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

Using forecasting, they can learn this. Forecasting is the technique of estimating important future occurrences based on an understanding of their behavior in the past and present. Without knowledge of how events have played out in the past and how they are now unfolding, it is impossible to predict the future. Analyzing occurrences from the past and present gives a solid foundation for learning more about their potential occurrence in the future. As a result, forecasting can be defined as the process of estimating the future typically utilizing calculations and predictions that consider historical performance, current trends, and projected changes in the near future.

rity Standard (PCI DSS) being held effective in such events. Its limitations are in its ability to oppose frauds calls when there are unique changes in human behaviour; thus, the use of Artificial Intelligence in banking services can be used to counter unauthorised credit or debit cards. By its nature, artificial intelligence can help manage financial institutions overnight by monitoring any behavioural patterns, identifying anomalies and recording any atypical changes in the systems’ functions. Furthermore, Artificial Intelligence spans wides in its use due to its ability of counterfactual thinking, analysing the past or historical events and making potential deductions as responses to such. This would then decrease the possibility of such intolerable events to occur again, simultaneously increasing consumers’ confidence in the economy. While it is undeniable that there are potential limitations to AI’s uses, it is currently the best viable approach to invigilating the degrading spread of fake news, preventing our economy from falling into a greater recession that the previous ones and highlighting abnormal behaviour during banking frauds by churning out warning methods to our global economy.

Rationality has not been alive for some time, at least within the lifespan of humanity; its last presence once came and left when Homo Economicus once lived. Humans have never conceived absolute rational behaviours, such theoretical ideas to model their demands would then require a priori quantitative economic model – using the concept of Artificial Intelligence to solve problems within the next 20 years would then need to make an initial assumption of ceteris paribus. Perhaps, it is irrationality and emotions that lead to majority of the economists to believe that markets are prominent reflections of both groups and individuals’ rationality as they perform transactions based on their ability and willingness to, thus creating incompetent frameworks such as perfect competition, even when it is known that many social events are pervaded by inherent indeterminacy. Our humanity’s tragic flaw is its inability to recognise that such non-radical and irrational assumptions are simply what led to the advent of Economics’ neoliberalism and the global financial crisis in 2008; so, perhaps, Artificial Intelligence can help economists to more accurately light an inc

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