Acc 300 Assignment 2
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This document provides information and instructions for 4 problems from an ACC-300 assignment on forecasting techniques. It includes time series data on airline passenger numbers, instructions to develop a 3-week forecast using an appropriate technique, data and instructions to generate forecasts for demand using exponential smoothing, instructions to calculate forecasts incorporating trend and seasonality for spa sales, and a question about calculating seasonal relatives for a restaurant's business across different nights.
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Trend adjusted exponential smoothing forecasting metho ds
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rl1an14 Assignment Print Viorvawarci5.00 pointsi!.docx
rl1an14 Assignment Print Viorv
awarci:
5.00 points
i! You did not receive credit for this question in a previous attempt
Problem 3-2
National Scan, lnc., sells radio frequency inventory tags. Monthly sales fora seven-month period were as
follows:
1.
Month
Feb.
Mar,
Apr.
May.
Jun.
J ul.
Aug.
Sales
(000)tJnfits
15
16
11
26
15
24
6
b. Forecat S€Ssnber sdes vdtrne rcirg edr d the fdloving:
(1) A lins fiend equrafm.(Round lour inbrmediab calculations and final answer lo 2 decimal
plam.)
Y1
(2) A fiv+month moving average. (Round your answer to 2 decimal places.)
Moving average thousands
(3) Exponential smoothing with a smoothing constant equal to .20, assuming a March forecast of
17(000). (Round your intermediate forecast values and final answer to 2 decimal places)
Forecast thousands
thousands
thousands
(4) The naive approach.
Naive approach
decimal places.)
Weighted average
i&://ezto.mhedffition.csn/fm.Fx
(5) A weighted average using .70 for August, .20 for July, and ,10 for June. (Round your answer to 2
thousands
114
11t12JN14 Assignment Print Vianv
y=$
Worksheet Difficulty:1 Easy
Learning Objeetive: 03-1 4
Problem 3-24 Compute and use regression and
correlation coeff cie nts.
award:
5. 5.00 points
i! Vou did not receive credit for this question in a previous attempt
Problem 3-15
The manager of a fashionable restaurant open Wedneday through Saturday says that the rcstaurant does
about 33 percent of its business on Friday night, 28 Frcent on Saturday night, and 19 percent on
Thursday night. What seasonal relatives would describe this situation?(Round your answers to 2
decimal places.)
Wednesday
Thursday
Friday
Saturday
Worksheet
Problem 3-1 5
Difrculty:2 Medium
Leaming Objedive: 03-1 3
Compub and use seasonal
relatives.
ittp://ezto.m heducation.com/hm.tpx 4t4
fitQtn14 Assignment PrintVia,v
Problem 3-20
An analyst must decide between two different forecasting techniques for weekly sales of roller blades: a
linear trend equation and the naive approach. The linear trend equation is F1 = 122 + 2.Ot, and it was
developed using data from periods '1 through 10. Based on data for penods 11 through 20 as shown in the
table, which of these two methods has the greater accuracy if MAD and MSE are used?
I Units Sold
11 147
12 148
13 151
14 146
15 155
16 152
17 156
18 157
19 158
20 167
MAD (Naive)
MAD (Linear)
MSE (Naive)
MSE (Linear)
(Click to select) v provides forecasts with less average enor and iess average squared enor.
Worksheet
Problem 3-20
Difficulty:2 Medium
Leaming Objective: 03-05
Summarize forecast enors and use
summaries to make decisions.
Leaming Objective:03-07 Use a
naive method to make a forecast.
Learning Objective: 03-11 Prepare
a linear trend forecast.
award:
4, 5.00 points
b. Determine the delivery charge for transporting a SGpound parcel 20
intermediate calculation. Round your answer to 2 decimal places.
response.)
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Dear students get fully solved SMU BBA Spring 2014 assignments
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The document provides step-by-step instructions for performing several mathematical operations and problem solving techniques, including:
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3) Simplifying rational expressions by finding common denominators.
4) Completing the square to rewrite a quadratic equation in standard form.
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Decimals Add and Subtract
1. This document provides a lesson on adding and subtracting decimals. It emphasizes lining up the decimals and notes that adding and subtracting decimals follows the same process as whole numbers.
2. Examples are provided that demonstrate adding decimals by lining them up under each other with the decimals in the same column before adding. Adding zeros as placeholders is explained to help line up the decimals.
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Trend adjusted exponential smoothing forecasting metho ds
This document describes the trend adjusted exponential smoothing forecasting method. It is a quantitative time series forecasting technique that calculates the weighted average of the current actual value and previous forecast, with an adjustment made for any trend present in the data. The trend adjustment is calculated using a smoothing constant and prior trend value. The method is preferred when a trend or seasonal pattern is evident in historical data. An example is provided to illustrate how to compute forecasts using this method by calculating the unadjusted forecast, trend, and adjusted forecast over multiple time periods.
rl1an14 Assignment Print Viorvawarci5.00 pointsi!.docx
rl1an14 Assignment Print Viorv
awarci:
5.00 points
i! You did not receive credit for this question in a previous attempt
Problem 3-2
National Scan, lnc., sells radio frequency inventory tags. Monthly sales fora seven-month period were as
follows:
1.
Month
Feb.
Mar,
Apr.
May.
Jun.
J ul.
Aug.
Sales
(000)tJnfits
15
16
11
26
15
24
6
b. Forecat S€Ssnber sdes vdtrne rcirg edr d the fdloving:
(1) A lins fiend equrafm.(Round lour inbrmediab calculations and final answer lo 2 decimal
plam.)
Y1
(2) A fiv+month moving average. (Round your answer to 2 decimal places.)
Moving average thousands
(3) Exponential smoothing with a smoothing constant equal to .20, assuming a March forecast of
17(000). (Round your intermediate forecast values and final answer to 2 decimal places)
Forecast thousands
thousands
thousands
(4) The naive approach.
Naive approach
decimal places.)
Weighted average
i&://ezto.mhedffition.csn/fm.Fx
(5) A weighted average using .70 for August, .20 for July, and ,10 for June. (Round your answer to 2
thousands
114
11t12JN14 Assignment Print Vianv
y=$
Worksheet Difficulty:1 Easy
Learning Objeetive: 03-1 4
Problem 3-24 Compute and use regression and
correlation coeff cie nts.
award:
5. 5.00 points
i! Vou did not receive credit for this question in a previous attempt
Problem 3-15
The manager of a fashionable restaurant open Wedneday through Saturday says that the rcstaurant does
about 33 percent of its business on Friday night, 28 Frcent on Saturday night, and 19 percent on
Thursday night. What seasonal relatives would describe this situation?(Round your answers to 2
decimal places.)
Wednesday
Thursday
Friday
Saturday
Worksheet
Problem 3-1 5
Difrculty:2 Medium
Leaming Objedive: 03-1 3
Compub and use seasonal
relatives.
ittp://ezto.m heducation.com/hm.tpx 4t4
fitQtn14 Assignment PrintVia,v
Problem 3-20
An analyst must decide between two different forecasting techniques for weekly sales of roller blades: a
linear trend equation and the naive approach. The linear trend equation is F1 = 122 + 2.Ot, and it was
developed using data from periods '1 through 10. Based on data for penods 11 through 20 as shown in the
table, which of these two methods has the greater accuracy if MAD and MSE are used?
I Units Sold
11 147
12 148
13 151
14 146
15 155
16 152
17 156
18 157
19 158
20 167
MAD (Naive)
MAD (Linear)
MSE (Naive)
MSE (Linear)
(Click to select) v provides forecasts with less average enor and iess average squared enor.
Worksheet
Problem 3-20
Difficulty:2 Medium
Leaming Objective: 03-05
Summarize forecast enors and use
summaries to make decisions.
Leaming Objective:03-07 Use a
naive method to make a forecast.
Learning Objective: 03-11 Prepare
a linear trend forecast.
award:
4, 5.00 points
b. Determine the delivery charge for transporting a SGpound parcel 20
intermediate calculation. Round your answer to 2 decimal places.
response.)
Htslleztc.mheducation.comlhm.tpx
S You did not receive cred ...
Imt 24
Dear students get fully solved SMU BBA Spring 2014 assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
MAT 540 Entire Course NEW
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Chapter-3_Heizer_S1.pptx
Here are 3 practice problems using quantitative forecasting methods:
1. Using simple exponential smoothing, forecast next period's sales given the following data with a smoothing constant of 0.3:
Period: Sales
1: 100
2: 110
3: 120
4: ?
Forecast: F1 = 100
F2 = 100 + 0.3(110 - 100) = 103
F3 = 103 + 0.3(120 - 103) = 108.9
F4 = 108.9 + 0.3(120 - 108.9) = 113.67
2. Using linear regression, forecast next year's profits based on advertising expenditures given:
Year: Prof
942015 Assignment Print Viewhttpezto.mheducation.com.docx
9/4/2015 Assignment Print View
http://ezto.mheducation.com/hm.tpx?todo=printview 1/8
1. Award: 25 out of 25.00 points
Score: 100/100 Points 100 %
Problem 3-2
National Scan, Inc., sells radio frequency inventory tags. Monthly sales for a sevenmonth period were as
follows:
Month
Sales
(000)Units
Feb. 19
Mar. 18
Apr. 15
May 20
Jun. 18
Jul. 22
Aug. 20
b. Forecast September sales volume using each of the following:
(1) A linear trend equation. (Round your intermediate calculations and final answer to 2 decimal
places.)
Yt 20.86 thousands
(2) A fivemonth moving average.
Moving average 19 thousands
(3) Exponential smoothing with a smoothing constant equal to .20, assuming a March forecast of
19(000). (Round your intermediate calculations and final answer to 2 decimal places.)
Forecast 19.26 thousands
(4) The naive approach.
Naive approach 20 thousands
(5) A weighted average using .60 for August, .30 for July, and .10 for June. (Round your answer to 2
decimal places.)
9/4/2015 Assignment Print View
http://ezto.mheducation.com/hm.tpx?todo=printview 2/8
Weighted average 20.40 thousands
References
Worksheet Learning Objective:
0307 Use a naive
method to make a
forecast.
Learning Objective: 0310 Prepare an
exponential smoothing forecast.
Problem 32 Learning Objective:
0308 Prepare a
moving average
forecast.
Problem 3-2
National Scan, Inc., sells radio frequency inventory tags. Monthly sales for a sevenmonth period were as
follows:
Month
Sales
(000)Units
Feb. 19
Mar. 18
Apr. 15
May 20
Jun. 18
Jul. 22
Aug. 20
b. Forecast September sales volume using each of the following:
(1) A linear trend equation. (Round your intermediate calculations and final answer to 2 decimal
places.)
Yt
20.86 ± 0.10 thousands
(2) A fivemonth moving average.
Moving average 19 thousands
(3) Exponential smoothing with a smoothing constant equal to .20, assuming a March forecast of
19(000). (Round your intermediate calculations and final answer to 2 decimal places.)
Forecast 19.26 ± 0.10
thousands
9/4/2015 Assignment Print View
http://ezto.mheducation.com/hm.tpx?todo=printview 3/8
2. Award: 25 out of 25.00 points
(4) The naive approach.
Naive approach 20 thousands
(5) A weighted average using .60 for August, .30 for July, and .10 for June. (Round your answer to 2
decimal places.)
Weighted average 20.40 ± 0.01
thousands
Explanation:
b.
(1)
t Y tY
1 19 19
2 18 36
3 15 45
4 20 80
5 18 90
6 22 132
7 20 140
28 132 542
with n = 7, Σt = 28, Σt2 = 140
b =
nΣty − ΣtΣy
=
7(542) − 28(132)
= .50
nΣt2 − (Σt)2 7(140) − 28(28)
a =
Σy − bΣt
=
132 − .50(28)
= 16.86
n 7
For Sept., t = 8, and Yt = 16.86 + .50(8) = 20.86 (000)
(2)
MA5 =
15 + 20 + 18 + 22 + 20 = 195
(3)
Month Forecast = F(old) + .20 [Act.
Demand forecasting methods 1 gp
This document provides an overview of demand forecasting methods. It discusses qualitative and quantitative forecasting models, including time series analysis techniques like moving averages, exponential smoothing, and adjusting for trends and seasonality. It also covers causal models using linear regression. Key steps in forecasting like selecting a model, measuring accuracy, and choosing software are outlined. The homework assigns practicing examples on least squares, moving averages, and exponential smoothing from a textbook.
Instructions After the student calculates the answers to this hom.docx
Instructions: After the student calculates the answers to this homework he/she must submit the answers by placing them in the Assignment # _ Submittal Link in Bb by midnight of the date shown in the Course Roadmap and Syllabus. The Instructor does not allow late submissions therefore a score of zero (0) will be recorded for the assignment if submitted after midnight Eastern Time of the due date.
Solution
s for the assignment will be released in Bb the morning after the due date for access by students. Total points for the assignment is 140. Note: There is no partial credit given; either the answer is correct or incorrect.
Scenario: Ajax Manufacturing is an electronic test equipment manufacturing firm that markets a certain piece of specialty test equipment.
Ajax has several competitors who currently market similar pieces of equipment.
While customers have repeatedly indicated they prefer Ajax’s test equipment, they have historically proven to be unwilling to wait for Ajax to manufacture this certain piece of equipment on demand and will purchase their test equipment from Ajax’s competitors in the event Ajax does not have the equipment available in inventory for immediate delivery.
Thus, the key to Ajax successfully maintaining market share for this particular piece of equipment has been to have it available in stock for immediate delivery.
Unfortunately, it is a rather expensive piece of equipment to maintain in inventory.
Period
Actual Number of Sold Units
Period
Actual Number of Sold Units
1
33
16
38
2
36
17
37
3
32
18
37
4
35
19
37
5
33
20
35
6
34
21
37
7
34
22
34
8
38
23
35
9
37
24
34
10
36
25
36
11
38
26
32
12
36
27
35
13
37
28
33
14
39
29
38
15
35
30
37
31
? Forecast ?
Thus, the president of Ajax Manufacturing is very interested in accurately forecasting market demand in order to ensure he has adequate inventory available to meet customer demand without incurring undue inventory costs.
His sales department has provided historical data (in the table above) regarding market demand for this certain piece of specialty electronics test equipment for the past 30 months.
Instructions for Homework Assignment: Use the provided Excel file to construct a spreadsheet for each of the four models (Models 1-4) then answer Questions 26 - 28 based upon your calculations.
Model 1: Answer the following questions.
Question 1. Calculate the projected demand for the test equipment for the time periods 4 - 31 based upon a 3-month moving average forecast model. What is the projected demand for period 31? (5 points)
Question 2. Calculate the mean absolute deviation (MAD) for the 3-month moving average forecast for each of the time periods 4 through 30 and for the model. (5 points)
Question 3. Calculate the mean squared error (MSE) for the 3-month moving average forecast for time periods 4 through 30 and for the model. (5 points)
Question 4. Calculate the mean absolute per.
Chap011
This document discusses various qualitative and quantitative forecasting methods including simple and weighted moving averages, exponential smoothing, and simple linear regression. It provides examples of how to calculate forecasts using each of these methods and evaluates forecast accuracy using metrics like MAD and tracking signal.
M&l case
- M&L Manufacturing produces printer and copier components for a major manufacturer as well as for replacement parts distribution. They produce about 20 different items.
- Due to competitive pressures and falling profits, the manager wants to introduce formal forecasting to improve production planning and inventory management for two products.
- Historical demand data for the past 14 weeks is provided for the two products. Forecasts for the next 4 weeks will be generated using linear regression for Product 1 and an intuitive average-based approach for Product 2.
test 1Suppose that you are using the simple mean to make a forecas.docx
test 1Suppose that you are using the simple mean to make a forecast. This period’s forecast was equal to 200 units, and it was based on 5 periods of demand. This period’s actual demand was 300 units. What is your forecast for next period?5*200+3001300simple mean = 1300/6= 216.7 217 rounded answerThe school’s cafeteria has three service lines (pizza, salads, and sandwiches). The pizza line has one server and serves 90 pizzas per hour. The salad line has two servers and they handle 140 customers in 70 minutes. The sandwich line has three servers and they supply 360 sandwiches in 90 minutes. Which service line has the highest hourly productivity?Your answer should just note down one of the three lines pizza, salads, or sandwich? For instance, if you conclude that the salads line has the highest productivity you will just write salads in your answer.Pizza P P= O/ISalad P=O/Isandwich P=O/I90/1140/(70/60)*2360/(90/60)*390 pizzas per hour 140/2.33360/4.560 salads per hour 80 slalads per hour pizza is the highestSuppose that you want to set up a 3-month weighted moving average forecasting system. You want the weights to be percentages (that add to 100%). Furthermore, you want weights for the most recent two months to be equal but you want each of those weights to be twice as large as the weight for the oldest month. What should the weight be for the oldest month?x=2yso, x+x+y = 100%or 2y +2y + y =100%y=20%Which of the following would not be considered a core competency that a company might have:insuffcient distribution centerThe definition of quality that involves the product functioning as expected without failure is:realiability Cover Me, Inc. sells umbrellas in three cities. Management assumes that annual rainfall is the primary determinant of umbrella sales, and it wants to generate a linear regression equation to estimate potential sales in other cities. Given the data below, what is the forecast for 20 in. of rain? (Round your answer to the nearest whole number) Rainfall Sales X Y b=3(152400)-(78)(5100)/3(2340)-(78)^2 City A 36 in. 2300b=63.5 City B 30 in. 2000 City C 12 in. 800y=63.5+50xSuppose that a product has two parts, both of which must be working in order for the product to function. The reliability of the first part is .85, and the reliability of the second part is .82. In addition, the second part comes with a backup that is 50% reliable. What is the overall reliability of the product? Please answer as a percentage to the second decimal place (so your answer should be of the form xx.xx%)0.82+0.5(1-0.82)=0.91(0.85)(0.91)=(0.7735)Which of the following forecasting methods would be most accurate if demand were rapidly decrea.
NCV 3 Mathematical Literacy Hands-On Support Slide Show - Module 3
This slide show complements the learner guide NCV 3 Mathematical Literacy Hands-On Training by San Viljoen, published by Future Managers. For more information visit our website www.futuremanagers.net
Week 4 Staffing BudgetsFTEs Variance AnalysisAssignment Guide.docx
Week 4: Staffing Budgets/FTEs/ Variance Analysis
Assignment Guidelines with Scoring RubricPurpose
This assignment provides learners with the opportunity to apply budgeting knowledge and skills by calculating full-time equivalents for a nursing unit, projecting FTE needs related to census changes, and analyzing a variance scenario.
Due Date: Sunday 11:59 p.m. MT at the end of Week 4Total Points Possible: 150 Requirements
Description of the Assignment
This assignment is in two scenario-based sections each related to staffing budgets. In the first, the student will perform multistep calculations of FTEs and projection of future FTE needs for a selected nursing unit. The second section involves calculation of budget variance and its analysis. Each section requires supported written interpretation of findings.
Criteria for Content
Answer the questions and complete the calculations required for the two sections of the assignment.
Key points related to Calculations:
1. When performing calculations, standard rounding rules apply. If the number to the right of the decimal is less than 5, round down to the nearest whole number, e.g., 33.4 = 33 If the number to the right of the decimal is 5 or greater, round up to the nearest whole number, e.g. 33.5 =34.
2. Read the question carefully. Pay close attention to the units be asked and keep them consistent. For example, hours vs FTEs; days vs months vs years.
3. Provide ALL formulas with references. Designate which formula associates with which source. It is not sufficient to simply list the source at the beginning of the section. Write out the formula used BEFORE filling in the numbers.
Example: Efficiency Variance + Volume Variance + Cost Variance = Total Variance
Rundio, A. (2016). The nurse manager’s guide to budgeting and finance. 2nd. Ed., Indianapolis, IN: Sigma Theta Tau International.
· Section One: Staffing Budget and FTEs
· Calculations of full-time equivalents (FTEs)
· Project FTE needs related to census changes
· Section Two: Variance Analysis
· Calculating variance
· Variance analysis with explanation
Download the Assignment Word Document and submit your answers directly onto the form. When completed, upload into the assignment portal in your course. For the questions requiring a written response, please adhere to proper grammar and syntax, and provide references. For the questions requiring calculations, show all your work including the formula used. Include the references for formula chosen.
Preparing the Assignment
Section One—Staffing Budget and FTEs
Personnel Budget Case Study
Sandra Chambers has recently accepted the position of assistant administrator for the department of nursing at Potter Regional Medical Center where she will oversee the operations of five medical units. As she evaluates the budgets for the different cost centers, she finds that all are being used at near or full capacity. The activity in four of the five has remained steady over the past 2 years. A fifth unit ...
Ch04pp
The document contains 8 practice problems related to forecasting techniques. Problem 1 asks to calculate a 3-week moving average of auto sales data. Problem 2 introduces a weighted moving average using different weights for the past 3 weeks. Problem 3 demonstrates simple exponential smoothing to forecast demand. Problem 4 evaluates the accuracy of 2 different alpha values for exponential smoothing of battery sales forecasts. Problem 5 involves calculating a least squares trend line and forecasting sales for 2003. Problem 6 provides data to calculate the tracking signal and mean absolute deviation for a demand forecast. Problems 7-8 involve seasonal indices and forecasting annual and quarterly sales with 10% growth.
PROBABILITYProbability of A P(A)= NFNPAdditi.docx
PROBABILITY
Probability of A P(A)= NF/NP
Addition rule P(A or B) = P(A) + P(B) – P(A and B)
Multiplication rule P(A and B) = P(A) . P(B\A)
Complementary principle P(A)+P(A’)=1
Independency P(A)=P(A\B) P(B)=P(B\A)
NUMERICAL MEASURES
PCS=
outliers
2k>n
PROBABILITY DISTRIBUTIONS
1. The following is the time, in minutes, 15 employees of accounting department of East Penn Manufacturing spent commuting to work this morning. [20pts]
28 25 48 37 41 19 32 26 16 23 23 29 36 31 26
1. Organize data into stemplot. [2pts]
1. Find the standard deviation, round it to 2 decimal places. [3pts]
1. Find value of the 85th percentile L85 (or P85) and write its brief interpretation. [3pts]
1. Find Q1, Q3, IQR and test possible outliers. Draw a conclusion clearly. [5pts]
1. Organize data into classes using an interval 15-23 as the first. [2pts]
1. What is a class width w in previous part e? [1pt]
1. Use the frequency table and find the standard deviation. Round the result to two decimal places if necessary. [3pts]
1. Compare results of b) and g). Which one is exact? [1pt]
_________________________________________________________________________________
2.Use the frequency table and find the median. Round only the last result to two decimal places. [4pts]
Class
frequency
0-4
6
5-9
7
10-14
12
15-19
8
_________________________________________________________________________________
3. Grace Andrews, a stockbroker, recommends two stocks (Hewlett Packard and Citibank) to her customers. Suppose that the probability that each stock’s price will go up next year is 0.6, and that each stock’s price behavior is independent of the price behavior of the other stock. Take a number of stocks that will go up next year as the random variable X and give a complete probability distribution.[6pts]
4. A survey of executives dealt with their loyalty to the company. One of the questions was, “If you were given an offer by another company equal to or slightly better than your present position, would you remain with the company or take the other position?” The responses of the 200 executives in the survey were cross-classified with their length of service with the company. Give all results rounded to 4 decimal places.[2+3+2+3+1+5=16pts]
LENGTH OF
SERVICE
LOYALTY
< 1year
1-5 years
6-10years
>10years
total
Would remain
10
30
5
75
120
Would not remain
25
15
10
30
80
total
35
45
15
105
200
a) What is the probability that randomly chosen executive is loyal to the company?
b) What is the probability that randomly chosen executive has more than 10 years of service OR is not loyal to the company?
c) What is the probability that randomly chosen executive has less than one year of service AND is not loyal to the company?
d) Two executives are chosen ra.
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NAA Maximize 2015 - Presentation on In-depth Analytics of Pricing Discovery
This session was the first ever math-based session for advanced revenue management practitioners to discuss computational challenges in revenue management and customer data analytics. Pricing isn’t just about looking at data on spreadsheets. You actually have to do math. Complex math. Well, you have to do complex math if your revenue management software system doesn’t bring the science and analytics together.
MAT 540 STR Great Stories /newtonhelp.com
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QNT 561 NERD Education System--qnt561nerd.com
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QNT 561 Final Exam Guide (New, 2017)
QNT 561 Week 1 Assignment Statistics Concepts and Descriptive Measures Instructions (Financial Data)
QNT 561 Week 1 Assignment Statistics Concepts and Descriptive Measures Instructions (Consumer Food)
QNT 561 Week 2 Case Study MBA Schools in Asia Pacific (2 Papers)
QNT 561 Week 3 Case Study SuperFun Toys (2 Papers)
QNT 561 Week 3 Assignment Expansion Strategy and Establishing a Re-Order Point
QNT 561 Week 4 Case the Payment Time
QNT 561 Week 5 Spicy Wings Case Study
demand forecasting
This document discusses various demand forecasting models and techniques. It covers qualitative and quantitative forecasting methods. Time series models like naive, moving average, weighted moving average and exponential smoothing are explained. The document also discusses adjusting forecasts for seasonality using seasonal indices. Linear regression is provided as an example of a causal forecasting model. Key steps in the forecasting process are identified as selecting the appropriate model, evaluating available data, and generating and monitoring forecasts. Factors to consider when choosing a model include the available data, required accuracy, forecast horizon and data patterns. Forecasting software options are also briefly outlined.
IPPTChap010.pptx
The document provides an overview of time-series forecasting techniques and their application to forecasting call volume for a computer warehouse. It discusses several forecasting methods including last-value, averaging, and moving average. For each method, it shows how to adjust for seasonality, apply the method to historical call volume data, and calculate forecast errors. The best method will be selected based on accuracy in predicting future call volume.
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Vertex42: Terms of Use and Copyright:
See the Terms Of Use worksheet and the license agreement on Vertex42.com. Copyright notices may not be removed.0HELP[Automation of Supply Chain Management System]Ray & Young Inc.Today's Date:10/22/15Thursday(vertical red line)Project Lead:Rodney Wheeler[42]Start Date:10/22/15Thursday[42]First Day of Week (Mon=2):210/19/1510/20/1510/21/1510/22/1510/23/1510/24/1510/25/1510/26/1510/27/1510/28/1510/29/1510/30/1510/31/1511/1/1511/2/1511/3/1511/4/1511/5/1511/6/1511/7/1511/8/1511/9/1511/10/1511/11/1511/12/1511/13/1511/14/1511/15/1511/16/1511/17/1511/18/1511/19/1511/20/1511/21/1511/22/1511/23/1511/24/1511/25/1511/26/1511/27/1511/28/1511/29/1511/30/1512/1/1512/2/1512/3/1512/4/1512/5/1512/6/1512/7/1512/8/1512/9/1512/10/1512/11/1512/12/1512/13/1512/14/1512/15/1512/16/1512/17/1512/18/1512/19/1512/20/1512/21/1512/22/1512/23/1512/24/1512/25/1512/26/1512/27/1512/28/1512/29/1512/30/1512/31/151/1/161/2/161/3/161/4/161/5/161/6/161/7/161/8/161/9/161/10/161/11/161/12/161/13/161/14/161/15/161/16/161/17/161/18/161/19/161/20/161/21/161/22/161/23/161/24/161/25/161/26/161/27/161/28/161/29/161/30/161/31/162/1/162/2/162/3/162/4/162/5/162/6/162/7/162/8/162/9/162/10/162/11/162/12/162/13/162/14/162/15/162/16/162/17/162/18/162/19/162/20/162/21/162/22/162/23/162/24/162/25/162/26/162/27/162/28/162/29/163/1/163/2/163/3/163/4/163/5/163/6/163/7/163/8/163/9/163/10/163/11/163/12/163/13/163/14/163/15/163/16/163/17/163/18/163/19/163/20/163/21/163/22/163/23/163/24/163/25/163/26/163/27/163/28/163/29/163/30/163/31/164/1/164/2/164/3/164/4/164/5/164/6/164/7/164/8/164/9/164/10/164/11/164/12/164/13/164/14/164/15/164/16/164/17/164/18/164/19/164/20/164/21/164/22/164/23/164/24/164/25/164/26/164/27/164/28/164/29/164/30/165/1/165/2/165/3/165/4/165/5/165/6/165/7/165/8/165/9/165/10/165/11/165/12/165/13/165/14/165/15/165/16/165/17/165/18/165/19/165/20/165/21/165/22/165/23/165/24/165/25/165/26/165/27/165/28/165/29/165/30/165/31/166/1/166/2/166/3/166/4/166/5/166/6/166/7/166/8/166/9/166/10/166/11/166/12/16WBS
Jon: Work Breakdown Structure
Level 1: 1, 2, 3, ...
Level 2: 1.1, 1.2, 1.3, ...
Level 3: 1.1.1, 1.1.2, 1.1.3, …
The WBS is automatically entered, but the formulas are different for different levels.TasksTask
LeadStart
Jon: Start Date
Enter the starting date for this task. To associate the start date with the end of another task, enter a formula in the start date that refers to the end date of that task.End
Jon: End Date
The ending date is calculated by adding the Duration (calendar days) to the Start date minus 1 day, because the task duration is from the beginning of the Start day to the end of the End day.
Duration (Days)
Jon: Duration (Calendar Days)
Enter the number of calendar days for the given task. Refer to the Working Days column or use a calendar to determine the corresponding working days. The duration is from the beginning of the Start date to the ending of the End Date.
When the d.
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Business Editorial
This document provides instructions for a business editorial assignment. Students must write a 3 to 5 page paper in APA style arguing a position on a topic of interest. The paper should include a clear statement of the issue, a discussion of premises with credible supporting evidence, responses to counterarguments with evidence, a logical connection between premises and conclusion, thorough research cited from at least 5 sources including 3 from the school library.
BIOL 250 Master Quiz
The document provides information about anatomy and physiology topics in multiple choice question format. It covers topics like the study of body structures, cells, influential medical textbooks, peer review in science, feedback loops in the body, anatomical terminology, locations of body cavities and organs, and tissues of the body.
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Chapter-3_Heizer_S1.pptx
Here are 3 practice problems using quantitative forecasting methods:
1. Using simple exponential smoothing, forecast next period's sales given the following data with a smoothing constant of 0.3:
Period: Sales
1: 100
2: 110
3: 120
4: ?
Forecast: F1 = 100
F2 = 100 + 0.3(110 - 100) = 103
F3 = 103 + 0.3(120 - 103) = 108.9
F4 = 108.9 + 0.3(120 - 108.9) = 113.67
2. Using linear regression, forecast next year's profits based on advertising expenditures given:
Year: Prof
942015 Assignment Print Viewhttpezto.mheducation.com.docx
9/4/2015 Assignment Print View
http://ezto.mheducation.com/hm.tpx?todo=printview 1/8
1. Award: 25 out of 25.00 points
Score: 100/100 Points 100 %
Problem 3-2
National Scan, Inc., sells radio frequency inventory tags. Monthly sales for a sevenmonth period were as
follows:
Month
Sales
(000)Units
Feb. 19
Mar. 18
Apr. 15
May 20
Jun. 18
Jul. 22
Aug. 20
b. Forecast September sales volume using each of the following:
(1) A linear trend equation. (Round your intermediate calculations and final answer to 2 decimal
places.)
Yt 20.86 thousands
(2) A fivemonth moving average.
Moving average 19 thousands
(3) Exponential smoothing with a smoothing constant equal to .20, assuming a March forecast of
19(000). (Round your intermediate calculations and final answer to 2 decimal places.)
Forecast 19.26 thousands
(4) The naive approach.
Naive approach 20 thousands
(5) A weighted average using .60 for August, .30 for July, and .10 for June. (Round your answer to 2
decimal places.)
9/4/2015 Assignment Print View
http://ezto.mheducation.com/hm.tpx?todo=printview 2/8
Weighted average 20.40 thousands
References
Worksheet Learning Objective:
0307 Use a naive
method to make a
forecast.
Learning Objective: 0310 Prepare an
exponential smoothing forecast.
Problem 32 Learning Objective:
0308 Prepare a
moving average
forecast.
Problem 3-2
National Scan, Inc., sells radio frequency inventory tags. Monthly sales for a sevenmonth period were as
follows:
Month
Sales
(000)Units
Feb. 19
Mar. 18
Apr. 15
May 20
Jun. 18
Jul. 22
Aug. 20
b. Forecast September sales volume using each of the following:
(1) A linear trend equation. (Round your intermediate calculations and final answer to 2 decimal
places.)
Yt
20.86 ± 0.10 thousands
(2) A fivemonth moving average.
Moving average 19 thousands
(3) Exponential smoothing with a smoothing constant equal to .20, assuming a March forecast of
19(000). (Round your intermediate calculations and final answer to 2 decimal places.)
Forecast 19.26 ± 0.10
thousands
9/4/2015 Assignment Print View
http://ezto.mheducation.com/hm.tpx?todo=printview 3/8
2. Award: 25 out of 25.00 points
(4) The naive approach.
Naive approach 20 thousands
(5) A weighted average using .60 for August, .30 for July, and .10 for June. (Round your answer to 2
decimal places.)
Weighted average 20.40 ± 0.01
thousands
Explanation:
b.
(1)
t Y tY
1 19 19
2 18 36
3 15 45
4 20 80
5 18 90
6 22 132
7 20 140
28 132 542
with n = 7, Σt = 28, Σt2 = 140
b =
nΣty − ΣtΣy
=
7(542) − 28(132)
= .50
nΣt2 − (Σt)2 7(140) − 28(28)
a =
Σy − bΣt
=
132 − .50(28)
= 16.86
n 7
For Sept., t = 8, and Yt = 16.86 + .50(8) = 20.86 (000)
(2)
MA5 =
15 + 20 + 18 + 22 + 20 = 195
(3)
Month Forecast = F(old) + .20 [Act.
Demand forecasting methods 1 gp
This document provides an overview of demand forecasting methods. It discusses qualitative and quantitative forecasting models, including time series analysis techniques like moving averages, exponential smoothing, and adjusting for trends and seasonality. It also covers causal models using linear regression. Key steps in forecasting like selecting a model, measuring accuracy, and choosing software are outlined. The homework assigns practicing examples on least squares, moving averages, and exponential smoothing from a textbook.
Instructions After the student calculates the answers to this hom.docx
Instructions: After the student calculates the answers to this homework he/she must submit the answers by placing them in the Assignment # _ Submittal Link in Bb by midnight of the date shown in the Course Roadmap and Syllabus. The Instructor does not allow late submissions therefore a score of zero (0) will be recorded for the assignment if submitted after midnight Eastern Time of the due date.
Solution
s for the assignment will be released in Bb the morning after the due date for access by students. Total points for the assignment is 140. Note: There is no partial credit given; either the answer is correct or incorrect.
Scenario: Ajax Manufacturing is an electronic test equipment manufacturing firm that markets a certain piece of specialty test equipment.
Ajax has several competitors who currently market similar pieces of equipment.
While customers have repeatedly indicated they prefer Ajax’s test equipment, they have historically proven to be unwilling to wait for Ajax to manufacture this certain piece of equipment on demand and will purchase their test equipment from Ajax’s competitors in the event Ajax does not have the equipment available in inventory for immediate delivery.
Thus, the key to Ajax successfully maintaining market share for this particular piece of equipment has been to have it available in stock for immediate delivery.
Unfortunately, it is a rather expensive piece of equipment to maintain in inventory.
Period
Actual Number of Sold Units
Period
Actual Number of Sold Units
1
33
16
38
2
36
17
37
3
32
18
37
4
35
19
37
5
33
20
35
6
34
21
37
7
34
22
34
8
38
23
35
9
37
24
34
10
36
25
36
11
38
26
32
12
36
27
35
13
37
28
33
14
39
29
38
15
35
30
37
31
? Forecast ?
Thus, the president of Ajax Manufacturing is very interested in accurately forecasting market demand in order to ensure he has adequate inventory available to meet customer demand without incurring undue inventory costs.
His sales department has provided historical data (in the table above) regarding market demand for this certain piece of specialty electronics test equipment for the past 30 months.
Instructions for Homework Assignment: Use the provided Excel file to construct a spreadsheet for each of the four models (Models 1-4) then answer Questions 26 - 28 based upon your calculations.
Model 1: Answer the following questions.
Question 1. Calculate the projected demand for the test equipment for the time periods 4 - 31 based upon a 3-month moving average forecast model. What is the projected demand for period 31? (5 points)
Question 2. Calculate the mean absolute deviation (MAD) for the 3-month moving average forecast for each of the time periods 4 through 30 and for the model. (5 points)
Question 3. Calculate the mean squared error (MSE) for the 3-month moving average forecast for time periods 4 through 30 and for the model. (5 points)
Question 4. Calculate the mean absolute per.
Chap011
This document discusses various qualitative and quantitative forecasting methods including simple and weighted moving averages, exponential smoothing, and simple linear regression. It provides examples of how to calculate forecasts using each of these methods and evaluates forecast accuracy using metrics like MAD and tracking signal.
M&l case
- M&L Manufacturing produces printer and copier components for a major manufacturer as well as for replacement parts distribution. They produce about 20 different items.
- Due to competitive pressures and falling profits, the manager wants to introduce formal forecasting to improve production planning and inventory management for two products.
- Historical demand data for the past 14 weeks is provided for the two products. Forecasts for the next 4 weeks will be generated using linear regression for Product 1 and an intuitive average-based approach for Product 2.
test 1Suppose that you are using the simple mean to make a forecas.docx
test 1Suppose that you are using the simple mean to make a forecast. This period’s forecast was equal to 200 units, and it was based on 5 periods of demand. This period’s actual demand was 300 units. What is your forecast for next period?5*200+3001300simple mean = 1300/6= 216.7 217 rounded answerThe school’s cafeteria has three service lines (pizza, salads, and sandwiches). The pizza line has one server and serves 90 pizzas per hour. The salad line has two servers and they handle 140 customers in 70 minutes. The sandwich line has three servers and they supply 360 sandwiches in 90 minutes. Which service line has the highest hourly productivity?Your answer should just note down one of the three lines pizza, salads, or sandwich? For instance, if you conclude that the salads line has the highest productivity you will just write salads in your answer.Pizza P P= O/ISalad P=O/Isandwich P=O/I90/1140/(70/60)*2360/(90/60)*390 pizzas per hour 140/2.33360/4.560 salads per hour 80 slalads per hour pizza is the highestSuppose that you want to set up a 3-month weighted moving average forecasting system. You want the weights to be percentages (that add to 100%). Furthermore, you want weights for the most recent two months to be equal but you want each of those weights to be twice as large as the weight for the oldest month. What should the weight be for the oldest month?x=2yso, x+x+y = 100%or 2y +2y + y =100%y=20%Which of the following would not be considered a core competency that a company might have:insuffcient distribution centerThe definition of quality that involves the product functioning as expected without failure is:realiability Cover Me, Inc. sells umbrellas in three cities. Management assumes that annual rainfall is the primary determinant of umbrella sales, and it wants to generate a linear regression equation to estimate potential sales in other cities. Given the data below, what is the forecast for 20 in. of rain? (Round your answer to the nearest whole number) Rainfall Sales X Y b=3(152400)-(78)(5100)/3(2340)-(78)^2 City A 36 in. 2300b=63.5 City B 30 in. 2000 City C 12 in. 800y=63.5+50xSuppose that a product has two parts, both of which must be working in order for the product to function. The reliability of the first part is .85, and the reliability of the second part is .82. In addition, the second part comes with a backup that is 50% reliable. What is the overall reliability of the product? Please answer as a percentage to the second decimal place (so your answer should be of the form xx.xx%)0.82+0.5(1-0.82)=0.91(0.85)(0.91)=(0.7735)Which of the following forecasting methods would be most accurate if demand were rapidly decrea.
NCV 3 Mathematical Literacy Hands-On Support Slide Show - Module 3
This slide show complements the learner guide NCV 3 Mathematical Literacy Hands-On Training by San Viljoen, published by Future Managers. For more information visit our website www.futuremanagers.net
Week 4 Staffing BudgetsFTEs Variance AnalysisAssignment Guide.docx
Week 4: Staffing Budgets/FTEs/ Variance Analysis
Assignment Guidelines with Scoring RubricPurpose
This assignment provides learners with the opportunity to apply budgeting knowledge and skills by calculating full-time equivalents for a nursing unit, projecting FTE needs related to census changes, and analyzing a variance scenario.
Due Date: Sunday 11:59 p.m. MT at the end of Week 4Total Points Possible: 150 Requirements
Description of the Assignment
This assignment is in two scenario-based sections each related to staffing budgets. In the first, the student will perform multistep calculations of FTEs and projection of future FTE needs for a selected nursing unit. The second section involves calculation of budget variance and its analysis. Each section requires supported written interpretation of findings.
Criteria for Content
Answer the questions and complete the calculations required for the two sections of the assignment.
Key points related to Calculations:
1. When performing calculations, standard rounding rules apply. If the number to the right of the decimal is less than 5, round down to the nearest whole number, e.g., 33.4 = 33 If the number to the right of the decimal is 5 or greater, round up to the nearest whole number, e.g. 33.5 =34.
2. Read the question carefully. Pay close attention to the units be asked and keep them consistent. For example, hours vs FTEs; days vs months vs years.
3. Provide ALL formulas with references. Designate which formula associates with which source. It is not sufficient to simply list the source at the beginning of the section. Write out the formula used BEFORE filling in the numbers.
Example: Efficiency Variance + Volume Variance + Cost Variance = Total Variance
Rundio, A. (2016). The nurse manager’s guide to budgeting and finance. 2nd. Ed., Indianapolis, IN: Sigma Theta Tau International.
· Section One: Staffing Budget and FTEs
· Calculations of full-time equivalents (FTEs)
· Project FTE needs related to census changes
· Section Two: Variance Analysis
· Calculating variance
· Variance analysis with explanation
Download the Assignment Word Document and submit your answers directly onto the form. When completed, upload into the assignment portal in your course. For the questions requiring a written response, please adhere to proper grammar and syntax, and provide references. For the questions requiring calculations, show all your work including the formula used. Include the references for formula chosen.
Preparing the Assignment
Section One—Staffing Budget and FTEs
Personnel Budget Case Study
Sandra Chambers has recently accepted the position of assistant administrator for the department of nursing at Potter Regional Medical Center where she will oversee the operations of five medical units. As she evaluates the budgets for the different cost centers, she finds that all are being used at near or full capacity. The activity in four of the five has remained steady over the past 2 years. A fifth unit ...
Ch04pp
The document contains 8 practice problems related to forecasting techniques. Problem 1 asks to calculate a 3-week moving average of auto sales data. Problem 2 introduces a weighted moving average using different weights for the past 3 weeks. Problem 3 demonstrates simple exponential smoothing to forecast demand. Problem 4 evaluates the accuracy of 2 different alpha values for exponential smoothing of battery sales forecasts. Problem 5 involves calculating a least squares trend line and forecasting sales for 2003. Problem 6 provides data to calculate the tracking signal and mean absolute deviation for a demand forecast. Problems 7-8 involve seasonal indices and forecasting annual and quarterly sales with 10% growth.
PROBABILITYProbability of A P(A)= NFNPAdditi.docx
PROBABILITY
Probability of A P(A)= NF/NP
Addition rule P(A or B) = P(A) + P(B) – P(A and B)
Multiplication rule P(A and B) = P(A) . P(B\A)
Complementary principle P(A)+P(A’)=1
Independency P(A)=P(A\B) P(B)=P(B\A)
NUMERICAL MEASURES
PCS=
outliers
2k>n
PROBABILITY DISTRIBUTIONS
1. The following is the time, in minutes, 15 employees of accounting department of East Penn Manufacturing spent commuting to work this morning. [20pts]
28 25 48 37 41 19 32 26 16 23 23 29 36 31 26
1. Organize data into stemplot. [2pts]
1. Find the standard deviation, round it to 2 decimal places. [3pts]
1. Find value of the 85th percentile L85 (or P85) and write its brief interpretation. [3pts]
1. Find Q1, Q3, IQR and test possible outliers. Draw a conclusion clearly. [5pts]
1. Organize data into classes using an interval 15-23 as the first. [2pts]
1. What is a class width w in previous part e? [1pt]
1. Use the frequency table and find the standard deviation. Round the result to two decimal places if necessary. [3pts]
1. Compare results of b) and g). Which one is exact? [1pt]
_________________________________________________________________________________
2.Use the frequency table and find the median. Round only the last result to two decimal places. [4pts]
Class
frequency
0-4
6
5-9
7
10-14
12
15-19
8
_________________________________________________________________________________
3. Grace Andrews, a stockbroker, recommends two stocks (Hewlett Packard and Citibank) to her customers. Suppose that the probability that each stock’s price will go up next year is 0.6, and that each stock’s price behavior is independent of the price behavior of the other stock. Take a number of stocks that will go up next year as the random variable X and give a complete probability distribution.[6pts]
4. A survey of executives dealt with their loyalty to the company. One of the questions was, “If you were given an offer by another company equal to or slightly better than your present position, would you remain with the company or take the other position?” The responses of the 200 executives in the survey were cross-classified with their length of service with the company. Give all results rounded to 4 decimal places.[2+3+2+3+1+5=16pts]
LENGTH OF
SERVICE
LOYALTY
< 1year
1-5 years
6-10years
>10years
total
Would remain
10
30
5
75
120
Would not remain
25
15
10
30
80
total
35
45
15
105
200
a) What is the probability that randomly chosen executive is loyal to the company?
b) What is the probability that randomly chosen executive has more than 10 years of service OR is not loyal to the company?
c) What is the probability that randomly chosen executive has less than one year of service AND is not loyal to the company?
d) Two executives are chosen ra.
BSOP 330 Entire Course NEW
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NAA Maximize 2015 - Presentation on In-depth Analytics of Pricing Discovery
This session was the first ever math-based session for advanced revenue management practitioners to discuss computational challenges in revenue management and customer data analytics. Pricing isn’t just about looking at data on spreadsheets. You actually have to do math. Complex math. Well, you have to do complex math if your revenue management software system doesn’t bring the science and analytics together.
MAT 540 STR Great Stories /newtonhelp.com
For more course tutorials visit
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This Tutorial contains 20 Sets of Final Exam (800 Questions
QNT 561 NERD Education System--qnt561nerd.com
FOR MORE CLASSES VISIT
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QNT 561 Final Exam Guide (New, 2017)
QNT 561 Week 1 Assignment Statistics Concepts and Descriptive Measures Instructions (Financial Data)
QNT 561 Week 1 Assignment Statistics Concepts and Descriptive Measures Instructions (Consumer Food)
QNT 561 Week 2 Case Study MBA Schools in Asia Pacific (2 Papers)
QNT 561 Week 3 Case Study SuperFun Toys (2 Papers)
QNT 561 Week 3 Assignment Expansion Strategy and Establishing a Re-Order Point
QNT 561 Week 4 Case the Payment Time
QNT 561 Week 5 Spicy Wings Case Study
demand forecasting
This document discusses various demand forecasting models and techniques. It covers qualitative and quantitative forecasting methods. Time series models like naive, moving average, weighted moving average and exponential smoothing are explained. The document also discusses adjusting forecasts for seasonality using seasonal indices. Linear regression is provided as an example of a causal forecasting model. Key steps in the forecasting process are identified as selecting the appropriate model, evaluating available data, and generating and monitoring forecasts. Factors to consider when choosing a model include the available data, required accuracy, forecast horizon and data patterns. Forecasting software options are also briefly outlined.
IPPTChap010.pptx
The document provides an overview of time-series forecasting techniques and their application to forecasting call volume for a computer warehouse. It discusses several forecasting methods including last-value, averaging, and moving average. For each method, it shows how to adjust for seasonality, apply the method to historical call volume data, and calculate forecast errors. The best method will be selected based on accuracy in predicting future call volume.
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See the Terms Of Use worksheet and the license agreement on Vertex42.com. Copyright notices may not be removed.0HELP[Automation of Supply Chain Management System]Ray & Young Inc.Today's Date:10/22/15Thursday(vertical red line)Project Lead:Rodney Wheeler[42]Start Date:10/22/15Thursday[42]First Day of Week (Mon=2):210/19/1510/20/1510/21/1510/22/1510/23/1510/24/1510/25/1510/26/1510/27/1510/28/1510/29/1510/30/1510/31/1511/1/1511/2/1511/3/1511/4/1511/5/1511/6/1511/7/1511/8/1511/9/1511/10/1511/11/1511/12/1511/13/1511/14/1511/15/1511/16/1511/17/1511/18/1511/19/1511/20/1511/21/1511/22/1511/23/1511/24/1511/25/1511/26/1511/27/1511/28/1511/29/1511/30/1512/1/1512/2/1512/3/1512/4/1512/5/1512/6/1512/7/1512/8/1512/9/1512/10/1512/11/1512/12/1512/13/1512/14/1512/15/1512/16/1512/17/1512/18/1512/19/1512/20/1512/21/1512/22/1512/23/1512/24/1512/25/1512/26/1512/27/1512/28/1512/29/1512/30/1512/31/151/1/161/2/161/3/161/4/161/5/161/6/161/7/161/8/161/9/161/10/161/11/161/12/161/13/161/14/161/15/161/16/161/17/161/18/161/19/161/20/161/21/161/22/161/23/161/24/161/25/161/26/161/27/161/28/161/29/161/30/161/31/162/1/162/2/162/3/162/4/162/5/162/6/162/7/162/8/162/9/162/10/162/11/162/12/162/13/162/14/162/15/162/16/162/17/162/18/162/19/162/20/162/21/162/22/162/23/162/24/162/25/162/26/162/27/162/28/162/29/163/1/163/2/163/3/163/4/163/5/163/6/163/7/163/8/163/9/163/10/163/11/163/12/163/13/163/14/163/15/163/16/163/17/163/18/163/19/163/20/163/21/163/22/163/23/163/24/163/25/163/26/163/27/163/28/163/29/163/30/163/31/164/1/164/2/164/3/164/4/164/5/164/6/164/7/164/8/164/9/164/10/164/11/164/12/164/13/164/14/164/15/164/16/164/17/164/18/164/19/164/20/164/21/164/22/164/23/164/24/164/25/164/26/164/27/164/28/164/29/164/30/165/1/165/2/165/3/165/4/165/5/165/6/165/7/165/8/165/9/165/10/165/11/165/12/165/13/165/14/165/15/165/16/165/17/165/18/165/19/165/20/165/21/165/22/165/23/165/24/165/25/165/26/165/27/165/28/165/29/165/30/165/31/166/1/166/2/166/3/166/4/166/5/166/6/166/7/166/8/166/9/166/10/166/11/166/12/16WBS
Jon: Work Breakdown Structure
Level 1: 1, 2, 3, ...
Level 2: 1.1, 1.2, 1.3, ...
Level 3: 1.1.1, 1.1.2, 1.1.3, …
The WBS is automatically entered, but the formulas are different for different levels.TasksTask
LeadStart
Jon: Start Date
Enter the starting date for this task. To associate the start date with the end of another task, enter a formula in the start date that refers to the end date of that task.End
Jon: End Date
The ending date is calculated by adding the Duration (calendar days) to the Start date minus 1 day, because the task duration is from the beginning of the Start day to the end of the End day.
Duration (Days)
Jon: Duration (Calendar Days)
Enter the number of calendar days for the given task. Refer to the Working Days column or use a calendar to determine the corresponding working days. The duration is from the beginning of the Start date to the ending of the End Date.
When the d.
MAT 540 Str Inspiring Innovation--mat540.com
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GanttChartGantt ChartVersion 1.7.3© 2006-2014 Vertex42 LLCVert.docx
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