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Given the following history, use a three-quarter moving average to forecast the demand for the third quarter of this year. Note, the 1st quarter is Jan, Feb, and Mar; 2nd quarter Apr, May, Jun; 3rd quarter Jul, Aug, Sep; and 4th quarter Oct, Nov, Dec.

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Last year

100

125

135

175

185

200

150

140

130

200

225

250

This year

125

135

135

190

200

190

Short Answer

Expert verified

Forecasting is the act of predicting demand, supply, and pricing within an industry. It involves investigating the competition, collecting supplier data, and analyzing past patterns to predict the future of an industry.

Step by step solution

01

Moving average

When demand for a product is neither growing nor declining rapidly, and if it doesn't have seasonal characteristics, a moving averagewill be useful in removing the random fluctuations for forecasting.

02

Explanation

Oct

200

Nov

225

Dec

250

Sum of (200+225+250)=675

Jan

125

Feb

135

Mar

135

Sum of localid="1657685832893" (125+135+135)=395

Apr

190

May

200

Jun

190

Sum of (190+200+190)=580

The three quarter moving average is =675+395+5803=550

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Most popular questions from this chapter

Not all the items in your office supply store are evenly distributed as far as demand is concerned, so you decide to forecast demand to help plan your stock. Past data for legal-sized yellow tablets for August are

Week 1

300

Week 2

400

Week 3

600

Week 4

700

  1. Using a three-week moving average, what would you forecast the next week to be?
  2. Using exponential smoothing witha =0.20, if the exponential forecast for week 3 was estimated as the average of the first two weeks [(300 + 400)/2 = 350], what would you forecast week 5 to be?

Sales data for two years are as follows. Data are aggregated with two months of sales in each 鈥減eriod.鈥

Months

Sales

闯补苍耻补谤测鈥揊别产谤耻补谤测

109

惭补谤肠丑鈥揂辫谤颈濒

104

惭补测鈥揓耻苍别

150

闯耻濒测鈥揂耻驳耻蝉迟

170

厂别辫迟别尘产别谤鈥揙肠迟辞产别谤

120

狈辞惫别尘产别谤鈥揇别肠别尘产别谤

100

Months

Sales

闯补苍耻补谤测鈥揊别产谤耻补谤测

115

惭补谤肠丑鈥揂辫谤颈濒

112

惭补测鈥揓耻苍别

159

闯耻濒测鈥揂耻驳耻蝉迟

182

厂别辫迟别尘产别谤鈥揙肠迟辞产别谤

126

狈辞惫别尘产别谤鈥揇别肠别尘产别谤

106

a. Plot the data.

b. Fit a simple linear regression model to the sales data.

c. In addition to the regression model, determine multiplicative seasonal index factors. A full cycle is assumed to be a full year.

d. Using the results from parts (b) and (c), prepare a forecast for the next year.

Question: What part of SAP gives users personalized access to a range of information, applications, and services supported by the system?

Semans is a manufacturer that produces bracket assemblies. Demand for bracket assemblies (X) is 130 units. The following is the BOM in indented form:

ITEMS

DESCRIPTION

USAGE

X

Bracket assembly

1

A

Wall board

4

B

Hanger sub-assembly

2

D

Hanger casting

3

E

Ceramic knob

1

C

Rivet Head screw

3

F

Metal tong

4

G

Plastic cap

2

Below is a table indicating current inventory levels:

ITEMS

X

A

B

C

D

E

F

G

Inventory

25

16

60

20

180

160

1000

100

a.Using Excel, create the MRP using the information provided.

Question: What supply chain metric measures how many complete orders were filled and shipped on time?

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