forecasting cpim

5/21/2018 Forecasting CPIM

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McGraw-Hill/Irwin Copyright 2008 by The McGraw-Hill Companies, Inc. All rights reserved.

Demand Management

Processing,Influencing, &Anticipating

Demand

BuySell BuyStore

MoveMake Sell Store

MoveSell

MakeMake

MoveMoveBuy


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Managing the sell side of a business

Plant

Plant

Plant

Warehouse

Suppliers

Customer

s

Supply-Demand Management

"Make, Move, Store"

SupplierRelationship

Management

"Buy"

CustomerRelationshipManagement

"Sell"


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4-3

Key questions1. What is the scope of demand management?

2. What does order processing involve; why is it an important area for management

attention?3. What is customer profit potential, & how is it relevant for influencing demand?

4. What are 5 alternatives for improving forecast accuracy, what do they mean, & howcan they be applied?

5. How do the tactics ofpart standardization&postponement of form or placehelpimprove forecast accuracy?

6. What is the difference between long term & short term forecasting?

7. What are 4 long term forecasting methods; what are the risks ofsalesperson/customer input?

8. What are the components of demand, & which component is not forecasted?

9. How do the moving average, Winters, & focus forecasting methods work?

10. What is the role of the number of periods in the moving average method, & thesmoothing parameters in the Winters method?

11. What is the purpose of filtering, & why is it important for computer-basedforecasting?

12. What do the following principles of nature mean & how are they relevant fordemand management? (1) law of large numbers, (2) trumpet of doom, (3) recencyeffect, (4) hockey stick effect, (5) Pareto phenomenon

13. What are the managerial insights from the chapter?


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Road map

Processing Demand

Influencing Demand

How to Improve Forecast Accuracy

Long Term Forecasting

Short Term Forecasting

Summary


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Scope of demand management

So what is demand management?

Concerned with processing, influencing,and anticipating demand

Well begin with processing demand or,in more common terms, order

processingor order fulfillment


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Order processing

Order processing is usually viewed to spanorder booking to order shipment

Example steps?

Customer validation, order entry, credit checking,pricing, design changes, availability checks, deliverytime estimation, notification of shipment, notificationof delays

Processing Demand


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Processing Demand

CUSTOMER ORDER ENTRY AND

CHECKING

Customer Validation

Credit Control Operations

ER

P

INVOICING

SHIPPING

CUSTOMER SERVICE

ORDER

INTERRUPTION

ORDERPICKING AND

ASSEMBLY

RETURNS


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Characteristics Can be a complex & time consuming process

dealing largely with information flow

Susceptible to ad hoc modifications over time inresponse to problems (e.g., extra credit check addeddue to expensive nonpaying customer a few yearsago)

A major customer contact point withorganization

Can significantly impact customer perceptions

IT advances & high customer impact

A potential profitable target for improvement

Processing Demand


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Example 1

Benetton

Electronic loop linking sales agent, factory, & warehouse

If not available, measurements transferred to knitting

machine for production

Benetton uses a single warehouse

Staffed by 8 people & about 230,000 pieces shipped daily

Processing Demand


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Example 2

K-Mart and MasterLock

Policy for mistake in shipment or invoice

Strike 1: $10,000, Strike 2: $50,000, Strike 3: lose

business

MasterLock revamped their order processing function

Processing Demand


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Example 3customer tools

Processing Demand

Amazon online order tracking


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Example 4customer tools

Processing Demand

UPS online order tracking


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Example 4continued

Processing Demand

UPS online tools


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Road map Processing Demand

Influencing Demand




Summary


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Measure customer profit potential

Some customers are more profitable than others

Advancing technologies more practical to estimate profitpotential of individual customers

Can guide efforts/investments for customer retention &acquisition . . . investments to influence demand

E.g.,

Electronics manufacturer: reviews historical customer profit beforesending service contract renewal

Wireless phone firm: churn scores& lifetime valueestimatesinfluence # of customer contacts & attractiveness of offerings

Ongoing development of data mining methods

Influencing Demand

A simple idea


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Road map

Processing Demand

Influencing Demand




Summary

F ti Alt ti


17/1004-17

Motivating example 1

Sunbeam

Improved forecasting led to 45% reduction ininventory

Included estimates from top 200 customers

Forecasting Alternatives

F ti Alt ti


18/1004-18


Apple

A history of problems forecasting demand

Many components sourced from 1 supplier -accurate forecasts are critical

Over $1 billion in unfilled orders during thecrucial holiday season. The CEO (Spindler)ousted a few months later


F ti Alt ti


19/1004-19


IBM

Badly misjudged demand in PC business in 1996went from being profitable in 1995 to a $200million loss through 1sthalf of 1996


F ti Alt ti


20/1004-20


Christmas 1999 & e-commerce takesoff

Large unanticipated increase in Internet orders

didnt ship on time

E.g., Many Toys R Us Christmas orders not delivereduntil March I will never buy online again


ForecastingAlternatives


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Improvement alternatives

Change the forecasting method

Collect more or different data

Analyze the information differently

E.g., involve more people, new forecasting software, spend moretime manually reviewing, focus groups etc.

Change operations or operating policies

Introduce early warning mechanisms

Take advantage of the law of large numbers

Reduce information delays & leadtimes (trumpet of doom)

Reduce demand volatility




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Early warning

Change policies so that some (or more)customers provide earlier commitmentoffuture demand, e.g.,

Early bird program for builder markets discount for60-day advance order

Invite large buyers to Aspen in February to view nextyears skiwear line, & encourage orders

Commitment asking customers how muchthey are likely to buy next quarter




23/1004-23

Law of large numbers

As volume increases, relative variability decreases

Postponement in form or place, e.g.,

Dell configure your own PC

From full product line at 12 regional DCs to full product line at asingle super DC, with 10% of product line stocked at 11 regionalDCs (i.e., fast movers that account for 70% of sales)

Part standardization, e.g.,

Arbys sandwich wrappers; plastic lids with push down drinkindicator

Intel Pentium processors all the same size

- 2.8 GHz tests out below 2.8 spec can be sold as a 2.66 GHz chip (down-binning)


Principle of Nature



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Trumpet of doom

As forecast horizonincreases, accuracydecreases, e.g.,

Reduce production & delivery leadtimes

Dell pick-to-light system for assembly

Reduce information delays

EDI transmission of daily consumer demand up

through multiple levels in the supply chain


0

Forecast Error Range over Time

Time Until Forecast Event0

PercentageForecast

Error

Principle of Nature



25/1004-25

Reduce demand volatility

Beware of product proliferation

Pareto analysis separating the important few from the trivial many

Periodic length of line analysis to critically assess whether to continuallyoffer slow movers

Principle of Nature: Pareto phenomenonthe lions share of anaggregate measure is determined by relatively few factors

E.g., the 80-20 rule 80% of demand is due to 20% of product line

Beware of perverse cycle of promotions customers wait forsale before buying, thereby forcing a sale

A step further dynamic pricing to stabilize demand & align with supply

Reduce the hockey stick effect


2 Principles of Nature



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Hockey stick effect

Volume tends to pick up towards the endof a reporting period . . . why?

Look for ways to lessen the effect contributes to demand volatility,inefficiency, poor service

Jan Feb

Principle of Nature




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Channel stuffing

Lots of sales booked near the end of a quarter,then sales drop off at the start of the nextquarter

E.g.,

A large brewer offered a vacation to the salespersonin each region who sold the most beer to stores over

a 3 month period

One winner was able to convince a few stores to freeup backroom space and fill it entirely with beer

One contributor to the hockey stick effect




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Were about to focus onmethods for predictingdemand


Improvement alternatives

short pork bellies

But, important to remember . . . many creative ways toimprove forecast accuracy that have nothing to do withmethod

E.g., early warning incentives, law of large numbers, trumpet ofdoom, reduce demand volatility


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Influencing Demand




Summary



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Characteristics of long term forecasts

Single or multi-year horizon

Monthly or annual time bucket

Aggregate units

Input to long term decisions

Accuracy generally more important than short termforecasts . . . why?

Tend to use expensive & time consuming methods . . .due to the preceding point & due to a PON . . . which is?




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Recency effect

Humans tend to overreact to (or be overlyinfluenced by) recent events

E.g.,

Hughes Electronics Corp. developed an artificialintelligence based financial trading system. Thedevelopers did this by encoding the wisdom of

Christine Downton, a successful portfolio manager.One motivation for creating the system is that it isimmune to the recency effect, i.e., humans tend toget overly fixated on the most recent information.

g g

Principle of Nature



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Some alternative methods

Judgment

Salesperson & customer input

Great information source, but beware of bias potential& recency effect= humans tend to be overlyinfluenced by recent events

Outside services

Causal methods . . . examples?

g g


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Influencing Demand




Characteristics

Components of demand

Moving average

Winters method

Focus forecasting

Filtering

Summary



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Long term/short term characteristics

Long term forecasts

Single or multi-year horizon

Monthly or annual time bucket

Aggregate units (e.g., product/service categories)

Input to long term decisions

Expensive & time consumingmethods

Accuracy importance

Trumpet of doom

Short term forecasts

Weekly or monthly horizon

Daily & weekly time bucket

Detailed units (e.g., SKU)

Input to short term decisions

Inexpensive & quick methods

Accuracy importance

Trumpet of doom

Could argue using 2 different principles of nature that its [easier?/harder?] to be

accurate with short term forecasting than with long term forecasting

g


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Definition of the Forecasting Process

The Art and Science of Predicting FutureEvents

Forecasting vs. Predicting

Based on Past Data

Economic vs. Demand Forecasting


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Elements of Demand Forecasting

Dynamic in Nature

Consider Uncertainty (Stochastic)

Rely on Information contained in PastData

Applied to various time horizons

short term

medium term forecasts

long term forecasts


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Steps in the Forecasting Process

Determine the Use of the Forecast Select the Items to be Forecasted Determine a Suitable Time Horizon Select an appropriate Set of Forecasting Models

Gather Relevant Data Conduct the Analysis Validate the Model - Assess its Accuracy Make the Forecast Implement the Results


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Independent Demand:What a firm can do to manage it?

Can take an active role to influencedemand

FORECASTING

Can take a passive role and simplyrespond to demand


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Types of Forecasts

Qualitative (Judgmental)

Quantitative Time Series Analysis

Causal Relationships

Simulation


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Qualitative Methods

Grass Roots

Market Research

Panel Consensus

Executive Judgment

Historical analogy

Delphi Method

Qualitative

Methods


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Delphi Method

1. Choose the experts to participate representing avariety of knowledgeable people in differentareas

2. Through a questionnaire (or E-mail), obtainforecasts (and any premises or qualifications forthe forecasts) from all participants

3. Summarize the results and redistribute them tothe participants along with appropriate newquestions

4. Summarize again, refining forecasts andconditions, and again develop new questions

5. Repeat Step 4 as necessary and distribute thefinal results to all participants


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Quantitative Forecasting Models

Both Pattern Based and CorrelationalModels rest on the assumption that therelationships of the past will continueinto the Future

Both can Mathematically Characterize theProbabilistic Nature of the Forecast

Both Use Information from RelevantTime Frames


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Influencing Demand




Characteristics


Moving average

Winters method

Focus forecasting

Filtering

Summary


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Components of Demand

Average demand for a period oftime

Trend

Seasonal element

Cyclical elements

Random variation

Autocorrelation


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Pattern Based Analyses

Definition Identifying an underlying pattern in historical

data, describe it in mathematical terms, andthen extrapolate it into the future

Uses a Time Series of Past Data


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Time Series Variation

Time Series of Demand Data TypicallyContain Four Components of VariationAbout the Mean or Average

Pattern Based Forecasting Needs toMathematically Characterize Each ofthese

Fi di C t f D d


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Finding Components of Demand

1 2 3 4

x

x xx

xx

x xx

xxx x x

xxxxxx x x

xx

x x xx

xx

xx

x

xx

xx

xx

x

xx

x xx

x

x

Year

Sales

Seasonal variation

Linear

Trend

Average


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Time Series Analysis

Time series forecasting models try topredict the future based on past data

You can pick models based on:

1. Time horizon to forecast2. Data availability

3. Accuracy required

4. Size of forecasting budget

5. Availability of qualified personnel


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Simple Moving Average Formula

F =A + A + A +...+A

nt

t-1 t-2 t-3 t-n

The simple moving average model assumes

an average is a good estimator of futurebehavior

The formula for the simple moving averageis:

Ft= Forecast for the coming periodn = Number of periods to be averaged

A t-1= Actual occurrence in the past period for up to n

periods


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Simple Moving Average Problem (1)

Week Demand

1 650

2 678

3 720

4 785

5 859

6 920

7 850

8 758

9 892

10 920

11 789

12 844

F =A + A + A +...+A

nt t-1 t-2 t-3 t-n

Question: What are the 3-week and 6-week movingaverage forecasts fordemand?

Assume you only have 3weeks and 6 weeks ofactual demand data for the

respective forecasts

Calculating the moving averages gives us:


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Week Demand 3-Week 6-Week

1 650

2 678

3 720

4 785 682.67

5 859 727.67

6 920 788.00

7 850 854.67 768.67

8 758 876.33 802.00

9 892 842.67 815.3310 920 833.33 844.00

11 789 856.67 866.50

12 844 867.00 854.83

F4=(650+678+720)/3

=682.67

F7=(650+678+720

+785+859+920)/6

=768.67


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500

600

700

800

900

1000

1 2 3 4 5 6 7 8 9 10 11 12

Week

Deman

d Demand

3-Week

6-Week

Plotting the moving averages and comparing

them shows how the lines smooth out to reveal

the overall upward trend in this example

Note how the

3-Week issmoother than

the Demand,

and 6-Week is

even smoother


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Simple Moving Average Problem (2) Data

Week Demand

1 820

2 775

3 680

4 655

5 620

6 600

7 575

Question: What is the 3week moving averageforecast for this data?

Assume you only have3 weeks and 5 weeksof actual demanddata for therespective forecasts


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Simple Moving Average Problem (2) Solution

Week Demand 3-Week 5-Week

1 820

2 7753 680

4 655 758.33

5 620 703.33

6 600 651.67 710.00

7 575 625.00 666.00

F4=(820+775+680)/3

=758.33 F6=(820+775+680

+655+620)/5

=710.00

W i ht d M i A F l


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Weighted Moving Average Formula

F = w A + w A + w A + ...+ w At 1 t -1 2 t -2 3 t -3 n t - n

w = 1ii=1

n

While the moving average formula implies an equalweight being placed on each value that is being

averaged, the weighted moving average permits an

unequal weighting on prior time periods

wt = weight given to time period t

occurrence (weights must add to one)

The formula for the moving average is:


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Weighted Moving Average Problem (1) Data

Weights:t-1 .5

t-2 .3

t-3 .2

Week Demand

1 650

2 678

3 720

4

Question: Given the weekly demand and weights, what is

the forecast for the 4thperiod or Week 4?

Note that the weights place more emphasis on the

most recent data, that is time period t-1


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Weighted Moving Average Problem (1) Solution

Week Demand Forecast

1 650

2 6783 720

4 693.4

F4= 0.5(720)+0.3(678)+0.2(650)=693.4


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Weighted Moving Average Problem (2) Data

Weights:

t-1 .7

t-2 .2t-3 .1

Week Demand

1 820

2 775

3 680

4 655

Question: Given the weekly demand information and

weights, what is the weighted moving average forecast

of the 5thperiod or week?


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Weighted Moving Average Problem (2) Solution

Week Demand Forecast

1 820

2 775

3 680

4 655

5 672

F5= (0.1)(755)+(0.2)(680)+(0.7)(655)= 672

Short Term ForecastingMoving Average and Weighted Moving Average


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Some pros/cons

1. Simple (+)

2. Designated weights of history (-)

3. History cut-off beyond mperiods (-)

Exponential Smoothing Model


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Exponential Smoothing Model

Premise: The most recent observations mighthave the highest predictive value

Therefore, we should give more weight to themore recent time periods when forecasting

Ft= Ft-1 + a(At-1 - Ft-1)

constantsmoothingAlpha

periodepast t timin theoccuranceActualA

periodpast time1inalueForecast vF

periodt timecomingfor thelueForcast vaF

:Where

1-t

1-t

t

a


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Exponential Smoothing Problem (1) Data

Week Demand

1 820

2 775

3 6804 655

5 750

6 802

7 7988 689

9 775

10

Question: Given theweekly demand data,what are theexponential smoothingforecasts for periods 2-

10 using a=0.10 anda

=0.60?Assume F1=D1

Answer: The respective alphas columns denote the forecast


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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

7 798 785.38 770.498 689 786.64 787.00

9 775 776.88 728.20

10 776.69 756.28

p p

values. Note that you can only forecast one time period into

the future.


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Exponential Smoothing Problem (1) Plotting

500

600

700

800

900

1 2 3 4 5 6 7 8 9 10

Week

Demand Demand

0.1

0.6

Note how that the smaller alpha results in a smoother line inthis example


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Exponential Smoothing Problem (2) Data

Question: What are

the exponential

smoothing forecastsfor periods 2-5 using

a =0.5?

Assume F1=D1

Week Demand

1 8202 775

3 680

4 6555


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Exponential Smoothing Problem (2) Solution

Week Demand 0.5

1 820 820.00

2 775 820.00

3 680 797.504 655 738.75

5 696.88

F1=820+(0.5)(820-820)=820 F3=820+(0.5)(775-820)=797.75


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Seasonal Adjustments

Applied to Moving Averages and TimeSeries Regression

First, Calculate a Seasonal Index (SI)Factor for Each Relevant Time Period(day, week, month, quarter)

Each Seasonal Periods SI isCalculated by Averaging the Ratio ofits Actual Demand to the ForecastDemand for all Corresponding Periods

S l Adj t t


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Forecast for Future Periods is Calculatedby Multiplying the Unadjusted MovingAverage or Time Series Forecast for a

given Period by the CorrespondingSeasonal Index for that Period

i.e. if the SMA forecast for the month ofMarch is 27 and the SI for March is

1.125, then

Emar= 27*1.125 = 30.375

Seasonal Adjustment Example


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Seasonal Adjustment Example


Sales Demand

Month 1993 1994Monthly

Average

Overall

AverageSeasonal Index

SI Adjusted

Forecast

Jan 80 100 90.00 94.00 0.96 86.17

Feb 75 85 80.00 94.00 0.85 68.09

Mar 80 90 85.00 94.00 0.90 76.86

Apr 90 110 100.00 94.00 1.06 106.38May 115 131 123.00 94.00 1.31 160.95

Jun 110 120 115.00 94.00 1.22 140.69

Jul 100 110 105.00 94.00 1.12 117.29

Aug 90 110 100.00 94.00 1.06 106.38

Sep 85 95 90.00 94.00 0.96 86.17

Oct 75 85 80.00 94.00 0.85 68.09

Nov 75 85 80.00 94.00 0.85 68.09

Dec 80 80 80.00 94.00 0.85 68.09

Average 87.92 100.08

Expected Demand for 1995 = 1153.23



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Seasonal AdjustmentsExample Graph

Seasonal Adjusted Forecasting

50

70

90

110

130

150

170

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1993

1994

SI Adjusted

Forecast

Overall

Average

Evaluating Forecast Accuracy


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Use of Residuals Analyses Residuals are the Difference Between the

Forecast and the Actual Demand for a GivenPeriod

Assessed by Several Measures Mean Absolute Deviation - MAD

Mean Squared Error - MSE

Tracking Signal


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The MAD Statistic to DetermineForecasting Error

MAD =

A - F

n

t t

t=1

n

1 MAD 0.8 standard deviation

1 standard deviation 1.25 MAD

The ideal MAD is zero which wouldmean there is no forecasting error

The larger the MAD, the less theaccurate the resulting model

MAD Problem Data


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MAD Problem Data

Month Sales Forecast

1 220 n/a

2 250 255

3 210 205

4 300 320

5 325 315

Question: What is the MAD value given

the forecast values in the table below?

MAD Problem Solution


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MAD Problem Solution

MAD =

A - F

n=

40

4= 10

t t

t=1

n

Month Sales Forecast Abs Error1 220 n/a

2 250 255 5

3 210 205 5

4 300 320 20

5 325 315 10

40

Note that by itself, the MAD

only lets us know the mean

error in a set of forecasts


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Evaluating Forecast AccuracyMean Absolute Deviation - MAD

Exponentially Smoothed MAD

MADt= aMAD|Dt- Forecastt| + (1- aMAD)MADt-1



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g yMean Squared Error - MSE

MSE = ((Di- Forecasti)

2

)/nPeriod

Actual

Demand

Time

Series

Forecast

Time

Series

Residual

Squared

Error

1 12 12.16 -0.16 0.03

2 13 12.13 0.87 0.76

3 10 12.09 -2.09 4.39

4 11 12.06 -1.06 1.135 10 12.03 -2.03 4.12

6 14 12.00 2.00 4.01

7 16 11.97 4.03 16.28

8 15 11.93 3.07 9.40

9 13 11.90 1.10 1.21

10 8 11.87 -3.87 14.97

11 10 11.84 -1.84 3.37

12 12 11.80 0.20 0.04

13 9 11.77 -2.77 7.69

14 13 11.74 1.26 1.59

15 13 11.71 1.29 1.67

MSE = 4.71

RMSE = 2.17

Tracking Signal Formula


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Tracking Signal Formula

The Tracking Signal or TS is a measure thatindicates whether the forecast average iskeeping pace with any genuine upward ordownward changes in demand.

Depending on the number of MADs selected, theTS can be used like a quality control chart

indicating when the model is generating toomuch error in its forecasts.

The TS formula is:

TS =RSFE

MAD=

Running sum of forecast errors

Mean absolute deviation


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Evaluating Forecast AccuracyTracking Signal

Tracking Signal = Running Sum ofForecast Error / MAD = RSFE/MAD

Period Actual

Demand

Time

Series

Forecast

Time

Series

Residual

RSFE MAD Tracking

Signal1 12 12.16 -0.16 -0.16 0.03 -5.00

2 13 12.13 0.87 0.72 0.20 3.58

3 10 12.09 -2.09 -1.38 0.58 -2.38

4 11 12.06 -1.06 -2.44 0.68 -3.61

5 10 12.03 -2.03 -4.47 0.95 -4.72

6 14 12.00 2.00 -2.47 1.16 -2.13

7 16 11.97 4.03 1.57 1.73 0.90

8 15 11.93 3.07 4.63 2.00 2.329 13 11.90 1.10 5.73 1.82 3.15

10 8 11.87 -3.87 1.86 2.23 0.84

11 10 11.84 -1.84 0.03 2.15 0.01

12 12 11.80 0.20 0.22 1.76 0.13

13 9 11.77 -2.77 -2.55 1.96 -1.30

14 13 11.74 1.26 -1.29 1.82 -0.71

15 13 11.71 1.29 0.00 1.72 0.00

Road map


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Road map

Processing Demand

Influencing Demand




Characteristics


Moving average

Winters method

Focus forecasting

Filtering

Summary

Old i t

Short Term ForecastingWinters


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Old man winters

Winters method used to forecast one period into the future

See how method detects patterns & adapts to market changes overtime

Old Man Winters in Action

0.00

100.00

200.00

300.00

400.00

500.00

600.00

0 20 40 60 80 100

Time

Volume

Actual

Forecast



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Key to Winters method

Winters is an exponential smoothingmethod

Smoothing is based on a key idea

For each component (which are?), a portionof difference between estimate & actual isdue to randomness& certain portion due

to real change



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Smoothing in action...

New estimate = old estimate + (somepercentage)(error)

Smoothes out peaks & valleys (i.e.,randomness) of actual

Road map


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Influencing Demand




Characteristics


Moving average

Winters method

Focus forecasting

Filtering

Summary

B i i i ht

Short Term ForecastingFocus


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Bernies insight

An intuitive & successful idea

Regularly use a # of different methods togenerate forecasts

Maintain historical accuracy information on each

method

Use the most accurate method to generateofficial forecasts

or what is focus forecasting?

Short Term ForecastingFocus


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Advertisementappearing in

APICS The

Performance

Advantage

Road map


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Road map

Processing Demand

Influencing Demand




Characteristics


Moving average

Winters method

Focus forecasting

Filtering

Summary

Short Term ForecastingFiltering


87/100

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Two types of filters

An important feature of computer-based forecastingsystems

Large amounts of data impractical to manually review all

1. For data input errors (e.g., typos, scanner errors)

If |actual - forecast| > limit, then report

2. For unacceptable forecast errors (e.g., warranting

management attention)

If average absolute error > limit, then report

If average error (i.e., bias) > limit, then report

R d


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Influencing Demand




Dependent Demand

Correlational Forecasting Summary

Demand Management


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Demand ManagementBill of Materials (BOM)

A

B(4) C(2)

D(2) E(1) D(3) F(2)

Dependent Demand:

Raw Materials,

Component parts,Sub-assemblies, etc.

Independent Demand:

Finished Goods

Web-Based Forecasting: CPFR


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Web Based Forecasting: CPFR

Collaborative Planning, Forecasting, and

Replenishment(CPFR) a Web-based tool used tocoordinate demand forecasting, production andpurchase planning, and inventory replenishmentbetween supply chain trading partners.

Used to integrate the multi-tier or n-Tier supplychain, including manufacturers, distributors andretailers.

CPFRs objective is to exchange selected internalinformation to provide for a reliable, longer termfuture views of demand in the supply chain.

CPFR uses a cyclic and iterative approach to

derive consensus forecasts.


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Web-Based Forecasting:Steps in CPFR

1. Creation of a front-end partnership

agreement

2. Joint business planning

3. Development of demand forecasts

4. Sharing forecasts

5. Inventory replenishment

Correlational Forecasting


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g

Assumes an Outcome is Dependent anExisting Relationship Between theDemand Variable and Some otherIndependent Variable(s) Demand Variable is Dependent Variable Other Related Variables are Independent

Variables Generally Expressed as a Multiple Linear

Regression Model Y =

+

X1+ X2+ X2+ . . . nXn+ i

Simple Linear Regression Model


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Simple Linear Regression Model

Yt= a + bx

0 1 2 3 4 5 x (Time)

YThe simple linear regression

model seeks to fit a line

through various data over

time

Is the linear regression model

a

- Ytis the regressed forecast value or dependentvariable in the model

-a is the intercept value of the the regression line, and-b is similar to the slope of the regression line.

- However, since it is calculated with the variability ofthe data in mind, its formulation is not as straightforward as our usual notion of slope.

Si l Li R i F l f


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Simple Linear Regression Formulas forCalculatinga and b

a = y - b x

b =xy- n(y)(x)

x - n(x2 2

)

Simple Linear Regression Problem Data


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Simple Linear Regression Problem Data

Week Sales

1 150

2 157

3 1624 166

5 177

Question: Given the data below, what is the simple linearregression model that can be used to predict sales in future

weeks?

Answer: First, using the linear regression formulas, we

can compute a and b


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Week Week*Week Sales Week*Sales

1 1 150 1502 4 157 314

3 9 162 486

4 16 166 664

5 25 177 8853 55 162.4 2499

Average Sum Average Sum

b = xy - n( y)(x)x - n( x

= 2499 - 5(162.4)(3) =

a = y - bx = 162.4 - (6.3)(3) =

2 2

) ( )55 5 96310

6.3

143.5

can compute a and b

Y = 143 5 + 6 3xThe resulting regression model

97


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Yt= 143.5 + 6.3x

180

Perio

d

135

140145

150

155

160165

170

175

1 2 3 4 5

Sales Sales

Forecast

is:

Now if we plot the regression generated forecasts against the

actual sales we obtain the following chart:

Statistical Assumptions of Multiple LinearR i


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Regression

The Error Term (the residual i) isNormally Distributed

There is no Serial Correlation Among

Error Terms Magnitude of the Error Term is

Independent of the Size of Any of theIndependent Variables - Xi

Assumptions Can be Tested ThroughAnalyses of the Residuals - i

Major Statistical Problems of Multiple


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Major Statistical Problems of MultipleLinear Regression

Multicolinarity

Use of Time-Lagged IndependentVariables

Both of These Problems Result in Modelswith Potentially Valid Predictions, but theReliability of the Coefficients isQuestionable

Demand Management


100/100

gThe End

Processing,Influencing, &Anticipating

Demand

Store SellMake

forecasting cpim

Documents

long term forecasting

demand managementprocessing

components of demand

scope of demand management

road map processing

customer perceptions

customer profit potential

expensive nonpaying