Master Planning of Resources

Session 2

Forecasting Demand

What is a Forecast?

Forecast – An estimate of future demand. A forecast can be determined by mathematical means using historical data, it can be created subjectively by using estimates from informal sources, or it can represent a combination of both techniques.

Forecast Error – The difference between actual demand and forecast demand, stated as an absolute value or as a percentage.

Forecast Management – The process of making, checking, correcting, and using forecasts. It also includes determination of the forecast horizon.

Why Forecast? To plan for the future by reducing uncertainty To facilitate a company in taking control of operations.

Without forecast, it would be a chaos. To anticipate and manage change To increase communication and integration of planning

teams To anticipate inventory and capacity demands and

manage lead times To project costs of operations into budgeting processes To improve competitiveness and productivity through

decreased costs and improved delivery and responsiveness to customer needs

Areas Impacted by the Forecast

Investment decisions Capital equipment decisions Inventory planning Capacity planning Operations budgets Lead-time management

Forecast System Design Issues

Determine information that needs to be forecasted Assign responsibility for the forecast Set up forecast system parameters Select forecasting models and techniques Collect data Test models Record actual demand Report accuracy Determine root cause of variance Review forecasting system for improved performance

General Forecasting Techniques

Qualitative Techniques—based on intuitive or judgmental evaluation

Quantitative Techniques—based on computational projection of a numeric relationship

Qualitative Techniques

Expert opinion Market research Focus groups Historical analogy Delphi method Panel consensus

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Quantitative Techniques

Moving average Exponential smoothing Regression analysis Adaptive smoothing Graphical methods Econometric modeling Life-cycle modeling

General Forecasting Data Methods

Intrinsic forecasting methods are based on historical patterns of the data itself from company data

Extrinsic forecasting methods are based on external patterns from information outside the company such as published data and data available from the Internet

Qualitative and quantitative forecasts may be generated based on intrinsic or extrinsic information.

Internal (Intrinsic) Factors

Product life-cycle management

Planned price changes

Changes in the sales force

Resource constraints Marketing and sales

promotion Advertising

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External (Extrinsic) Factors

Competition New customers Plans of major

customers Government policies Regulatory concerns Economic conditions Environmental issues Weather conditions Global trends

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Leading Indicators

Indicator(Causal Factor)

Housing starts

Birth rateHealth trends

Desire for Healthier lifestyle

Influences volume of

Building materialsHome furnishingsBaby productsMedical suppliesNutritional productsFitness products

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Demand

A need for a particular product or component

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Independent demand is demand for an item that is unrelated to the demand for other items. Independent demand items are saleable products or services that areadded to the master schedule.

Dependent demand can be calculated directly from the demand for other products.It is related to the bill of material structure.

Sources of Demand

Demand can come from many sources: Consumers Customers Referrers Dealers Distributors Interplant Service parts

Demand Characteristics

Internal Factors Product promotion Product substitution

External Factors Random fluctuation Seasonality Trend Economic cycle Changing customer

preferences and demands

Seasonality

0100

200

300

400

500600

700

800

J F M A M J J A S O N D

Sales in cases by month

Year 1Year 2

Seasonality Calculation

Measures seasonal variation of demand

Relates the average demand in a particular period to the average demand for all periods

The Seasonal Indexperiod average demand

average demand for all periods

Calculation of Seasonal IndexSales of Ice Cream

Month Year 1 Year 2 Total Calculation Index

January 10 12 22 22/409 0.05

February 10 12 22 22/409 0.05

March 10 12 22 22/409 0.05

April 50 55 105 105/409 0.26

May 150 160 310 310/409 0.76

June 400 420 820 820/409 2.00

July 600 620 1220 1220/409 2.98

August 700 730 1430 1430/409 3.49

September 350 360 710 710/409 1.74

October 100 105 205 205/409 0.50

November 10 12 22 22/409 0.05

December 10 12 22 22/409 0.05

Total 2400 2510 4910

Average 409.17 Round to 409

Seasonality Exercise

Economic Cycle

0

5

10

15

20

25

30

35

1 3 5 7 9 11 13 15 17 19

Quarter

Sales by Quarter

Pyramid Forecasting

Product/item volume(units)

Product family volume(units/dollars)

Totalbusinessvolume(dollars)

Rol

l Up

Fore

cast

Force Dow

n Adjustm

ent

Pyramid Forecasting

Pyramid Forecasting

Technique—Pyramid Forecasting Example

ROLL-UPProduct-level forecast

X1 units—8,200

price—$20.61 Family-level forecast

Family-adjusted forecastFORCE-DOWN

X1

X2

15,00013,045

15,00013,045

× 4,845 = 5,571 units

× 8,200 = 9,429 units

X2 units—4,845 price—$10.00 —units—13,045 Family avg price—$16.67 —units—15,000

Pyramid Forecasting Using Revenue

A B C D E F

X1 X2 Totals

units price units price Qty $

1 8,200 $20.61 4,845 $10.00 13,045 $217,452

2 1.15

3 9,429 $20.61 5,571 $10.00 15,000 $250,042

4 $250,070

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Pyramid Forecasting Exercise

Historical DemandProduct A

Region 1 150Region 2 300Selling Price $4.50

Management has determined that next year’s demand will be $10,000 total.

CALCULATE the projected demand in units for products A and B in each region.

Product BRegion 1 300Region 2 450Selling Price $8.50

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Pyramid Forecasting Exercise—Solution

Based upon historical demandA = 150 + 300 = 450 × $4.50 = $2,025B = 300 + 450 = 750 × $8.50 = $6,375

Total = $8,400

A: Region 1 = 1.19 × 150 = 178.5Region 2 = 1.19 × 300 = 357.0

B: Region 1 = 1.19 × 300 = 357.0Region 2 = 1.19 × 450 = 535.5

178.5 + 357.0 = 535.5 × $4.50 = $2,409.75357.0 + 535.5 = 892.5 × $8.50 = $7,586.25

= 1.19 (19% increase)$10,000

$8,400

$9,996.00

Moving Average Forecasting

Advantages A simple technique that is easy to calculate It can be used to filter out random variation Longer periods provide more smoothing

Limitations If a trend exists, it is hard to detect Moving averages lag trends

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Moving Average ExerciseActual sales Next month’s

forecastvariation

Jan 100

Feb 500

Mar 1000

Apr 1500

May 2800

June 5100

Jul 6200

Aug 5700

Sep 3200

Oct 1200

Nov 500

Dec 100

Exponential Smoothing

Provides a routine method of updating item forecasts

Alpha is a weighting factor applied to the demand element

Works well for items with fairly constant demand

Is satisfactory for short-range forecasts Lags trends

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New Forecast = ∝x Actual Demand + (1 - ) x Old Forecast∝

New Forecast = Old Forecast + x (Actual Demand – Old Forecast)∝

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Smoothing Factor

Referred to as Alpha ( Determines the weight of historical

data on projection Sets responsiveness to changes in

demand Range 0 1

=

2n + 1

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Smoothing Factor (cont.)

Determines how many periods of actual demand will influence forecast

1.00 = 1 period

0.50 = 3 periods

0.29 = 6 periods

0.15 = 12 periods

0.10 = 19 periods

0.1 Low weighting -most smoothing

0.9 High weighting - close to actual

Comparison of Exponential Smoothing Alpha Factors

Actual sales

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Exponential Smoothing Examples

New forecast = Old forecast + smoothing factor ( (actual demand - old forecast)

Example: old forecast = 160, actual = 200, = 0.1

new forecast = 160 + (0.1 (200 - 160))

= 160 + (0.1 40) = 164

Example: old forecast = 160, actual = 200, = 0.8

new forecast = 160 + (0.8 (200 - 160))

= 160 + (0.8 40) = 192

Adapted from: Manufacturing for Survival, B.R. Williams, Addison Wesley, 1996

New Product Introduction

Every new product/service is a calculated risk.

Every new product/service has the potential to be the next

Blockbuster Lifesaver Money loser Disaster Liability nightmare.

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Product Life Cycle

Introduction Growth Maturity Decline

Product Life Cycle Stages

Volume

Time

Focus Forecasting—Assumptions/Methods

Assumptions The most recent past is the best indicator of the

future One forecasting model is better than the others

Methods All forecasting models for all items forecasted will

be compared against recent sales history The model that achieves the closest fit will be

used to forecast this item this time Next time, a different model may be selected

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Data Issues for Forecasting

Availability of data Consistency of data Amount of history required Forecast frequency Frequency of model reevaluation Cost and time issues Recording true demand Order date vs. ship date Product units vs. financial units Level of aggregation Customer partnering

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Planning Horizon and Time Periods

Time Periods (week numbers)

Forecast Length

Short Mid Long

Weeks Months Quarters

1 2 3 4 5 6 7 8 9 1011 12 13 17 21 25 29 33 37 41 45 49 53 65 78 91 104

PlanningHorizon

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Data Preparation and Collection Record sales data in same

periods as forecast data (daily, weekly, or monthly)

Monitor demand, not sales and/or shipments

Record the circumstances of exceptional demand

Record demand separately for unique customer groupings and market sectors

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Dealing with Outliers

0

5

10

15

20

25

J F M A M J J A S O N D J F M A M J J A S O N D

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50

55

Decomposition of Data Purify the data Adjust the data Take out the baseline and components Identify demand components

– Trend

– Seasonality

– Nonannual cycle

– Random error Measure the random error Project the series Recompose

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Session 2 Review

You should now be able to Explain why forecasting is important Identify and describe general methods of

forecasting Identify factors influencing demand Describe considerations in using data for

forecasts Outline the process of data decomposition

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