely as possible
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Three Major Types of Forecasts
Judgmental
Uses subjective, qualitative judgment (opinions, surveys,experts, managers, others). Most useful when there is limiteddata and with New Product Introductions
Time series
Observes what has occurred over previous time periods andassumes that future patterns will follow historical patterns
Associative Models
Establishes cause and effect relationships betweenindependent and dependent variables (rainy days and umbrellasales, pricing and sales volume, attendance at sporting eventsand food sold, others)
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Forecasting techniques (Table 3.6)
Approach Technique Brief Description
Consumer surveys Questioning consumers on future plans
Direct-contact composites Joint estimates obtained from sales or customer service
Executive opinionFinance, marketing, and manufacturing managers join to prepare
forecast
Delphi technique
Series of questionnaires answered anonymously by knowledgeable
people; successive questionnaires are based on information obtained
from previous surveys
Outside opinion Consultants or other outside experts prepare the forecast
Time series: NaveNext value in a series will equal the previous value in a comparable
period
Time series: Moving Averages Forecast is based on an average of recent values
Time series: ExponentialSmoothing
Sophisticated form of weighted moving average
Associative Models: Simple
Regression
Values of one variable are used to predict values of a dependent
variable
Associative Models: Multiple
Regression
Two or more variables are used to predict values of a dependent
variable
Judgment /
opinion:
QUALITATIVE
Statistical:QUANTITATIVE
* From Stevenson, Operations Management, Ninth Edition, McGraw Hill Irwin
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Elements of any Good Forecast
Timely
AccurateReliable
Written
* From Stevenson, Operations Management, Ninth Edition, McGraw Hill Irwin
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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 Obtain, clean and analyze data
Step 5 Make the forecast
Step 6 Monitor the forecast
* From Stevenson, Operations Management, Ninth Edition, McGraw Hill Irwin
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Forecast Factors (Table 3.5)
Factor Short Range Intermediate Range Long Range
Frequency Often Occasional Infrequent
Level ofAggregation Item Product FamilyTotal Output, Type of
product / service
Type of ModelSmoothing, Projection,
Regression
Projection, Seasonal,
RegressionManagerial Judgment
Degree ofManagement Involvement Low Moderate High
Cost per Forecast Low Moderate High
Forecasts are established with two (2) Units of Measure:
1. Units
2. Dollars
Both have significance to the Enterprise
* From Stevenson, Operations Management, Ninth Edition, McGraw Hill Irwin
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Start with what you KNOW
How many people will attend the next Giants game?
Tickets already sold
Patterns of walk up sales
Visiting team
Weather
School day
Other
How many Sewing Machines will Singer sell this week? Orders in Backlog
Inventory in Stores
Production capacity
Household Budget Rent
Car Payment
Bills
Rest of money
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A Demand Forecast serves many Purposes
RegionProduct
LineChannel Features Product Customer
Scheduling Factory Volumes
Materials Planning
Balancing Factory Capacity
Assessing Direct Cost @ MixesAnalyzing Absorption implications
Revenue Planning
Revenue Scenarios
Allocation Criteria
Commissions &Quotas
Estimating TAM and Share
Pricing Targets
Programs & Promotions
Margins @ MixesMessage to Analysts
Business Need / Benefit
WHAT is done and WHY?
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How different Functions use Forecast information
ORGANIZATION KEY VALUE OF A FORECAST
Sales & Marketing Pricing, Promotions, Quotas, Commissions
Operations Schedules, Capacity, Capital
Materials Continuous supply, Inventory
Logistics Transportation Planning
Finance & Accounting Cash flow, cost, profits, PE estimates
HR Hiring, recruiting, training
MIS Hardware, connectivity, support
Design New products and services
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Forecast accuracy varies over time
0 +1 +2 +3 +4 +n
ExpectedE
rrors
Over
Under
Time in Future (Weeks)
The further into the future, the harder
to predict details with accuracy
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Detailed Product Forecast Accuracy will vary by Time Horizon
5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%
Week TBD
5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%
Month
5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%
Quarter
Known High Prob.
Known High Probability / Influence TBD
Known High Probability and/or can Influence To Be Determined
Current Week should approach 100%
Current Month should be greater than 80%
Quarter should be at least 70%
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Tracking Forecast Accuracy
Level of Aggregation
Item (Mix of individual SKUs)
Family
Product Line
Channel
Customers
Quantity
Time Buckets
Final consumer sales
Regular tracking and monitoring with enable Demand SENSING,
as well as contribute to increased accuracy of future forecasts
Absolute values and square roots eliminate the
possibility of positive and negative variances
canceling each other out key for Mix tracking;
less critical for Revenue tracking
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Relationship of Lead Time, Forecast, Inventory, and Cost
Need toForecast
InventoryLevels in
Pipeline
Cost toManage
Risk ofExcess
Long Lead Time
Short Lead Time
High High Higher Higher
Low Low Lower Lower
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Time Series Forecasts (and Behaviors)
Trend - long-term movement in data
Seasonality - short-term regular variations in data
Cyclewavelike variations of more than one years duration
Irregular variations- caused by unusual circumstances
Random variations- caused by chance
G h h l i Ti S i d (Fi 3 1)
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Graphs help interpret Time Series data (Figure 3.1)
Trend
Irregular
variation
Seasonal variations
90
8988
Cycles
* From Stevenson, Operations Management, Ninth Edition, McGraw Hill Irwin
R l f SUPPLY F t
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Relevance of SUPPLY on Forecasts
Historical Sales does not always equal historical Demand
Stockouts Substitutions
Causal Factors may distort the analysis (pricing,promotions, competitor performance)
Scarcity Behavior
Allocation
Advance buying
Hedging
Hording
G id t l ti F ti th d (T bl 3 4)
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Guide to selecting Forecasting methods (Table 3.4)
Forecasting
Method
Amount of
Historical DataData Pattern Forecast Horizon Preparation Time
Personnel
Background
Moving Average 2 - 3 observations Data should bestationary Short Short Little sophistication
Simple exponential
smoothing5 - 10 observations
Data should be
stationaryShort Short Little sophistication
Trend-adjusted
exponential
smoothing
10 - 15 observations Trend Short to medium ShortModerate
sophistication
Trend models
10 - 20; for
seasonality at least
5 per season
Trend Short to medium ShortModerate
sophistication
SeasonalEnough to see 2
peaks and troughs
Handles cyclical
and seasonal
patterns
Short to medium Short to moderate Little sophistication
Causal regression
models
10 observations per
independent
variable
Can handle complex
data patterns
Short, medium, or
long
Long development
time, short time for
implementation
Considerable
sophistication
* From Stevenson, Operations Management, Ninth Edition, McGraw Hill Irwin
S l ti th t f l F ti t h i ( )
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Selecting the most useful Forecasting technique(s)
No single technique works in every situation
Two most important factors
Cost
Accuracy
Other factors include the availability of:
Historical data
Computers
Time needed to gather and analyze the data Forecast horizon
C l F t
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Causal Factors
External
Market conditions (e.g. paintings when the Painter passesaway)
New competition
Competitors cannot supply
Internal Pricing
Promotions
Incentives
B B d H R T t l
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0
75
150
225
300
375
450
525
600
675
750
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
Barry Bonds Home Run Totals
Age
Home
Runs
?????????
Oth P i t t id
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Other Points to consider
Do not second guess the forecast
significant judgment and even debate contribute to the final forecast.Once the forecast is finalized it then becomes the Demand Plan ofRecord for the enterprise
and do not say, If only we got a better forecast
The forecast should be generated as a team and managed as a team
It is helpful to provide a range of expected Demand
A useful application of Confidence Intervals from Statistics
Product Transitions are very difficult to forecast, but require specialattention and monitoring
New Product Introduction
End Of Life
Peter Drucker: The best way to predict the future is to CONTROL it