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1. How Hermitage can codify and manage the knowledge of experienced maintenance technicians

Hermitage Escalator Company can follow a structured approach utilizing both existing data and insights from their most experienced personnel. This can be achieved through knowledge capture, knowledge management systems, implementing predictive maintenance with IoT, and continuous improvement. Through knowledge capture, a database can be created to store information gathered from maintenance technicians. Conducting interviews and historical data analysis are some approaches that can be used in knowledge capture. In this case, interviews can be organized where the most experienced maintenance technicians document their decision-making criteria, troubleshooting processes, and other common issues they encounter and how they address them. Examining historical maintenance records can also help identify common failures, frequent problems, and the effectiveness of past repair actions.

The next step is developing a knowledge base; it involves creating a centralized digital knowledge base where all captured knowledge is stored. Storing information in a knowledge base makes it easily accessible to other technicians. The data is also categorized for quick reference (Wang & Meng, 2019). For instance, it can be referenced based on common issues, symptoms, etc. The knowledge is then codified into explicit rules. Developing decision trees can also help guide technicians through a series of questions and diagnostic steps based on observed symptoms. Implementing predictive maintenance with IoT involves sensor placement and data integration (Pech et al., 2021). A platform that integrates data from IoT sensors with the knowledge base is implemented which allows collection of relevant data. Additionally, predictive analytics can be used to identify patterns in sensor data that correlate with known issues. For continuous improvement, a feedback mechanism can be created where technicians can suggest improvements. As new issues are encountered and resolved the knowledge base is updated to include these new learning. According to Wang & Meng (2019), continuously refining the maintenance rules based on feedback and new data insights helps in ensuring the knowledge base remains updated.

1. How big data analytics might be more costly

Big data analytics offer numerous benefits. However, it can lead to increased costs when it comes to maintaining escalators. Some of the factors that can contribute to higher costs include investment and implementation costs, data management and storage costs, operational costs, and integration issues. Installing hardware, software and other infrastructure often involves significant upfront costs. Using big data often requires an organization to have the necessary hardware including sensors, IT infrastructure such as servers and storage, and big data analytics software. According to Naeem et al., (2022), big data analytics involves collecting, storing, and processing large volumes of data. Therefore, extensive storage solutions and robust data management systems are required which can be expensive. Maintaining and updating the IoT sensors, software, and hardware systems also involves ongoing costs. The large volume of data raises cybersecurity concerns and thus needs to be protected. Protecting the data from breaches and enhancing cybersecurity involves additional costs for security systems. Training existing staff to use new technologies and data analytics tools also requires time and resources further increasing the cost of maintaining the escalators. Additionally, integrating new big data analytics solutions with existing legacy systems can be complex and costly.

1. Value from analyzing data on past maintenance calls

Analyzing data on past maintenance calls can provide significant value in improving maintenance efficiency and reducing downtime. Understanding the most frequent problems can help in prioritizing maintenance efforts allowing focus on the most critical areas. It also provides insights on maintenance. For instance, by analyzing the trends, it is possible to know when the failures are likely to occur allowing for proactive maintenance. Analyzing data is also essential in ensuring resources are allocated efficiently. For instance, identifying the most common issues and their resolutions can help in better resource planning and ensure that the right parts and personnel are available when needed. Understanding the root causes of frequent breakdowns can also lead to measures that prevent future occurrences, thus reducing downtime. Reduced downtime enhances customer satisfaction and reduces complaints.

Specific data that have value

Analyzing past maintenance call data provides actionable insights that enhance the overall maintenance strategy. Someof the data that has value includes data on the type of issues, frequency of when these issues happen, resolution methods, technician performance, and maintenance cost. The data helps in enhancing efficiency, support decision-making, increase uptime, and reduce cost. Detailed logs of all maintenance calls, categorized by type of issue help in identifying the most common problems and help prioritize maintenance efforts. Data such as the time taken to resolve each issue and the methods used for repair highlight inefficiencies in current maintenance processes and identify best practices. Records of parts that were replaced or repaired during each maintenance help in managing inventory and ensuring critical parts are always available, reducing downtime. Information on which technician handled each maintenance and the outcomes can also help in identifying training needs and recognizing high-performing technicians who can mentor others. Additionally, past data allows proactive and targeted maintenance ensuring escalators remain operational, increasing reliability and user satisfaction.

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