Process Performance Indicators Research Articles

Optimizing Player Management Processes in Sports: Translating Lessons from Healthcare Process Improvements to Sports

Abstract Typical player management processes focus on managing an athlete’s physical, physiological, psychological, technical and tactical preparation and performance. Current literature illustrates limited attempts to optimize such processes in sports. Therefore, this study aimed to analyze the application of Business Process Management (BPM) in healthcare (a service industry resembling sports) and formulate a model to optimize data driven player management processes in professional sports. A systematic review, adhering to PRISMA framework was conducted on articles extracted from seven databases, focused on using BPM to digitally optimize patient related healthcare processes. Literature reviews by authors was the main mode of healthcare process identification for BPM interventions. Interviews with process owners followed by process modelling were common modes of process discovery. Stakeholder and value-based analysis highlighted potential optimization areas. In most articles, details on process redesign strategies were not explicitly provided. New digital system developments and implementation of Business Process Management Systems were common. Optimized processes were evaluated using usability assessments and pre-post statistical analysis of key process performance indicators. However, the scientific rigor of most experiments designed for such latter evaluations were suboptimal. From the findings, a stepwise approach to optimize data driven player management processes in professional sports has been proposed.

ВЗАИМОСВЯЗЬ БУХГАЛТЕРСКОГО И СТАТИСТИЧЕСКОГО УЧЕТА ЛОГИСТИЧЕСКИХ ЗАТРАТ В ИНТЕГРИРОВАННОЙ СТРУКТУРЕ БИЗНЕСА

For economic entities, business partnerships are not based on individual competition or improving individual business processes, but on improving supply chains. When making management decisions, the question arises about the performance indicators of business processes both at the level of an economic entity and at the macro level. When assessing the effectiveness of supply chains, it is necessary to assess the costs arising in the course of financial and economic activities. The logistics component has a large share in the supply costs. A full accounting of logistics costs allows you to obtain reliable information for the analysis and development of measures to reduce them, which increases the efficiency of the entire economic activity of an economic entity. The article proposes a classification of logistics costs according to the degree of added value creation and dependence on the volume of production, including the allocation of transaction costs, which make up a significant share in the overall structure of logistics costs. The classification of logistics costs should be transformed depending on the organizational and legal form of the organization and the field of activity. The system of accounting for the logistics costs of economic entities should highlight them when implementing all logistics functions; study information about the most significant logistics costs and the nature of their occurrence; study the interdependencies of logistics costs throughout the entire supply chain. The developed system allows keeping separate accounting of logistics costs on a separate account, which makes it possible to control them. In modern conditions, organizations are faced with the need to integrate various types of accounting, including accounting, management and statistical. The algorithm for the implementation of this task includes four stages: development of a methodology for the integration of accounting, management and statistical accounting; formation of management accounting policies; development of data transformation regulations; selection of software for the automation of statistical, accounting and management accounting

Drill bit deterioration estimation with the Random Forest Regressor

Blastholes drilling performance is crucial for ensuring good performance of the whole excavation process, the correctness of which demands ‘healthy’ drill bit and appropriate behavior of an operator. Given the large volume of non-linear parameters describing the process, it appears reasonable to employ supervised learning methods to obtain drilling performance insights. Random Forest Regressor model has been trained on the dataset corresponding to correct performance of blastholes drilling and its hyperparameters have been tuned to obtain the highest possible accuracy. It has been later tested on three datasets corresponding to a good performance of drilling, and two cases of its non-optimal execution. Estimation errors are proposed to be used as bit technical state condition indicators (or more generally - process performance indicators). Root Mean Squared Error has been proven to differ significantly when compared estimation based on datasets corresponding to execution of drilling with ‘healthy’ drill bit, and its execution with worn-off one, however, it has been not sufficient to distinguish non-optimal drilling when additional feed pressure has been exerted by an operator to compensate the reduced pace of drilling. It has been, however, possible when the mean of absolute estimation errors has been used.

Determining Performance Metrics of Supply Chain Management in Make-to-Order Small-Medium Enterprise Using Supply Chain Operation Reference Model (SCOR Version 12.0)

Performance measurement in supply chain management is essential to facilitate the company to achieve effectiveness and efficiency to meet customer satisfaction. One of the models to measure performance in the supply chain is SCOR version 12. This model presents a business process framework, performance indicators, best practices, and unique technologies to support communication and collaboration between supply chain partners to increase the effectiveness of supply chain management and the effectiveness of supply chain improvements. This research used SCOR 12.0 to identify the performance metrics within the supply chain. A make-to-order small-medium enterprise (SMEs) in Yogyakarta, Indonesia, is the object of the research. We portrayed the business scope diagram by identifying the process elements in each tier (plan, source, make, deliver, return, enable) and decomposing each Process into performance attributes, i.e., Reliability, responsiveness, agility, cost, and asset management efficiency. We obtained three performance attributes (Reliability, responsiveness, and cost) based on observation and interviews, 52 performance metrics spread into 47 process elements. The SMEs can use the performance metric framework to measure supply chain management performance in make-to-order products.

Performance indicators for lifecycle process of public private partnership (PPP) projects in Malaysia

PurposeThis paper has two objectives. The first objective is to examine the important performance indicators of the lifecycle process of public private partnership (PPP) projects. The second objective is to investigate the difference in the perception of the importance of the performance indicators between the public and private sectors.Design/methodology/approachTo achieve the research objectives, the study used a questionnaire survey. The questionnaire was distributed via postal mail to officers of government departments and private sector companies who may have been involved in PPP projects. A total of 237 completed questionnaires were received, representing a 51.52% response rate. To examine the importance of performance indicators, the descriptive statistical tests of mean, standard deviation and mean score ranking were used. Independent t-tests were conducted to investigate the differences in the perceptions of the importance of performance indicators between the two respondent groups.FindingsThe findings show that all the 16 performance indicators are perceived as important and very important. The top five important performance indicators for a PPP project lifecycle process are “Time management”, “Contractual management”, “Cost management”, “Safety management” and “Effective risk management system”, while “stress or conflicts management” is the least important. In terms of the differences in the perception of the public and private sector groups, the results indicate that four indicators (“environment protection”, “cost management”, “effective risk management system” and “good work environment”), show a significant statistical difference between the perception of the public and the private sector respondents.Originality/valueThis study offers empirical evidence on key performance indicators for a PPP project that are crucial throughout its lifecycle as perceived by two key parties in a PPP contract, i.e. the public and the private sectors.

Experimental Investigation on Dry Routing of CFRP Composite: Temperature, Forces, Tool Wear, and Fine Dust Emission.

This article presents the influence of machining conditions on typical process performance indicators, namely cutting force, specific cutting energy, cutting temperature, tool wear, and fine dust emission during dry milling of CFRPs. The main goal is to determine the machining process window for obtaining quality parts with acceptable tool performance and limited dust emission. For achieving this, the cutting temperature was examined using analytical and empirical models, and systematic cutting experiments were conducted to assess the reliability of the theoretical predictions. A full factorial design was used for the experimental design. The experiments were conducted on a CNC milling machine with cutting speeds of 10,000, 15,000, and 20,000 rpm and feed rates of 2, 4, and 6 µm/tooth. Based on the results, it was ascertained that spindle speed significantly affects the cutting temperature and fine particle emission while cutting force, specific cutting energy, and tool wear are influenced by the feed rate. The optimal conditions for cutting force and tool wear were observed at a cutting speed of 10,000 rpm. The cutting temperature did not exceed the glass transition temperature for the cutting speeds tested and feed rates used. The fine particles emitted ranged from 0.5 to 10 µm aerodynamic diameters with a maximum concentration of 2776.6 particles for those of 0.5 µm diameters. Finally, results of the experimental optimization are presented, and the model is validated. The results obtained may be used to better understand specific phenomena associated with the milling of CFRPs and provide the means to select effective milling parameters to improve the technology and economics of the process.

Assessing and improving measurability of process performance indicators based on quality of logs

The efficiency and effectiveness of business processes are usually evaluated by Process Performance Indicators (PPIs), which are computed using process event logs. PPIs can be insightful only when they are measurable, i.e., reliable. This paper proposes to define PPI measurability on the basis of the quality of the data in the process logs. Then, based on this definition, a framework for PPI measurability assessment and improvement is presented. For the assessment, we propose novel definitions of PPI accuracy, completeness, consistency, timeliness and volume that contextualise the traditional definitions in the data quality literature to the case of process logs. For the improvement, we define a set of guidelines for improving the measurability of a PPI. These guidelines may concern improving existing event logs, for instance through data imputation, implementation or enhancement of the process monitoring systems, or updating the PPI definitions. A case study in a large-sized institution is discussed to show the feasibility and the practical value of the proposed framework.

Dual-Input Slope Seeking Control of Continuous Micro-Algae Cultures with Experimental Validation

This paper investigates the application of adaptive slope-seeking strategies to dual-input single output dynamic processes. While the classical objective of extremum seeking control is to drive a process performance index to its optimum, this paper also considers slope seeking, which allows driving the performance index to a desired level (which is thus sub-optimal). Moreover, the consideration of more than one input signal allows minimizing the input energy thanks to the degrees of freedom offered by the additional inputs. The actual process is assumed to be locally approachable by a Hammerstein model, combining a nonlinear static map with a linear dynamic model. The proposed strategy is based on the interplay of three components: (i) a recursive estimation algorithm providing the model parameters and the performance index gradient, (ii) a slope generator using the static map parameter estimates to convert the performance index setpoint into slope setpoints, and (iii) an adaptive controller driving the process to the desired setpoint. The performance of the slope strategy is assessed in simulation in an application example related to lipid productivity optimization in continuous cultures of micro-algae by acting on both the incident light intensity and the dilution rate. It is also validated in experimental studies where biomass production in a continuous photo-bioreactor is targeted.

Evaluation of the Performance of Mining Processes after the Strategic Innovation for Sustainable Development

The article summarizes the arguments within the scientific discussion about performance management in mining companies and their significance for obtaining competitiveness in the market of mining companies in the direction of sustainable development and economic growth. The main goal of the paper is to evaluate the performance indicators of mining processes after the implementation of strategic innovation—a new layout of the mining area focused on a combination of stationary and mobile mining equipment and their influence on the environment in a selected mining company in Slovakia in area of mining of limestone. Methods of research were focused on using economic indicators for the valuation of the efficiency and functionality of the mining processes. We used Pareto analysis for evaluation that points to critical mining processes and their significance in the financial area with orientation to costs, revenues. This research was used economic analysis with direction to efficiency, an indicator of cost and profit. Those indicators create a base for effective business in the mining area. The research empirically confirms that the new innovation of layout of mining place brings improvement of mining processes and indicators point to effective (over limit 0.70) and functional (over limit 0.90) mining processes in the year 2020. Pareto analysis showed the best processes (mining, expedition, transport, sorting) for financial benefits, the volume of production, demand, the satisfaction of customers, the cover needs of industries but at the same they are processes with high costs. Strategic innovation brought improvement too in the area of the environment. The results of the research can be useful for other mining companies in performance management and achievement mining market position.

Development and qualification of a representative scale-down model of automated Ficoll-based processing of a cell-based therapeutic according to quality by design principles

Background aimsThis article describes the development of a small-scale model for Ficoll-based cell separation as part of process development of an advanced therapy medicinal product and its qualification. Because of the complexity of biological products, their manufacturing process as well as characterization and control needs to be accurately understood. Likewise, scale-down models serve as an indispensable tool for process development, characterization, optimization and validation. This scale-down model represents a cell processor device widely used in advance therapies. This approach is inteded to optimise resources and to focus its use on process characterisation studies under the paradigm of the Quality by design. A scale-down model should reflect the large manufacturing scale. Consequently, this simplified system should offer a high degree of control over the process parameters to depict a robust model, even considering the process limitations. For this reason, a model should be developed and qualified for the intended purpose. MethodsProcess operating parameters were studied, and their resulting performance at full scale was used as a baseline to guide scale-down model development. Once the model was established, comparability runs were performed by establishing standard operating conditions with bone marrow samples. These analyses showed consistency between the bench and the large scale. Additionally, statistical analyses were employed to demonstrate equivalence. ResultsThe process performance indicators and assessed quality attributes were equivalent and fell into the acceptance ranges defined for the large-scale process. ConclusionsThis scale-down model is suitable for use in process characterization studies.

THE MANAGEMENT OF PROCESS LEAD TIME IN THE RELEASE OF STERILE PHARMACEUTICAL BATCHES: A CASE STUDY OF OPTIMIZATION IN AN OFFICIAL BRAZILIAN PHARMACEUTICAL LABORATORY

Medicines must comply with quality, safety, and efficacy pillars. Nowadays, organizations seek to incorporate new management models encouraged by quality program following the world trend regarding the technological revolution. The present research aims to improve the sterile pharmaceutical product batches release process, using the Failure Mode Effects Analysis (FMEA) method. This study addresses the gap in literature on quality risk management during batch release. The methodology uses a form adapted to the process in order to systematize the information, improving its comparison and analysis, thus estimating, the identification of potential failure modes and their effects on their performance. Made it possible to assign values for the severity, occurrence, and failure modes detection, to then determine the risk level and the priority of risk level. The results obtained showed the mitigation and elimination of failures in the process, as well as opportunities for improvement and causes of failures identification, improvement in the process performance indicators, greater reliability, and reduction in batch release time. Keywords: good manufacturing practices pharmaceutical industry, risk management, risk management tools

Defining the relationship between IT Service management and knowledge management: towards improved performance

ABSTRACT Regarding the continuous changes in Information Technologies and the necessity for managing them, IT service management (ITSM) should be enriched with knowledge management (KM) to align with the requirements of the digital era. The aim of this research is to identify the relationship between ITSM and KM in order to set up a foundation for designing an integrated ITSM–KM model. Unlike the previous endeavours that only considered the general relationship between ITSM and KM, we focus on the relationship between processes and activities of them in detail. We applied fuzzy cognitive map (FCM) to identify the relationships in both process and activity levels. Also, we specified the key performance indicators of ITSM processes which will be improved in response to this integration using FAHP technique. The results of this research indicate that the efficiency and effectiveness of ITSM processes will increase when they are integrated with KM activities.

Monitoring and Evaluation of the Production Process of Automotive Connectors

Paper deals with the regulation of the production process in the production of connectors for the automotive industry. Connectors are produced by spraying metal contacts with thermoplastic material using overmolding technology. For the analysis of the measurement system and the evaluation of the process performance, a qualitative characteristic was selected, namely the length dimension. The analyzed product is produced in an injection mold with two cavities. Products from individual cavities were analyzed separately. In the first phase, the analysis of the measurement system (MSA) was evaluated. In the second phase, process performance was evaluated using process performance indices. After the first phase, the measurement system was found to be satisfactory. In the second phase, it was found that the production process is not in very good condition. This means that the products produced in this process may show poor quality properties.

Artificial Neural Correlation Analysis for Performance-Indicator-Related Nonlinear Process Monitoring

In this article, a novel fault detection and process monitoring method referred to as artificial neural correlation analysis (ANCA) is proposed. Because nonlinear characteristics are common in complex industrial processes, the classic canonical correlation analysis (CCA) always perform poorly. Many scholars have noticed the nonlinear problem of the process and have also proposed some improved schemes, such as the kernel method. However, the selection of suitable parameters in the kernel method is extremely difficult, so most of the kernel learning methods are slightly unsatisfactory. Considering that the artificial neural network (ANN) can well extract the required feature components from the nonlinear data, we combined ANN and CCA from their respective principles, and proposed a new nonlinear monitoring method and the detailed gradient descent method derivation for the ANCA network is presented. In addition, we have designed two indices to monitor the changes of process variables and performance indicators. Finally, a numerical example, the Tennessee Eastman benchmark, and the Zhoushan thermal power plant process illustrate the superiority of the proposed method.

Catalytic hydrothermal carbonization of microalgae biomass for low-carbon emission power generation: the environmental impacts of hydrochar co-firing

This work aims to improve the synthesis of renewable hydrochar (HC) co-fired with coal to reduce grenhouse gas (GHG) emission. Acetic acid catalyzed hydrothermal carbonization (cHTC) of Chlorella vulgaris microalgae biomass was investigated based on a 33−1 fractional statistical design of the experiment to examine the effects of hydrothermal reaction temperature (T = 180–220 °C), biomass-to-suspension- (BSR = 5–25 wt.%), and catalyst-to-suspension (CSR = 0–10 wt.%) ratios on process performance indicators. Analysis of variance was used to assess the experimental data. The results show that the application of homogeneous catalyst improves the fuel ratio and energy recovery efficiency up to 0.38 and 36.3%. Ex-ante cradle-to-gate life cycle assessment was performed to evaluate the impacts of co-firing ratio (CFR) and hydrochar quality on multi-perspective mid-, and endpoint environmental indicators. The highest decarbonization potential (−1.54 kg CO2,eq kWh−1) is achieved using catalytic hydrochar biofuel produced at 195 °C, 25 wt.% BSR, and 8 wt.% CSR levels. The application of catalytic and autocatalytic hydrochar blends improves the overall environmental impacts and greenhouse gas footprint of solid fuel firing facilitating the transition toward low-carbon emission power generation.

Integrated Design of Working Fluid Mixtures and Absorption Refrigeration Cycles

This work presents a CAMD (computer-aided molecular design) approach for the design of working fluid mixtures used in ABR (absorption refrigeration) cycles. Compared to previous works, the proposed approach introduces two major improvements. It employs for the first time an ABR process model in the course of CAMD, hence enabling the evaluation of the generated mixtures considering process performance indicators. It enables for the first time the simultaneous generation and evaluation of molecular structures for both refrigerants and absorbents. The employed model and CAMD optimization problem formulation incorporates major ABR operational driving forces pertaining to efficient refrigeration, sufficient solubility of mixture components and ease of separation in the generator. The approach employs a multicriteria assessment methodology both during CAMD and for the evaluation of selected mixtures using a more rigorous ABR model at a second stage. The work identifies novel mixtures, with Acetaldehyde/2-Methoxyethyl acetate and Acetaldehyde/Methanediol exhibiting the highest performance. The latter exhibits 3% higher COP (coefficient of performance) and cooling output than the reference mixture NH3/H2O, whereas it operates at 87 and 89% lower high and low cycle pressures. The novel mixtures are also compared with novel mixtures previously identified through a heuristic approach by the authors. The latter mixtures indicate overall higher ABR performance but similar or worse performance in safety, health and environmental impact indices. Further performance improvements may be achieved by including into CAMD additional chemical groups to be able to simulate the complex absorbent structures available in published works.

Sorbitol production optimization in B2B industry with six sigma approach

The purpose of this study is to examine quality control in the sorbitol industry using the six-sigma method to determine the causes of wasteful production costs and factors that influence them. This research collected 300 data with the details of 150 data in each phase, before and after the implementation of improvement action, and interviewed 12 employees in the sorbitol department. Six sigma approach consists of five phases: define tested by statistic descriptive, measure analyzed by process capability, analyze phase analyzed by fishbone diagram and action priority matrix, improve analyzed by process capability, and control phase tested by X Bar R chart. The results of this study are DPMO (Defect Per Million Opportunity) decreased from 994,497 to 543,058, sigma level increased from - 1.04 to 1.39, standard deviation from 0.0072196 to 0.01, Pp (Process Performance) from 0.11 to 0.22, Ppk (Process Performance Index) from - 0.85 to 0.09, average reducing sugar from 0.11 to 0.14, and the use of nickel from 298 to 221 Kg.

KINERJA SUPPLY CHAIN MANAGEMENT GLASS EEL (BENIH SIDAT) UNTUK MENUNJANG INDUSTRI PEMBESARAN SIDAT DI PALABUHANRATU

The utilization of glass eel (eel seeds) in the Cimandiri River Palabuhanratu has a very important role to support the supply chain for the eel culture industry. The purpose of this study was to estimate the amount of glass eel catch, describe the glass eel eel supply chain system, and measure the performance of the supply chain in eel culture. The research method uses quantitative methods through direct observation and interviews, as well as case studies of supply chain actors. The results showed that the highest catch of glass eel in 2019 was in June at an average of 14,859 grams (14.9 kg). The highest catch of glass eel occurred in the dry season (June - August), with a total catch of 21,884 grams (21.8 kg). The glass eel supply chain mechanism consists of supply chain flow pattern on the product flow, a good and transparent flow of information on each link, and the distribution of two payment systems, namely cash and credit payments. The supply chain performance in the eel culture industry obtained a performance value of 82.63 which indicates that the achievement of the company's supply chain performance is in the good category, and improvements need to be made especially on the deliver process performance indicators with a value of 2.70.
 Keywords: : glass eel, supply chain, eel, Cimandiri River

Regression and ANFIS-based models for predicting of surface roughness and thrust force during drilling of biocomposites

Biocomposites are increasingly being applied in the automotive industry. For assembly purpose, drilling process is often performed in these biocomposites parts to produce holes. The machining behavior of biocomposites is affected by the matrix, the reinforcements (nature and size), by the cutting conditions and the mechanical properties of biocomposites. Some of the newly developed biocomposites are made of a polypropylene/polyolefin blend matrix, which has low thermal conductivity and low softening temperature. Therefore, the selection of cutting parameters should be done with care to avoid thermal-induced damages occurred during drilling biocomposites. Also, due to the heterogeneous and anisotropic properties of biocomposites, it is essential to develop models for predicting the effects of these factors on the machining process performance indicators. In this research work, a full factorial design is used to investigate the effects of machining parameters (feed rate, drill diameter, spindle speed, and reinforcement type) on machinability indicators during the drilling of the three new biocomposites: Polypropylene/Polyolefin matrix reinforced with biocarbon particles and/or miscanthus fibers. The regression and ANFIS-based models are established for predicting thrust force (Ft) and surface roughness (Ra) based on empirical data. The results obtained show that, for each tested biocomposite, the proposed ANFIS models have better predictive accuracy for thrust force and surface roughness than the regression-based models. Furthermore, the drilling parameters and reinforcements used in these biocomposites considerably affect their machinability.

The study of palm kernel shell updraft gasification for supplying heat to an asphalt mixing plant

Air-gasification converts biomass using air into combustible gaseous components which also known as producer gas. The combustion of those gases provides energy for any purposes. In an asphalt mixing plant, this technology is implemented using palm kernel shell as feedstock to generate heat for aggregate heating as a part of hot-mixed asphalt production. Both Cold Gas Efficiency (CGE) and Carbon Conversion Efficiency (CCE) are the common performance indicators of such process. This study examines a performance of an updraft fixed-bed gasifier to convert the shell into producer gas in supplying heat for increasing aggregate temperature from ambient to about 145 – 165°C at capacity of 800 kg/min. The feedstock flowrate was set at 990 kg/hour and equivalence ratios were varied at 0.04, 0.05 and 0.08. Because the gasification reactions were in equilibrium, the producer gas composition for calculating the CGE was predicted using thermodynamic model with minimizing Gibbs free energy. The feedstock and solid residue of the process were analysed to determine the carbon content for calculating the CCE. It is indicated that the performance of the gasification operation for supplying heat in an asphalt mixing plant is limited by operating conditions.