Reply of at least 500 words to this post. Reply must be supported by at least 3scholarly peer-reviewed sources in the current APA format. Any sources cited must have beenpublished within the last five years. and must contain biblical integration.The Bible must be cited to support your assertions regarding biblical integration and be includedin references in addition the number of required scholarly peer-reviewed sources.
Discussion: Hermitage Case Study
As the escalator industry evolves with the rise of the Internet of Things (IoT) and big data analytics, companies like Hermitage Escalator Company must rethink how they approach maintenance and customer service. With thousands of escalators in operation and a growing demand for reliability and safety, Hermitage faces the challenge of maintaining performance while integrating new digital technologies. At the heart of this transformation is the need to capture and leverage the data generated by smart sensors and the hands-on expertise of seasoned maintenance technicians (Lu et al., 2020). This case explores how Hermitage can codify technician knowledge, manage the costs and complexities of big data, and extract value from its historical maintenance data to build a more innovative, more predictive maintenance model for the future (Medeiros et al., 2020).
Hermitage can codify and manage the knowledge of its experienced maintenance technicians by capturing their expertise and translating it into structured systems such as decision rules, algorithms, and predictive models (Medeiros et al., 2020; Rawat & Yadav, 2021). One approach is to document how expert technicians diagnose and troubleshoot problems, often over the phone, by creating decision trees or rule-based expert systems from their diagnostic questioning methods (Veganzones & Severin, 2021). In addition to interviews and observations, Hermitage can use its extensive maintenance history to identify common problems, practical solutions, and patterns in equipment failure (Lu et al., 2020). By digitizing and analyzing this data, they can create a baseline for predictive maintenance strategies (Medeiros et al., 2020). Machine learning can further enhance this effort by identifying correlations between sensor readings and actual mechanical issues (Bankins et al., 2024). When combined with technician experience, these models can form the basis of a hybrid AI-human knowledge system (Bankins et al., 2024). Hermitage should also establish a feedback loop where technicians can flag false alarms or confirm successful diagnoses, helping to refine the system over time. Through this approach, Hermitage can formalize the tribal knowledge of its technicians into actionable rules that will guide responses in the era of big data and IoT integration.
While big data analytics promise efficiency and proactive maintenance, it can increase costs if not carefully managed (Rawat & Yadav, 2021). One primary concern is the issue of false positives, situations where sensors indicate a problem that doesn’t exist. Responding to every sensor alert without proper validation can result in unnecessary dispatches, increasing labor and travel costs (Lu et al., 2020). Another potential cost driver is misinterpreting normal variability or “noise” in equipment behavior as failures, triggering needless interventions. Additionally, the upfront investment required to install sensors, integrate systems, and build analytics infrastructure, especially for retrofitting older, legacy escalators, can be substantial (Rawat & Yadav, 2021). Big data analytics also adds complexity, requiring new training for technicians to interpret data and operate new tools (Rawat & Yadav, 2021). Lastly, by increasing the frequency of alerts and maintenance calls, analytics could unintentionally overwhelm the maintenance workforce with minor or non-urgent issues. If every slight anomaly prompts an immediate response, operational efficiency could suffer, and costs may escalate rather than decline (Lu et al., 2020). Therefore, implementing big data solutions must include intelligent filtering, prioritization, and bundling of maintenance actions to truly reduce expenses (Rawat & Yadav, 2021).
Analyzing data from past maintenance calls offers significant value by helping Hermitage understand failure patterns, root causes, and service effectiveness. This historical insight allows the company to pinpoint the components most prone to failure, recognize recurring issues, and detect trends in equipment performance. Such analysis can also help optimize technician dispatch by matching specific problems with technicians with a proven track record of resolving them efficiently (Medeiros et al., 2020). Additionally, understanding common sequences of events leading to failure can guide sensor placement and the development of predictive models. Specific data types that would be valuable include the escalator model, component involved, time and date of the call, reported symptoms, technician notes, resolution time, and repair cost. These data points enable Hermitage to benchmark normal failure rates, predict future issues, and design more cost-effective maintenance schedules (Medeiros et al., 2020). By using this information, the company can reduce downtime, improve customer satisfaction, and ensure that the right technician is sent at the right time for the right issue, all while preparing the groundwork for full IoT integration.
Hermitage Escalator Company stands at the intersection of tradition and innovation as it prepares to embrace the opportunities and challenges of the Internet of Things. To navigate this transition successfully, the company must focus on capturing the invaluable knowledge of its experienced technicians and translating it into codified rules and predictive maintenance systems (Veganzones & Severin, 2021). At the same time, Hermitage must be cautious in its use of big data analytics to avoid unnecessary costs stemming from false positives, over-maintenance, or inefficient responses. The company’s rich history of maintenance records holds untapped potential data that, when properly analyzed, can drive more intelligent decisions, optimize technician dispatch, and enhance overall service efficiency (Medeiros et al., 2020). By combining human expertise with intelligent data systems, Hermitage can build a maintenance model that is not only predictive and cost-effective but also positioned to deliver more excellent value to customers in the digital age (Veganzones & Severin, 2021). From a biblical perspective, 1 Thessalonians 5:21 “Examine everything carefully; hold fast to that which is good” should be considered by companies, since yes, big data is helpful, but the privacy and security concerns exist (English Standard Version Bible, 2001,1 Thessalonians 5:21)
References:
Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel
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Lu, Q., Xie, X., Parlikad, A. K., Schooling, J. M., & Konstantinou, E. (2020). Moving from
building information models to digital twins for operation and maintenance. Proceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction, 174(2), 46-56. to an external site.
Medeiros, M. M. D., Hoppen, N., & Maçada, A. C. G. (2020). Data science for business:
benefits, challenges, and opportunities. The Bottom Line, 33(2), 149-163. to an external site.
Rawat, R., & Yadav, R. (2021). Big data: Big data analysis, issues and challenges and
technologies. In IOP Conference Series: Materials Science and Engineering (Vol. 1022, No. 1, p. 012014). IOP Publishing.
Veganzones, D., & Severin, E. (2021). Corporate failure prediction models in the twenty-first
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