Important Key Performance Indicators Research Articles

Measuring supply chain resilience along the automotive value chain — A comparative research on literature and industry

About three years after the start of the COVID-19 pandemic disrupting global supply chains, companies increasingly focus on creating supply chains that are resilient to the next disruption. While researchers have developed multiple frameworks and quantitative models for assessing risk in supply chains, the question of how to measure supply chain resilience (SCR) with key performance indicators (KPIs) remains unanswered. This research provides answers on how to measure SCR in the automotive industry. Researchers investigated literature’s perspective through text mining on 195 published papers on SCR and compared that to the industry’s perspective. The Analytical Hierarchy Process is applied to text mining results to find the most suitable combination of KPIs. For the industry data, a conjoint method analyzed via an ordinal regression is applied in interviews. The research reveals that the most important KPIs, according to literature, are lead time variation, OTIF (On time in full), and volume flexibility of suppliers. At the same time, the industry also assigns the greatest contribution to OTIF and volume flexibility, and to the stock level of high-risk parts. This study also investigates the different priorities of OEMs, Tier 1 and Tier 2 suppliers when measuring SCR. Perspectives on how to measure resilience vary within the industry as well as between industry and academia. This research reveals the need for a greater exchange between industry and academia as well as a more structural discussion of resilience KPIs and their application within the industry.

A Comprehensive Analysis of Airline Passenger's Satisfaction through Classification Model

Passengers' satisfaction with airlines is one of the important determinants of competitive advantage for airlines with globalization and customer-oriented aviation industries of today. Customer satisfaction directly impacts brand loyalty, customer retention, and profitability, becoming an important key performance indicator for airline operators. The research examines a dataset of over 120,000 passengers, evaluating various aspects of their flying experience such as inflight services, comfort, punctuality, and overall satisfaction. An in-depth analysis of the key factors for customer satisfaction is done based on several data analyses and using different machine learning techniques like Logistic Regression, K-Nearest Neighbors, Decision Tree, and Random Forest. Through these techniques, our results show that, among others, service quality, punctuality, and comfort are the essential requirements for customer satisfaction. Conclusively, this study offers actionable recommendations for airlines that are vital in improving passenger satisfaction, which is an important factor in developing effective customer loyalty in the highly competitive airline business. The K-Nearest Neighbors (KNN) model performed best with an F1 score of 0.93, excelling in balancing precision and recall. Other models like Decision Tree and Random Forest were also used, with Random Forest providing robustness due to its ability to handle large datasets without overfitting, while Logistic Regression gave interpretable results but had the lowest F1 score at 0.89.

Analysis Risk Mitigation of Delay in Delivery Using House of Risk Method (Case: Air Cargo Transportation)

Logistics Service Provider Companies are responsible for ensuring the delivery of goods using land modes, sea modes and air modes on time. However, in reality there are many problems and obstacles faced in order to guarantee one of these important key performance indicators. This research uses the house of risk method to identify the causes of the risk of delays and design mitigation strategies that can be done to overcome this problem by conducting interviews and filling out house of risk questionnaires with respondents who understand the problem. In this research, 13 risk events and 19 risk agents were found, and 9 priority risk agents were found to cause the problem. After that, activities were carried out to design mitigation strategies that could be carried out and 8 preventive actions were identified and the sequence that must be carried out to overcome the risk of delays.

The variation in post-endoscopy upper gastrointestinal cancer rates among endoscopy providers in England and associated factors: a population-based study.

Post-endoscopy upper gastrointestinal cancer (PEUGIC) is an important key performance indicator for endoscopy quality. We examined variation in PEUGIC rates among endoscopy providers in England and explored associated factors. The was a population-based, retrospective, case-control study, examining data from National Cancer Registration and Analysis Service and Hospital Episode Statistics databases for esophageal and gastric cancers diagnosed between 2009 and 2018 in England. PEUGIC were cancers diagnosed 6 to 36 months after an endoscopy that did not diagnose cancer. Associated factors were identified using multivariable logistic regression analyses. The national PEUGIC rate was 8.5%, varying from 5% to 13% among endoscopy providers. Factors associated with PEUGIC included: female sex (odds ratio [OR] 1.29 [95%CI 1.23-1.36]); younger age (age >80 years, OR 0.52 [0.48-0.56], compared with ≤60 years); increasing comorbidity (Charlson score >4, OR 5.06 [4.45-5.76]); history of esophageal ulcer (OR 3.30 [3.11-3.50]), Barrett's esophagus (OR 3.21 [3.02-3.42]), esophageal stricture (OR 1.28 [1.20-1.37]), or gastric ulcer (OR 1.55 [1.44-1.66]); squamous cell carcinoma (OR 1.50 [1.39-1.61]); and UK national endoscopy accreditation status - providers requiring improvement (OR 1.10 [1.01-1.20]), providers never assessed (OR 1.24 [1.04-1.47]). PEUGIC rates varied threefold among endoscopy providers, suggesting unwarranted differences in endoscopy quality. PEUGIC was associated with endoscopy findings known to be associated with upper gastrointestinal cancer and a lack of national endoscopy provider accreditation. PEUGIC variations suggest an opportunity to raise performance standards to detect upper gastrointestinal cancers earlier and improve outcomes.

Development of a Performance Assessment Model for Contractors in Saudi Arabian Construction Projects

Saudi Arabia leads the Gulf Cooperation Council countries in the construction industry, with 63% of the region’s future projects and $1.4 trillion in building and transportation projects. Previous studies have shown that 60% of construction projects are behind the schedule. One of the reasons behind this delay is the inappropriate selection of contractors for construction projects; therefore, the development of a contractor performance assessment model is needed for construction projects in Saudi Arabia. The proposed model includes the major key performance indicators (KPIs) used to measure contractors’ performance in construction projects. Afterward, a questionnaire is conducted with construction professionals to recommend the important KPIs to consider while developing the model. The analytic hierarchy process decision-making technique is applied for assessing relative priorities among the identified KPIs based on expert or decision-maker consensus. The results show that the time category is the most important in selecting the best contractor, emphasizing the significance of timely project completion.

КЛЮЧОВІ ПОКАЗНИКИ ЕФЕКТИВНОСТІ РОЗВИТКУ ТУРИСТИЧНИХ ДЕСТИНАЦІЙ

The article examines the problem of evaluating the effectiveness of the functioning of tourist destinations, collects and analyzes the main indicators that are used to evaluate the importance of tourist activity for the territory and proposes key performance indicators (KPI). Worldwide, tourism is one of the fastest growing industries. The increase in the number of tourists in tourist destinations requires increased attention from the authorities to ensure the sustainable development of recreational areas. The formation of the tourist product and tourist offer as a whole should be accompanied by the establishment of certain criteria for evaluating the effectiveness of tourism development in the destination from the point of view of sustainability and competitiveness. We included economic, environmental, socio-cultural and management KPIs among the most important key performance indicators. To assess the economic impact of tourism in a destination, KPIs should assess whether new business is created in the destination due to the presence of tourism needs, what percentage of tourism purchases are made from local producers, the dynamics of the number of skilled workers involved in tourism and tourism infrastructure. Also, in any destination, a system should be built to determine the risks and opportunities associated with the environmental consequences of the implementation of tourist activities. Such a system aims to analyze many factors: water use, climate action, solid waste management, air, land and sea pollution, as well as habitat and biodiversity conservation. The essence of socio-cultural KPIs is to empower communities. The impact of tourism on local communities goes beyond economic benefits, affecting wider sections of society through social dynamics, culture and quality of life. Management KPIs may include the implementation of the destination management plan, the amount of funding for tourism development, the amount of funds specially allocated to overcome the negative consequences of tourism. It is noted that the use of a holistic approach to planning the development of tourist destinations, which includes key performance indicators in the economy, environment, socio-cultural sphere and management, enables destinations to make informed decisions that benefit both visitors and local residents.

Investigation of key performance indicators for performance management of the manufacturing industry in the era of the COVID-19 pandemic

AbstractThe execution of constructive Key Performance Indicators (KPIs) is a critical tool for the Performance Management (PM) of the manufacturing industry to regulate operations. The companies rely on the PM strategies grounded on conventional KPIs assessment to achieve sustainability although the current dynamic manufacturing environment is undergoing complexities. The KPIs used in the past for PM are not mutually dependent, as they have not been adequately measured and updated to address emergency situations like the COVID-19 pandemic, particularly for the Leather Products Industry (LPI). Monitoring of plentiful KPIs is inconceivable and literature is also not available. Realizing these gaps, this study accumulates suggestions from a wide-ranging context of 25 experts’ feedback. Initially, a set of KPI was identified through literature review and experts survey. Later, employing a Pareto analysis, 15 KPIs were identified from 48 KPIs. Then the finalized KPIs investigated utilizing linguistic Z-digits and Decision-Making Trial and Evaluation Laboratory (DEMATEL) to find the “Cause-Effect” relationship. An industrial chronology is conferred to demonstrate the potency and functionality of the suggested method. The upshot signifies the “Target fulfillment within the delivery time during COVID-19” as the most important KPI for the studied case. The outcomes will assist the LPI managers to dictate crucial KPIs suitably and flourish the PM in attaining the goals and objectives.

Does Corporate Governance and Quality of Disclosure Affect Firm Performance? An Empirical Study on Indian Banks

Financial markets, particularly in the developed markets, have started including non-quantitative disclosures as one of the important key performance indicators and valuation drivers. Notably, the quality of corporate governance, reflected in the board of directors and audit quality, serves as a key performance metric. This paper explores the relationship between market performance, corporate governance practices, and quality of disclosures (QOD) (measured through an index) in Indian financial firms (government-owned banks, private banks, and NBFCs). We assess market performance using price–book value and its dependence on qualitative variables such as corporate governance index (CGI) and QOD. Findings indicate that the market performance of financial firms is influenced by standard accounting ratios such as return on assets, capital adequacy ratio, and non-performing assets, as expected. CGI and QOD have improved over time across all financial firm categories. However, while CGI exhibits a positive relationship with firm performance, the link between market performance and QOD is less definitive. This study, limited to select banks based on market capitalization, explores the relationship between market and accounting performance over five years (2016–2020), with a focus on three consecutive years (2012, 2016, and 2020) to explore the correlation between their market performance and key qualitative factors such as CGI and QOD.

A protocol to study the impact of implementation of National Accreditation Board for Hospitals & Healthcare Providers (NABH) standards among health care workers in a tertiary care hospital in India

Background: The healthcare system is now transforming widely with new technology including the introduction of a variety of medical gadgets, clinical trials, telemedicine, health insurance, health tourism, and outsourcing programs. Recent research focused on the quality of healthcare has proved useful for evaluating satisfaction and improvement of services in an organization. The World Medical Assembly asserts that in order to enhance patient outcomes on an individual level and community health, healthcare providers and organizations have an ethical and professional commitment to continuously improve the quality of services and patient safety. The National Accreditation Board for Hospitals & Healthcare Providers - Quality Council of India - (NABH) formed a constituent board to design and oversee an accreditation scheme for healthcare institutions to ensure the quality of care. Healthcare accreditation organizations and certification programs have progressed international efforts to raise the standard of healthcare since the 1970s. The accreditation process, which is carried out by qualified external peer reviewers, allows a healthcare organization to be officially recognized for achieving performance standards. The objectives of this protocol are to understand the perception and impact of healthcare workers towards implementation of NABH standards and to study the impact of pre and post assessment of NABH with the help of 10 important key performance indicators in hospitals. Methods: A descriptive cross-sectional design will be adopted in the study. It includes collection of data from the medical record department records for pre-assessment and direct questionnaire to the participants for post-assessment of NABH standards. The pre-assessment data of quality indicators will be collected from the previous records of NABH files from the quality assurance department. Expected result: The expected result of the study will identify the different impacts of implementation of NABH standards after pre and post assessment in an organization.

LEAF: Navigating Concept Drift in Cellular Networks

Operational networks commonly rely on machine learning models for many tasks, including detecting anomalies, inferring application performance, and forecasting demand. Yet, model accuracy can degrade due to concept drift, whereby the relationship between the features and the target to be predicted changes. Mitigating concept drift is an essential part of operationalizing machine learning models in general, but is of particular importance in networking's highly dynamic deployment environments. In this paper, we first characterize concept drift in a large cellular network for a major metropolitan area in the United States. We find that concept drift occurs across many important key performance indicators (KPIs), independently of the model, training set size, and time interval---thus necessitating practical approaches to detect, explain, and mitigate it. We then show that frequent model retraining with newly available data is not sufficient to mitigate concept drift, and can even degrade model accuracy further. Finally, we develop a new methodology for concept drift mitigation, Local Error Approximation of Features (LEAF). LEAF works by detecting drift; explaining the features and time intervals that contribute the most to drift; and mitigates it using forgetting and over-sampling. We evaluate LEAF against industry-standard mitigation approaches (notably, periodic retraining) with more than four years of cellular KPI data. Our initial tests with a major cellular provider in the US show that LEAF consistently outperforms periodic and triggered retraining on complex, real-world data while reducing costly retraining operations.

Identification and practical validation of spectrally efficient non-orthogonal frequency shaping waveform

Signal waveform is the basic physical layer element that fundamentally determines the most important key performance indicator in communication systems, namely spectral efficiency. Traditional waveforms rely on orthogonal Nyquist shaping but result in restricted spectral efficiency. Non-orthogonal waveforms have been proposed to enhance spectral efficiency via compressing either time or frequency resources. However, beyond 25% efficiency improvements in this way, classical Mazo theory states that performance will start to degrade. Here we use a robust and low-complexity neural network modulator to create asymmetric sub-carrier shapes, for multi-carrier non-orthogonal frequency shaping (NOFS) signals. The result is a non-Nyquist waveform which achieves 150% spectral efficiency improvement and still operates well with simple receiver processing. We set up a hardware communication link with over-the-air image transmission that practically validates the spectral efficiency improvement. This spectral efficiency will save communication resources for future communications systems such as 6G services.

On the modeling of continuous kraft pulp digesters: Inclusion of wood characteristics

In order to improve the pulp quality, it is essential to understand the influence of wood characteristics in the Kraft process, highlighting the methylglucuronic (MeGlcA) and hexenuronic (HexA) acids contents, and the syringyl/guaiacyl (S/G) ratio. Since their monitoring is still inefficient, it becomes difficult to use fully data-driven modeling approaches. Therefore, it is important to improve consolidated phenomenological models. In this work, an extended Purdue model was developed to simulate a continuous kraft pulp digester. Unlike most works, we considered the formation of HexA from MeGlcA and the dissolution of both compounds. We also considered the contribution of HexA to the kappa number, an important key performance indicator. As an innovation, we proposed the addition of the S/G ratio influence on the delignification rate of hardwoods by using experimental data taken from literature. The steady-state profiles and dynamic responses were similar to those found in the base work. The blow-line kappa number was 20% higher than that obtained disconsidering HexA. By performing a sensitivity analysis, the steady-state profile of kappa number proved to be very sensitive to variations in the S/G ratio. Thus, the results confirmed the importance of wood characteristics in the kraft pulping.

Comparing forecasting models for predicting nursery mortality under field conditions using regression and machine learning algorithms

Swine nursery mortality is one of the most important key performance indicators (KPIs) for commercial swine production systems and, for the purpose of this study, refers to the number of pigs that died during the first 60 days during the post-weaning phase (approximately 170-day period). Likewise, nursery mortality accounts for almost 50% of the mortality that occurs throughout the post-weaning phase. Despite being a metric concerning the post-weaning phase of production, previous studies have demonstrated that nursery mortality is highly correlated with the previous performance of the piglets in the pre-weaning phase and to stocking conditions at placement in nursery sites. However, there is no evidence of that predictive models are currently routinely utilized in the swine industry to predict the nursery performance of individual groups of pigs based on their previous performance in the pre-weaning phase. One obstacle to building such predictive models is that health and management data as well as production data for the pre and post-weaning phases are collected with separate record-keeping programs and stored in unconnected databases.Thus, the objective of this study was to measure and compare the performance of 5 forecasting models for predicting nursery mortality using a master table containing data collected from a single swine production system for 1,831 groups of pigs. An automated model was built to integrate information for 48 variables included in the master table that was previously disconnected within the production system. After the model and parameter tuning process, the model with the best overall prediction performance was the Support Vector Machine (SVM) in terms of Root Mean Squared Error (RMSE=0.3929), Mean Absolute Error (MAE=0.2956), and coefficient of determination (R2=0.7761). This study demonstrated the ability to use an integrated master table containing whole-herd information until the placement event of nursery groups to predict their downstream nursery mortality through the initial 60 days of the post-weaning phase.

Operational space efficiency (OpSE): a structured metric to evaluate the efficient use of space in industrial workstations

PurposeThis paper proposes a new metric, named Operational Space Efficiency (OpSE), intended to diagnose and quantify the inefficient use of floor space for stocking materials in industrial workstations. OpSE presents a formulation analogous to the well-known Overall Equipment Effectiveness and can be obtained as the product of three distinct indicators: Standard Compliance Effectiveness, Standards Selection Effectiveness and Design Space-usage Effectiveness.Design/methodology/approachThis indicator scrutinizes how usefully floor space in workstations is used to temporarily stock materials in the form of raw materials, semi-finished products, parts and components. It is suited for analyzing fixed-position layouts as well as product layouts typical of repetitive manufacturing settings, such as assembly lines in the automotive sector. The proposed indicator leverages an appropriate loss structure that features those factors affecting floor space utilization in workstations with regard to supplying and stocking materials.FindingsAn Italian manufacturer in the field of electro-technology was used as an industrial case study for the application of the methodology. The application shows how the three indicators work in practice, the effectiveness of OpSE and the methodology as a whole, in diagnosing floor space usage inefficiencies and in properly addressing improvement actions of the internal logistics in industrial settings.Originality/valueThe paper scrutinizes some important Key Performance Indicators (KPIs) dealing with space usage efficiency and identifies some significant drawbacks. Then it suggests a new, inclusive structure of losses and a KPI that not only measures efficiency but also allows to identify viable countermeasures.

ED Revisits Within 72 Hours to a Tertiary Health Care Facility in Dubai: A Descriptive Study.

Unplanned emergency department (ED) revisit is one of the major challenges faced by emergency care facilities and reflects their quality of care. It is an important key performance indicator (KPI) for emergency medical care. Often, inadequate medical care by physicians is claimed to be the main cause of unplanned ED revisits, yet this assumption is not well studied in the literature. Thus, this study aimed to identify the causes of unplanned ED revisits within 72 hours from the initial visit to the emergency department which could help in developing an action plan and improve quality of care and patient safety. A retrospective study was conducted in Rashid Hospital Trauma Center, from December 2019 to January 2020, using electronic medical records reviewed by two independent investigators. The reasons for the ED revisits were categorized into the following four domains: illness, physician, patient, and system related. A total of 584 revisits were found which accounted for 1.9% of ED attendance from December 2019 to January 2020. Majority of them were male patients, and 63% of the population had a mean age of 33 years. Majority of the ED revisits were due to illness (54%), followed by patient related (20%), physician related (18%), andsystem related (8%) factors. Most of the patients were discharged on the second visit. The two most common reasons for revisits in the ED department that were seen within the 72 hours were illness related and patient related, followed by physician related. The cause is mainly rooted in suboptimal discharge plans.

LECast: A Low-Energy-Consumption Broadcast Protocol for UAV Blockchain Networks

With the continuous development of communication technology, drones are playing an important role in many fields, such as power transmission line inspection and agricultural pesticide spraying. In order to protect the data privacy and communication security of drones, many experts are considering blockchain as its enabling technology. However, due to their small size and limited power storage, drones cannot support energy-intensive blockchain applications. In addition, the future 6G communications need to implement an important key performance indicator, namely extremely low-power communications (ELPCs). As a consequence, research into green blockchain is becoming more and more popular. The broadcast of the blockchain is one of the most energy-intensive parts because it entails flooding and there are a lot of unnecessary communication processes. Therefore, in order to make blockchain more suitable for ELPC requirements in 6G communications and unmanned aerial vehicle (UAV) networks, we took the blockchain broadcast as an improvement candidate and designed LECast, a low-energy-consumption protocol. LECast first analyzes the energy consumption model of the communication between two drones and constructs the shortest-path broadcast tree (SPB Tree) for the UAV networks to minimize energy consumption. Meanwhile, to make the sending drone address the receiving drone in a more convenient way, we proposed an extended Huffman coding (EHC) scheme to name the drones. Furthermore, the other issues with the broadcast tree are reliability and security. When a channel fails, subsequent drones cannot smoothly receive the transaction or block data. As a result, we introduced multichannel transmission with splitting data (MTSD); that is, the transaction or block data are divided into segments and transmitted in parallel multiple times over multiple channels. Finally, through the analysis and simulation of LECast in terms of energy consumption, latency, throughput, reliability, security, and coverage rate, the advantages of LECast were confirmed, which could meet the requirements of ELPCs and be well applied to UAV networks.

Accuracy of key performance indicators in solar-assisted heating systems due to measurement uncertainties

In this paper, the influence of measurement uncertainties on the key performance indicators fsav and FSC in solar-assisted heating systems is investigated. The impact of temperature, volumetric flow rate, and irradiance sensors with different measurement accuracies is analysed with a combination of Gaussian error propagation and Monte Carlo Analysis. To identify the sensor equipment with best cost–benefit-ratio (BCBR), different sensor combinations are investigated. If fsav and FSC are to be measured, additional sensors must be installed. The resulting costs range from 250€ for an inexpensive equipment to 330€ for recommended BCBR equipment and up to 780€ for the maximum accuracy equipment. For smaller economically dimensioned systems the use of inexpensive sensors results in large uncertainties, e.g. fˆsav=(15.2±13.7)%. The uncertainties for larger systems are significantly lower, e.g. fˆsav=(56.7±6.3)%. This is true as well if better sensors are used. Regardless of the system’s size, with the BCBR sensor equipment the resulting expanded uncertainties can be reduced by 60% (relative) compared to the results with the inexpensive sensor equipment. With maximum accuracy sensor equipment the resulting expanded uncertainties can even be reduced by 77% (relative). The results show the importance of a carefully selected sensor equipment and the influence of the system’s size. Inexpensive sensors can lead to large uncertainty ranges for the important key performance indicators fsav and FSC. Thus, these key figures effectively cannot be used for assessing the performance of a solar-assisted heating system.

Visualization Method of Product Design Intent and Examples of Application in Product Development: Concept and Application of Design Map

It is said that design involves deciding the composition and structure of the things to be made, determining the details of each part and the methods of making them, and recording them in the form of documents and drawings. The designer should create a series of scenarios in which the desired function is broken down into steps according to the specifications of each material. The material is then embodied and assembled to make the desired function and is socially implemented. The series of scenarios mentioned here, which the designer should consider, is the design intent. Instead of discussing strategies and products, this article discusses a methodology for visualizing “design intent,” which is essential for teams to implement them in the society systematically and how teams can share it. Furthermore, this article shows that the product design intentions of designers could be decomposed using two methods, product design and process design, regarding the visualization methods of product design intentions. Using these two methods, we present the idea of a “design map” that visualizes product design intent in the form of a tree diagram, and a total “Himozuke” method that includes other visualization tools. As an example application, we show that these methods work effectively in the development of superlarge flat panel displays, and as a result, both “shortening the development period” and “improving the yield in mass-production prototypes,” which are important key performance indicators during the development period, were significantly improved.

USING KEY PERFORMANCE INDICATORS IN CONSTRUCTION PROJECT LITERATURE

Key performance indicators (KPIs) are one of the prominent concepts used for performance measurement, which is an integral part of project management. KPIs are widely and effectively used to monitor, measure, and evaluate the performance and production of project management team in the construction industry. It is therefore necessary for construction projects to complete the project in the budget, time, quality, safety, and satisfaction specified in the contract. This paper provides a review to explore KPIs used in construction project management to measure the performance of construction projects. A comprehensive review was performed to compile KPIs proposed by relevant studies from the Scopus database. Based on the comprehensive literature review, performance of cost, time/schedule and quality, client satisfaction, and effective communication were identified as the most important KPIs in the construction project management domain. Overall, this study presents insights into KPIs and can give an idea to identify trends of KPIs, which is of great importance for the construction project management in terms of effectiveness, efficiency, and quality.

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning.