APICS ForecastingDemand

Master Planning of Resources

Session 2

Forecasting Demand

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2-2Master Planning of Resources, ver. 2.0 – December 2001

Master Planning of Resources

Session 1: The Business Planning Process

Session 2: Forecasting Demand

Session 3: Demand Management and Customer Service

Session 4: Distribution Planning

Session 5: The Sales and Operations Planning Process

Session 6: The Master Scheduling Process

Session 7: Managing the Master Scheduling Process

Session 8: Measuring Performance and Validatingthe Plan

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Session 2 Objectives

Explain why forecasting is important Identify and describe general methods of

forecasting Identify demand characteristics Describe considerations in using data for

forecasts Outline the process of data decomposition

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What Is a 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.”

— APICS Dictionary, 9th ed.

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What Is a Forecast?

“Lead Time is reason for planning and forecasting. Forecasting is an important aid in effective and efficient planning

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Why Forecast? To plan for the future by reducing uncertainty 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

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Areas Impacted by the Forecast

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

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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

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General Forecasting Techniques

Qualitative—based on intuitive or judgmental evaluation Quantitative—based on computational projection of a numeric

relationship Quantitative: time series predicting the continuation of historical

pattern Explanatory understanding relation between dependent and

independent variables

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General Forecasting Data Sources

Intrinsic—based on historical patterns of the data itself from company data

Extrinsic—based on external patterns from information outside the company

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Master Planning of Resources, ver. 2.0 – December 2001

Demand

A need for a particular product or component

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Sources of Demand

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

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Demand Characteristics

Internal Factors Product promotion Product substitution

External Factors Random fluctuation Seasonality Trend Economic cycle Changing customer

preferences and demands

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General Forecasting Steps

Problem definition Gathering information Preliminary ( Exploratory ) analysis Choosing and fitting model Using and evaluating model

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Time series : Historical data will consist of a sequence of observations over time .This is called such a sequence as time series

Assumption the past pattern repeat in future

The time scale is equally spaced

There are two types of data 1. Time related

2. Cross-sectional( Observation at same time)

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Four types of time series data pattern can be distinguished 1. Horizontal(random). 2.Seasonal. 3. Cyclic . 4. Trend

Difference between seasonal and cyclic

Measure of error MAD mean absolute deviation, MSD mean square deviation

Both MAD and S provide measure of spread

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Approximately 2/3 of observations must lie within one S from mean

Approximately 95 % of observations within 2 S from mean.

Plot of ACF it provides useful check for seasonality and other pattern.

Measure of accuracy and precision

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Accuracy ME,MAE,MSE Relative accuracy MPE,MAPE For Forecasting data to be divided into two

sets one for forecasting model and other for assessing error ( first set is called initializing and other set is called as test or holdout)

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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

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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

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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 3.50

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 409.1667 Round to 409

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Economic Cycle

0

5

10

15

20

25

30

35

1 3 5 7 9 11 13 15 17 19

Quarter

Sales by Quarter

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Pyramid Forecasting

Product/item volume(units)

Product family volume(units/dollars)

Totalbusinessvolume(dollars)

Rol

l Up

Fore

cast

Force Dow

n Adjustm

ent

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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

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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 9,430 $20.61 5,572 $10.00 15,002 $250,000

4 $250.070

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Master Planning of Resources, ver. 2.0 – December 20012-26

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 = 536.5 × $4.50 = $2,414.25357.0 + 535.5 = 892.5 × $8.50 = $7,586.25

$10,000.50

= 1.19 (19% increase)$10,000

$8,400

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Quantitative Techniques

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

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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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NEW FORECAST = ACTUAL DEMAND + (1-) OLD FORECASTNEW FORECAST = OLD FORECAST + (ACTUAL DEMAND - OLD FORECAST)

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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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

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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 sales - 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

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Qualitative Techniques

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

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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 rateHits on a Web siteHealth trends

Healthier lifestyle

Influences volume of

Building materialsHome furnishingsBaby productse-commerce salesMedical suppliesNutritional productsFitness products

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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

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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 26 30 34 39 43 47 52 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

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Decomposition of Data Purify the data Adjust the data Take out the baseline 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 demand characteristics Describe considerations in using data for

forecasts Outline the process of data decomposition

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APICS ForecastingDemand

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