MANAGEMENT’S FORECAST

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1. Sales of quilt covers at Bud Banis’ department store in Carbondale over the past year are shown below. Management prepared a forecast using a combination of exponential smoothing and its collective judgment for the 4 months (March, April, May, and June):

MONTH UNIT SALES MANAGEMENT’S FORECAST
July 100
August 93
September 96
October 110
November 124
December 119
January 92
February 83
March 101 120
April 96 114
May 89 110
June 108 108

a. Compute MAD and MAPE for management’s technique.
b. Do management’s results outperform (i.e., have smaller MAD and MAPE than) a naive forecast?
c. Which forecast do you recommend, based on lower forecast error? PX

2. Attendance at Orlando’s newest Disney like attraction, Lego World, has been as follows:

QUARTER GUESTS (IN THOUSANDS) QUARTER GUESTS (IN THOUSANDS)
Winter Year 1 73 Summer Year 2 124
Spring Year 1 104 Fall Year 2 52
Summer Year 1 168 Winter Year 3 89
Fall Year 1 74 Spring Year 3 146
Winter Year 2 65 Summer Year 3 205
Spring Year 2 82 Fall Year 3 98

Compute seasonal indices using all of the data. PX

3. Storrs Cycles has just started selling the new Cyclone Mountain bike, with monthly sales as shown in the table. First, co-owner Bob Day wants to forecast by exponential smoothing by initially setting February’s forecast equal to January’s sales with α=.1. Co-owner Sherry Snyder wants to use a three-period moving average.

SALES BOB SHERRY BOB’S ERROR SHERRY’S ERROR
January 400 —
February 380 400
March 410
April 375
May

a. Is there a strong linear trend in sales over time?
b. Fill in the table with what Bob and Sherry each forecast for May and the earlier months, as relevant.
c. Assume that May’s actual sales figure turns out to be 405. Complete the table’s columns and then calculate the mean absolute deviation for both Bob’s and Sherry’s methods.
d. Based on these calculations, which method seems more accurate? PX

 

Sample Solution

ver, with the increase in media attention on the infestation, the increase in shortage has, according to the law of supply and demand, cause prices to shoot up at a high rate, inevitably, lower-income families are unable to purchase them for their personal well-being, leading to significant welfare loss as the provision of such necessary products become limited and in high demand. Perhaps, such supply is low in regions or provinces with low income due to the Friedman Theory which states people will make decisions on consumption based on their income over time; thus, suppliers choose not to supply products at required areas, leading to biases and prejudices in where such products are supplied – all without taking into a rational account of how human behaviour may respond in such conditions. The predictable nature of human beings perhaps could allow artificial intelligence to estimate individualized demand and supply using the economic concept of game theory where it understands the current social environmental circumstances that inevitably cause individuals to decide, influencing a society’s microeconomic facets. On the contrary, it is also important to note that, very similar to those who are made aware of their biases, artificial intelligence could be fed statistical biases – which may skew the solution required to accurately target the output. Thus, AI could also have the ability to discriminate; for example, upon identifying that rural areas may have lower literacy rates, it may intensify the Lewis Turning Point situation where there is a surplus rural labour in the primary and secondary sector – hence an increasing in employment saturation of such jobs when there are other applicable job vacancies available or an economy without balanced growth policies. Despite this, when assessing the potential setbacks to using Artificial Intelligence as a data-analysis program to output an individual’s or firm’s interests, economists could perhaps considerable to say that the potential for biased data is, for now, negligible relative to describing our world using models.

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