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.
Hermitage Escalators
Hermitage Escalators is an organization that sells escalators in the international market and is owned by an elevator company seeking to provide a full sales portfolio of personnel lifting devices. As Hermitage sells lift devices internationally, naturally they provide services to these devices that can be found in a multitude of locations around the world. As the world was facing a change in technology, specifically the “Internet of Things” (IoT), Hermitage Escalators sought to take advantage of this change. One concern for Hermitage was ensuring the application of IoT to their devices would help prevent or reduce the need for emergency maintenance by being able to predict or see a problem before it becomes a bigger issue. The issue within that concern is the cost of the new IoT devices as well as knowing which parts to track through the new IoT devices which will be discussed.
Strategic Implementation
The future is being shaped by IoT, and organizations are seeking ways to navigate through this change as the organization will have to face how to adopt and implement IoT within its organization. IoT is able to provide valuable data to an organization when properly implemented and when analyzed properly multiple issues can be addressed in near real-time as data flows in. Hermitage faces not the issue of adopting IoT but how to use IoT in a more productive manner to better serve their customers by reducing the need to respond to emergency repairs by gathering data on their products. However, Hermitage does not know which areas to focus on to add IoT devices to track data for the purpose mentioned previously. One way that could help Hermitage decide where to attach IoT devices for critical data collection points is to ask their experienced technicians what problems they face more frequently and how they can identify issues before they arise so Hermitage may be able to add IoT devices to components identified as more critical through these questions and when data comes back the technicians can validate or invalidate the data patterns identified by the IoT devices. This is a method that can turn technicians from not only just experts in their field but as curators of organizational knowledge helping to turn their experience and field wisdom into predictive maintenance schedules.
However, this is not always feasible as not all technicians are instructors or able to properly convey their experience into formal documentation, therefore an organization must seek out members within them who have a “librarian mindset”. A “librarian mindset” can be described as a cognitive and behavioral concept in which an individual proactively seeks to acquire and document processes and procedures. Much like a librarian, these individuals seek to preserve valuable data preserving institutional memory through data stewardship. These individuals are especially important in environments where turnover can be high as “tribal knowledge” can lead to lost data that can hamper an organizations productivity should it be lost and undocumented. This kind of data is known as organizational memory and is crucial to maintaining continuity in management’s ability to make decisions (Walsh & Ungson, 1991). To further promote the “librarian mindset” organizations should seek to adopt a culture that participates in communities of practice (Bootz et al., 2022) and learning organization principles (Kristensen et al., 2022), allowing for knowledge to flow freely while preserving data within the organization. It must also be noted that data stewardship should be managed responsibly to provide useful models in IoT environments (Donge et al., 2022)
It must also be noted that gathering data, especially big data, can become costly. More data can equal more potential efficiency within the organization, the costs must be feasible and allow for a return on investment that will justify the costs of the data collection and analysis. Such costs can come from labor of installation, integration of sensors, and upgrading old devices that did not come with IoT preinstalled. Caution should be heeded when attempting to implement sensors into older devices as the device must not be nearing its obsolescence as the cost of implementation may not outweigh the remaining life span of the products profitability through maintenance. These investments must not be implemented rashly as misaligned investments often produce diminishing returns (Hunter, 2015). This can be truer if the data collected will not be able to be implemented or provide useful long-term knowledge.
Yet, this does not mean data must be brand new and can only be sourced through IoT. Historical service records are a form of data that can be collected and analyzed. The longer back service records go the more data can be used to start as a basis point to build a maintenance schedule based off long term trends, common failure points, and indicators of future problems. In the Daft textbook, Daft (2021) mentions that there is so much data that data is like “dirt”, but when “mined” properly, this dirt may reveal “gold”. For Hermitage Escalators, data points such as repair frequency, part replacements, and resolution timeframes can form protocols and inventory planning crucial to keeping their devices running and avoiding emergency situations and repairs. Knowledge sharing networks are important in creating this process (Cross et al., 2006) and to better assist in converting data “dirt” into data “gold” Artificial Intelligence is a good support system that can increase organizational value (Zapata-Cotes et al., 2021).
These strategies reflect the Bibles description of wisdom and stewardship. As stated in the Bible, “The heart of the discerning acquires knowledge, for the ears of the wise seek it out.” (Proverbs 18:15, New International Version). Proverbs 18:15 describes an ethical approach to acquiring knowledge as those with good judgement gain knowledge and sharing the information with wise ones. A librarian mindset falls in line as individuals with the mindset enable responsible contributions to the organization through their acquisition, preservation, and spread of knowledge.
Hermitage Escalators can make the most efficient use and value out of their data by combining big data with human expertise. This can be achieved by fostering a culture of data stewardship such as encouraging employees to adopt a librarian mindset and being careful on where and how to invest into IoT analytics. If these practices and strategies are implemented Hermitage Escalators would be positioned to succeed in IoT age of the industry by being an adaptive, resilient, and “intelligent” organization.
Garcia_J-BUSI735(D03)-Module-5.docx
References
Angelopoulos, S., Brown, M., McAuley, D., Merali, Y., Mortier, R., & Price, D. (2021). Stewardship of personal data on social networking sites. International Journal of Information Management, 56, 102208.
Bootz, J.-P., Borzillo, S., & Raub, S. (2022). Leaders of organisational communities of practice: Their characteristics, activities, and fit with their communities. Knowledge Management Research & Practice, 21(5), 972–982.
Cross, R., Parker, A., Prusak, L., & Borgatti, S. P. (2006). Knowing what we know: Supporting knowledge creation and sharing in social networks. Knowledge Management and Organizational Learning, 270–294.
Hunter, S. D. (2015). Combining theoretical perspectives on the organizational structure-performance relationship. Journal of Organization Design, 4(2), 24.
Kristensen, T. B., Saabye, H., & Edmondson, A. (2022). Becoming a learning organization while enhancing performance: The case of Lego. International Journal of Operations & Production Management, 42(13), 438–481.
Rennstam, J., & Kärreman, D. (2019). Understanding control in communities of practice: Constructive disobedience in a high-tech firm. Human Relations, 73(6), 864–890.
van Donge, W., Bharosa, N., & Janssen, M. F. W. H. A. (2022). Data-driven government: Cross-case Comparison of Data Stewardship in data ecosystems. Government Information Quarterly, 39(2), 101642.
Zapata-Cortes, J. A., Alor-Hernández, G., Sánchez-Ramírez, C., & García-Alcaraz, J. L. (2021). New Perspectives on enterprise decision-making applying Artificial Intelligence Techniques. Springer International Publishing.
