Relevant Key Performance Indicators Research Articles

Towards sustainable manufacturing: A comprehensive analysis of circular economy key performance indicators in the manufacturing industry

In the pursuit of sustainable development, the concept of Circular Economy (CE) has emerged as a transformative alternative to the traditional linear economic model characterized by take, make, use, and dispose practices. However, there is a growing recognition of the limitations of the linear economy in addressing contemporary global challenges, leading to a pressing need for a more regenerative approach. This study aims to explore and analyze Key Performance Indicators (KPIs) associated with the circular economy, particularly focusing on the manufacturing industry, to provide a valuable guide for enterprises looking to integrate CE principles effectively and reinforce sustainability. The novelty of this study lies in its comprehensive examination of KPIs that embrace sustainability's triple bottom lines - environmental, economic, and social dimensions. The systematic selection of relevant KPIs and the utilization of Social Network Analysis (SNA) to identify the most influential CE KPIs within the manufacturing industry add depth to the research. By identifying and upgrading significant KPIs representing various facets of CE development, this study contributes to a more meticulous understanding of how enterprises can measure their progress towards circularity. In each of the triple bottom lines of sustainability, five significant KPIs were recognized and upgraded to the second assessment process, where these 15 KPIs are analyzed using SNA to obtain 5 aggregated KPIs. The comprehensive analysis of the obtained fifteen KPIs culminates in aggregated CE indicators, primarily strategies and initiatives (SI), material efficiency (ME), productivity of remanufacturing (OR), technology investment (ROR), and eco-innovation (EI). The aggregated Key Performance Indicators (KPIs) displayed an extreme value of the weighted average index attributed to strategies and initiatives KPI with a value of 0.067. These aggregated KPIs provide a practical framework for manufacturers to assess and optimize their circular economy practices. It's essential to note that all binary values incorporated in the proposed approach are derived from an extensive literature review. These values may vary depending on the specific objectives of individual manufacturing firms or focus groups. As a result, the proposed approach is adaptable to diverse manufacturing industries, allowing customization to suit the unique input criteria of each firm or group. Significantly, the current work emphasizes the influential role of manufacturers in shaping a sustainable future in alignment with development goals.

Key Performance Indicators: A Framework for Allied Healthcare Educational Institutions.

Performance evaluation in the allied healthcare education sector is complex, making it essential for policymakers and managers to approach it comprehensively and thoughtfully to understand their performance. Hence, the development and monitoring of Key Performance Indicators (KPIs) in this domain must be considered one of the key priorities for the policymakers in AHIs. This study aims to develop a framework for the AHIs to extract and profile the indicators, measure, and report the results appropriately. The authors adopted a general review of the literature approach to study the primary goals of the institutional KPI framework, emphasizing the need for benchmarking while implementing KPIs and how to track performance using a KPI dashboard. The study provides the scope, relevant KPI categories, and a list of KPIs for evaluating the effectiveness of allied healthcare programs. The study findings also emphasized the need for benchmarking the KPIs and establishing a KPI dashboard while measuring and monitoring performance. KPIs are considered an invaluable tool that contributes immensely to the performance monitoring process of AHIs, irrespective of the specialties. This helps to identify and guide AHIs for developing KPIs and the associated minimum data set to measure organizational performance and monitor the quality of teaching and learning. In addition, the KPI framework reported in this study is a tool to assist performance monitoring that can subsequently contribute to the overall quality of AHIs.

Mapping ward managers' core competencies using the Balanced Scorecard model.

Nurse ward managers have a crucial role in the effective functioning of hospital wards and in organising the work of the nursing team. The Balanced Scorecard model is a management framework that enables strategy to be translated into action by setting goals that lead to measurable outcomes. Few studies have used the Balanced Scorecard model to map the competencies of healthcare professionals. To use the Balanced Scorecard model to map the core competencies of nurse ward managers and determine relevant key performance areas (KPAs) and key performance indicators (KPIs). This mixed-methods study was conducted in the surgical department of an Italian hospital using a qualitative and quantitative exploratory sequential approach. For the quantitative part, the authors asked a convenience sample of various clinical staff to rate a list of nine competencies in terms of their importance for the work of nurse ward managers. For the qualitative part, two focus group discussions were held with nurse ward managers. Responses to the questionnaires showed that all nine competencies were important to respondents, with no statistically significant differences between roles. Findings from the focus group discussions broadly confirmed the relevance and importance of the nine competencies. Based on these findings, the authors formulated a strategic map of the nurse ward manager role, with core competencies and associated objectives mapped against the four dimensions of the Balanced Scorecard model. A strategic map of the nurse ward manager role informed by the Balanced Scorecard model can help measure nurse ward managers' performance and provide a benchmark for them to improve their competencies.

New consensus definition on defining and measuring care for children with paediatric feeding disorder.

This study addresses the absence of a definition of care for children with feeding disorders, limited agreement on key performance indicators (KPIs), and the lack of data linked to those KPIs. Clinicians, consumers and researchers involved in outpatient feeding care in New South Wales (NSW), Australia were invited to participate in a two-Phase study. In Phase 1, a modified Delphi method was used. Two rounds of voting resulted in a new consensus definition of a multidisciplinary paediatric feeding clinic. Three further rounds voting determined relevant KPIs. In Phase 2, the KPIs were piloted prospectively in 10 clinics. Twenty-six clinicians, consumers and researchers participated in Phase 1. Participation across five voting rounds declined from 92% to 60% and a valid definition and KPI set were created. In Phase 2, the definition and KPIs were piloted in 10 clinics over 6 weeks. Data for 110 patients were collected. The final KPI set of 28 measures proposed covers clinical features, patient demographics and medical issues, parent-child interaction and outcome measures. A new definition of a multidisciplinary paediatric feeding clinic is now available, linked to a standardised KPI set covering relevant performance measures. These proved viable in baseline data collection for 10 clinics across NSW. This sets a foundation for further data collection, systematic measurement of care provision and outcomes, and research needed to deliver care improvement for children with paediatric feeding disorder.

ESG Reporting and Metrics: From Double Materiality to Key Performance Indicators

This article conceptualises the link between firms’ value chains and distribution networks and the requirements for double-materiality assessments in contemporary reporting regulations worldwide. The new European Sustainability Reporting Standards (ESRS) and the standards for sustainability reporting issued by the International Sustainability Standards Board (ISSB), called IFRS S1 and IFRS S2, require companies to report their own direct (scope 1) and indirect (scope 2) greenhouse gas (GHG) emissions as well as GHG emissions in their value chains and distribution networks (both scope 3). However, GHG emissions comprise just one dimension of information that is relevant to understand when assessing, managing and reporting the footprints and impacts of a firm and are, therefore, only a fraction of the key performance indicators (KPIs) related to ESG that should be disclosed. Through a case study, this article demonstrates the connection between a due diligence analysis of a firm’s value chains and distribution networks; an analysis of the competitive parameters of its business model; the identified impacts, risks and opportunities; and the double-materiality perspective. The double-materiality perspective prioritises actions based on probability and significance, creating a natural space to identify KPIs. The implication of this study is that firms can be assisted in identifying relevant KPIs based on double-materiality assessments aided by applying the REGS model because it guides firms in choosing the most relevant KPIs.

A Multi-Criteria Approach to Evaluate Sustainability: A Case Study of the Navarrese Wine Sector

The present work introduces a multi-criteria approach focused on the evaluation of wine production, considering the three dimensions of sustainability: environmental, economic and social. In this sense, the most relevant key performance indicators were selected within each dimension and disaggregated into sub-indicators to address the different sustainability aspects within the wine value chain. Furthermore, the analytic hierarchy process was applied as the method to weight the relevance of the three dimensions and corresponding key performance indicators, in order to allow the producers to understand which aspects need to be covered to improve their production sustainability. Results demonstrate how the approach proposed is able to identify, for both the vineyard and winery, the strengths and weaknesses regarding the sustainability performance of their production. Additionally, this study also contributes by shedding light on the most suitable and recommended actions to increase the company’s sustainability from a sustainable perspective. Additionally, it is important to highlight that, although the developed approach is specifically designed for the wine sector in the Navarrese region, it can be replicated by adjusting the key performance indicator selection to apply this methodology to the reality in other wine production regions.

Overcoming Head Contractor Barriers to Sustainable Waste Management Solutions in the Australian Construction Industry

The construction industry has one of the highest waste intensities in Australia. While there are barriers to the implementation of sustainable waste management (WM) practices, there is a lack of viable solutions for head contractors to overcome these barriers. This research investigates the role of incentives in achieving sustainable WM in the Australian commercial construction industry. A qualitative approach was adopted through interviews with experts in the field to explore the role of incentives as possible solutions to the barriers presented. The findings show that participants are willing to use more sustainable WM practices. However, the barriers are perceived to be too substantial. Many types of incentives can encourage changes in behavior, which contribute to better waste outcomes. The findings also indicate key stakeholders such as the client, government, and industry regulators may provide incentives, including enhancing relevant key performance indicators, amending existing legislations, and implementing government programs to foster a Circular Economy to improve sustainable WM practices. This study contributes to the field by raising awareness about the role of incentives for head contractors to achieve sustainable WM practices.

Modelling Patients Waiting and Service Time by ARIMA Model: A Case of Federal University Gusau Clinic

The ability to model and forecast waiting and service time to increase patients' satisfaction, reduce waiting time, avoid casualties, and increase efficiency in service delivery is crucial. It encourages the identification of future pressure by using the relevant key performance indicators. In this paper, the ARIMA model is used to study the waiting and service time of patients at the {\it Federal University Gusau} Health Services Clinic. The system was a single, time-independent arrival with many service points. Based on the results found in the waiting and service processes, the service time has a lower mean and variance when compared to the waiting time. The waiting time has a lower skewness and kurtosis when compared to the service time. The Ljung-Box (Q) Statistic test shows that the correlation in the time series has been adequately captured for the waiting and service time processes, though the waiting and service time processes have 4 and 10 outliers respectively. The ARIMA (0,1,2) and ARIMA (2,1,1) are selected for modelling the waiting and service time respectively based on the evaluation metrics.

Impact analysis of cooperative perception on the performance of automated driving in unsignalized roundabouts.

This paper reports the implementation and results of a simulation-based analysis of the impact of cloud/edge-enabled cooperative perception on the performance of automated driving in unsignalized roundabouts. This is achieved by comparing the performance of automated driving assisted by cooperative perception to that of a baseline system, where the automated vehicle relies only on its onboard sensing and perception for motion planning and control. The paper first provides the descriptions of the implemented simulation model, which integrates the SUMO road traffic generator and CARLA simulator. This includes descriptions of both the baseline and cooperative perception-assisted automated driving systems. We then define a set of relevant key performance indicators for traffic efficiency, safety, and ride comfort, as well as simulation scenarios to collect relevant data for our analysis. This is followed by the description of simulation scenarios, presentation of the results, and discussions of the insights learned from the results.

Performance Evaluation of Photonics-Based Coherent MIMO Radar Systems for Maritime Surveillance

Multiple-input multiple-output (MIMO) radars offer significant advantages over conventional standalone radars in terms of target detection and localization capabilities. However, to fully exploit their potential, such systems require excellent time and phase synchronization among the central unit and the radar heads. Only recently, thanks to microwave photonics (MWP) techniques, it has been possible to develop the first coherent MIMO radar demonstrators. In this paper, a simulation tool in MATLAB programming language is proposed to model coherent MIMO radars based on MWP techniques in maritime surveillance applications. Moreover, a novel approach for estimating the radar cross section of extended maritime targets is presented. The system performance in co-located and distributed MIMO configuration, as well as in single- and multi-band operations, is evaluated by means of relevant key performance indicators (KPIs). Simulations are carried out in two close-to-reality scenarios. In the first, a co-located MIMO radar is mounted on top of a patrolling vessel. In the second, a distributed MIMO radar is deployed inside a port. The proposed KPIs are analyzed in terms of both geometric and frequency diversities of the system, laying the foundations for general system optimization criteria valid in any given surveillance application.

What are the essential elements of a digital transformation process to develop an efficient digitization strategy for management?

PurposeConsidering the COVID-19 pandemic, digital transformation has taken on heightened importance. This paper aims to identify the key components of successful digital transformations. As the IT industry is at the forefront of digital transformation, the insights and outcomes of this study can be advantageous to other sectors. This can assist other organizations in expediting their transformation journey and reducing costs.Design/methodology/approachThe research was conducted through a quantitative survey, which underwent a rigorous development process. The questionnaire was designed with clear objectives, informed by a thorough review of existing literature and a qualitative survey. Initial questions were formulated and reviewed internally before being pretested with a small group. Based on feedback, the questions were refined, and clarifying hints were added. The final survey was conducted, and the resulting data were analyzed to identify essential components of digital transformations and relevant key performance indicators (KPIs) for measuring success.FindingsA digital transformation's leading and essential components are mindsets, communication, strategy, technology and people. This relates well to the meta-study. The result of the determined KPIs is also interesting. According to the survey, the degree of automation, efficiency gain, employer survey and financial numbers are identified as important factors. Moreover, the increasing number of companies embracing digital transformation is a promising trend, indicating the relevance of this study's content for the future and its potential to assist in successful transformation.Research limitations/implicationsThe respondents in Germany showed a distinct pattern, but the outcomes could vary if the questionnaire had a more equitable representation of regions and countries worldwide. A similar situation applies to company size, as many participants were from large enterprises. Analyzing medium and small businesses may alter the findings. It is debatable whether KPIs alone can effectively facilitate a successful digital transformation. Besides the components, tracking the transformation's outcome and progress thoroughly is crucial to ensure the process aligns from start to finish.Originality/valueOverall, these results provide very precise and valid points that are necessary for digital transformation. With the knowledge gained from the IT sector, companies from other industries can already take the first step and plan for a digital transformation. In summary, relevant building blocks for a digital transformation have been found with the help of the survey.

Analysis of Key Performance Indicators during and Post the Covid-19 Pandemic (Case Msmes in Indonesia)

This study aims to identify the Key Performance Indicators (KPI) of each of the internal and external environmental factors used to measure the performance of Micro, Small, and Medium Enterprises (MSMEs) during and after the COVID-19 pandemic. The population in this study are the owners and managers of MSMEs in Banyumas Regency in December 2020 and 2021. The data analysis technique uses the Mc-Nemar Test Method. The results of the study indicate that KPIs which include production; GMP Good Manufacturing Practices; quality control; Brand; packaging; labeling and intellectual property; marketing; Financial management; capital literacy; Human Resources; character; Relevant agencies, and permits to measure MSME performance during and after the COVID-19 pandemic. These KPIs contain implementation in the short term (during the COVID- 19 pandemic) and medium or long term (post-COVID-19 pandemic). This study proves that KPIs relevant to measuring short-term performance are very important to develop a model for the recovery of MSME performance during the COVID-19 pandemic. Relevant KPIs to measure the performance of MSMEs in the medium or long term are very important to determine the post- COVID-19 recovery strategy.

Analysis and Modelling of Road Traffic Using SUMO to Optimize the Arrival Time of Emergency Vehicles

Traffic simulation tools are used by city planners and traffic professionals over the years for modelling and analysis of existing and future infrastructural or policy implementations. There are numerous studies on emergency vehicle (EV) prioritization in cities all over the world, but every area is unique and requires the data collection and simulation to be done separately. In this case, the focus area is the Mörfelder Landstraße in Frankfurt am Main, Germany, one of the busiest streets in this city. Thestudy illustrates demand modelling, simulation and evaluation of a traffic improvement strategy for EVs. Vehicular traffic such as passenger cars and trams are simulated microscopically. To perform accurate traffic simulation, input data quality assurance and cleansing of Master Data is required. Therefore, the data is adapted to reproduce the real-world scenario and transformed into the readable format for the simulation model. Vehicular demand is calibrated by traffic count data provided by the Frankfurt Traffic Department. To model road traffic and road network, origin destination matrices using the Gravity Mathematical Model and Open Street Maps are generated, respectively. This process is time-consuming and requires effort. However, this process is critical to get realistic results. In the next step, the road traffic is simulated using SUMO (Simulation of Urban mobility). Finally, EV relevant key performance indicators (KPIs): total trip time and total delay time are derived from simulations. The real-world scenario is compared with five alternative scenarios. The comparison of the KPIs revealed that the real-world scenario results in longer travel times compared to the EV-prioritization scenario. In the least case, the overall travel times for EV has decreased significantly and, as we know, in the case of EVs, even a few seconds saved could prove crucial for a person in need.

Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis

<p><big>Credit scoring remains one of the most important subjects in financial risk management. Although the methods in this field have grown in sophistication, further improvements are necessary. These advances could translate in major gains for financial institutions and other companies that extend credit by diminishing the potential for losses in this process. This research seeks to compare statistical and artificial intelligence (AI) predictors in a credit risk analysis setting, namely the discriminant analysis, the logistic regression (LR), the artificial neural networks (ANNs), and the random forests. In order to perform this comparison, these methods are used to predict the default risk for a sample of companies that engage in trade credit. Pre-processing procedures are established, namely in the form of a proper sampling technique to assure the balance of the sample. Additionally, multicollinearity in the dataset is assessed via an analysis of the variance inflation factors (VIFs), and the presence of multivariate outliers is investigated with an algorithm based on robust Mahalanobis distances (MDs). After seeking the most beneficial architectures and/or settings for each predictor category, the final models are then compared in terms of several relevant key performance indicators (KPIs). The benchmarking analysis revealed that the artificial intelligence methods outperformed the statistical approaches.</big></p>

Techno-Economic Analysis of a Hydrogen-Based Power Supply Backup System for Tertiary Sector Buildings: A Case Study in Greece

In view of the European Union’s strategy on hydrogen for decarbonization and buildings’ decarbonization targets, the use of hydrogen in buildings is expected in the future. Backup power in buildings is usually provided with diesel generators (DGs). In this study, the use of a hydrogen fuel cell (HFC) power supply backup system is studied. Its operation is compared to a DG and a techno-economic analysis of the latter’s replacement with an HFC is conducted by calculating relevant key performance indicators (KPIs). The developed approach is presented in a case study on a school building in Greece. Based on the school’s electricity loads, which are calculated with a dynamic energy simulation and power shortages scenarios, the backup system’s characteristics are defined, and the relevant KPIs are calculated. It was found that the HFC system can reduce the annual CO2 emissions by up to 400 kg and has a lower annual operation cost than a DG. However, due to its high investment cost, its levelized cost of electricity is higher, and the replacement of an existing DG is unviable in the current market situation. The techno-economic study reveals that subsidies of around 58–89% are required to foster the deployment of HFC backup systems in buildings.

A comprehensive framework tool for performance assessment of NBS for hydro-meteorological risk management

This paper describes an assessment framework tool to analyze the performance of nature-based solutions (NBS) for hydro-meteorological risk management. The tool is based on multi-criteria decision analysis within the context of NBSs, an umbrella concept currently in focus that promotes nature and provides ecological and socio-economic benefits. The proposed tool includes the selection and application of key performance indicators (KPIs) for the co-benefits and costs associated with the implementation of NBSs. To ensure high societal impact, the tool relies on a participatory approach. Stakeholder preferences are taken into account within the assessment process. As such, the assessment framework can be used as a design and selection tool for NBSs and other alternative measures, including grey and hybrid solutions. The proposed procedure can be adapted to the specific socio-environmental context and hydro-meteorological risk by tailoring the set of relevant KPIs. The assessment framework is useful for monitoring the implemented measures and to document their effectiveness. The methodology provides quantitative and transparent documentation of hydro-meteorological risk management processes, useful for decision- and policy-makers, and stakeholders dealing with NBS measures.

Combining phytoremediation with bioenergy production: developing a multi-criteria decision matrix for plant species selection

The use of plants to extract metal contaminants from soils has been proposed as a cost-effective means of remediation, and utilizing energy crops for this phytoextraction process is a useful way of attaining added value from the process. To simultaneously attain both these objectives successfully, selection of an appropriate plant species is crucial to satisfy a number of imporTant criteria including translocation index, metal and drought tolerance, fast growth rate, high lignocellulosic content, good biomass production, adequate calorific value, second generation attribute, and a good rooting system. In this study, we proposed a multi-criteria decision analysis (MCDA) to aid decision-making on plant species based on information generated from a systematic review survey. Eight species Helianthus annuus (sunflower), Brassica juncea (Indian mustard), Glycine max (soybean), Salix spp. (willow), Populus spp. (poplar), Panicum virgatum (switchgrass), Typha latifolia (cattails), and Miscanthus sinensis (silvergrass) were examined based on the amount of hits on a number of scientific search databases. The data was normalized by estimating their min–max values and their suitability. These criteria/indicators were weighted based on stipulated research objectives/priorities to form the basis of a final overall utility scoring. Using the MCDA, sunflower and silvergrass emerged as the top two candidates for both phytoremediation and bioenergy production. The multi-criteria matrix scores assist the process of making decisions because they compile plant species options quantitatively for all relevant criteria and key performance indicators (KPIs) and its weighing process helps incorporate stakeholder priorities to the selection process.

Characterization of Laboratory Flow and Performance for Process Improvements via Application of Process Mining.

The rising level of laboratory automation provides an increasing number of logged events that can be used for the characterization of laboratory performance and process improvements. This abundance of data is often underutilized for improving laboratory efficiency. The first aim of this descriptive study is to provide a structured approach for transforming raw laboratory data to data that is suitable for process mining. The second aim is to describe a process mining approach for mapping and characterizing the sample flow in a clinical chemistry laboratory to identify areas for improvement in the testing process. Data were extracted from instrument log files and the middleware between laboratory instruments and information technology infrastructure. Process mining was used for automated process discovery and analysis. Laboratory performance was quantified in terms of relevant key performance indicators (KPIs): turnaround time, timeliness, workload, work-in-process, and machine downtime. The method was applied to two Dutch university hospital clinical chemistry laboratories. We identified areas where alternative routes might increase laboratory efficiency and observed the negative effects of machine downtime on laboratory performance. This encourages the laboratory to review sample routes in its analyzer lines, the routes of high priority samples during instrument downtime, as well as the preventive maintenance policy. This article provides the first application of process mining to event data from a medical diagnostic laboratory for automated process model discovery. Our study shows that process mining, with the use of relevant KPIs, provides valuable insights for laboratories that motivates the disclosure and increased utilization of laboratory event data, which in turn drive the analytical staff to intervene in the process to achieve the set performance goals. Our approach is vendor independent and widely applicable for all medical diagnostic laboratories.

KEY PERFORMANCE INDICATORS SPECIFIC TOTHE HUMAN RESOURCES DEPARTMENT

In this paper I want to present the value that the human resources department brings to the organization of which it is part. It consists in its ability to fulfill its role of activating the development of human capital and its sustainability. Long-term performance and market leadership could not be achieved by organizations without motivated, hard-working employees and a strong sense of loyalty to the employer. Throughout the process that employees go through since joining the organization, the human resources module needs to be aware of the importance of actively implementing performance management tools in order to monitor key activities. In order for performance management to function optimally in a department, the objectives and functions to be performed by this team must be identified in accordance with the organization'sstrategy. Then, the most representative and relevant key performance indicators are chosen for the mutually agreed objectives. And in the second part of the paper, I will present a case study of the application of these key performance indicators on the units of the Romanian Army.

Coproducing and Implementing Person-Centered Key Performance Indicators in Ambulatory Cancer Nursing.

Policy advocates person-centered healthcare for people living with cancer. Although nurses and patients alike recognize that a good care experience cannot be measured solely by clinical outcomes, the difficulty in finding indicators that measure the delivery of effective person-centered care remains a challenge. The aim of this study was to explore the impact of a coproduced implementation project using the person-centered nursing key performance indicators to support the development of person-centered practice across ambulatory chemotherapy units. The study adopted an evaluation approach derived from work of the Medical Research Council. A mobile app was used to collect and analyze 3 cycles of data using 4 measurement tools, with the aim of informing quality improvement activities. Six implementation teams were recruited from chemotherapy units across a region in the United Kingdom. Qualitative interviews were used to evaluate the experience of participants. Data analysis revealed 4 themes: building relationships that nurture the care experience, inspiring nursing staff to flourish, shaping practice and service changes through the nature of the conversations, and becoming person-centered through coproduction. This study provides evidence of the value of implementing these person-centered key performance indicators using a model of coproduction in cancer nursing. The data generated by the key performance indicators offer valuable feedback to nurses that can inform the development of person-centered practice and contribute to an enhanced patient experience. This study provides an innovative and transferable approach for implementing relevant and appropriate key performance indicators within cancer nursing, which can contribute to developing person-centered cultures.