Student Replies: Module 4 Discussion - Business Analytics Trends

This assignment presents student replies to a discussion on recent trends in business analytics. The first student reply discusses the use of AI and machine learning for predictive maintenance in the U.S. Air Force to improve aircraft availability. The second reply focuses on how governments are leveraging big data to make policy decisions, specifically analyzing R&D investments in Korean small and medium-sized enterprises. The student replies highlight the importance of AI in various sectors like military and government, emphasizing the need for data quality, accurate prediction models, and the potential for increased efficiency and return on investment.

Student Replies for Module 4
Discussion: Recent Trends in Business
Analytics
Week 2: Respond to at least two of your classmates’ initial posts. Your response should be
substantive and further the discussion. It is OK to be critical and use critical thinking. That
means you can question what your classmates say. Do they provide their own critical thinking
and logic?
The US Air Force has over 5,400 aircraft in its inventory, with an average airframe age of 29 years. The
Air Force cited in 2017 that “we are at our lowest state of full spectrum readiness in history.”
(Heritage.Org, 2019) The availability of serviceable aircraft used to conduct combat and training
operations, is measured by metrics such as mission capable (MC) status and aircraft availability. These
rates have been in steady decline across the fleet and in key mission series over the past several years
with an average MC rate in 2018 of 69.97%, down 1.33% from the prior year. (Losey, 2019) One of the
ways that the Air Force is seeking to address this decline is adopting AI and machine learning to develop
predictive maintenance models to increase aircraft availability rates.
Predictive maintenance uses historical data on part failure rates, mean time between failure, metadata
of the equipment, etc. to accurately predict maintenance. The goal is to replace components “Just in
Time” in order to gain efficiencies in cost and down time. There are three important considerations for
implementing predictive maintenance models:
1. The problem must be able to be predicted
2. The data record is of sufficient quality and incorporates good and bad outcomes (both the
discrepancy and the repair)
3. Domain experts who understand the issue, processes, and systems that can assist analysts in
structuring the algorithms
Continual model evaluation and optimization is necessary when implementing predictive maintenance
models:
1. Accuracy – the ability of the algorithm to make correct decisions from a sample data set (and
theoretically perform correctly in the future based on real time data inputs)
2. Precision – the rate of false alarms
3. Recall – correct identification of failures
4. F1 score – average of precision and recall rates from 0 (worst) to 1 (best)
(Microsoft Azure, 2020)

References
Heritage.Org. (2019, 10 30). An Assessment of U.S. Military Power. Retrieved from Heritage.Org:
https://www.heritage.org/military-strength/assessment-us-military-power/us-air-force
Losey, S. (2019, 7 26). Aircraft mission-capable rates hit new low in Air Force, despite efforts to improve.
Retrieved from Air Force Times:
https://www.airforcetimes.com/news/your-air-force/2019/07/26/aircraft-mission-capable-rates-hit-
new-low-in-air-force-despite-efforts-to-improve/
Microsoft Azure. (2020, 1 9). Azure AI guide for predictive maintenance solutions. Retrieved from
Microsoft Azure: https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-
process/cortana-analytics-playbook-predictive-maintenance#business-case-for-predictive-maintenance
Reply Here:
The post significantly highlights the different use of technological advancements, and
particularly artificial intelligence in the U.S Air Force. It has been observed that the number of
mission capable aircrafts available to provide service or training have had a steady decline. The
U.S. Air Force has attempted to combat this situation through the adoption of advanced
technology such as machine learning and artificial intelligence. The application of these tools
helps in predictive maintenance which in turn helps in the increase of the rates of aircraft
availability.
The scope of such predictive maintenance involves the usage of historical data regarding rates
of failure, mean time between the cases of failures and the like so that the maintenance can be
accurately computed and predicted. The essential aspect to be noted from this post is the
degree to which artificial technology can be useful for the prevention of mishaps and
discrepancies in the operations of an important body like the U.S. Air Force. The post has also
identified the various attributes for the measurement of the accuracy of the artificial
intelligence to predict maintenances. Such assessment of the accuracy of the prediction system
can aid in its developing its efficiency further and thereby aid the U.S. Air Force to perform in a
more efficient manner.

2nd Student
Governments these days are looking, not only to protect citizens from big data, they
are looking at ways to leverage it to help make public asset investment and policy decisions.
The sampled data must be appropriate for use and analysis clustered to produce the best
possible probability.
Kim, E. S., Choi, Y., & Byun, J. (2019) analyzed Korean small and medium-sized
enterprises using data of the National Science and Technology Information Service (NTIS),
over the years of 2013-2017. The analysis was to help determine if research and development
(R&D) products the Korean government were investing during that time, provided an
acceptable return on investment. Through the use of python, the use of self-organizing map
algorithm cluster analysis, classification and regression trees decision tree analysis, and to
predict outcomes a receiver operating characteristic curve. Science and technology are
leading nations into the future, shadowing all other factors of production.
The Korean government was the fifth in the world to invest in R&D projects and first
in the world when compared by gross domestic product. Although, the big data has been used
successfully by governments in the medical, public safety, and security arenas, this study
failed to appreciate a return on investment for the Korean government concerning the R&D
programs they made a considerable investment. They found “it is necessary to fully review
the achievability of objectives and possibility of the realization of performance when selecting
projects for practical use…” (Kim, Choi, & Byun, pg. 12, 2019) Furthermore, the data itself
should be of concern when governments make a data-based policy.
The use of Self-organizing map algorithm clustering provided for efficient data
categorizing of vast information. The information provided by the NTIS and collection
method of this study may be flawed by other motivations for investment in any small to
medium enterprise R&D Project. The data may not present the full reasoning behind the
support of a particular project.
References
Kim, E. S., Choi, Y., & Byun, J. (2019). Big data analytics in government: Improving
decision making for R&D investment in korean SMEs. Sustainability, 12(1), 202.
doi:10.3390/su12010202
James

Reply Here:
This post highlights other aspects of using advanced technology and science in order for the
governments to realize the potential of the country in terms of product development, health
and medicine, security and public safety. The main functions of such technology lie in the field
of research and development in order to analyze means to maximize the returns on investment
made. The focus of this post is on the small and medium sized enterprises of Korea and the
usage of the data from the National Science and Technology Information Service (NTIS). The
various tools that have been used in order to evaluate the efficiency of the research and
development systems to bring about returns on investment include python, classification and
regression trees decision tree analysis and self organizing map algorithm cluster analysis.
The purpose of the project is to fully utilize the available technological and scientific resources
of the country to fully scrutinize and evaluate the potential of the small and medium sized
enterprises. Such an evaluation would further aid in the overall economic growth and
development of the country.