demand management

05/01/23 1

Demand Planning and Inventory Management

Presented by: dr. Tom Bramorski

05/01/23 2

Helpful References1. Chase, Aquilano and Jacobs, “Operations Management for

Competitive Advantage, 10th Edition, McGraw Hill, 20042. Chase, and Aquilano, “Production and Operations Management”,

7th Edition, McGraw Hill, 1995 3. Vollmann, Berry, and Whybark, “Manufacturing Planning and

Control Systems,” 5th Edition, McGraw Hill, 2005 4. Tersine, “Principles of Inventory and Materials Management,” 3rd

Edition, Elsevier Science Publishing, 19885. Gaither and Frasier, “Operations Management,”9th Edition, South-

Western Publishing, 20026. Krajewski and Ritzman, “Operations Management,” 6th Edition,

Prentice Hall, 20027. http://www.vendormanagedinventory.com/setup.htm

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Demand Planning The MPC systemMaster Production SchedulingOrder promising Demand management Forecasting

Evaluating accuracy of forecasts

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The MPC System Structure

Front End

Engine

Front End

Long-term,High uncertainty

Short-term,Low uncertainty

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The MPC System: Front End

Resource Planning

Production Planning

Demand Management

Market Internal & External

Customers

Master Production Scheduling (MPS)

MPC Boundary

Rough-cut Capacity Planning

Engine

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The MPC System: Engine

Detailed Material Planning

Time-Phased Requirement (MRP) Records

Material & Capacity Plans


Back End

Detailed Capacity Planning

Inventory Status Data

Routing File

Bills of Material

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The MPC System: Back End

Shop-floor Systems

Material & Capacity Plans

Inventory Status Data

Vendor Systems

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The MPC System: Front End

Resource Planning

Production Planning

Demand Management


Customers


MPC Boundary


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Fully load facilities and minimize overloading and under loading

Make sure enough capacity available to satisfy expected demand

Plan for the orderly and systematic change of production capacity to meet the peaks and valleys of expected customer demand

Get the most output for the amount of resources available

Production Planning

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A forecast of aggregate demand covering the selected planning horizon (6-18 months)

The alternative means available to adjust short- to medium-term capacity, to what extent each alternative could impact capacity and the related costs

The current status of the system in terms of workforce level, inventory level and production rate

Production Planning: Inputs

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A production plan: aggregate decisions for each period in the planning horizon about workforce level inventory level production rate

Projected costs if the production plan was implemented

Production Planning: Outputs

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Medium-Term Capacity AdjustmentsWorkforce level adjustments

Hire or layoff full-time workers Hire or layoff part-time workers Hire or layoff contract workers

Utilizing existing work force Overtime Idle time (undertime) Reduce hours worked

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Medium-Term Capacity Adjustments Inventory level adjustments

Finished goods inventory Backorders/lost sales

Subcontracting

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Demand Management in the MPC System

Resource Planning

Production Planning

Demand Management


Customers


Front End

MPC Boundary


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Demand Management in the MPC System

Resource Planning

Production Planning

Demand Management


Customers


Front End

MPC Boundary


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Demand ManagementForms a link between a company and

the market Determines the quantities and timing of

all demands Provides a framework for coordination

between functional areas in a way consistent with the market needs

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Demand Management In manufacturing, it enables

coordination between the supply chain elements Specific demands initiate actions

throughout MPC systems leading to product delivery and consumption of materials and capacities

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Demand ManagementConsists of:

Forecasting Order entry Order promising Customer order service Physical distribution Other customer-contact-related activities

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Demand ManagementProvides inputs to:

Master Production Scheduling (MPS) for end items

Manufacturing Resource Planning (MRP) for spare parts and lower-level items

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Demand Management Accounts for all sources of demand,

including: Finished products Spare and service parts Intra-company requirements Product samples Pipeline inventory Scrap & rework Product returns

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Demand Management & Production PlanningSuppose that the production plan states

aggregate quarterly output in dollarsThen, demand planning must

synchronize actual production with the aggregate target This requires real-time efficient

communication and data exchange throughout the entire supply chain

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Demand Management & MPS

MPS is an anticipated build schedule for manufacturing end products or product options MPS is a statement of production, not a

statement of market demand, i.e. it is not a forecast

MPS considers capacity limitations and resource efficiency/utilization issues

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MPS forms a critical communication link with manufacturing in terms of resources needed to build orders

MPS is stated in terms of part numbers for which bills of material exist (physical parts) rather than aggregate units (dollars)

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Specific products may be groups of items (e.g. J-body cars) rather than individual end items In such cases the exact product mix (e.g.

the number of 2-door & 4-door models all using J-body design) is determined at the latest possible moment with a Final Assembly Schedule (FAS)

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Forecasting is used: For estimating future levels of activities,

e.g. demand As a basis for business planning As a basis for decisions regarding:

Process selection Facility layout Production planning Scheduling, etc.

Forecasting as a Part of Strategic Business Planning

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ForecastingMethod(s) Outputs

SalesForecast

ManagementTeam

Inputs

BusinessStrategy

Production ResourceForecasts

Forecast errors & feedback

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Market conditions Competitor actions Consumer tastes Products’ life cycles Season Customers’ plans

Inputs to Forecasting

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Economic outlook Business cycle status Leading indicators

Stocks Prices Bond yields Material prices Business failures Money supply Unemployment


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Other factors Legal Political Sociological Cultural


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SalesForecast

ManagementTeam

Inputs

BusinessStrategy



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Qualitative ApproachesQuantitative Approaches

Forecasting Methods

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SalesForecast

ManagementTeam

Inputs

BusinessStrategy



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Estimated demand For each product or product family

(group technology) For each time period Over the forecasting horizon

Other outputs

Outputs From Forecasting

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SalesForecast

ManagementTeam

Inputs

BusinessStrategy



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Makes decisions regarding: Production capacity Available resources Levels of acceptable business risk

Management Team

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Based on: Experience Personal values and motives Organizational values and culture Societal values and needs Other factors

Management Team

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SalesForecast

ManagementTeam

Inputs

BusinessStrategy



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Determines the level of demand: For each product or product family In each time period (bucket) over the

planning horizon

Sales Forecast

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SalesForecast

ManagementTeam

Inputs

BusinessStrategy



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Suppose that today you develop a forecast of $1.5 million for next month dollar value of OEM equipment repairs

What is the probability that the actual value will be exactly $1.5 million?

Answer: zero

Forecast Error and Feedback

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$1.5 million$1.5 million

demand

Forecast Error and Feedback Demand is Normally distributed. Probability is the area under the

bell-shaped curve. Use ranging (e.g. demand less than $2 million), as the area corresponding to any number is zero

$2 million$2 million

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Monitoring forecast errors and analyzing feedback provides a basis for: Analyzing and influencing inputs (promotions,

bundling, etc.) Modifying management decisions in terms of:

Capacities Capabilities Resources Risk


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Forecasting models are evaluated on the basis of three characteristics: Impulse response Noise-dampening ability Accuracy


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SalesForecast

ManagementTeam

Inputs

BusinessStrategy



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The formation of and changes to: Marketing plan

Advertising Sales efforts Product and service pricing

Business Strategy

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The formation of and changes to: Production plan

Quality levels Customer service Capacity Costs

Business Strategy

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The formation of and changes to: Financial plan

Credit policies Billing policies

Business Strategy

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SalesForecast

ManagementTeam

Inputs

BusinessStrategy



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Forecasting

Forecasting as a part of strategic business planning

Types of demand – what to forecast? Components of demand Overview of forecasting techniques Evaluating accuracy of forecasts Forecast ranging – prediction intervals

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Sales views orders from the point of view of order value, profit margins, etc.

Production views orders from the point of view of order makeup (use of resources) Sales forecasts (in dollars, units, etc.) must

be translated into production resource forecasts (in machine hrs, labor hrs, etc.)

Production Resource Forecasts

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Production Resource Forecasts Examples

Long-Range Medium-Range Short-RangeFacility capacitiesCapital needs Facility needs Other

Workforce level Department

capacities Purchased

materials Inventories Other

Labor, by skill class Machine capacities Cash Inventories Other

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Production Resource Forecasts Examples

Forecast Horizon

Time Span Item Being Forecast Units of

Measure

Long-Range Years

Product lines Factory capacities Planning for new products Capital expenditures Facility location or expansion R&D

Dollars, tons, etc.

Medium-Range Months

Product groups Department capacities Sales planning Production planning and budgeting

Dollars, tons, etc.

Short-Range Weeks

Specific product quantities Machine capacities Planning Purchasing Scheduling Workforce levels Production levels Job assignments

Physical units of products

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Strategic Importance ofForecasting Capacity planning

New facility planning Facility expansion/contraction

Production planning What to produce? How much to produce? Where to produce?

Workforce scheduling Labor issues

Resource utilization issues

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Forecasting



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A

B(4) C(2)

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

Types of Demand

Independent Demand(finished goods and spare parts)

Dependent Demand

(components)

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Types of DemandIndependent demand items

need to be forecastThese items include:

Finished goods andSpare parts

Dependent (component) demand items must be calculated

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Forecasting



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Components of Demand Q

uant

ity

Time

(a) Error: Data cluster about a horizontal line with no information (white noise process)

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uant

ity

Time

(b) Irregular: Sudden change in process level due to assignable causes, e.g. strike

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uant

ity

Time

(c) Trend: Data consistently increase or decrease, not necessarily in a linear fashion

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uant

ity

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

Months(d) Seasonal: Data consistently show peaks and valleys

with a one-year cycle

Year 1

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uant

ity

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

Months

Year 1

Year 2

(d) Seasonal: Data consistently show peaks and valleys with a one-year cycle

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uant

ity

| | | | | |1 2 3 4 5 6

Years(e) Cyclical: Data reveal gradual increases and

decreases over extended periods of more than one-year in length

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Forecasting



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ForecastingQualitative approaches

No past sales data is available Appropriate for forecasting technology

change, new product sales, consumer tastes change, etc.

Quantitative approaches Sales history is available Product design is stable

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Qualitative Approaches Usually based on judgments about causal

factors that underlie the demand of particular products or services

Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events

The approach/method that is appropriate depends on a product’s life cycle stage

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Educated guess intuitive hunches Executive committee consensusDelphi methodSurvey of sales forceSurvey of customers Historical analogyMarket research scientifically conducted surveys

Qualitative Approaches

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Based on the assumption that the “forces” that generated the past demand will generate the future demand, i.e., history will tend to repeat itself

Analysis of the past demand pattern provides a good basis for forecasting future demand

Majority of quantitative approaches fall in the category of time series analysis

Quantitative Approaches

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Regression Models Simple regression

One independent variable Multiple regression

More than one independent variables Linear regression

All variables are of power of 1, e.g. X Nonlinear regression

At least one independent variable is of power different than 1 or interaction terms are present in the model, e.g. X2, X1X2

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Causal Methods: Simple Linear Regression

Dep

ende

nt v

aria

ble

Independent variableX

Y

Actualvalueof Y

Estimate ofY from regressionequation

Value of X usedto estimate Y

Deviation,or error

{

Regressionequation:Y = a + bX

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Simple Linear Regression Example Using the data below estimate point sales

(in 000 of units) when budgeted advertising expenditure is $2,300Examples.xls

Sales AdvertisingMonth (000 units) (000 $)

1 264 2.52 116 1.33 165 1.44 101 1.05 209 2.0

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Simple Linear Regression Example

Sales vs. Advertising

0.000

50.000

100.000

150.000

200.000

250.000

300.000

0.000 0.500 1.000 1.500 2.000 2.500 3.000

Sales (000 units)

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Simple Linear Regression Example

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.979564766

R Square 0.959547131

Adjusted R Square 0.946062842

Standard Error 15.60273574

Observations 5

ANOVA

df SS MS F Significance F

Regression 1 17323.66391 17323.66391 71.1603775 0.003495969

Residual 3 730.3360882 243.4453627

Total 4 18054

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept -8.134986226 22.3524729 -0.363941219 0.74003927 -79.27059775 63.0006253 -79.27059775 63.0006253

Advertising (000 $) 109.2286501 12.94843989 8.435661059 0.003495969 68.0208968 150.4364035 68.0208968 150.4364035

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Simple Linear Regression Example The regression line equation is:

F = -8.135 + 109.229 X Regression is significant overall as p-value = .003 Advertising is a significant predictor of sales as

p-value = .003 94.61 percent of variability in sales is explained by

advertising (good model!) If X = 2,300 the expected sales is:

F(23) = -8.135 + 109.229 (2.3) = $243,091

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Time-Series Regression If the independent variable is time (t) the

regression is called time-series regression

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Time-Series Regression Example Using the data below estimate point sales

(in 000 of units) in month 6Examples.xls

SalesMonth (000 units)

1 150

2 163

3 182

4 191

5 209

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Time Series Regression Example

Sales vs. Time

0.000

50.000

100.000

150.000

200.000

250.000

0 1 2 3 4 5 6

Sales (000 units)

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Time Series Regression Example

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.995711736

R Square 0.99144186

Adjusted R Square 0.988589147

Standard Error 2.476556749

Observations 5

ANOVA

df SS MS F Significance F

Regression 1 2131.6 2131.6 347.5434783 0.000336881

Residual 3 18.4 6.133333333

Total 4 2150

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 135.2 2.597434632 52.05135804 1.5617E-05 126.933796 143.466204 126.933796 143.466204

Month 14.6 0.783156008 18.64251802 0.000336881 12.10764572 17.09235428 12.10764572 17.09235428

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Time Series Regression Example The regression line equation is:

F = 135.2 + 14.6 t Regression is significant overall as

p-value = .0003 Time is a significant predictor of sales as

p-value = .0003 98.86 percent of variability in sales is explained by

time (good model!) If t = 6 the expected sales is:

F(6) = 135.2 + 14.6 (6) = $222.800

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Time Series Methods: Simple Moving AverageSpecify the AP factorCalculate the forecast as an average of

AP most recent observations Use the forecast if actual value is not

available

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Simple Moving Average Example Calculate a 3-week simple moving

average forecast for part demand in weeks 4 and 5 Examples.xls

Week Demand

1 400

2 380

3 411

4 4155 ?

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Simple Moving Average Example

Week

450 —

430 —

410 —

390 —

370 —

| | | | | |0 5 10 15 20 25 30

Actual shipmentarrivals

Ship

men

t arr

ival

s

05/01/23 83

Simple Moving Average Example


450 —

430 —

410 —

390 —

370 —

Week

| | | | | |0 5 10 15 20 25 30

ShipmentWeek Arrivals

1 4002 3803 4114 415

F4=(400+380+411)/3 = 397F5=(380+411+415)/3 = 402

Ship

men

t arr

ival

s

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Simple Moving Average ExampleF4 = 397 F5 = 402Observe that as forecasts are made

further into the future the forecast error increases

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Time Series Methods: Weighted Moving Average Specify the AP factor Specify the weights

Weights are positive fractions summing up to one Calculate the forecast as a weighted average

of AP most recent observations Assign a larger weight to a more recent

observation Use the forecast if actual value is not available

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Weighted Moving Average Example Calculate a 3-week weighted moving

average forecast for part demand in weeks 4 and 5. The weights are: w1=.70, w2=.20, w3=.10. Examples.xls

Week Demand

1 400

2 380

3 411

4 4155 ?

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Weighted Moving Average Example

Week

450 —

430 —

410 —

390 —

370 —

| | | | | |0 5 10 15 20 25 30


3-week MAforecast

6-week MAforecast

Ship

men

t arr

ival

s

Weighted Moving Average assigned weights

Time Period Weightt .70t-1 .20t-2 .10

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Weighted Moving Average Example

450 —

430 —

410 —

390 —

370 —

Week

| | | | | |0 5 10 15 20 25 30


3-week MAforecast

6-week MAforecast

Ship

men

t arr

ival

s

Weighted Moving Average

Time Period Weightt .70t-1 .20t-2 .10

F4 = 0.70(411) + 0.20(380) + 0.10(400) = 403.70F5 = 0.70(415) + 0.20(411) + 0.10(380) = 410.70

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Specify the smoothing constant It must be a positive fraction, 0<<1

Specify the starting forecast value Usually F1 = A1

Calculate the forecast using the equation Ft+1 = Ft + (At – Ft)

Time Series Methods: Single Exponential Smoothing

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Calculate a single exponential smoothing forecast for period 5 assuming = 0.1 and F1 = A1Examples.xls

Single Exponential Smoothing Example

Week Demand

1 400

2 380

3 411

4 4155 ?

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Single Exponential Smoothing Example

450 —

430 —

410 —

390 —

370 —

Week

| | | | | |0 5 10 15 20 25 30

Ship

men

t arr

ival

s

Exponential Smoothing, = 0.10

F t +1 = Ft + (At – Ft )

F1 = 400.00F2 = 400+0.10(400-400) = 400.00F3 = 400+0.10(380-400) = 398.00F4 = 398+0.10(415-398) = 399.30F5 = 399.30+.10(415-399.3) = 400.87

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Model Comparisons

450 —

430 —

410 —

390 —

370 —Ship

men

t arr

ival

s

Week

| | | | | |0 5 10 15 20 25 30

3-week MAforecast

3-week weighted MAforecast

Exponential smoothing = 0.10

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Model ComparisonsThese models can be extended to

cover more complex cases Which model is the best for

forecasting?The choice of the weights, the AP

factor and the smoothing constant determines the noise dampening ability of the forecasting model

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Time Series Methods: Seasonal InfluencesConsider the following data. Determine the quarterly seasonally adjusted forecast for year 5 if expected demand is 2,600 units

Examples.xls

Quarter Year 1 Year 2 Year 3 Year 41 45 70 100 1002 335 370 585 7253 520 590 830 11604 100 170 285 215

Total 1000 1200 1800 2200 Average 250 300 450 550

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Seasonal Influences Example

Quarterly Sales

0

200

400

600

800

1,000

1,200

1,400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

time

sale

s

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Quarter Year 1 Year 2 Year 3 Year 41 45 70 100 1002 335 370 585 7253 520 590 830 11604 100 170 285 215

Total 1000 1200 1800 2200 Average 250 300 450 550

Seasonal Index = Actual DemandAverage Demand

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Quarter Year 1 Year 2 Year 3 Year 41 45/250 = 0.18 70 100 1002 335 370 585 7253 520 590 830 11604 100 170 285 215

Total 1000 1200 1800 2200 Average 250 300 450 550

Seasonal Index = = 0.1845250

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Quarter Year 1 Year 2 Year 3 Year 41 45/250 = 0.18 70/300 = 0.23 100/450 = 0.22 100/550 = 0.182 335/250 = 1.34 370/300 = 1.23 585/450 = 1.30 725/550 = 1.323 520/250 = 2.08 590/300 = 1.97 830/450 = 1.84 1160/550 = 2.114 100/250 = 0.40 170/300 = 0.57 285/450 = 0.63 215/550 = 0.39

Quarter Average Seasonal Index1 (0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20234

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Quarter Average Seasonal Index1 (0.18 + 0.23 + 0.22 + 0.18)/4 = 0.202 (1.34 + 1.23 + 1.30 + 1.32)/4 = 1.303 (2.08 + 1.97 + 1.84 + 2.11)/4 = 2.004 (0.40 + 0.57 + 0.63 + 0.39)/4 = 0.50

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Quarter Average Seasonal Index Forecast1 (0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20 650(0.20) = 1302 (1.34 + 1.23 + 1.30 + 1.32)/4 = 1.303 (2.08 + 1.97 + 1.84 + 2.11)/4 = 2.004 (0.40 + 0.57 + 0.63 + 0.39)/4 = 0.50

Projected Annual Demand = 2600Average Quarterly Demand = 2600/4 = 650

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Quarter Average Seasonal Index Forecast1 (0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20 650(0.20) = 1302 (1.34 + 1.23 + 1.30 + 1.32)/4 = 1.30 650(1.30) = 8453 (2.08 + 1.97 + 1.84 + 2.11)/4 = 2.00 650(2.00) = 13004 (0.40 + 0.57 + 0.63 + 0.39)/4 = 0.50 650(0.50) = 325

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Seasonal Patterns

Period

Dem

and

(a) Multiplicative pattern

| | | | | | | | | | | | | | | |0 2 4 5 8 10 12 14 16

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Seasonal Patterns

Period

| | | | | | | | | | | | | | | |0 2 4 5 8 10 12 14 16

Dem

and

(b) Additive pattern

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Forecasting



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Factors in Choosing a Forecasting Method CostAccuracyData availableTime spanNature of products and services Impulse response and noise dampening

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There is a trade-off between cost and accuracy. Generally, more forecast accuracy can be obtained at a cost

High-accuracy approaches have disadvantages: Use more data Data are ordinarily more difficult to obtain The models are more costly to design, implement,

and operate Take longer to use

Cost & Accuracy

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Low/Moderate-Cost Approaches Statistical models, historical analogies,

executive-committee consensusHigh-Cost Approaches

Complex econometric models, Delphi, and market research

Cost & Accuracy

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Accuracy is the typical criterion for judging the performance of a forecasting approach

Accuracy is how well the forecasted values match the actual values

Cost & Accuracy

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Is the necessary data available or can it be economically obtained?

If the need is to forecast sales of a new product, then a customer survey may not be practical. Instead, historical analogy or market research may have to be used

Data Available

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What operations resource is being forecast and for what purpose?

Short-term staffing needs might best be forecast with moving average or exponential smoothing models

Long-term factory capacity needs might best be predicted with regression or executive committee consensus methods

Time Span

05/01/23 111

Is the product/service high cost or high volume?

Where is the product/service in its life cycle?

Does the product/service have seasonal demand fluctuations?

Nature of Products and Services

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An appropriate balance must be achieved between How responsive we want the forecasting

model to be to changes in the actual demand data

Our desire to suppress undesirable chance variation or noise in the demand data

Impulse Response & Noise Dampening

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If forecasts have little period-to-period fluctuation, they are said to be noise dampening

Forecasts that respond quickly to changes in data are said to have a high impulse response

A forecast system that responds quickly to data changes necessarily picks up a great deal of random fluctuation (noise)

Hence, there is a trade-off between high impulse response and high noise dampening

Impulse Response & Noise Dampening

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Measures of Forecast Error Residual, Et = At – Ft Running sum of residuals, CFE = Et Mean Squared Error, MSE = (Et

2)/n Mean Absolute Deviation,

MAD = (|Et|)/n

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Measures of Forecast Error Standard deviation, =

Mean Absolute Percentage Error,

MAPE =

(Et – E )2

n – 1

[ |Et | (100) ] / At

n

05/01/23 116

Measures of Forecast Error Example

Absolute Error Absolute Percent

Month, Demand, Forecast, Error, Squared, Error, Error, t Dt Ft Et Et

2 |Et| (|Et|/Dt)(100)1 200 225 –25 625 25 12.5% 2 240 220 20 400 20 8.3 3 300 285 15 225 15 5.0 4 270 290 –20 400 20 7.4 5 230 250 –20 400 20 8.7 6 260 240 20 400 20 7.7 7 210 250 –40 1600 40 19.0 8 275 240 35 1225 35 12.7

Total –15 5275 195 81.3%

MSE = = 659.45275

8

CFE = – 15

Measures of Error

MAD = = 24.41958

MAPE = = 10.2%81.3%8

E = = – 1.875– 15 8

= 27.4

05/01/23 117

Monitoring and Controllinga Forecasting ModelThe Tracking Signal measures the

cumulative forecast error over n periods in terms of MAD If the forecasting model is performing well,

the TS should be around zero The TS indicates the direction of the

forecasting error If the TS is positive -- increase the forecasts If the TS is negative -- decrease the forecasts

05/01/23 118

The Tracking SignalPercentage of the Area of the Normal Probability Distribution within the Control Limits of the Tracking Signal

Control Limit Spread Equivalent Percentage of Area(number of MAD) Number of 2 within Control Limits

± 1.0 ± 0.80 57.62± 1.5 ± 1.20 76.98± 2.0 ± 1.60 89.04± 2.5 ± 2.00 95.44± 3.0 ± 2.40 98.36± 3.5 ± 2.80 99.48± 4.0 ± 3.20 99.86

05/01/23 119

The Tracking Signal Example

Tracking Signal = CFE / MAD

+2.0 —

+1.5 —

+1.0 —

+0.5 —

0 —

–0.5 —

–1.0 —

–1.5 —| | | | |

0 5 10 15 20 25 Observation number

Trac

king

sig

nal

Control limit

Control limit

05/01/23 120

The Tracking Signal Example

+2.0 —

+1.5 —

+1.0 —

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0 5 10 15 20 25 Observation number

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king

sig

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Control limitOut of control

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Forecasting



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Forecast Ranging Under the assumption that the distribution of

demand is Normal it is necessary to assign a degree of confidence that future demand will be covered by an interval of a certain width

The procedure for determining this interval is called forecast ranging Prediction interval Confidence interval

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Forecast RangingConfidence interval states that that the

mean demand in some future period will be covered Example: The manager is 95 percent

confident that mean sales (i.e. the average for all sales people) next month will be between $480,000 and $520,000

The manager will be wrong in 5 out of 100 cases

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Forecast Ranging Prediction interval allows the decision maker

to state that the individual value of demand in some future period will be covered Example: The manager is 95 percent confident

that John’s sales (i.e. the individual sales) next month will be between $470,000 and $530,000

The manager will be wrong in 5 out of 100 cases

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Forecast Ranging For all things being equal, the prediction

interval is always wider than the confidence interval since there is more variability

Increasing the sample size, n, reduces variability and narrows both intervals

Forecasting far into the future increases variability and widens both intervals

Computer forecasting software packages use forecast ranging without explicitly involving statistics

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Computer Software For Forecasting Examples of computer software with

forecasting capabilities Forecast Pro Autobox SmartForecasts for Windows SAS SPSS SAP (part of ERP software) Minitab

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Reasons For Ineffective Forecasting Not involving a broad cross section of people Not recognizing that forecasting is integral to

business planning Not using forecast ranging Not forecasting the right demands Not selecting an appropriate forecasting

method Not tracking the accuracy of the forecasting

models and failure to update the forecasting model

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The End