Expert answer:Operations Forecasting

Expert answer:Purpose of Assignment The purpose of this assignment is for students to learn how to apply Operations Forecasting.Assignment Steps Resources: Microsoft® Excel® (Template provided)Select a business
operations dataset from the internet, which can be used for forecasting
what the results may be at a future state (monthly [for two years],
quarterly [for five years], annually [for 20 years]. (See example in the
Template). Use caution in using the template, as some of the cells
contain formatted functions for performing calculations. You should only
need to enter data into Column A and B only. Review the template and
the associated data. This should give you an idea of the types of data
you will require, and what the results columns reveal. You are required
to provide an APA formatted reference to the location of the source data
(Do Not just enter the Web Link). The returned work from previous
week’s work, should give you some insight as to what an APA reference
should look like. Develop a run over a given period, upper limit and lower limit as to confidence in the forecast using Microsoft® Excel®.TemplateCompare and contrast each quadrant of the forecast.Evaluate the impact this forecast would have on the firm from a
financial metrics standpoint, by writing a 300-word report in which you
describe your forecasting project and what it means in terms of
favorable or unfavorable forecast and why or why not..Format your assignment consistent with APA guidelines.
_operations_forecasting_week5.doc

forecast_ets_example.xlsx

Unformatted Attachment Preview

Operations Forecasting Grading Guide
OPS/571 Version 8
Operations Management
Copyright
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Edited in accordance with University of Phoenix® editorial standards and practices.
Operations Forecasting
Grading Guide
OPS/571 Version 8
Individual Assignment: Operations Forecasting
Purpose of Assignment
The purpose of this assignment is for students to learn how to apply Operations Forecasting.
Resources Required
Microsoft® Excel®
Content
Met
Partially
Met
Not Met
Total
Available
Total
Earned
5
#/5
Partially
Met
Not Met
Comments:
Selected a business operations dataset from
the internet or other sources which can be
used for forecasting in the University Library.
Developed a minimum of three quantitative
forecasts using Microsoft® Excel®.
Compared and contrasted each quantitative
forecast developed.
Chose the one forecast determined to be the
best for the firm and be prepared to explain
why you chose this.
Evaluated the impact this forecast would have
on the firm from a financial metrics
standpoint.
Developed a 700-word report describing your
Forecasting Project including details on all the
assignment steps.
Writing Guidelines
The paper—including tables and graphs,
headings, title page, and reference page—is
consistent with APA formatting guidelines and
meets course-level requirements.
Intellectual property is recognized with in-text
citations and a reference page.
Paragraph and sentence transitions are
present, logical, and maintain the flow
throughout the paper.
Sentences are complete, clear, and concise.
Met
Comments:
2
Operations Forecasting
Grading Guide
OPS/571 Version 8
Writing Guidelines
Met
Partially
Met
Not Met
Total
Available
Total
Earned
2
#/2
7
#/7
Rules of grammar and usage are followed
including spelling and punctuation.
Assignment Total
Additional comments:
#
Comments:
3
Forecast based on a historical time seri
Date
Airport Passengers
Forecast (Airport Passengers)
Jan-09
2,644,539
Feb-09
2,359,800
Mar-09
2,925,918
Apr-09
3,024,973
May-09
3,177,100
Jun-09
3,419,595
Jul-09
3,649,702
Aug-09
3,650,668
Sep-09
3,191,526
Oct-09
3,249,428
Nov-09
2,971,484
Dec-09
3,074,209
Jan-10
2,785,466
Feb-10
2,515,361
Mar-10
3,105,958
Apr-10
3,139,059
May-10
3,380,355
Jun-10
3,612,886
Jul-10
3,765,824
Aug-10
3,771,842
Sep-10
3,356,365
Oct-10
3,490,100
Nov-10
3,163,659
Dec-10
3,167,124
Jan-11
2,883,810
Feb-11
2,610,667
Mar-11
3,129,205
Apr-11
3,200,527
May-11
3,547,804
Jun-11
3,766,323
Jul-11
3,935,589
Aug-11
3,917,884
Sep-11
3,564,970
Oct-11
3,602,455
Nov-11
3,326,859
Dec-11
3,441,693
Jan-12
3,211,600
Feb-12
Mar-12
Apr-12
May-12
Jun-12
Jul-12
Aug-12
Sep-12
Oct-12
Nov-12
Dec-12
Jan-13
Feb-13
Mar-13
Apr-13
May-13
Jun-13
Jul-13
Aug-13
Sep-13
Oct-13
Nov-13
Dec-13
Jan-14
Feb-14
Mar-14
Apr-14
May-14
Jun-14
Jul-14
Aug-14
Sep-14
Oct-14
Nov-14
Dec-14
Jan-15
Feb-15
Mar-15
Apr-15
May-15
Jun-15
Jul-15
2,998,119
3,472,440
3,563,007
3,820,570
4,107,195
4,284,443
4,356,216
3,819,379
3,844,987
3,478,890
3,443,039
3,204,637
2,966,477
3,593,364
3,604,104
3,933,016
4,146,797
4,176,486
4,347,059
3,781,168
3,781,168
3,858,196
3,562,680
3,633,798
3,366,457
3,110,903
3,614,670
3,666,432
3,960,805
4,182,886
4,367,447
4,363,455
3,954,015
4,031,044
3,735,527
3,806,646
3,539,305
3,283,750
3,787,518
3,839,280
4,133,652
4,355,733
4,540,295
Aug-15
Sep-15
4,536,303
4,126,863
on a historical time series
Lower Confidence Bound (Airport Passengers)
Upper Confidence Bound (Airport Passengers)
3,781,168
3,695,827
3,395,234
3,461,388
3,189,182
2,928,857
3,427,939
3,475,095
3,764,936
3,982,553
4,162,715
4,154,383
3,740,659
3,813,424
3,781,168
4,020,566
3,730,125
3,806,209
3,543,732
3,292,949
3,801,401
3,857,769
4,156,674
4,383,219
4,572,180
4,572,528
4,167,372
4,248,664
3,513,726
3,580,709
3,309,277
3,049,673
3,549,430
3,597,219
3,887,654
4,105,830
4,286,519
3,957,329
4,032,582
3,769,332
3,517,827
4,025,605
4,081,340
4,379,651
4,605,636
4,794,070
4,278,685
3,865,432
4,793,920
4,388,294
1
This example contains sample monthly
passenger data for an airport from
January 2009 – September 2013.
2
If you scroll down, beginning in row 61
you’ll see how we used FORECAST.ETS
functions to calculate the forecast
passenger traffic through September
2015.
Or click the button >
3
There is an accompanying line chart
displaying forecast traffic
4
Review the FORECASTING function
reference on support.office.com by
clicking this box.
C
Forecast (Airport Passengers)
Column C uses the FORECAST.ETS
function. See cell C61.
D
Lower Confidence Bound (Airport
Passengers)
Column D uses the
FORECAST.ETS.CONFINTfunction.
See cell D61.
E
Upper Confidence B
Passengers)
Column D uses the
FORECAST.ETS.CONF
See cell E61.
For the same data and date ranges, FORECAST.ETS.SEASONALITY finds the seasonality detected in the data.
The FORECAST.ETS.STAT function finds a specific stat from the ETS algorithm for a given set of data and date ranges
this example, the 3rd argument (1) tells the function to return the Alpha parameter of the ETS algorithm. The 4th
can be 1 to calculate the seasonality, 0 to use no seasonality, or a positive integer to specify the length of the seasona
pattern. In this example, it refers to J48, which is the searonality calculation (same as if you set it to 1).
Seasonality & STAT
Seasonality:
STAT:
Forecast chart >
12
0.251
=FORECAST.ETS.SEASONALITY($B$4:$B$60,$A$4:$A$60,1,1)
=FORECAST.ETS.STAT($B$4:$B$60,$A$4:$A$60,1,J48,1,1)
Upper Confidence Bound (Airport
Passengers)
Column D uses the
FORECAST.ETS.CONFINTfunction.
See cell E61.
y detected in the data.
data and date ranges. In
he ETS algorithm. The 4th parameter
ecify the length of the seasonal
ou set it to 1).
Forecast based on a historical time ser
Airport Passenger Fo
6,000,000
5,000,000
4,000,000
3,000,000
2,000,000
1,000,000
0
Airport Passengers
Forecast (Airport Passengers)
< Home Airport Passenger Forecasting Lower Confidence Bound (Airport Passengers) Upper Confidence Bound (Airport Passengers) ... Purchase answer to see full attachment

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