Improvements In Key Performance Indicators Research Articles

Enhancing Magnetic Material Data Analysis with Genetic Algorithm-Optimized Variational Mode Decomposition

As the application of machine learning technology in predicting and optimizing material performance continues to grow, handling the electromagnetic data of magnetic materials, especially in removing unavoidable data noise and accurately extracting resonance peaks in the imaginary part of electromagnetic information, has become a significant challenge. These steps are crucial for revealing the deep electromagnetic behavior of materials and optimizing their performance. In response to this challenge, this study introduces an innovative approach—Genetic Algorithm-Optimized Variational Mode Decomposition for Signal Enhancement (GAO-VMD-SE). This method, through the Variational Mode Decomposition (VMD) technique optimized by genetic algorithms, not only effectively reduces noise in the data, thereby improving the Signal-to-Noise Ratio (SNR) and reducing the Mean Absolute Error (MAE), but also significantly enhances the hidden resonance peak information in complex permittivity and permeability data to achieve a comprehensive improvement in key performance indicators. Experimental results prove that this method surpasses traditional analysis techniques in key performance metrics such as the peak width ratio, peak overlap ratio, and the number of peaks. Especially in identifying characteristic peaks related to the Snoek limit, GAO-VMD-SE can effectively reveal the peak features hidden in complex data, thus providing important insights for evaluating the performance of materials at specific frequencies. Moreover, the effectiveness of this method in denoising not only enhances the quality and accuracy of material data analysis but also achieves a 1% to 10% enhancement in peak information extraction. This optimized data processing capability and versatility make GAO-VMD-SE not only suitable for evaluating the performance of magnetic materials but also show significant practical application value in processing spectral data and other time series signal data applications.

IoT-BIM and blockchain integration for enhanced data traceability in offsite manufacturing

The transformation of dynamic supply chains within the realm of offsite manufacturing presents a unique and compelling opportunity to effectively respond to shifts in market demand. However, this paradigm shift also entails novel challenges and prospects for streamlining processes, augmenting efficiency, and ultimately driving success across production workflows and logistical operations. Dynamic supply chains necessitate unimpeded access to high-quality, dependable data to facilitate real-time, data-driven decision-making. Furthermore, owing to their inherently intricate nature, these supply chains are subject to a heightened vulnerability to disruptions and delays, surpassing those encountered in traditional supply chains. Against this backdrop, this paper proposes a framework that synthesises the convergence of the Internet of Things (IoT), Building Information Modelling (BIM), and blockchain technology (BCT) into a cohesive and integrated system, thereby optimising the efficiency and transparency of supply chain management operations. The proposed framework takes cognisance of the challenges inherent in ensuring information traceability, mitigating fragmented information, enhancing interoperability, and fortifying reliability throughout the design, production, and implementation phases of assets within dynamic supply chains operating in the offsite manufacturing domain. To illustrate the efficacy of our proposition, we present a framework that exemplifies the seamless integration of a permissioned, open-source distributed ledger, which forms the bedrock of our support infrastructure for IoT and BIM integration.Through rigorous analysis, we showcase the effectiveness of the developed framework in the form of a designed Supply Chain Management Model (SCMM-PDO), which harmonises the physical, digital, and operational layers within a robust system architecture (BCT.IoT-BIM). The integration of these technologies provides a decentralised solution, reinforcing supply chains with improved security, reliability, and quality control mechanisms, thus enhancing the efficiency of the supply chain network. It is anticipated that this transformative integration will yield substantial improvements in key performance indicators (KPIs) such as operational efficiency, cost reduction, and the cultivation of trust and accountability across supply chain activities.The research proposes an IoT and BIM conceptual framework that simulates the dynamic supply chain operations, facilitating real-time analysis and prediction of supply chain participants' behaviours from resources, processes, and assets. Furthermore, the integration of IoT-BIM (digital twinning) with a blockchain-based system, such as Hyperledger Fabric, engenders a secure and transparent framework for the diligent tracking of assets and information throughout the supply chain. The findings of this study offer an all-encompassing conceptual framework for the secure and transparent tracking of asset information by integrating Blockchain-based technologies with IoT and BIM. This research endeavour thus expands the existing body of knowledge, establishing a foundation for future investigations within this proliferating field.

(VISIION-S): Viz.ai Implementation of Stroke augmented Intelligence and communications platform to improve Indicators and Outcomes for a comprehensive stroke center and Network – Sustainability

ObjectivesAs Comprehensive Stroke Centers (CSCs) strive to improve neuro-intervention (NIR) times, process improvements are put in place to streamline workflows. Our prior publication (VISIION) demonstrated improvements in key performance indicators (KPIs). The purpose VISIION-S was to analyze whether those results were sustainable. Materials and methodsConsecutive Direct Arriving LVO (DALVO) and telemedicine transfer LVO (BEMI) stroke NIR cases were assessed, including subgroups of DALVO-OnHours, DALVO-OffHours, BEMI-OnHours, and BEMI-OffHours. We analyzed times for the original 6 months pre (6/10/20-1/15/21) and compared them to a 17 month post-implementation period (1/16/21- 6/25/22) to evaluate for sustainability. Mann-Whitney U was utilized. Results150 NIR cases were analyzed pre (n = 47) v. post (n = 103) implementation (DALVO-OnHours 7 v. 20, DALVO-OffHours 10 v. 25, BEMI-OnHours 13 v. 20, BEMI-OffHours 17 v. 38). For Door-to-groin (DTG), improvement was noted for DALVO-OffHours 39%(157 min,96 min;p < 0.001), DALVO-ALL 25%(127 min,95 min;p = 0.006), BEMI-OffHours 46%(45 min,25 min;p = 0.023), and BEMI-ALL 40%(42 min,25 min;p = 0.005). Activation-to-groin (ATG), door-to-device (DTD), and door-to-recanalization (DTR) also showed statistical improvements. For DALVO-OffHours, there were reductions in door to CT (DTC) 80%(26 min,5 min;p < 0.001), ATG 32%(90 min,61 min;p = 0.036), DTG 39%(157 min,96 min;p < 0.001), DTD 31%(178 min,123 min;p = 0.002), and DTR 32%(197 min,135 min;p = 0.003). ConclusionsWe noted sustainability over a 17 month period with sustained reduction in KPIs for even more NIR time interval comparisons. In the greatest opportunity subgroup (DALVO-OffHours), we noted a reduction in all 5 time interval metrics. Our sustainability finding is important to show that process improvements continued even after the immediate period, adding credibility to the results. Models such as this could be useful for other centers striving to optimize workflow and improve times.

Abstract WP45: VISIION-S: Viz.ai Implementation Of Stroke Augmented Intelligence And Communications Platform To Improve Indicators And Outcomes For A Comprehensive Stroke Center And Network - Sustainability

Background: As Comprehensive Stroke Centers (CSCs) strive to improve neurointerventional (NIR) times, process improvements have been put in place to streamline workflows. Our prior publication (VISIION) demonstrated an improvement in key performance indicators (KPIs) in our CSC. The purpose of this study is to analyze whether the positive results demonstrated were sustainable. Methods: Sequential stroke NIR patients being Direct Arriving LVO (DALVO) and telemedicine transfer LVO (BEMI) cases were assessed, including subgroups of DALVO-OnHours, DALVO-OffHours, BEMI-OnHours, and BEMI-OffHours. We analyzed times for the original 6 months pre (6/10/20-1/15/21) and compared them to a 17 months post-implementation (1/16/21- 6/25/22) to evaluate for sustainability. Mann-Whitney U was utilized. Results: 150 NIR cases were analyzed pre (n=47) v. post (n=103) Viz.ai implementation (DALVO-OnHours 7 v. 20, DALVO-OffHours 10 v. 25, BEMI-OnHours 13 v. 20, BEMI-OffHours 17 v. 38). For Door-to-groin (DTG) assessments, improvement was noted for DALVO-OffHours 39% (157min,96min;p&lt;0.001), DALVO-ALL 25% (127min,95min;p=0.006), BEMI-OffHours 46% (45min,25min;p=0.023), and BEMI-ALL 40% (42min,25min;p=0.005). Activation-to-groin (ATG), door-to-device (DTD), and door-to-recanalization (DTR) showed similar improvements. For DALVO-OffHours, there were significant reductions in door to CT (DTC) 81% (26min,5min;p&lt;0.001), ATG 32% (90min,61min;p=0.036), DTG 39% (157min,96min;p&lt;0.001), DTD 31% (178min,123min;p=0.002), and DTR 32% (197min,135min;p=0.003). Conclusions: Consistent with our initial 6 month post-implementation pilot, we noted sustainability over a 17 month period with sustained reduction in KPIs for numerous key NIR subgroups. In the greatest opportunity subgroup (DALVO-OffHours), requiring team mobilization off hours without benefit of telemedicine transfer lead time, we noted a significant reduction in all 5 time metrics. Our sustainability finding is important to show that process improvements continued even after the immediate period, making a Hawthorne effect less likely and adding credibility to the results. Models such as this, could be useful for other centers striving to optimize workflow and improve NIR times.

Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing

With the spread of the Industry 4.0 concept, implementing Artificial Intelligence approaches on the shop floor that allow companies to increase their competitiveness in the market is starting to be prioritized. Due to the complexity of the processes used in the industry, the inclusion of a real-time Quality Prediction methodology avoids a considerable number of costs to companies. This paper exposes the whole process of introducing Artificial Intelligence in plastic injection molding processes in a company in Portugal. All the implementations and methodologies used are presented, from data collection to real-time classification, such as Data Augmentation and Human-in-the-Loop labeling, among others. This approach also allows predicting and alerting with regard to process quality loss. This leads to a reduction in the production of non-compliant parts, which increases productivity and reduces costs and environmental footprint. In order to understand the applicability of this system, it was tested in different injection molding processes (traditional and stretch and blow) and with different materials and products. The results of this document show that, with the approach developed and presented, it was possible to achieve an increase in Overall Equipment Effectiveness (OEE) of up to 12%, a reduction in the process downtime of up to 9% and a significant reduction in the number of non-conforming parts produced. This improvement in key performance indicators proves the potential of this solution.

The Development of a Cross-Border Energy Trade Cooperation Model of Interconnected Virtual Power Plants Using Bilateral Contracts

By coordinating the operation of regionally interconnected virtual power plants (VPPs), the growing penetration problem of renewable energy sources (RESs) into the power system can be addressed. This study presents an interactive trading cooperation model of regionally interconnected VPPs using bilateral contracts. The proposed model maximizes overall electricity market social welfare (SW) (i.e., maximization of consumer benefits while minimizing energy costs). The focus of the proposed approach is to design and develop a parallel energy exchange cooperation model of interconnected VPPs, ensuring the operational efficiency and reliability of interconnected power systems over the planning horizon. Given that adjacent VPPs may have differences in their energy generation and usage patterns, a scenario tree method is used to model the uncertainties associated with solar irradiation and load demand. A case study of two interconnected VPPs is used, the operational scenario is designed, and the corresponding computational model is developed. The results highlight that the proposed approach gives VPPs the option to utilize their internal network’s maximum capacity. As a result, there will be less reliance on the main grid for interconnected VPPs, and an improvement in key performance indicators, including the cost of the VPPs systems and renewable power variations.

Agronomic gain: Definition, approach, and application.

Meeting future global staple crop demand requires continual productivity improvement. Many performance indicators have been proposed to track and measure the increase in productivity while minimizing environmental degradation. However, their use has lagged behind theory, and has not been uniform across crops in different geographies. The consequence is an uneven understanding of opportunities for sustainable intensification. Simple but robust key performance indicators (KPIs) are needed to standardize knowledge across crops and geographies. This paper defines a new term 'agronomic gain' based on an improvement in KPIs, including productivity, resource use efficiencies, and soil health that a specific single or combination of agronomic practices delivers under certain environmental conditions. We apply the concept of agronomic gain to the different stages of science-based agronomic innovations and provide a description of different approaches used to assess agronomic gain including yield gap assessment, meta-data analysis, on-station and on-farm studies, impact assessment, panel studies, and use of subnational and national statistics for assessing KPIs at different stages. We mainly focus on studies on rice in sub-Saharan Africa, where large yield gaps exist. Rice is one of the most important staple food crops and plays an essential role in food security in this region. Our analysis identifies major challenges in the assessment of agronomic gain, including differentiating agronomic gain from genetic gain, unreliable in-person interviews, and assessment of some KPIs at a larger scale. To overcome these challenges, we suggest to (i) conduct multi-environment trials for assessing variety × agronomic practice × environment interaction on KPIs, and (ii) develop novel approaches for assessing KPIs, through development of indirect methods using remote-sensing technology, mobile devices for systematized site characterization, and establishment of empirical relationships among KPIs or between agronomic practices and KPIs.

Automated Dispensing Cabinet Optimization in a Level 2 Trauma Center

Introduction Opportunities have been identified regarding timely delivery of medications due to lack of ADC inventory related to stock outs or other variances in pharmacy workflow (i.e. compounding and distribution times). These opportunities for improvement impact patient care and result in both nursing and pharmacy staff frustration. Additionally, there are significant costs associated with unused medications stored in ADCs, taking up valuable real estate that would otherwise house more opportune inventory. This situation has created a need for more efficient management of the ADC inventory. Currently, research in the topic area is sparse. We hypothesize that the systematic management and oversight of ADC inventory will demonstrate a significant improvement in key performance indicators and provide insight to the current gaps in knowledge. Methods This study will be a continuous quality improvement project with a combination of retrospective data review and prospective optimization interventions of automated dispensing cabinets (Pyxis®). Interventions include review and adjustment of ADC par levels, removal of unused/stagnate medications, standardization of stock, and continual review of ADC inventory turns and associated optimization opportunities. The primary outcomes are the change in vend/fill ratio from baseline, change in medication stockout percentage from baseline. Results There was no significant difference in vend/fill ratio after the optimization phase compared with baseline [Difference 0.13 (11.56 ± 6.1 vs. 11.43 ± 5.41) respectively, (p=0.84)]. Medication stockout percentage was also found to be similar with baseline [Difference -0.05 (0.71% ± 0.12 vs. 0.76% ± 0.08) respectively, (p=0.37)]. For secondary outcomes, the change in blind stockout percentage from baseline was -0.04 [0.13 ± 0.02 vs. 0.17 ± 0.02, (p=0.004)] and the change in medications dispensed per day from baseline was 317 [2656 ± 143 vs. 2339 ± 200, (p=0.0002)]. Conclusion Optimization of automated dispensing cabinets yielded marginal improvements in vend/fill ratio and stockout percentage and significantly improved overall efficiency through an increase in the number of medications stocked in ADCs and number of medications dispensed per day from ADCs. Evaluation of more clinically significant performance indicators may better characterize the benefits from the optimization process.

Performance Analysis and Improvement of a B2B’s Supplier-Side E-Tender Driven Internal Process: A Discrete Event Simulation Study

In business-to-business (B2B), e-tendering is often mandated in situations where significant amount of money is involved in procurement of goods and services. This study focuses on Order-to-Cash (O2C), a sub-process of the larger Inquiry-to-Cash (I2C) process – an e-tender driven internal marketing process of a typical B2B company. The complete I2C is managed as two sub-processes – Inquiry-to-Order and Order-to-Cash. This study attempts to model, analyse, and improve the O2C sub-process of a typical original equipment manufacturer (OEM) using stakeholder-based discrete event simulation modelling (DESM). In recent times, engaging stakeholders in pre-model, model, and post-model coding stages is gaining attention which significantly impacts the study’s design as well as the interpretation of results. The existing and improved process is modelled using ARENA simulation software with inputs from stakeholders. In this study different scenarios are developed based on manpower combination of technical (T) and non-technical (N) skills in both existing and improved process to find out the near optimal scenario suitable for implementation by the organization. For optimal scenario in both existing and improved process, there is large improvement in key performance indicators (KPIs) such as waiting time, cycle time, and WIP (nos.) but with a trade-off in resource utilization of manpower acceptable to stakeholders.

The impact of a quality management program for patients undergoing head and neck resection with free-flap reconstruction: longitudinal study examining sustainability

BackgroundCare pathways (CPs) are helpful in reducing unwanted variation in clinical care. Most studies of CPs show they improve clinical outcomes but there is little known about the long-term impact of CPs as part of a sustained quality management program. Head and neck (HN) surgery with free flap reconstruction is complex, time-consuming and expensive. Complications are common and therefore CPs applied to this patient population are the focus of this paper. In this paper we report outcomes from a 9 year experience designing and using CPs in the management of patients undergoing major head and neck resection with free flap reconstruction.MethodsThe Calgary quality management program and CP design is described the accompanying article. Data from CP managed patients undergoing major HN surgery were prospectively collected and compared to a baseline cohort of patients managed with standard care. Data were retrospectively analyzed and intergroup comparisons were made.ResultsMobilization, decannulation time and hospital length of stay were significantly improved in pathway-managed patients (p = 0.001). Trend analysis showed sustained improvement in key performance indicators including complications. Return to the OR, primarily to assess a compromised flap, is increasing.ConclusionsCare pathways when deployed as part of an ongoing quality management program are associated with improved clinical outcomes in this complex group of patients.

Integrated project delivery implementation framework for water and wastewater treatment plant projects

Purpose The public nature of water and wastewater construction capital projects has rendered design-bid-build (DBB) as the delivery method of choice for such projects over the past years. Shortcomings inherent to DBB have had a negative effect on the key performance indicators (KPIs) of these projects. Numerous studies have been published about the benefits offered by integrated project delivery (IPD) in improving the delivery of DBB projects. Links correlating IPD principles to improvements in KPIs of DBB construction projects have not been established scientifically, thus leaving owners with no guidance on which IPD principle(s) to implement in order to improve a particular project KPI. The paper aims to discuss this issue. Design/methodology/approach Actual data were collected from 43 water and wastewater construction projects – including two control projects with full implementation of selective IPD principles – and used to compute major projects KPIs. Regression analysis and a focus group are then utilized to determine the effect of each implemented IPD principle on various project KPIs. Findings Implementation of open communication was found to have a significant effect on reducing project cost overrun, and the co-location of teams significantly reduced the time to respond to RFIs. Collaborative decision making significantly reduced the cost of field rework. Other IPD principles showed less-significant effects on project KPIs and were concluded to be ineffective at improving the projects’ performance. Practical implications An implementation framework is developed that can guide utility owners on which IPD principle(s) to implement in order to improve specific project KPIs. Originality/value This study demonstrates that not all IPD principles will result in performance improvement of a project. A selective and cost-effective implementation of certain IPD principle will have to be based on the nature of the project and the particular KPIs targeted for improvement.

Merenje i unapređenje indikatora ključnih performansi u savremenoj organizaciji

In the turbulent business conditions, modern systems of performance measurement is to provide high quality information management in the decision making process. Through key performance indicators to measure achievement of the vision, strategy and objectives of the company. When an organization chooses key performance indicators should take into account that these indicators provide information that will be measurable, reliable, realistic and usable for the implementation of appropriate corrective actions when performance is not compliant with the aim of organization. It is necessary for adequate attention to the improvement of key performance indicators with the aim of adapting and achieving better results in today's turbulent business conditions. There are a number of approaches for the development of key performance indicators, and companies should, in accordance with the business they do, identify approaches that would be most relevant to them. The basic tendency of contemporary organizations is harmonization of key performance indicators with modified business conditions, which is the basis for their improvement.

Evaluation of the impact of promotional campaign through a social networks on the key performance indicators of website for online of group-buying in Brazil

Relevance of the study: Based on data collection and analysis, present research made it possible to identify how the activities devised by a group-buying website on Facebook may exert influence on the KPIs for success.&#x0D; Purpose: The main task of present research is to answer the following question: can a digital social network be considered an effective tool for the improvement of key performance indicators (KPI) of a group-buying website?&#x0D; Findings: The research was conducted by considering data collected via mechanical observation using the computational tools Facebook Dashboard and Google Analytics. Data were analyzed using the means of comparison and a Pearson correlation coefficient, which demonstrated positive results of the campaign. When compared, the key performance indicators of the web site relating to Facebook displayed a larger dynamics than the general performance indicators of this web site. By the correlation coefficient, it was found that a higher power range of the Facebook Enterprise´s fan page could result in the increased traffic page hits of the examined web site, and an increase, mainly, in the number of new visitors.&#x0D; Originality / value: This paper analyzes some key performance indicators of a promotional campaign on Facebook for an online group-buying website in the city of Ribeirão Preto, São Paulo State, Brazil.&#x0D; Practical implications: Based on the collected data and performed analysis, it was found that the promotional activities on Facebook can increase the flow of new visitors and attract potential buyers to a group-buying website.&#x0D; Future research: It is recommended to perform further research for other social networks and in other countries.

Surgeon and Breast Unit Volume-Outcome Relationships in Breast Cancer Surgery and Treatment

To determine whether surgeon case volume and Unit case volume affected specific recognized key performance indicators (KPIs) of breast cancer surgical management. An increasing body of evidence suggests that a higher standard of cancer care, demonstrated by improved outcomes, is provided in high-volume units or by high-volume surgeons. The volume-outcome relationship pertaining to screen-detected breast cancers has yet to be thoroughly established and remains a pertinent issue in view of the debate surrounding breast cancer screening. The study population comprised all women with a new screen diagnosed breast cancer between 2004-2005 and 2009-2010. Surgeons' mean annual patient volumes were calculated and grouped as very low (<5), low (5-15), medium (16-49), or high volume (>50). The effect of breast screening unit volume was also evaluated. Statistical analyses were performed using Minitab V16.0 software (State College, PA) and R V2.13.0. There were 81,416 patients aged 61 (±6.8) years treated by 682 surgeons across 82 units. There were 209 very low-, 126 low-, 295 medium-, and 51 high-volume surgeons. The proportion of patients managed by very low-, low-, medium-, and high-volume surgeons was 1.2%, 6.9%, 65.5%, and 25.7%, respectively. Patients managed by high-volume surgeons were more likely to have breast-conserving surgery (BCS) than those managed by low-volume surgeons (P < 0.001). There was a higher proportion of sentinel lymph node biopsies (SLNB) performed by high-volume surgeons in invasive cancers (P = 0.005). High-volume units performed more BCS and SLNB than low-volume units (P < 0.001 and P < 0.001, respectively). Even in a setting with established quality control measures (KPIs) surgeon and unit volume have potent influences on initial patient management and treatment.

Free HUGS: Two years of sustained improvement in key performance indicators as a result of the FAST-HUGS-WIPP mnemonic device

Free HUGS: Two years of sustained improvement in key performance indicators as a result of the FAST-HUGS-WIPP mnemonic device