The final project is individual work.
Pick any national or international company with a product and/or service and do a report on it. Regardless of the project you use, you must answer the following SAMPLE questions (service vs. manufacturing)
For example, Dubai airport (service): what are the steps
Â· Give background on product/service/company
Â· Research the planning, coordination, communication, issues, etc. that occu
ed prior, during, and after this project.
Â· Provide your feedback with respect to the effectiveness, efficiency, productivity, and success/failure of this project.
Â· Identify any operations methods/concepts applied prior, during, and after the completion of this project.
Â· What have you learned while completing this assignment?
Â· Identify any methods and concepts you have learned that you can apply locally and personally.
Or, for example, the production of the Corvette Z06 (production); Chevrolet manufactures and sells a sports car called Corvette Z06. You are to complete the following:
Â· Give background on product/service/company
Â· Research the planning, coordination, processes, communication, issues, etc. that take place with respect to the operations and production for this vehicle (YouTube is a good start).
Â· Provide your feedback with respect to the effectiveness, efficiency, productivity, and success or failure of the operation.
Â· Provide your feedback on the operations methods/concepts applied in this production.
Â· What have you learned while completing this assignment?
Â· Identify any methods and concepts you have learned that you can apply locally and personally.
RULES:
Â· Write a minimum three-to-five pages (not including title page, references page, and appendix page) report on this project.
Â· You must provide a minimum of three references.
Â· The paper must be Times New Roman, 12pt SINGLE SPACED.
Â· Please send your assignment as an MS Word attachment via BlackBoard AND hard-copy during class.
(you can use the above examples)
Chapter 3
Forecasting
Chapter 3
Copyright Â© 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.
1
You should be able to:
LO 3.1 List features common to all forecasts
LO 3.2 Explain why forecasts are generally wrong
LO 3.3 List elements of a good forecast
LO 3.4 Outline the steps in the forecasting process
LO 3.5 Summarize forecast e
ors and use summaries to make decisions
LO 3.6 Describe four qualitative forecasting techniques
LO 3.7 Use a naÃ¯ve method to make a forecast
LO 3.8 Prepare a moving average forecast
LO 3.9 Prepare a weighted-average forecast
LO 3.10 Prepare an exponential smoothing forecast
LO 3.11 Prepare a linear trend forecast
LO 3.12 Prepare a trend-adjusted exponential smoothing forecast
LO 3.13 Compute and use seasonal relatives
LO 3.14 Compute and use regression and co
elation coefficients
LO 3.15 Construct control charts and use them to monitor forecast e
ors
LO 3.16 Describe the key factors and trade-offs to consider when choosing a forecasting technique
Chapter 3: Learning Objectives
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Forecast
Forecast â€“ a statement about the future value of a variable of interest
We make forecasts about such things as weather, demand, and resource availability
Forecasts are important to making informed decisions
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Two Important Aspects of Forecasts
Expected level of demand
The level of demand may be a function of some structural variation such as trend or seasonal variation
Accuracy
Related to the potential size of forecast e
o
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Plan the system
Generally involves long-range plans related to:
Types of products and services to offe
Facility and equipment levels
Facility location
Plan the use of the system
Generally involves short- and medium-range plans related to:
Inventory management
Workforce levels
Purchasing
Production
Budgeting
Scheduling
Forecast Uses
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Techniques assume some underlying causal system that existed in the past will persist into the future
Forecasts are not perfect
Forecasts for groups of items are more accurate than those for individual items
Forecast accuracy decreases as the forecasting horizon increases
Features Common to All Forecasts
LO 3.1
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Forecasts are not Perfect
Forecasts are not perfect:
Because random variation is always present, there will always be some residual e
or, even if all other factors have been accounted for.
LO 3.2
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The forecast
should be timely
should be accurate
should be reliable
should be expressed in meaningful units
should be in writing
technique should be simple to understand and use
should be cost-effective
Elements of a Good Forecast
LO 3.3
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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 e
ors
Steps in the Forecasting Process
LO 3.4
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Forecast Accuracy and Control
Allowances should be made for forecast e
ors
It is important to provide an indication of the extent to which the forecast might deviate from the value of the variable that actually occurs
Forecast e
ors should be monitored
E
or = Actual â€“ Forecast
If e
ors fall beyond acceptable bounds, co
ective action may be necessary
LO 3.5
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Forecast Accuracy Metrics
MAD weights all e
ors evenly
MSE weights e
ors according to their squared values
MAPE weights e
ors according to relative e
o
LO 3.5
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Period Actual
(A) Forecast
(F) (A-F) E
or |E
or| E
or2 [|E
or|/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%
Forecast E
or Calculation
LO 3.5
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Qualitative Forecasting
Qualitative techniques permit the inclusion of soft information such as:
Human factors
Personal opinions
Hunches
These factors are difficult, or impossible, to quantify
Quantitative Forecasting
These techniques rely on hard data
Quantitative techniques involve either the projection of historical data or the development of associative methods that attempt to use causal variables to make a forecast
Forecasting Approaches
LO 3.6
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Qualitative Forecasts
Forecasts that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts
Executive opinions
a small group of upper-level managers may meet and collectively develop a forecast
Sales force opinions
members of the sales or customer service staff can be good sources of information due to their direct contact with customers and may be aware of plans customers may be considering for the future
Consumer surveys
since consumers ultimately determine demand, it makes sense to solicit input from them
consumer surveys typically represent a sample of consumer opinions
Other approaches
managers may solicit 0pinions from other managers or staff people or outside experts to help with developing a forecast.
the Delphi method is an iterative process intended to achieve a consensus
LO 3.6
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Time-Series Forecasts
Forecasts that project patterns identified in recent time-series observations
Time-series - a time-ordered sequence of observations taken at regular time intervals
Assume that future values of the time-series can be estimated from past values of the time-series
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Trend
Seasonality
Cycles
I
egular variations
Random variation
Time-Series Behaviors
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Trend
A long-term upward or downward movement in data
Population shifts
Changing income
Seasonality
Short-term, fairly regular variations related to the calendar or time of day
Restaurants, service call centers, and theaters all experience seasonal demand
Trends and Seasonality
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Cycle
Wavelike variations lasting more than one yea
These are often related to a variety of economic, political, or even agricultural conditions
I
egular variation
Due to unusual circumstances that do not reflect typical behavio
Labor strike
Weather event
Random Variation
Residual variation that remains after all other behaviors have been accounted fo
Cycles and Variations
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NaÃ¯ve 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 periodâ€™s value
Can be used with
a stable time series
seasonal variations
trend
Time-Series Forecasting - NaÃ¯ve Forecast
LO 3.7
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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
Time-Series Forecasting - Averaging
LO 3.8
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Technique that averages a number of the most recent actual values in generating a forecast
Moving Average
LO 3.8
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As new data become available, the forecast is updated by adding the newest value and dropping the oldest and then re-computing the average
The number of data points included in the average determines the modelâ€™s sensitivity
Fewer data points used-- more responsive
More data points used-- less responsive
Moving Average
LO 3.8
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The most recent values in a time series are given more weight in computing a forecast
The choice of weights, w, is somewhat a
itrary and involves some trial and e
o
Weighted Moving Average
LO 3.9
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A weighted averaging method that is based on the previous forecast plus a percentage of the forecast e
o
Exponential Smoothing
LO 3.10
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Linear Trend
A simple data plot can reveal the existence and nature of a trend
Linear trend equation
LO 3.11
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Slope and intercept can be estimated from historical data
Estimating slope and intercept
LO 3.11
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Trend-Adjusted Exponential Smoothing
The trend adjusted forecast consists of two components
Smoothed e
o
Trend facto
LO 3.12
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Trend-Adjusted Exponential Smoothing
Alpha and beta are smoothing constants
Trend-adjusted exponential smoothing has the ability to respond to changes in trend
LO 3.12
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Seasonality â€“ regularly repeating movements in series values that can be tied to recu
ing events
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
Techniques for Seasonality
LO 3.13
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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 co
esponding seasonal relative
Seasonal Relatives
LO 3.13
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Associative techniques are based on the development of an equation that summarizes the effects of predictor variables
Predictor variables - variables that can be used to predict values of the variable of interest
Home values may be related to such factors as home and property size, location, number of bedrooms, and number of bathrooms
Associative Forecasting Techniques
LO 3.14
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Regression - a technique for fitting a line to a set of data points
Simple linear regression - the simplest form of regression that involves a linear relationship between two variables
The object of simple linear regression is to obtain an equation of a straight line that minimizes the sum of squared vertical deviations from the line (i.e., the least squares criterion)
Simple Linear Regression
LO 3.14
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Least Squares Line
LO 3.14
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Co
elation,
A measure of the strength and direction of relationship between two variables
Ranges between -1.00 and +1.00
2, square of the co
elation coefficient
A measure