CHAPTER 3-Forecasting PRESENTATIONS OF CLASS

quality & supply chain management

1. Chapter 3
Forecasting
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2. Pham Thi Xuan
Mai
MBAIU23030
Vu Thi Xuan Thu
MBAIU23036
Trinh Thi Yen
Anh
MBAIU23020
Duong Hoang Bao
Vy
MBA23041
Team members
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3. FORECAST
a statement about the future value
of a variable of interest
Wemakeforecastsaboutsuchthings
asweather,demand, andresource
availability
Forecasts are an important element
in making informed decisions
Forecast
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4. Two Important Aspects of Forecasts
01
02
The level of demand may be a function of some
structural variation such as trend or seasonal variation
Related to the potential size of forecast error
Expected level of demand
Accuracy
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5. 01 02
04 03
Features Common to All Forecasts
1.Forecasts
are not
perfect
Forecasts for
groupsofitemsare
more accurate
than thosefor
individualitems
Techniquesassume
some underlying
causalsystemthat
existedinthepast
willpersistintothe
future
Forecastaccuracy
decreasesasthe
forecastinghorizon
increases
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6. Elements of a Good Forecast
The forecast
1.Should be timely
2.Should be accurate
3.Should be reliable
4.Should be expressed in meaningful units
5.Should be in writing
6.Technique should be simple to understand
and use
7.Should be cost effective
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7. Determine the
purpose of the
forecast
Establish a time
horizon
Obtain, clean,
and analyze
appropriate data
Select a
forecasting
technique
Make the
forecast
Monitor the
forecast
Steps in the Forecasting Process
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8. Forecasterswanttominimizeforecasterrors
Itisnearlyimpossibletocorrectlyforecastreal-world
variablevaluesonaregularbasis
So,itisimportanttoprovideanindicationoftheextent
towhichtheforecastmightdeviatefromthevalueof
thevariablethatactuallyoccurs
Forecastaccuracyshouldbeanimportantforecasting
techniqueselectioncriterion
Error = Actual –Forecast
Iferrorsfallbeyondacceptablebounds,corrective
actionmaybenecessary
Forecast Accuracy and Control
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9. MAD weights all
errors evenly
MSE weights errors
according to their
squared values
MAPE weights
errors according
to relative error
Forecast Accuracy Metrics
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10. Forecast Error Calculation
Period
Actual
(A)
Forecast
(F)
(A-F) Error |Error| Error
2
[|Error|/Actual]x100
1 107 110 -3 3 9 2.80%
2 125 121 4 4 16 3.20%
3 115 112 3 3 9 2.61%
4 118 120 -2 2 4 1.69%
5 108 109 1 1 1 0.93%
Sum 13 39 11.23%
n = 5 n-1 = 4 n = 5
MAD MSE MAPE
= 2.6 = 9.75 = 2.25%
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11. QualitativeForecasting
Qualitativetechniquespermitthe
inclusionofsoftinformationsuch
as:
1.Humanfactors
2.Personalopinions
3.Hunches
Thesefactorsaredifficult,or
impossible,toquantify
Forecasting Approaches
QuantitativeForecasting
●Quantitative techniques
involveeithertheprojectionof
historicaldata or the
development ofassociative
methodsthatattempttouse
causalvariablestomakea
forecast
●Thesetechniquesrelyonhard
data
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12. Operations Strategy
isaplanthatoutlineshowanorganization
willuseitsproductionorservicecapabilities
tosupportitsoverallbusinessstrategyand
gainacompetitiveadvantageinthemarket.
Itinvolvesmakingkeydecisionsaboutthe
design,management, andimprovementof
operationalprocessesthatcreategoodsor
provideservices.

13. Time-series Forecast
❖Forecasts that project patterns identified in recent
time-series observations.
➢Time-series -a time-ordered sequence of
observations taken at regular time intervals.
➢Every day we count how many customers you
have; every day is a time series. You could also
do it every hour, every week, or whatever that
time series is.
❖Assume that future values of the time-series can be
estimated from past values of the time-series
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14. Time-Series Behaviors
Seasonality
Short-term, fairly regular variations related to
the calendar or time of day.
Restaurant, service call center, theaters all
experience seasonal demand.
A long term upward or downward movement
in data.
-Population shifts
-Changing income
Trend

15. Cycle:
Wavelike variations lasting more than one year
●These are often related to a variety of economic, political, or
even agricultural conditions
Irregular variation:
Due to unusual circumstances chat do not reflect typical behavior
●Labor strike
●Weather event
Random variation:
Residual variation that remains after all other behaviors have been
accounted for.
Time-Series Behaviors

16. Time-Series
Behaviors

17. Time-Series Forecasting -Naïve Forecast
Uses a single previous value of a time series as the basis for a forecast.
●The forecast for a time period is equal to the previous time period’s
value
Can be used with:
●A stable time series
●Seasonal variations
●Trend

18. Time-Series Forecasting -Averaging
These Techniques work best when a series tends to vary about an
average.
●Averaging techniques smooth variations in the data
●They can handle step changes or gradual changes in the level of a
series
●Techniques:
○Moving average
○Weighted moving average
○Exponential smoothing

19. Moving Average
Technique that averages a number of the most recent actual
values in generating a forecast.

20. Moving Average
Asnewdatabecome available,theforecastis
updatedbyaddingthenewestvalueanddropping
theoldestandthenre-computingtheaverage.
The number of data points included in the average
determines the model’s sensitivity:
●Fewer data points used -more responsive
●More data points used -less responsive

21. Weighted Moving Average
Themostrecentvaluesinatimeseriesaregivenmoreweightin
computingaforecast
➢The choice of weights, w, is somewhat arbitrary and involves some trial
and error

22. Exponential Smoothing
Aweightedaveragingmethodthatisbasedonthepreviousforecast
plusapercentageoftheforecasterror

23. Technique for trend
A simple data plot can reveal the existence
and nature of a trend
Linear trend equation:
F
t=a+bt
Where
F
t=Forecastforperiod
a=ValueofF
tatt=0
b=Slopeoftheline
t=Specifiednumberoftimeperiods
Linear trend equation

24. Estimating slope and intercept
Slope and intercept can be estimated from
historical data
series

25. Trend-Adjusted Exponential Smoothing
The trend adjusted forecast consists of two components:
●Smoothed error
●Trend factor
error

26. Trend-Adjusted Exponential Smoothing
Alpha and beta are smoothing constants
Trend-adjusted exponential smoothing has the ability to
respond to changes in trend

27. Techniques for Seasonality
●Expressed in terms of the amount that actual values deviate from
the average value of a series
●Models of seasonality
➢Additive
Seasonality is expressed as a quantity that gets added to or
subtracted from the time-series average in order to
incorporate seasonality
➢Multiplicative
Seasonality is expressed as a percentage of the average (or
trend) amount which is then used to multiply the value of a
series in order to incorporate seasonality
Seasonality –regularly repeating movements in series values
that can be tied to recurring events

28. Model of Seasonality

29. ●Seasonal relatives
➢The seasonal percentage used in the multiplicative seasonally
adjusted forecasting model
●Using seasonal relatives
➢To deseasonalize data
-Done in order to get a clearer picture of the nonseasonal
(e.g., trend) components of the data series
-Divide each data point by its seasonal relative
➢To incorporate seasonality in a forecast
-Obtain trend estimates for desired periods using a trend
equation
-Add seasonality by multiplying these trend estimates by
the corresponding seasonal relative
Seasonal relative

30. Techniques for Cycles
-Cycles are similar to seasonal variations but are of longer duration
-Explanatory approach
Search for another variable that relates to, and leads, the variable of
interest
➔Housing starts precede demand for products and services directly
related to construction of new homes
➔If a high correlation can be established with a leading variable, an
equation can be developed that describes the relationship,
enabling forecasts to be made

31. Associative Forecasting Techniques
Definition:Associativetechniquesarebasedonthedevelopmentofan
equationthatsummarizestheeffectsofpredictorvariables.Alsoknownas
“causal”modelsinvolvetheidentificationofvariablesthatcanbeusedto
predictanothervariableofinterest.
Independent Variables
These are the
predictorvariables,
suchasmarketing
spend, competitor
pricing,orseasonal
trends.
Dependent Variable
Thisisthevariable
we want to
predict,likefuture
salesorproduct
demand.
Key Assumptions:
Relationships between variables remain stable, and predictor variables can be
accurately forecast.

32. What is Simple Linear Regression?
Simple linear regressionis the most elementary form of regression analysis
where the relationship between two continuous variables is modeled using a
straight line, characterized by a slope and an intercept.
Dependent Variable Independent Variable
Equation:
Where:
-Yis the dependent variable (predicted value)
-Xis the independent variable (predictor)
-??????
�is the intercept (the value of Y when X = 0)
-??????
�is the slope of line, which indicates how Y
changes as X changes.
�=??????
0+??????
1�

33. Week Customer (X) Demand (Y)
1 x1 y1
2 x2 y2
… x… y…
52 x52 y52
(x1,y1)
(x2,y2)
(xn,yn)
Least Square Line
Equation for finding out ??????
�&??????
�:
Example:

34. Definition:Ameasureofthescatterorvariabilityofdatapointsaroundtheregressionline.
Standard Error
Interpretation:Asmallerstandarderrorindicatesatighterfitoftheregression
linetothedatapoints.
Impact:Alowerstandarderror
suggestsmoreaccuratepredictions
usingthelinearequation.

35. Correlation Coefficient
Coefficient of Determination (r
2
)
Represents the proportion of variance in the dependent variable explained by the
independent variable, ranging from 0 to 1.
Correlation (r)
A metric ranging from -1.00 to 1.00, indicating the strength and direction of the
linear relationship between two variables.

36. Simple Linear Regression Assumptions

37. Always plot the line to verify
that a linear relationship
The data may be time-
dependent
Small correlation may
indicate that other variables
are important
Issues to consider

38. Monitoring the Forecast
Importance:
Monitoring forecasts is crucial to ensure they
reflect reality.
Sources of Errors:
Tracking forecasts helps pinpoint
inaccuracies and areas for improvement.
Real-Life Example:
A grocery store might see a sales spike
during a holiday, requiring adjustments to
future forecasts.
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39. Choosing a Forecasting Technique
Factors to consider
•Cost
•Accuracy
•Availability of historical data
•Availability of forecasting software
•Time needed to gather and analyze data and prepare a forecast
•Forecast horizon

40. Using Forecast Information
•View forecasts as probable future demand
•React to meet that demand
Reactive approach
•Seeks to actively influence demand
•Advertising
•Pricing
•Product/service modifications
•Generally,requires either an explanatory model or a subjective assessment of the
influence on demand
Proactive approach

41. KEY POINTS OF OPERATION
STRATEGY
1.AlignmentwithBusinessStrategy
2.CapacityPlanning
3.ProductandServiceDesign
4.ProcessDesignandLayout
5.SupplyChainManagement
6.QualityManagement
7.InventoryManagement :
8.LeanOperations
9.FlexibilityandAdaptability
10.Sustainability
11.CostManagement
12.PerformanceMeasurement
1.CompetitivePriorities
2.CapacityPlanning
3.ProcessDesign
4.SupplyChainManagement
5.LeanOperations
6.Just-In-Time(JIT)
7.TotalQualityManagement (TQM)
8.SixSigma
9.Flexibility
10.Sustainability
11.Outsourcing
12.VerticalIntegration
13.CoreCompetencies
14.OrderWinnersandOrderQualifiers
15.Benchmarking
KEY TERMS OF OPERATION
STRATEGY
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