Process Performance Indicators Research Articles

An HPLC-SEC-based rapid quantification method for vesicular stomatitis virus particles to facilitate process development

Virus particle (VP) quantification plays a pivotal role in the development of production processes of VPs for virus-based therapies. The yield based on total VP count serves as a process performance indicator for evaluating process efficiency and consistency. Here, a label-free particle quantification method for enveloped VPs was developed, with potential applications in oncolytic virotherapy, vaccine development, and gene therapy. The method comprises size exclusion chromatography (SEC) separation based on high-performance liquid chromatography (HPLC) instruments. Ultraviolet (UV) was used for particle quantification and multi-angle light scattering (MALS) for particle characterization. Consistent recoveries of over 97% in the SEC were achieved upon mobile phase screenings and addition of bovine serum albumin (BSA) as sample stabilizer. A calibration curve was generated, and the method's performance and applicability to in-process samples were characterized. The assay’s repeatability variation was < 1% and its intermediate precision variation was < 3%. The linear range of the method spans from 7.08×108 to 1.72×1011 VP/mL, with a limit of detection (LOD) of 7.72×107 VP/mL and a lower limit of quantification (LLOQ) of 4.20×108 VP/mL. The method, characterized by its high precision, requires minimal hands-on time and provides same-day results, making it efficient for process development.

Effective management of business processes in the modern world

The article examines the key aspects of effective business process management in modern conditions. Emphasizes the importance of analyzing and optimizing work processes as the key to increasing company productivity and profitability. Provides an overview of the benefits of business process management, including increased efficiency, reduced costs, and improved customer service. A systematic approach to building such a system is described, including process mapping, implementation of regulations and the use of modern technologies. The impact of enterprise modernization and organizational changes on the efficiency of business processes is also considered. In addition, attention is paid to the role of people in this process, emphasizing the importance of staff training and developing a culture of continuous improvement. Finally, the article analyzes Key Performance Indicators of business processes and recommendations for conducting internal audit.

Predictive Ppk calculations for biologics and vaccines using a Bayesian approach - a tutorial.

In pharmaceutical manufacturing, especially biologics and vaccines manufacturing, emphasis on speedy process development can lead to inadequate process development, which often results in less robust commercial manufacturing process after launch. Process performance index (Ppk) is a statistical measurement of the ability of a process to produce output within specification limits over a period of time. In biopharmaceutical manufacturing, progression in process development is based on Critical Quality Attributes meeting their specification limits, lacking insight into the process robustness. Ppk is typically estimated after 15-30 commercial batches at which point it may be too late/too complex to make process adjustments to enhance robustness. The use of Bayesian statistics, prior knowledge, and input from Subject matter experts (SMEs) offers an opportunity to make predictions on process capability during the development cycle. Developing a standard methodology to assess long term process capability at various stages of development provides several benefits: provides opportunity for early insight into process vulnerabilities thereby enabling resolution pre-licensure; identifies area of the process to prioritize and focus on during process development/process characterization (PC) using a data-driven approach; and ultimately results in higher process robustness/process knowledge at launch. We propose a Bayesian-based method to predict the performance of a manufacturing process at full manufacturing scale during the development and commercialization phase, before commercial data exists. Under Bayesian framework, limited development data for the process of interest at hand, data from similar products, general SME knowledge, and literature can be carefully formulated into informative priors. The implementation of the proposed approach is presented through two examples. To allow for continuous improvement during process development, we recommend to embed this approach of using predictive Ppk at pre-defined commercialization stage-gates, for example, at completion of process development, prior to and completion of PC, prior to technology transfer runs (Engineering/Process Performance Qualification, PPQ), and prior to commercial specification setting.

Modelling and optimisation of power consumption and productivity in milling of thin-walled parts

A high percentage of mechanical parts in the metalworking industry (automotive, medical, energetic sector, and etc.), refers to thin-walled structures with optimized shapes and dimension, which produce on high or semi batch production principle. Production processes management, in mentioned industry and production systems, requires the development of appropriate information technologies based automatic systems. Planning and control systems are based on computer-aided technologies. Basis of this system are mathematical-logical models commonly, which is quantitatively link between input process parameters and process performance indicators (output parameters). In this study, modelling of total power consumption and process productivity was performed. Total power consumption and the process productivity were modelled, as very important output indicators in efficient and sustainable production. The modelling is based on the experimental analysis of the machining process during machining of thin-walled parts made of C45E (AISI 1045) carbon steel. Modelling was performed using the least squares method, with the use of ANOVA statistical analysis for the purpose of defining the input factors significance. Depth of cut, milling width and feed per tooth were used as input cutting parameters. Input cutting parameters combined experimental analysed according to Taguchi experimental plan. Based on developed adequate and sufficiently accurate mathematical models, optimization procedure was performed. The goal of optimisation was to minimize the power consumption and maximise productivity.

Optimizing [formula omitted] capture in pressure swing adsorption units: A deep neural network approach with optimality evaluation and operating maps for decision-making

This study presents a methodology for surrogate optimization of cyclic adsorption processes, focusing on enhancing pressure swing adsorption units for carbon dioxide (CO2) capture. We developed and implemented a multiple-input, single-output (MISO) framework comprising two deep neural network (DNN) models, predicting key process performance indicators. These models were then integrated into an optimization framework, leveraging particle swarm optimization (PSO) and statistical analysis to generate a comprehensive Pareto front representation. This approach delineated feasible operational regions (FORs) and highlighted the spectrum of optimal decision-making scenarios. A key aspect of our methodology was the evaluation of optimization effectiveness. This was accomplished by testing decision variables derived from the Pareto front against a phenomenological model, affirming the surrogate model’s reliability. Subsequently, the study delved into analyzing the feasible operational domains of these decision variables. A detailed correlation map was constructed to elucidate the interplay between these variables, thereby uncovering the most impactful factors influencing process behavior. The study offers a practical, insightful operational map that aids operators in pinpointing the optimal process location and prioritizing specific operational goals.

Design and development of a digital diagnostic clinical pathway: evidence from an action research study

PurposeThe primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.Design/methodology/approachThe methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.FindingsThe methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.Originality/valueThe study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.

A Bottom-Up Methodology for Identifying Key Performance Indicators for Sustainability Monitoring of Unit Manufacturing Processes

With growing environmental concerns and regulatory requirements, manufacturers are increasingly required to monitor and reduce the environmental impacts of their production processes. Despite increasing digitalization and data-collection capabilities, manufacturers are challenged in collecting the right data and framing process improvement targets. To address this challenge, this paper presents a bottom-up methodology based on the life cycle assessment for identifying performance indicators with the goal of monitoring and reducing the overall environmental impacts of a manufacturing process. More specifically, process performance indicators are defined as a set of controllable process parameters, and their suitability for sustainability monitoring is evaluated based on their sensitivity, measurability, actionability, reliability, timeliness, and human-centricity with respect to a chosen environmental impact category. The bottom-up formulation of process performance indicators is demonstrated through a real-world case study on an infeed centerless grinding process in a large manufacturing company. Results from the case study show that the process performance indicators with regards to climate change impacts included (i) reduction in grinding time, (ii) reduction in total grinding power, (iii) reduction in sparkout time, and (iv) increase in batch size.

Geographical Information System Modeling for Planning Internal Transportation in a Manufacturing Plant’s Outdoor Area

A geographical information system (GIS) is an advanced tool for collecting, managing, and analyzing spatially-referenced data. The contribution of GIS use to process performance indicators can be improved by combining it with multi-criteria decision analysis (MCDA). Combining a GIS and MCDA is, in the scientific literature, rarely discussed for planning an internal transportation system in a manufacturing plant’s outdoor area. The purpose of this article is to clarify what mangers can expect from using a combined approach when deciding on a transport fleet and the operational routing of vehicles. Beside the simulation of MCDA, the computer software ArcGIS Pro 3.0.2 with the Network Analyst extension was used for modelling the transportation system in the form of a case study. The article demonstrates the feasibility and effectiveness of GIS and MCDA use and reveals the extent of the challenge of how decision makers could make the most of ArcGIS functionality. The final solution for an internal transportation system in a manufacturing plant’s outdoor area includes such a vehicle fleet and the set time windows of orders for transport services, so that there are no violations of time windows and the work is completed within the work shift while minimizing costs, time, and distance. Decision makers can use the program without advanced knowledge of optimization approaches, following a procedure that does not differ much from that of learning to use other business software tools. On the contrary, the listed disadvantages can be summarized as the rigidity of setting detailed boundary conditions for a specific simulation scenario.

Statistical process control and PDCA for quality improvement in the mexican automotive industry

Despite the advancements witnessed in the realm of quality improvement, Statistical Process Control (SPC) perseveres as a universally embraced and extensively employed tool within manufacturing companies across the globe. This enduring popularity can be attributed to its adherence to the time-tested principles established by Shewhart, which have withstood the test of time since their inception in 1924. Over the years, SPC has consistently proven its efficacy in optimizing manufacturing processes, enhancing product quality, and fostering continuous improvement initiatives. Quality is an utmost necessity in the automotive industry, which ranks as the 3rd most significant industry in Mexico and the 5th globally in terms of volume and transactional activity. This research focuses on a successful real case study that demonstrates the enhancement of capability in a critical machining operation within an automotive plant located in Mexico, specifically dedicated to truck engine remanufacturing. This improvement was achieved through the analysis of x̅ and R control charts, followed by the implementation of the Plan-Do-Check-Act (PDCA) cycle. The results obtained were derived from a comparative analysis of data from two distinct time periods in the machining process of bushing diameters. This analysis revealed a significant increase in process capacity, with a 13.08% improvement in the Process Capability Index (Cpk) and a 17.2% enhancement in the Process Performance Index (Ppk). The decrease in expected out-of-specification parts showed a remarkable improvement of 72.7%. Additionally, there was an enhancement in process variability, with a notable 8.33% reduction in the mean range. The significance of this research lies in its empirical identification of gaps and opportunities within the realms of quality assurance and quality control, utilizing methodologies for quality improvement. By uncovering these gaps and opportunities, this study provides valuable insights that can guide future efforts to enhance quality practices and drive continuous improvement in the field.

Enhancing Process Stability and Quality Management: A Comprehensive Analysis of Process Capability Indices

Process Capability Indices such as Process Capability Index (Cp) and Corrected Process Capability Index (Cpk), along with Process Performance Index (Pp) and Process Performance Corrected Index (Ppk), are most commonly used tools in quality management within manufacturing processes. They determine whether a process is capable of producing products within established tolerance limits, addressing both short-term and long-term variability. Despite widespread use, the analysis of Process Capability Indices often overlooks special variability within processes, which may lead to misleading interpretations of a process's capability, especially when the determination of tolerance limits is inadequately conducted. This article aims to verify the hypothesis that current analyses of Process Capability Indices fail to consider special variability, which could mislead the interpretation of a process's ability to meet its specifications. Statistical analyses were conducted using the Minitab software, based on dynamic viscosity measurements from the production process of solvent-based paint, to explore the implications of special variability on the interpretation of Process Capability Indices. The study revealed that while Process Capability Indices are useful for identifying quality management opportunities, their effectiveness is limited when special variability is present, often resulting in misinterpretations of a process's true capabilities. The findings highlight the need for methodologies that incorporate considerations of all forms of variability to ensure accurate process capability assessments. This gap in traditional analyses can affect the strategic decision-making in quality management, suggesting a critical area for further research and methodological development. The research confirms the need for a revised approach in analyzing Process Capability Indices, advocating for advanced methods that accurately reflect all forms of variability to improve quality management practices. Future research should focus on developing these methodologies to ensure more reliable and effective use of Process Capability Indices in quality management.

МОДЕЛЮВАННЯ ПОКАЗНИКІВ ЕФЕКТИВНОСТІ ЗВЕДЕННЯ ЖИТЛОВОГО КОМПЛЕКСУ «АВІНЬЙОН»

This article is dedicated to modeling performance indicators of the comprehensive process of constructing a multi-story building while varying influencing factors, taking into account existing constraints, and utilizing modern software complexes. During the modeling process, the following solution for wall construction was employed as a component of the comprehensive building process: the installation of lightweight steel thin-walled structures (LSTW) with non-removable formwork cladding and filling with polystyrene concrete. Previous research has established that this construction and technological solution is the most effective among the compared alternatives. Building upon prior investigations, experimental-statistical modeling of the «construction duration», «construction cost», «average intensity of funding», and «maximum intensity of funding» indicators was performed, varying factors such as the «overlap coefficient of processes» and «number of working hours per week». Modeling was conducted using the COMPEX software complex. The values of efficiency indicators were obtained according to selected experimental plans by constructing economic (budgetary calculations) and organizational-technological (work performance schedules) models using the AVK 5 and Microsoft Project software complexes, respectively. During the experimental-statistical modeling, extremes of efficiency indicators and corresponding values of factor combinations influencing them were determined. The nature and degree of the factors' impact on the investigated efficiency indicators were identified. The «construction cost» indicator was subjected to an existing constraint in the form of using credit funds. The calculation of the impact of imposing this existing constraint was performed in the Microsoft Excel software complex. While varying the factorial space, diagrams of changes in the budgetary cost of building construction and the amount of funds needed to secure the loan were constructed. Combining these diagrams resulted in values for the «construction cost with consideration of credit funds». It was determined that the use of credit funds at a level of 90% significantly increases the cost of building construction. On average, this increase amounted to 183,8 million UAH. Graphical dependencies were constructed using the CorelDraw X6 software complex.

Including CFD rigorous models in the optimal design of carbon capture plants through trust-region methods

Efficient technology alternatives are required to fulfill the environmental goals of reducing CO2 emissions at short and medium term. In this context, carbon capture using hollow fiber membrane contactors is an interesting alternative due to its operational flexibility, modularity, and high interfacial area. To elucidate the true potential of this technology, cutting-edge modeling and optimization techniques must be applied to improve key process performance indicators. In the present study, we develop a CFD rigorous model for a MEA-based absorber using membrane contactors. This model is included as an external function in the formulation of an optimization problem through the trust-region filter method, to minimize the CO2 avoided cost of the capture plant. As results, we obtain an optimal design with a CO2 avoided cost of 54.16 $/t-CO2 and a specific energy demand of 3.52GJ/t–CO2.

In situ process analytical technology for real time viable cell density and cell viability during live-virus vaccine production

Viable cell density (VCD) and cell viability (CV) are key performance indicators of cell culture processes in biopharmaceutical production of biologics and vaccines. Traditional methods for monitoring VCD and CV involve offline cell counting assays that are both labor intensive and prone to high variability, resulting in sparse sampling and uncertainty in the obtained data. Process analytical technology (PAT) approaches offer a means to address these challenges. Specifically, in situ probe-based measurements of dielectric spectroscopy (also commonly known as capacitance) can characterize VCD and CV continuously in real time throughout an entire process, enabling robust process characterization. In this work, we propose in situ dielectric spectroscopy as a PAT tool for real time analysis of live-virus vaccine (LVV) production. Dielectric spectroscopy was collected across 25 discreet frequencies, offering a thorough evaluation of the proposed technology. Correlation of this PAT methodology to traditional offline cell counting assays was performed, in which VCD and CV were both successfully predicted using dielectric spectroscopy. Both univariate and multivariate data analysis approaches were evaluated for their potential to establish correlation between the in situ dielectric spectroscopy and offline measurements. Univariate analysis strategies are presented for optimal single frequency selection. Multivariate analysis, in the form of partial least squares (PLS) regression, produced significantly higher correlations between dielectric spectroscopy and offline VCD and CV data, as compared to univariate analysis. Specifically, by leveraging multivariate analysis of dielectric information from all 25 spectroscopic frequencies measured, PLS models performed significantly better than univariate models. This is particularly evident during cell death, where tracking VCD and CV have historically presented the greatest challenge. The results of this work demonstrate the potential of both single and multiple frequency dielectric spectroscopy measurements for enabling robust LVV process characterization, suggesting that broader application of in situ dielectric spectroscopy as a PAT tool in LVV processes can provide significantly improved process understanding. To the best of our knowledge, this is the first report of in situ dielectric spectroscopy with multivariate analysis to successfully predict VCD and CV in real time during live virus-based vaccine production.

CO2 Adsorption on Biomass-Derived Carbons from Albizia procera Leaves: Effects of Synthesis Strategies.

CO2 capture is a useful strategy for controlling the risks associated with global warming. The design of an adsorbent is essential for clean and potentially energy-efficient adsorption-based carbon capture processes. This study reports a facile and moderately temperature single-stage combined pyrolysis and activation strategy for the synthesis of nitrogen-doped carbons for high-performance CO2 capture. Using nitrogen-rich Albizia procera leaves as the precursor and carrying out single-stage pyrolysis and activation at temperatures of 500, 600, and 700 °C in the presence NaHCO3 as an activating agent, carbons with different surface characteristics and ultrahigh weight percentage (22-25%) of nitrogen were obtained. The subtle differences in surface characteristics and nitrogen content had a bearing on the CO2 adsorption performance of the resultant adsorbents. Outstanding results were achieved, with a CO2 adsorption capacity of up to 2.5 mmol/g and a CO2 over N2 selectivities reaching 54. The isotherm results were utilized to determine the performance indicators for a practical vacuum swing adsorption process. This study provides a practical strategy for the efficient synthesis of nitrogen-doped carbons for various adsorption applications.

Методическая основа комплексной оценки результативности бизнес-процессов в управлении качеством предприятия

The article explains the specifics of forming a methodology for the comprehensive assessment of key perfor-mance indicators of business processes in quality management. The relevance of the study is due to the fact that the existing methods of foreign and domestic scientists for calculating and evaluating business process indicators do not affect their relationship with the quality management of enterprises and do not describe the procedure for their automation. In addition, the author argues for the methodology of comprehensive assess-ment of key performance indicators of business processes of the quality management system, consisting of several stages: identification of processes, determination of their performance, expert evaluation of indicators, carrying out calculations according to specified formulas. An example of automating the calculation of key per-formance indicators of the business process “monitoring and measurement” of an industrial enterprise is pre-sented. This methodology allows to consider the strengths and weaknesses of the enterprise’s business pro-cesses for further development of corrective action plans to improve the quality management system.

Process Capability and Performance Indices for Discrete Data

Process capability and performance indices (PCIs and PPIs) are used in industry to provide numerical measures for the capability and performance of several processes. The majority of the literature refers to PCIs and PPIs for continuous data. The aim of this paper is to compute the classical indices for discrete data following Poisson, binomial or negative binomial distribution using various transformation techniques. A simulation study under different situations of a process and comparisons with other existing PCIs for discrete data are also presented. The methodology of computing the indices is easy to use, and as a result, one can have an assessment of the process capability and performance without difficulty. Three examples are further provided to illustrate the application of the transformation techniques.

Performance indicators of medical processes with strategic alignment in patient care

Performance indicators of medical processes with strategic alignment in patient care

Tribological aspects of nano-particle fluid based cooling and lubrication in milling process

The introduction of different additives into the cooling and lubrication systems in the metal cutting process, contributed to improving the machining process sustainability. Different types of additives can help to reduce friction, reduce wear of contact surfaces, improve heat dissipation from the cutting zone, etc. With the development of nanotechnology, great focus has been placed on the investigation and using of nano-particles, nano-additives and nanofluids in industry. In this paper, the effects of using the nano-particle based cooling and lubrication fluid are investigated. There are performed comparison effects of standard cooling and lubrication fluids versus nanofluids based cooling and lubrication on cutting process performance. Molybdenum disulphide particles are used as nano-particles, which dispersed in a standard cooling and lubrication fluid. As workpiece was used alloyed tool steel. Milling process performance indicators was analysed based on Taguchi experimental plan, for different cooling and lubrication condition and process parameters. Cutting forces component in three perpendicular axes, and machined surface parameters were measured. Experimental analysis has shown that the use of nano-particles, which mixed in a standard cooling and lubrication fluid, significantly contributes to the reduction of cutting forces, friction coefficients, more efficient cutting and generation of machined surfaces.

Anaerobic digestion of untreated manure: Environmental risk assessment of resultant digestates

Anaerobic digestion (AD) of poultry and goat manure was performed to estimate the biochemical methane potential (BMP) and biosafety of the digestates using standard analytical and microbiological methods. The effects of residence time (RT) on BMP, process performance indicators, potential pathogens and indicator bacteria reduction as well as heavy metals concentrations were determined. The experiment was performed in a semi-batch mode at mesophilic temperature of 30 ± 0.2°C using 20 L prototype biodigesters over 45 days RT. The cumulative biogas yield from goat manure (31,703 ml/gVS) was &gt; yield from poultry manure (30,275 ml/gVS). The process performance indicators after digestion revealed a minimal variation in pH (6.0 to 7.5) with notable reduction in total solids (55.0% in goat manure &gt;50.6% in poultry manure) and volatile solid (56.1% in goat manure &gt; 44.2% in poultry manure). Besides methanogens (Methanothrix, Methanobacterium and Methanosarcina species), Bacillus (100%) and Clostridium (87.5%) species were the most predominant bacterial genera. Sanitary assessment revealed a significant reduction (p &lt; 0.05) of indicator and potentially pathogenic bacteria at residence time ≥ 30 days. At 45 days RT, faecal coliform, Staphylococcus and Vibrio species were undetected in both poultry and goat manure digestates, while total coliforms (3.6 log CFU/ml) and Salmonella count (3.2 log CFU/ml) in poultry manure digestate were above tolerable limit. A negligible amount (p &lt; 0.05) of heavy metals was observed with higher zinc and copper concentrations in poultry and goat manure respectively. Extension of residence time and/or further treatment is critical to ensure digestate meets the United States EPA/EU permissible limit of 3.0 log CFU/ml before farmland application

Data mining in traditional Chinese medicine product quality review

The traditional Chinese medicine(TCM) enterprises have accumulated a large amount of product quality review(PQR) data. Mining these data can reveal the hidden knowledge in production and helps improve pharmaceutical manufacturing technology. However, there are few studies involving the mining of PQR data and thus enterprises lack the guidance to analyze the data. This study proposed a method to mine the PQR data, which consisted of 4 functional modules: data collection and preprocessing, risk classification of variables, risk evaluation by batches, and the regression analysis of quality. Further, we carried out a case study of the formulation process of a TCM product to illustrate the method. In the case study, the data of 398 batches of products during 2019-2021 were collected, which contained 65 process variables. The risks of variables were classified according to the process performance index. The risk of each batch was analyzed through short-term and long-term evaluation, and the critical variables with the strongest impact on the product quality were identified by partial least square regression. The results showed that 1 variable and 13 batches were of high risk, and the critical process variable was the quality of the intermediates. The proposed method enables enterprises to comprehensively mine the PQR data and helps to enhance the process understanding and improve the quality control.