Demand Management Qualitative Forecasting Methods Simple & Weighted Moving

Average Forecasts Exponential Smoothing Simple Linear Regression Web-Based Forecasting

OBJECTIVES

Role of Forecasting in a Supply Chain

The basis for all strategic and planning decisions in a supply chain

Used for both push and pull processes Examples:

Production: scheduling, inventory, aggregate planning

Marketing: sales force allocation, promotions, new production introduction

Finance: plant/equipment investment, budgetary planning

Personnel: workforce planning, hiring, layoffs All of these decisions are interrelated

Characteristics of Forecasts

Forecasts are always wrong (rarely correct). Should include expected value and measure of error.

Long-term forecasts are less accurate than short-term forecasts (forecast horizon is important)

Aggregate forecasts are more accurate than disaggregate forecasts

Basic Approach to Demand Forecasting

Understand the objectives of forecasting Integrate demand planning and forecasting Identify major factors that influence the demand

forecast Understand and identify customer segments Determine the appropriate forecasting technique Establish performance and error measures for the

forecast

Forecasting and Demand Planning Forecasting is the process of projecting the values of one or more

variables into the future.

Poor forecasting can result in poor inventory and staffing decisions, resulting in part shortages, inadequate customer service, and many customer complaints.

Many firms integrate forecasting with value chain and capacity management systems to make better operational decisions.

Accurate forecasts are needed throughout the value chain, and are used by all functional areas of the organization, including accounting, finance, marketing, operations, and distribution.

One of the biggest problems with forecasting systems is that they are driven by different departmental needs and incentive systems.

Demand planning software systems integrate marketing, inventory, sales, operations planning, and financial data.

Demand Management

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

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

Can take an active role to influence demand

Can take a passive role and simply respond to demand

Types of Forecasts

Qualitative (Judgmental)

Quantitative Time Series Analysis Causal Relationships Simulation

Components of Demand

Average demand for a period of time

Trend Seasonal element Cyclical elements Random variation Autocorrelation

Finding Components of Demand

1 2 3 4

x

x xx

xx

x xx

xx x x x

xxxxxx x x

xx

x x xx

xx

xx

x

xx

xx

xx

xx

xx

xx

x

x

Year

Sale

s

Seasonal variation

Linear

Trend

Qualitative Methods

Grass Roots

Market Research

Panel Consensus

Executive Judgment

Historical analogy

Delphi Method

Qualitative

Methods

Delphi Method

l. Choose the experts to participate representing a variety of knowledgeable people in different areas

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

3. Summarize the results and redistribute them to the participants along with appropriate new questions

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

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

Time Series Analysis

Time series forecasting models try to predict the future based on past data

You can pick models based on:1. Time horizon to forecast2. Data availability3. Accuracy required4. Size of forecasting budget5. Availability of qualified personnel

Simple Moving Average Formula

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

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

The simple moving average model assumes an average is a good estimator of future behavior

The formula for the simple moving average is:

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

Simple Moving Average Problem (1)

Week Demand1 6502 6783 7204 7855 8596 9207 8508 7589 892

10 92011 78912 844

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

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

Question: What are the 3-week and 6-week moving average forecasts for demand?

Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts

Week Demand 3-Week 6-Week1 6502 6783 7204 785 682.675 859 727.676 920 788.007 850 854.67 768.678 758 876.33 802.009 892 842.67 815.33

10 920 833.33 844.0011 789 856.67 866.5012 844 867.00 854.83

F4=(650+678+720)/3

=682.67

F7=(650+678+720+785+859+920)/6

=768.67

Calculating the moving averages gives us:

The McGraw-Hill Companies, Inc., 2004

500

600

700

800

900

1000

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

Week

Dem

and 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 is smoother than the Demand, and 6-Week is even smoother

Simple Moving Average Problem (2) Data

Week Demand1 8202 7753 6804 6555 6206 6007 575

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

Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts

Simple Moving Average Problem (2) Solution

Week Demand 3-Week 5-Week1 8202 7753 6804 655 758.335 620 703.336 600 651.67 710.007 575 625.00 666.00

F4=(820+775+680)/3

=758.33

F6=(820+775+680+655+620)/5

=710.00

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 equal weight 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:

Weighted Moving Average Problem (1) Data

Weights: t-1 .5t-2 .3t-3 .2

Week Demand1 6502 6783 7204

Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4?

Note that the weights place more emphasis on the most recent data, that is time period t-1

Weighted Moving Average Problem (1) Solution

Week Demand Forecast1 6502 6783 7204 693.4

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

Weighted Moving Average Problem (2) Data

Weights: t-1 .7t-2 .2t-3 .1

Week Demand1 8202 7753 6804 655

Question: Given the weekly demand information and weights, what is the weighted moving average forecast of the 5th period or week?

Weighted Moving Average Problem (2) Solution

Week Demand Forecast1 8202 7753 6804 6555 672

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

Exponential Smoothing Model

Premise: The most recent observations might have the highest predictive value

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

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

constant smoothing Alphaperiod epast t tim in the occurance ActualA

period past time 1in alueForecast vFperiod t timecoming for the lueForcast vaF

:Where

1-t

1-t

t

===

=

Exponential Smoothing Problem (1) Data

Week Demand1 8202 7753 6804 6555 7506 8027 7988 6899 775

10

Question: Given the weekly demand data, what are the exponential smoothing forecasts for periods 2-10 using =0.10 and =0.60?

Assume F1=D1

Week Demand 0.1 0.61 820 820.00 820.002 775 820.00 820.003 680 815.50 820.004 655 801.95 817.305 750 787.26 808.096 802 783.53 795.597 798 785.38 788.358 689 786.64 786.579 775 776.88 786.61

10 776.69 780.77

Answer: The respective alphas columns denote the forecast values. Note that you can only forecast one time period into the future.

Exponential Smoothing Problem (1) Plotting

500550600650700750800850

1 2 3 4 5 6 7 8 9 10

Dem

an

d

Week

Demand

0.1

0.6

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

Exponential Smoothing Problem (2) Data

Question: What are the exponential smoothing forecasts for periods 2-5 using a =0.5?

Assume F1=D1

Week Demand1 8202 7753 6804 6555

Exponential Smoothing Problem (2) Solution

Week Demand 0.51 820 820.002 775 820.003 680 797.504 655 738.755 696.88

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

The MAD Statistic to Determine Forecasting Error

MAD = A - F

n

t tt=1

n

1 MAD 0.8 standard deviation1 standard deviation 1.25 MAD

The ideal MAD is zero which would mean there is no forecasting error

The larger the MAD, the less the accurate the resulting model

MAD Problem Data

Month Sales Forecast1 220 n/a2 250 2553 210 2054 300 3205 325 315

Question: What is the MAD value given the forecast values in the table below?

MAD Problem Solution

MAD = A - F

n=

404

= 10t t

t=1

n

Month Sales Forecast Abs Error1 220 n/a2 250 255 53 210 205 54 300 320 205 325 315 10

40

Note that by itself, the MAD only lets us know the mean error in a set of forecasts

Simple Linear Regression Model

Yt = a + bx

0 1 2 3 4 5 x (Time)

Y

The simple linear regression model seeks to fit a line through various data over time

Is the linear regression model

a

Yt is the regressed forecast value or dependent variable 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 of the data in mind, its formulation is not as straight forward as our usual notion of slope.

Simple Linear Regression Formulas for Calculating a and b

a = y - bx

b = xy - n(y)(x)x - n(x2 2

)

Simple Linear Regression Problem Data

Week Sales1 1502 1573 1624 1665 177

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

Week Week*Week Sales Week*Sales1 1 150 1502 4 157 3143 9 162 4864 16 166 6645 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 9

6310

6.3

143.5

Answer: First, using the linear regression formulas, we can compute a and b

37

Yt = 143.5+6.3x

180

Period

135

140

145

150

155

160

165

170

175

1 2 3 4 5

Sale

s

Sales

Forecast

The resulting regression model is:

Now if we plot the regression generated forecasts against the actual sales we obtain the following chart:

38

Slide Number 1Role of Forecasting in a Supply ChainCharacteristics of ForecastsBasic Approach to Demand ForecastingForecasting and Demand PlanningDemand ManagementIndependent Demand: What a firm can do to manage it?Types of ForecastsComponents of DemandFinding Components of DemandQualitative Methods Delphi MethodTime Series AnalysisSimple Moving Average FormulaSimple Moving Average Problem (1)Slide Number 16Slide Number 17Simple Moving Average Problem (2) DataSimple Moving Average Problem (2) SolutionWeighted Moving Average FormulaWeighted Moving Average Problem (1) DataWeighted Moving Average Problem (1) SolutionWeighted Moving Average Problem (2) Data Weighted Moving Average Problem (2) SolutionExponential Smoothing ModelExponential Smoothing Problem (1) DataSlide Number 27Exponential Smoothing Problem (1) PlottingExponential Smoothing Problem (2) DataExponential Smoothing Problem (2) SolutionThe MAD Statistic to Determine Forecasting ErrorMAD Problem DataMAD Problem SolutionSimple Linear Regression ModelSimple Linear Regression Formulas for Calculating a and bSimple Linear Regression Problem DataSlide Number 37Slide Number 38