Browsing by Author "Yang, Eunhwa"

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  • Challenges and gaps with user-led decision-making for prioritizing maintenance work orders
    Ensafi, Mahnaz; Thabet, Walid; Afsari, Kereshmeh; Yang, Eunhwa (Elsevier, 2023-05-01)
    A vast amount of work orders is submitted daily which is a critical component of management for any facility. The process taken for prioritizing work orders, however, shows a high dependency on the extent of knowledge and experience of responsible staff available and is challenged by inconsistency in data collection, and uncertainty in decision-making. Making decisions and responding to a high number of requests demand intensive labor hours resulting in delays causing issues for facility managers. The high number of service requests, various work orders, and the required balance between cost and budget highlight the importance of the need for improving work order processing to optimize time and cost of buildings' operation. Through review of the literature, unstructured and semi-structured interviews, and a qualitative analysis approach, this paper identifies various challenges and gaps in user-driven decision-making for processing work orders and determines best practices. The challenges identified include inconsistency in prioritizing orders, lack of data requirements and knowledge management, cognitive workload and biases, and inconsistency in data collection. Using data-driven decision-making methods can address existing challenges, improve the process of prioritizing work orders and enhance the quality of the work performed by timely responding to submitted requests. This will improve the operation and maintenance of building facilities and increase occupants’ satisfaction.
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    Work Order Prioritization Using Neural Networks to Improve Building Operation
    Ensafi, Mahnaz (Virginia Tech, 2022-10-20)
    Facility management involves a variety of processes with a large amount of data for managing and maintaining facilities. Processing and prioritizing work orders constitute a big part of facility management, given the large number of work orders submitted daily. Current practices for prioritizing work orders are mainly user-driven and lack consistency in collecting, processing, and managing a large amount of data. Decision-making methods have been used to address challenges such as inconsistency. However, they have challenges, including variations between comparisons during the actual prioritization task as opposed to those outside of the maintenance context. Data-driven methods can help bridge the gap by extracting meaningful and valuable information and patterns to support future decision-makings. Through a review of the literature, interviews, and survey questionnaires, this research explored different industry practices in various facilities and identified challenges and gaps with existing practices. Challenges include inconsistency in data collection and prioritizing work orders, lack of data requirements, and coping strategies and biases. The collected data showed the list of criteria and their rankings for different facilities and demonstrated the possible impact of facility type, size, and years of experience on criteria selection and ranking. Based on the results, this research proposed a methodology to automate the process of prioritizing work orders using Neural Networks. The research analyzed the work order data obtained from an educational facility, explained data cleaning and preprocessing approaches, and provided insights. The data exploration and preprocessing revealed challenges such as submission of multiple work orders as one, missing data for certain criteria, long durations for work orders' execution, and lack of correlation between collected criteria and the schedule. Through hyperparameter tuning, the optimum neural network configuration was identified. The developed neural network predicts the schedule of new work orders based on the existing data. The outcome of this research can be used to develop requirements and guidelines for collecting and processing work order data, improve the accuracy of work order scheduling, and increase the efficiency of existing practices using data-driven approaches.