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Week 5 Discussion Thread
Hermitage Escalator Company
School of Business, Liberty University
BUSI 735: Strategic Organization Design and Theory
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Introduction
Hermitage Escalator Company is a thriving company that installs approximately 2000
escalators in the United States per year in hotels, stadiums, airports, and government buildings
(Daft, 2021). Escalators are a more efficient way to move people between floors or horizontally
through a space, coordinating traffic flow and speed. There are many moving parts within a
single escalator, almost all mechanical, working in concert with each other, providing this service
seamlessly.
As with most mechanical contraptions with many components, maintenance is
paramount and the key to Hermitage’s escalators functioning as expected. Maintenance and
repairs require a person on-site for service, which costs time and money. In this article, the aim
is to leverage the Internet of Things (IoT) to enable predictive analytics that proactively address
potential breakdowns, thereby increasing customer satisfaction and ensuring equipment
operates with minimal downtime.
Knowledge Management
Effective knowledge sharing is essential for organizational coordination and
performance, integrating individuals’ expertise and skills (Hong, 2025). With Hermitage seeking
to leverage the capabilities of IoT, the need to build a program specific to the company’s needs
while implementing this new technology is imperative to ensure this process goes smoothly.
There are hundreds of experienced maintenance technicians at Hermitage who have
accumulated decades of service and a wealth of knowledge and expertise (Daft, 2021).
Gathering and compiling this information for use with the new technology will be critical during
the transition.
Compiling data from the most experienced personnel is a tedious task, but not
impossible. In addition to the obvious maintenance and service logs available within the
company, this data can also be collected through in-depth interviews, observations, and
document (existing service manuals) analysis (Maisiri & Ngulube, 2023).
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Managing collected data involves analyzing it, preserving what is useful, and ensuring it
can be written into the new software being created to monitor the escalator hardware to
predict problems. For instance, during data collection, if it is determined that a specific sensor
fails frequently, tailoring the IoT to monitor it for issues would be the likely approach.
Throughout the process, the strategy should identify and assess knowledge within the
company's targets based on practice-related concepts (Ben Zammel & Najar, 2021).
Cost to Serve
Daft (2021) writes, “If you take every single thing that was sensed and automatically
send a technician to check it out, you spend money for each visit with no benefit” (p. 397). This
is the most apparent way Hermitage will lose money in transitioning to a predictive analysis
model, so it is imperative that the new technology accurately predicts actual faults. False
positives have the potential to increase costs as well as the “noise”, which are issues that can
wait until regularly scheduled maintenance (Daft, 2021).
The use of historical data in predictive analytics is inherently conservative by reproducing
and reinforcing norms, practices, and traditions of the past (Martin, 2022). This is why it is
important to thoroughly assess the data being used to build the IoT tool to minimize false
positives, which lead to increased costs.
Maintenance workers make about $50 per hour and can earn over $100,000 per year
with overtime (Daft, 2021). Deploying these workers to service calls that turn out to be
unnecessary costs the company the workers' salaries and any parts ordered that are not
needed. Additionally, the time spent responding to these “false” calls prohibits the service
technician from answering an actual service call, which can erode customer satisfaction.
Value
Data from past maintenance calls can enhance the efficiency of IoT development.
Specifically, analyzing this data will allow Hermitage to input only what is necessary and discard
issues that are not frequent enough to include or do not add value. Analyzing callbacks and
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historical repairs can influence costs and outcomes (Daft, 2021). The compilation of data will
show trends and reveal the most utilized parts, and will also provide non-seasoned employees
with the information necessary to increase their job efficiency.
Data obtained from the analyses highlights the specific parts that would have the most
value in case of a break. The escalator has many parts that, when paired with a sensor, can
deliver the most significant benefit. The escalator comprises a series of control panels, drive
units, chains, sprockets, and safety switches (Daft, 2021). Knowing which parts malfunction
most and generate the most service calls is significant because it allows the IoT tool
development to focus on those parts. This can increase the tool's effectiveness and minimize
false-positive responses.
Conclusion
Escalator malfunctions lead to increased downtime, severe economic losses, threats to
passengers’ health and safety, and a significant decrease in overall end-user satisfaction (Zubair
& Zhang, 2022). It is not only the responsibility of the organization where the equipment is
installed to prioritize the safety of the public who use the facility; it is also the responsibility of
Hermitage to ensure that its product is safe, effective, and performs the job for which it is
employed. Responsibility can be a heavy burden, reminding us of the importance of living with
integrity and accountability (Manhood Journey, 2025). Perhaps, The Book of Luke teaches us
that integrity in small matters reflects one's character and ability to handle greater
responsibilities. Luke 16:10 states, “Whoever can be trusted with very little can also be trusted
with much, and whoever is dishonest with very little will also be dishonest with much.” (Bible
Hub, 2025).
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References
Ben Zammel, I., & Najar, T. (2021). Toward a conceptual framework of reflexivity and practices in
knowledge management. Management Decision, 59(12), 2809-2826.
https://doi.org/10.1108/MD-04-2020-0428.
Bible Hub (2025). Luke 16:10. https://biblehub.com/luke/16-10.htm.
Daft, R. L. (2021). Organization theory & design (Thirteenth ed.). Cengage.
Hong, J. H. (2025). How knowledge sharing shapes the performance of U.S. federal agencies:
The importance of agency task characteristics. International Public Management Journal,
1-17. https://doi.org/10.1080/10967494.2025.2531105.
Maisiri, E., & Ngulube, P. (2023). A framework for knowledge sharing in the art world in
Zimbabwe. Information Development., 39(3), 550–566.
https://doi.org/10.1177/02666669221084892.
Manhood Journey (2025). Responsibility. https://manhoodjourney.org/bible-verses-about-
responsibility/.
Martin, K. (2022). Creating Accuracy and The Ethics of Predictive Analytics. University of Notre
Dame. https://kirstenmartin.net/wp-content/uploads/2021/11/acm-Ethics-of-
Predictive-Analytics.pdf.
Zubair, M. U., & Zhang, X. (2022). Investigation and improvements of the existing best-value
selection criteria for elevator maintenance contractors. Journal of Management in
Engineering, 38(1)https://doi.org/10.1061/(ASCE)ME.1943-5479.0000987.
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