demand management
TRANSCRIPT
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Demand Planning and Inventory Management
Presented by: dr. Tom Bramorski
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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
Master Production Scheduling (MPS)
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
Market Internal & External
Customers
Master Production Scheduling (MPS)
MPC Boundary
Rough-cut Capacity Planning
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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
Market Internal & External
Customers
Master Production Scheduling (MPS)
Front End
MPC Boundary
Rough-cut Capacity Planning
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Demand Management in the MPC System
Resource Planning
Production Planning
Demand Management
Market Internal & External
Customers
Master Production Scheduling (MPS)
Front End
MPC Boundary
Rough-cut Capacity Planning
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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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Demand Management & MPS
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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Demand Management & MPS
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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Forecasting as a Part of Strategic Business Planning
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
Inputs to Forecasting
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Other factors Legal Political Sociological Cultural
Inputs to Forecasting
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Forecasting as a Part of Strategic Business Planning
ForecastingMethod(s) Outputs
SalesForecast
ManagementTeam
Inputs
BusinessStrategy
Production ResourceForecasts
Forecast errors & feedback
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Qualitative ApproachesQuantitative Approaches
Forecasting Methods
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Forecasting as a Part of Strategic Business Planning
ForecastingMethod(s) Outputs
SalesForecast
ManagementTeam
Inputs
BusinessStrategy
Production ResourceForecasts
Forecast errors & feedback
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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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Forecasting as a Part of Strategic Business Planning
ForecastingMethod(s) Outputs
SalesForecast
ManagementTeam
Inputs
BusinessStrategy
Production ResourceForecasts
Forecast errors & feedback
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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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Forecasting as a Part of Strategic Business Planning
ForecastingMethod(s) Outputs
SalesForecast
ManagementTeam
Inputs
BusinessStrategy
Production ResourceForecasts
Forecast errors & feedback
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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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Forecasting as a Part of Strategic Business Planning
ForecastingMethod(s) Outputs
SalesForecast
ManagementTeam
Inputs
BusinessStrategy
Production ResourceForecasts
Forecast errors & feedback
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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
Forecast Error and Feedback
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Forecasting models are evaluated on the basis of three characteristics: Impulse response Noise-dampening ability Accuracy
Forecast Error and Feedback
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Forecasting as a Part of Strategic Business Planning
ForecastingMethod(s) Outputs
SalesForecast
ManagementTeam
Inputs
BusinessStrategy
Production ResourceForecasts
Forecast errors & feedback
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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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Forecasting as a Part of Strategic Business Planning
ForecastingMethod(s) Outputs
SalesForecast
ManagementTeam
Inputs
BusinessStrategy
Production ResourceForecasts
Forecast errors & feedback
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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
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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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
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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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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Components of Demand Q
uant
ity
Time
(b) Irregular: Sudden change in process level due to assignable causes, e.g. strike
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Components of Demand Q
uant
ity
Time
(c) Trend: Data consistently increase or decrease, not necessarily in a linear fashion
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Components of Demand Q
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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Components of Demand Q
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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Components of Demand Q
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
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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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
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Simple Moving Average Example
Actual shipmentarrivals
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
Actual shipmentarrivals
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
Actual shipmentarrivals
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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Seasonal Influences Example
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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Seasonal Influences Example
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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Seasonal Influences Example
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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Seasonal Influences Example
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 = 0.202 (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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Seasonal Influences Example
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 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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Seasonal Influences Example
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 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
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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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
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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
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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
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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
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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
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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
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The Tracking Signal Example
+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 limitOut of control
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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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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