forecast stevenson

7/28/2019 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



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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



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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



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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


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


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

forecast stevenson

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