By Kai Yin, and Jiayi Yan with George Broadbent, Dr. Jeff Chen, and Dr. Eve Lin
In the previous article, “Modular Data Analytics and Reporting in Facility Management – Five Modules,” we discussed utilizing five modules, including Asset Operation, Asset Maintenance, Safety, Real Estate, and Sustainability, from a modularized design perspective to encompass a holistic digital twin. Modular data analytics and reporting enable performing quantification analysis for each focus area in a facility. The modular framework also provides a foundation for the end-user to obtain the most straightforward understanding of facility performance when a corresponding scoring system is established. This article disassembles each module’s health status into subcategories and associated parameters to demonstrate how a health scoring system can support digital twin implementation and facilitate performance tracking in conjunction with facility operation and maintenance (O&M).
Decision-Making Support System (DMSS)
The quantifiable decision-making support system (DMSS) is usually described as an information system whose purpose is to provide partial or full support for decision-making phases, including but not limited to intelligence, design, choice, implementation, and learning. In the AECO industry, a DMSS should be designed with the following two considerations.
On one hand, it is common for current O&M practices to rely mostly on the facility operators’ knowledge and experience. According to the Operations and Maintenance Benchmarks issued by the International Facility Management Association (IFMA) average building operation and maintenance costs jumped by 72 percent between 2007 and 2017. The cost increases were mainly due to greater expenses associated with maintenance staff and building deterioration. This dilemma illustrates the imbalance between growing O&M needs and a limited pool of seasoned O&M staff. Therefore, high O&M costs and low O&M efficiency should be expected consequences that most facilities need to plan for now or the near future. To overcome the asymmetry of the supply and demand relationship, it is critical to shift from the traditional experience-oriented subjective environment to a data-oriented objective environment. Introducing a standardized scoring system based on comprehensive data collection and analysis to replace human judgment based on personal experience will be the next step to accelerate the change.
On the other hand, traditional Asset Management/Facility Management (AM/FM) data practices that were manual or semi-manual created information silos that prevent even seasoned O&M staff from effective query or cross-reference as well as a timely response. For example, a large transportation agency has structural information, inspection reports, and asset information stored in discrete databases which makes data integration or collaboration almost impossible. Moreover, information silos, even when connected, cannot provide information about the appearance of the facility. The essence or the correlation between data cannot be represented.
In another example, a traditional data reporting system could provide generic asset information, such as useful life, manufacturer, usage condition, and so on. However, it cannot connect all these pieces to inform whether the usage condition (public, private) will contribute to a shortened productive life. This will obviously impact the remaining value- and scheduled Preventive Maintenance (PM) routines, and in turn affect the AM/FM budget. The advent of Predictive Maintenance (PdM) is a solution through data collection via IoT, data analytics, AI algorithms, etc. The design of DMSS needs to be in line with the development of PdM, and all related data flows to ensure the data can be aggregated and interpreted to match the needs of the entire building/facility’s O&M assessment to the facility operator’s daily work and capability.
Design Methodology of Scoring System to Support DMSS
Modular Health Scoring System (MHSS)
The core concept of the Modular Health Scoring System (MHSS) is to provide comprehensive parameter ratings that cover three main categories – Present Status, Trend, and Risk – for each of the five modules within a digital twin. By assessing parameters or factors in all three categories, the MHSS can provide a thorough health score for each module and trace the problematic area based on the rating logic of the MHSS. Parameters under “Present Status” focus on presenting the most up-to-date operational performance based on the current value or real-time monitoring and analysis. Parameters in the “Trend” category indicate the directional trend and variations. Lastly, parameters under “Risk” are the correction factors of the overall health score. For example, in the Real Estate Module, the Current Occupancy Rate represents the present operational performance. The Rental Growth Rate parameter under the “Trend” category shows the future pattern, as well Rent Overdue Amount and Rent Overdue Frequency are factors under the “Risk” category.
The following example demonstrates the importance of including all three categories in the MHSS. Suppose the MHSS for the Asset Maintenance Module is only based on the parameters under the “Present Status” category, such as Equipment Maintenance Rate, Inspection Time, and Maintenance Quantities, and all are under the tolerable performance thresholds. In that case, the facility manager won’t pay attention to the operational performance. However, if the MHSS includes parameters under the “Trend” category, and all the assets are operating under normal conditions, some trending parameters might reveal potential maintenance hazards, such as the Returning Rate Growth. The comprehensive scoring system of “Present Status + Trend + Risk” can reflect potential problematic areas via different parameter combinations in time. The cumulated overall scores for every module can show the performance of each one. This allows the facility manager to locate the problem by looking up the scoring of individual parameters under different categories and modules and avoids guess work.
Table 1 provides a sample of modular scoring parameters, categories, and category coefficients of the five modules. With the digital twin O&M management system accumulating data, including the decisions made by the facility managers for different events, and algorithms, coefficients, and threshold adjustment of the scoring system can become training data to adjust the O&M management system to meet the current project needs.
MHSS Scoring Mechanism
After establishing MHSS methodology, the next step is to consider its practical application mechanism. As the two previous articles mentioned, there is a need for multi-level user data management, as shown in Figure 1. According to the decision maker’s role and responsibility, user requirements can be divided into three levels: (1) the technical level for daily O&M; (2) the middle-level for project integration and management; and (3) the owner-level for overall decision-making and control. However, the MHSS with the five listed modules cannot fully satisfy these requirements based on these multi-level user data management requirements. Therefore, it is essential to include weighted coefficients based on the users’ level and their roles to accurately obtain the evaluation scores for the overall health of the current project.
For companies or organizations with multiple projects and facilities, the overall corporation scores can be the aggregate of each individual project score multiplied by its weighted coefficient, as shown in Figure 2. Based on these scores, facility managers can quickly locate the problem with an individual project or module, specific piece of equipment, or personnel issue and make timely decisions.
Benefits of MHSS in AM/FM
MHSS utilizes a standardized scoring system to enable dedicated AM/FM management supported by objective facility data. It creates a friendly onboarding process for unseasoned staff with a gentle learning curve and comprehensive facility information. It allows the users to access different data aggregations from multiple perspectives to deepen their understanding of the whole building/facility. Most importantly, it avoids an overdependence on experience-oriented O&M practices to make the FM/AM program flexible, adaptive, and more resilient. Its direct contributions are cost-savings and enhanced O&M efficiency/efficacy. Another notable benefit of MHSS lies in its capability of storing, analyzing, learning, and then providing predictive decision-making support via years of iteration. As illustrated in Figure 3, information from historical archives, discrete data silos, historic sensor readings, or daily reports from the Computerized Maintenance Management system (CMMS) will be gradually integrated and purified from “disordered and fragmented assets” into “intangible and decision-making supported digital assets,” which are as crucial as physical ones.
Additionally, the MHSS powered DMSS is an essential component of Enterprise Asset Management (EAM) that oversees the entire facility from a high level. Most of the time, detailed asset data requires corresponding Subject Matter Experts (SMEs) (i.e., Structure, MEP, HVAC, etc.) to utilize. For non-technical/engineering staff, the scoring system is a quantifiable method that is straightforward enough to make high-level decisions or plan strategies. Recalling the “3X3” principles discussed in the first article, the MHSS powered DMSS is the final execution of that concept. With the creation of different permission groups, the user will be able to log into their own dashboard tailored specifically to their needs. Achieving this goal breaks down the “knowledge/technology barriers” between all staff and stakeholders to let them communicate and share insights in an open environment. Lastly, DMSS should also leave space for the user to customize the formulas or parameters behind the scoring system to continuously optimize O&M methods. The score could be used as a KPI for single module performance or a reference to compare one module to another. All of these will make the FM/AM more controllable, quantifiable, and targetable.
This article expands upon the topics covered previously and presents the implementation of an MHSS powered DMSS built on top of a previously established framework. We showcase how it can potentially support different user groups’ needs, and how it benefits and facilitates O&M. The next article shifts the focus to the Sustainability Module and explains how a data management framework can empower sustainable-related performance tracking and support high-level sustainable strategies and Problem-Cause-Remedy (PCR) definitions.
Partial project and data contributions are credited to Jiancheng Cai from Rutgers, The State University of New Jersey.
Kai Yin co-founded China-based ZhiuTech in 2017 and is Vice President of Product Development. The company’s mission is adopting technology for the AECO industry, creating customized solutions and bringing value to clients’ real life work cases. He previously worked as a mechanical engineer at Arup, and technology consultant at Microdesk. Kai earned a bachelor’s degree in civil engineering from Purdue University, and master’s degree in building science from the University of Southern California.
Jiayi Yan is the co-founder and senior engineer at Beijing ZhiuTech Co. Ltd. in China. Jiayi graduated from University of Southern California specializing in building science and she is a PhD student of University College London dedicated to digital twin topics. She has years of experiences working as a building technology consultant providing technical solutions and implementation plans for world-leading enterprises in the United States and China. Jiayi specializes in BIM, sustainability, and city-level digital twin development in urban regeneration from multi-stakeholder perspective.
George Broadbent is Microdesk’s Vice President of Asset Management and has worked on a variety of projects including the rollout of Microdesk’s Maximo and Revit integration solution, ModelStream. George works closely with key stakeholders to identify strategies for asset management projects and manages the effort to build out new systems.
Dr. Xifan Jeff Chen is the EAM Assistant Director at Microdesk, and head of EAM Strategic Advisory Service. Jeff specializes in providing strategic consulting services for clients, conducting and implementing BIM, EAM and GIS integrated solutions, and developing digital twin methodologies for lifecycle BIM implementation.
Dr. Eve Lin is a EAM Strategy Consultant and Sustainability Lead at Microdesk, Dr. Eve Lin specializes in providing strategic and technical solutions for clients to facilitate sustainable practices throughout the project lifecycle. Her involvement includes building performance simulation, design automation, BIM and GIS integration and development of digital twin solutions.