Multiple Key Performance Indicators Research Articles

Introduction of ultra-hypofractionation in breast cancer: Implications for costs and resource use

PurposeA shift towards (ultra-)hypofractionated breast irradiation can have important implications for the practice of contemporary radiation oncology. This paper presents a systematic analysis of the impact of different fractionation schedules on multiple key performance indicators, namely resource use, costs, work times, throughput and waiting times. Materials and MethodsTime-driven activity-based costing (TD-ABC) is applied to calculate the costs and resources consumed where the perspective of the radiotherapy department in adopted. Three fractionation regimens are considered: ultra-hypofractionation (5 x 5.2 Gy, UHF), moderate hypofractionation (15 x 2.67 Gy, HF) and conventional fractionation (25 x 2 Gy, CF). Subsequently, a discrete event simulation (DES) model of the radiotherapy care pathway is developed and scenarios are compared in which the following factors are varied: distribution of fractionation regimens, patient volume and operating hours. ResultsThe application of (U)HF can permit radiotherapy departments to reduce the use of scarce resources, realise work time and cost savings, increase throughput and reduce waiting times. The financial advantages of (U)HF are, however, reduced in cases of excess capacity and cost savings may therefore be limited in the short-term. Moreover, although an extension of operating hours has favourable effects on throughput and waiting times, it may also reduce cost differences between fractionation schedules by increasing the capacity of resources. ConclusionBy providing an in-depth analysis of the consequences associated with a shift towards (U)HF in breast cancer, the present study demonstrates how a DES model based on TD-ABC costing can assist radiotherapy professionals in making data-driven decisions.

Determining safety aspects of Differential Speed Limit on Indian Expressway

Optimization of operating speed limit on any class of road is the biggest task faced by the transportation experts. A variety of speed control or management techniques are available all around the globe to minimize the frequency of over-speeding or for increasing the acceptability of the posted speed limit. In this study, a comparison is carried out between Uniform Speed Limit (USL) and Differential speed Limit (DSL) on an Expressway in India, using microsimulation package PTV VISSIM. In USL, a single speed limit is implemented for all class of vehicles whereas DSL strategy have two distinct speed limit for two different class of vehicles i.e., passenger carrying vehicles and goods carrying vehicles. Multiple Key Performance Indicators (KPIs) were considered. Results suggested that DSL seems to be the better strategy on the roads accommodating high speeds like Expressways under heterogenous conditions. Four out of five KPIs were showing the results in favor of DSL strategy in comparison to USL strategy. KPIs like speed variance, Road Safety Index (RSI), no. of vehicles having Deceleration-Rate-to-Avoid-Collision (DRAC) > 3.35 m/sec2 and no. of lane changes were showcasing better results in case of DSL strategy in comparison to USL strategy.

A thermal performance standard for residential buildings in warm climates: Lessons learned in Brazil

This paper presents the main challenges faced and lessons learned when developing a new method to assess the thermal performance of residential buildings for the recently updated Brazilian building performance standard, NBR 15575–1:2021. It touches on subjects related to the representation of occupant behavior, climate, and thermal acceptability in building performance simulation, as well as the use of reference models, key performance indicators, and performance levels in policies. NBR15575 provides a thorough procedure to analyze thermal performance, allowing buildings to be assessed while in passive operation mode and also accounting for energy needs when active operation is necessary. Multiple key performance indicators are introduced to provide a comprehensive evaluation. The main difficulty found was dealing with diversity. Given the vast scale of Brazil’s territory, a high variation in culture, climate and construction techniques is expected, thus requiring a compromise between detail and scalability. There are still many opportunities for improvement, especially regarding the representation of occupants, and adjustments of the characteristics of reference models to promote high thermal performance, considering the diverse climates, regional practices, and economic needs. This paper might help researchers and other stakeholders to develop and improve other local standards and protocols, especially for warm climates.

A multi-dimensional performance assessment framework for microgrids concerning renewable penetration, reliability, and economics

Aligning with the government energy policies for achieving carbon neutrality, microgrids have been receiving increasing attention recently due to their economic and environmental potential. To facilitate their widespread applications, quantitative and effective performance assessment methods and tools at the planning and design stage are needed. These methods are highly valuable to provide insights for decision-makers regarding their multiple key performance indicators, including renewable penetration, reliability, and economics. This study, therefore, aims to develop a multi-dimensional performance assessment framework for the convenient assessment of microgrids concerning their key performance indicators. An empirical cost model is developed based on the Latin hypercube sampling (LHS) method, which can effectively reduce the computation cost and achieve acceptable accuracy, compared with the conventional exhaustive method. A vacation hotel microgrid on a remote island in Hong Kong is selected to test and verify the effectiveness of the assessment framework. Results show that the proposed framework can effectively quantify the overall microgrid performance concerning renewable penetration, reliability, and economics. Proper renewable energy penetration (about 60%) can achieve a good economic performance concerning different reliability requirements. Enlarging the backup power generation capacity solely is not a reasonable measure to enhance the system reliability, which may result in a significant increase of the overall cost (i.e., up to 6 times).

Performance Evaluation of Random Access Methods for IoT-over-Satellite

Low Earth orbit (LEO) satellite constellations are currently being explored to provide global and seamless coverage for IoT-over-Satellite applications. Random access techniques require low transmission overhead providing a compatible route for IoT-over-Satellite applications, however, coming at the expense of the offered quality-of-service. In this paper, we develop a realistic uplink performance framework that incorporates many practical parameters such as the satellite availability, packet collision and interference, Doppler shift, and impairments experienced in a typical Satellite-to-Ground channel. The framework is capable of assessing multiple key performance indicators of the overall IoT-over-Satellite random access system. The performance is presented in terms of the bit error rate, packet error rate, and the energy wasted per IoT device. To emulate a realistic IoT-over-Satellite network, LoRa modulated traffic is first generated and injected into the Satellite-to-Ground channel. The results indicate high resistance to Doppler shifts even without any Doppler correction and provide some resistance to highly congested environments.

Innovative PEDRERA Model Tool Boosting Sustainable and Feasible Renovation Programs at District Scale in Spain

In accordance with the new recovery plan, Next Generation EU (NGEU), and the need to speed up the transition of cities towards a new sustainable model, this paper provides an overview of the outcomes of the PEDRERA project, which is focused on the development of a novel tool able to calculate multiple key performance indicators that can support renovation actions at the district level, according to a Positive Energy District (PED) concept. The new tool is programmed in Python programming language and is useful to evaluate several strategies for the renovation of existing building stock. It moves from a quick list of input according to several Public Private Partnership (PPP) models, in addition to other potential business models. Furthermore, the design of the model is supported by a step-by-step methodology in order to deal with a “financial appraisal” that is interactive in each context, customizable for each stakeholder, and user-friendly. The paper describes this innovative tool and reports on the stronger potential that this model can offer when it runs in a QGIS software environment and interacts with a PostgreSQL database, as demonstrated in two case studies located in Spain.

Multi-method simulation approach: multiple restaurants to multiple customers on-demand food delivery services in Dubai

ABSTRACT Since early 2020, the COVID-19 outbreak has disrupted various supply chains including the on-demand food delivery sector. As a result, this service industry has witnessed a tremendous spike in demand that is affecting its delivery operations at the downstream level. Previous research studies have explored one-to-one and many-to-one solutions to the virtual food court delivery problem (VFCDP) to optimize on-demand food delivery services in different cities. However, research efforts have been limited to multiple restaurant orders from only one customer which does not apply to traditional systems where multiple customers request on-demand food delivery from multiple restaurants. This study rigorously analyses multiple restaurants to multiple customers (Many-to-many) food delivery simulation models in ideal weather conditions that are constrained with multiple key performance indicators (KPIs) such as delivery fleet utilization (the number of couriers utilized over the fleet size), average order delivery time, and fuel costs. This research also benchmarks the on-demand food delivery queueing methodologies using system dynamics and agent-based simulation modeling where three on-demand food delivery routing methodologies are simulated including First-in-First-Out (FIFO), Nearest, and Simulated Annealing using AnyLogic. The results suggest that the Many-to-many (Nearest) method outperforms other delivery routing methods which would have positive implications on optimizing existing food delivery systems and managerial decisions.

A data-driven method for unsupervised electricity consumption characterisation at the district level and beyond

A bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical learning analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information. To demonstrate the validity of this methodology, the characterisation of the electricity consumption of the whole province of Lleida, located in northeast Spain, is implemented and tested. The geographical aggregation level considered is the postal code since it is the highest data resolution available through the open data sources used in the research work. The development and the experimental tests are supported by a web application environment formed by interactive user interfaces specifically developed for this purpose. The paper’s novelty relies on the application of statistical data methods able to infer the main energy performance characteristics of a large number of urban districts without prior knowledge of their building characteristics and with the use of solely measured data coming from smart meters, cadastre databases and weather forecasting services. A data-driven technique disaggregates electricity consumption in multiple uses (space heating, cooling, holidays and baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from this disaggregated energy uses to obtain the energy characterisation of the buildings within a specific area. The potential reuse of this methodology allows for a better understanding of the drivers of electricity use, with multiple applications for the public and private sector.

What Affects Dewatering Performance of High Density Slurry?

The dewatering of flocculated high density slurry presents a significant challenge to most mining industries. The new technologies to treat high density slurry require a consistent and robust flocculation method in order to enter the market of tailings management. The flocculation of high density slurry, however, due to its complexity, is always a challenge to be undertaken appropriately and to evaluate the dewatering performance correctly. This paper probes the complexity by using a torque-controlled mixing technique to demonstrate the influence of feed properties, polymer type, polymer dosage, and mixing conditions on dewatering performance. The study shows that flocculant should be dosed at the optimal range to achieve the highest dewatering performance. A full dosage responsive curve including under dosage, optimal dosage, and overdosage is critical to evaluate the dewatering performance of high density slurries and flocculants. The mixing conditions such as mixing speed, mixing time, and geometry of the mixing impeller affect the flocculation efficacy. It was found that the dewatering performance of high density slurry is sensitive to solids content, water chemistry, and clay activity. High sodicity and high clay activity in the high density slurry decreases the dewatering performance. Therefore, it is critical to evaluate flocculants across multiple feeds and dosages with replication in order to select optimal dewatering performance. Using multiple key performance indicators (KPIs) to build technical and economic criteria is also critical for polymer evaluation.

A Data-Driven Method for Unsupervised Electricity Consumption Characterisation at the District Level and Beyond

A bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical learning analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information. To demonstrate the validity of this methodology, the characterisation of the electricity consumption of the whole province of Lleida, located in northeast Spain, is implemented and tested. The geographical aggregation level considered is the postal code since it is the highest data resolution available through the open data sources used in the research work. The development and the experimental tests are supported by a web application environment formed by interactive user interfaces specifically developed for this purpose. The paper's novelty relies on the application of statistical data methods able to infer the main energy performance characteristics of a large number of urban districts without prior knowledge of their building characteristics and with the use of solely measured data coming from smart meters, cadastre databases and weather forecasting services. A data-driven technique disaggregates electricity consumption in multiple uses (space heating, cooling, holidays and baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from this disaggregated energy uses to obtain the energy characterization of the buildings within a specific area. The potential reuse of this methodology allows for a better understanding of the drivers of electricity use, with multiple applications for the public and private sector.

Comparing train driving strategies on multiple key performance indicators

The driving strategy of train drivers has a large impact on the energy consumption. In recent studies the focus was on calculating the optimal eco-driving strategy, and measuring the exact amount of energy used during train runs. However, energy consumption is not the only key performance indicator that affects the operational performance of train or freight operating companies. In this study we define a set of key performance indicators, relevant to train operation, that are specific, measurable, assignable, realistic and time-related, and that are influenced by the driving strategy of the driver. These key performance indicators are safety, timeliness, energy consumption, workload of the driver, the environment, cost of maintenance and brand image. We chose four driving strategies that are most used in daily practice or most studied in the literature to assess these key performance indicators. Per key performance indicator we defined evaluation criteria to measure the impact of a driving strategy. We then defined key characteristics (e.g. track length, gradients), and conditions (e.g. speed restrictions, and load factor) based on which we defined test scenarios for three different train types. We then used the Radau Pseudospectral Method for solving the various optimal train control problems to compute the effect of the driving strategies on most of the key performance indicators. Our findings show amongst others, that a maximal coasting strategy causes the least environmental pollution, and in most scenarios its energy consumption coincided with the optimal energy-efficient train control strategy or it had an energy efficiency close to the optimal one. Furthermore, we found that on other key performance indicators there are differences between the driving strategies (e.g. in cost of maintenance), which should be considered when choosing a preferred driving strategy. Our results enable train and freight operating companies to make an informed decision when choosing a preferred driving strategy for their drivers, or when choosing a Driver Advisory System that supports this preferred driving strategy.

Stochastic Optimization Models for Risk-Based Reservoir Management*

The paper provides an overview of publications on reservoir management and formulates a novel stochastic dynamic optimization model for water balance management in the area affected. The proposed stochastic optimization approach allows multiple key performance indicators such as agriculture and energy production, wetland water and flood protection, biodiversity preservation, and reservoir storage. The two-stage feature of the proposed model induces safety constraints on water supply known as chance conditions in stochastic optimization: safety constraints in nuclear energy, stability constraints in insurance business, or constraints on the Conditional Value-at-Risk (CVaR) in finance. The original nonlinear, nonconvex and often discontinuous model can be reduced to linear programming problems.

Development of a central order processing system for optimizing demand-driven textile supply chains: a real case based simulation study

Nowadays, the demand of small-series production and quick response become more and more important in textile supply chains. To meet the increasing trend of customization in garment production, forecast based supply chain model is not suitable any more. Demand-driven garment supply chain is developed and employed more and more. However, there are still many defects in current model for demand-driven supply chains, e.g. long lead time, low efficiency etc. Therefore, in this study we proposed a new collaborative model with central order processing system (COPS) to optimize current demand-driven garment supply chain and improve multiple supply chain performances. Common and important supply chain collaboration strategies, including resource sharing, information sharing, joint-decision making and profit sharing, were merged into this system. Discrete-event simulation technology was utilized to experiment and evaluate the new collaborative model under different conditions based on a real case in France. Multiple key performance indicators (KPIs) were examined for the whole supply chain and also for individual companies. Based on the simulation experiment results, we found that new proposed collaborative model gain improvements in all examined KPIs. New model with COPS performed better under high workload condition than under low workload condition. It can not only increase overall profit level of the whole supply chain but also individual profit level of each company.

Simulation-based Six Sigma value function for system-level performance assessment and improvement

PurposeThe purpose of this paper is to develop a simulation-based value function (VF) that combines multiple key performance indicators (KPIs) into a unified Sigma rating (SR) for system-level performance assessment and improvement.Design/methodology/approachSimulation is used as a platform for assessing the multiple KPIs at the system level. A simple additive VF is formed to combine the KPIs into a unified SR using the analytical hierarchy process and the entropy method. Value mapping is utilized to resolve the conflict among KPIs and generate a unified value. These methods are integrated into the standard Six Sigma define-measure-analyze-improve-control (DMAIC) process.FindingsSimulation results provided the Six Sigma DMAIC process with system-level performance measurement and analysis based on multiple KPIs. The developed VF successfully generated unified SRs that were used to assess various performance improvement plans.Research limitations/implicationsThe accuracy and credibility of the results obtained from using the proposed VF are highly dependent on the availability of pertinent data and the accuracy of the developed simulation model.Practical implicationsThe proposed approach provides Six Sigma practitioners and performance mangers with a mechanism to assess and improve the performance of production and service system based on multiple KPIs when conducting Six Sigma studies.Originality/valueThis paper contributes to the previous research by handling multiple KPIs in Six Sigma studies conducted at the system level using simulation and VF. The research also provides guidelines for using the different methods of weights assessment to form the VF within the DMAIC process.

K-PdM: KPI-Oriented Machinery Deterioration Estimation Framework for Predictive Maintenance Using Cluster-Based Hidden Markov Model

Explosive increase of industrial data collected from sensors has brought increasing attractions to the data-driven predictive maintenance for industrial machines in cyber-physical systems (CPSs). Since machinery faults are always caused by performance deterioration of components, learning the deteriorating mode from observed sensor data facilitates the prognostics of impeding faults and predicting the remaining useful life (RUL). In modern CPSs, several key performance indicators (KPIs) are monitored to detect the corresponding fine-grained deteriorating modes of industrial machines. However, the overall deterioration estimation and RUL prediction based on these KPIs with various patterns have been a great challenge, especially without labels of deteriorating index or uninterpretable of root causes. In this paper, we proposed K-PdM , a cluster-based hidden Markov model for the machinery deterioration estimation and RUL prediction based on multiple KPIs. The method uncovers the fine-grained deteriorating modes of machines through each unlabeled KPI data and learns a mapping between each deteriorating KPI index and RULs. Accordingly, an overall deterioration estimation and RUL prediction of machine are able to be achieved based on the combination of each KPI’s deterioration estimation. Moreover, a set of interpretable semantic rules are setup to analyze the root cause of performance deterioration among KPIs. An experimental application is proposed to demonstrate its applicability based on the PHM08 data sets. The obtained results show their effectiveness to predict the RULs of machines.

119 Development of a Clinical Performance Dashboard to Empower Resident Education

To develop an emergency medicine (EM) resident clinical dashboard to address the need for more objective and formative feedback in medical education. This dashboard contains high-impact visual displays of select performance measures for EM residents based on data extracted from the electronic medical record (EMR). This was a feasibility study conducted at a four-year EM residency program at an academic tertiary care center. Resident performance data from July-Nov 2016 were extracted from the EMR. Residents’ names were replaced with a random two-letter code (“AA,” “DE,” etc) to protect resident confidentiality. A business analytics software was used to calculate 4 performance measures, or key performance indicators (KPIs). KPIs were designed to 1) reflect state or nationally reported quality metrics; 2) represent metrics supported by the American College of Emergency Physicians (ACEP) Clinical Emergency Data Registry (CEDR); or 3) represent Accreditation Council for Graduate Medical Education (ACGME) EM Milestones. These KPI were coded into an interactive interface to create the EM resident clinical Dashboard. Data filters were added to allow visualization based on resident training level, patient acuity, and ED location. Descriptive statistics were calculated for each KPI. We developed a prototype interactive clinical dashboard measuring the following KPIs: admission rate, time to disposition and time to discharge. Interactive graphs were generated for each KPI, and residents were ranked and color-coded by tertile for easy visual interpretation (see accompanying figure for example). Different colors were assigned to the top-performing tertile (lowest admission rates, shortest disposition and discharge times, and highest sepsis protocol compliance), the middle tertile, and the bottom tertile. For all patients, the average admission rate was 20% for PGY1s, 23% for PGY2s, 29% for PGY3s, and 23% for PGY4s. average time to disposition in minutes was 338 for PGY1s, 218 for PGY2s, 226 for PGY3s, and 216 for PGY4s. Average Time to Discharge in minutes was 436 for PGY1s, 305 for PGY2s, 310 for PGY3s, and 272 for PGY4s. In this feasibility study, we demonstrated that an EM Clinical resident dashboard can be generated from EMR data to display and manipulate resident performance measures across multiple KPIs. We hope that such a Dashboard can be further developed as an innovative tool to enhance and supplement usual feedback in order to improve resident learning and career preparedness. Future research plans include a pre-post analysis of resident behavior after incorporation of the dashboard into ACGME-mandated performance review sessions, the inclusion of additional KPIs such as opioid prescribing practices and CT utilization, and using objective dashboard data to quantify measures of competency within the ACGME Milestones.

The Impact of Joint Commission International Healthcare Accreditation on Infection Control Performance: A Study in Dubai Hospital

Objective - The United Arab Emirates (UAE) Vision 2021 aims to achieve a world-class healthcare system and the national agenda has set multiple Key Performance Indicators (KPIs) to accomplish accreditation by all healthcare facilities in the country by 2021. The purpose of this study was to assess the impact of Joint Commission International (JCI) accreditation on Infection Control (IC) KPIs in Dubai Hospital (DH). Methodology/Technique - A longitudinal case study covering 2007 to 2013 was conducted to examine this impact based on secondary data of the KPIs during this period and semi-structured interviews with senior health officials. Findings - The study reveals that the healthcare accreditation acts as a catalyst for implementing and monitoring changes but it also underscores some challenges. It proposes recommendations such as improving surveillance systems, ensuring proper resources allocation, and undertaking further research. Novelty - The study recommends improving the surveillance and reporting systems at the national level through frequent internal surveys. Type of Paper - Empirical Keywords: Dubai; Infection Control; Joint Commission International Accreditation; Key Performance Indicators; United Arab Emirates. JEL Classification: H51, I11, P46

Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication

The prediction and key factors identification for lot Cycle time (CT) and Equipment utilization (EU) which remain the Key performance indicators (KPI) are vital for multi-objective optimization in semiconductor manufacturing industry. This paper proposes a prediction methodology which predicts CT and EU simultaneously and identifies their key factors. Bayesian neural network (BNN) is used to establish the simultaneous prediction model for Multiple key performance indicators (MKPI), and Bayes theorem is key solution in model complexity controlling. The closed-loop structure is built to keep the stability of MKPI prediction model and the weight analysis method is the basis of identifying the key factors for CT and EU. Compared with Artificial neural network (ANN) and Selective naive Bayesian classifier (SNBC), the simulation results of the prediction method of BNN are proved to be more feasible and effective. The prediction accuracy of BNN has been obviously improved than ANN and SNBC.

Production and maintenance planning: an integrated approach under uncertainties

The current paper aims to develop an integrated plan for maintenance and production. It simultaneously determines production lot size, assembly-specific preventive maintenance schedule, and job sequencing for a multicomponent machine so as to optimize the total cost of operation. The work considers an environment which is characterized by stochastic processes and uncertainties related to demand, supply, machine yield, etc. The paper also evaluates the value of proposed approach over conventional operations planning policies for various machine and maintenance parameters. This evaluation is performed at multiple level of decision making such as strategic level, operational level, and tactical level. The paper further extends this evaluation for multiple key performance indicators such as machine availability, total number of failure, and total cost of operations to demonstrate the superiority of proposed approach.

Coordinating and Evaluating of Multiple Key Performance Indicators for Manufacturing Equipment: Case Study of Distillation Column

Manufacturing equipment takes the task of operation and directly effects on the manufacturing process. One single Key Performance Indicator (KPI) is mainly employed to evaluate equipment in most studies, neither integrating the KPIs into a completed evaluation system nor considering the impact and conflict among KPIs. In this paper, a KPI evaluation architecture is presented to define and analyze KPIs, and then a common structure for KPI to obtain the KPI set of manufacturing equipment is introduced. An available multi-KPI coordination model is proposed to discern and balance the relationship among multi-KPI. Finally, a case study is introduced to illustrate the applicability of the coordination model by using multi-objective optimization strategy and an efficient solution is obtained.