Process Performance Indicators Research Articles

A Machine Learning and Fuzzy Logic Model for Optimizing Digital Transformation in Renewable Energy: Insights into Industrial Information Integration

The most essential criteria to improve digital transformation in renewable energy projects should be identified. This situation helps the companies to use limited financial budgets and human resources in the most efficient way. Therefore, a new study is needed to analyze the performance indicators of the digital transformation process in renewable energy projects. Accordingly, this study aims to identify the most significant performance indicators of digital transformation for these projects. A three-stage machine learning and fuzzy logic-based decision-making model has been constructed in this process. The first stage includes the weight calculation of the experts by dimension reduction methodology. Secondly, essential factors of digital transformation in renewable energy projects are examined via Fermatean fuzzy criteria importance through intercriteria correlation (CRITIC). The final part consists of the ranking of emerging seven countries with Fermatean fuzzy weighted aggregated sum product assessment (WASPAS). On the other side, combined compromise solution (CoCoSo) method is also taken into consideration in this process to make a comparative evaluation. The main contribution of this study is the generation of novel machine learning and fuzzy logic integrated decision-making model to make evaluation related to the digital transformation of renewable energy projects. In this model, machine learning technique is used to determine the importance weights of the experts. Similarly, integrating Fermatean fuzzy numbers with CRITIC and WASPAS techniques also contributes to the literature by minimizing the uncertainty and identifying the relationship between the items. The findings demonstrate that employing qualified personnel plays the most critical role in increasing digital transformation in renewable energy projects. Additionally, government support is very critical in the successful implementation of digital transformation processes in renewable energy projects.

Parametric investigation of friction stir welding of aluminum alloy and Inconel 718 using finite element analysis

Friction Stir Welding (FSW) has emerged as a prominent technique for joining metallic materials, offering advantages in both similar and dissimilar material combinations. Despite its effectiveness, further advancements are needed to address industry demands and enhance the understanding of the FSW process. Welding nickel-based superalloys like Inconel 718 poses specific challenges, with existing literature suggesting that achieving successful welds requires high axial forces and precise process parameters, limiting its practical application in industries. To tackle these issues, this research proposes the utilization of 3D finite element models integrating multiphysics aspects, encompassing material flow and heat transfer mechanisms such as conduction, convection, and radiation. These models were validated against existing experimental data and employed to analyze temperature and strain distributions within the heat affected zone and weld nugget. The findings offer insights into the impact of various process parameters, including rotational speed, welding speed, normal force, cooling rate, and the effect of induction preheating, on key performance metrics like temperature profiles, grain size distribution, microhardness, and stress evolution. The outcomes underscore a notable correlation between process variables and performance indicators, facilitating a comprehensive understanding of the FSW process dynamics.

Data-driven Performance Indicators for SAGD Process of Oil Sands Using Support Vector Regression Machine with Parameter Optimization Algorithm

Abstract The rapid and accurate forecasting of performance in the Steam-Assisted Gravity Drainage (SAGD) process for oil sands is crucial for the reasonable design of the development plan. This study aims to address this need by presenting novel data-driven performance indicators based on support vector regression (SVR), a machine learning method that complements the traditional physics-driven approach. During the SAGD process, steam is injected into the reservoir to heat the bitumen, reducing its viscosity, and allowing it to flow towards a lower well where it can be collected. The performance of the SAGD process depends on various factors such as steam injection rate, reservoir heterogeneity, and operating conditions. Accurately forecasting the performance of the SAGD process can help optimize these parameters and improve the overall efficiency of oil sands recovery. The data-driven performance indicators proposed in this study utilize the SVR method to establish a relationship between input parameters and the desired performance outputs. In the constructing process, some parameter optimization algorithms, like grid search method, particle swarm optimization algorithm and genetic algorithm, are used to identify the optimal SVR model structure. The validation results show that the design meets the desired objectives. All in all, through proposed data-driven performance indicators, the performance of SAGD process in candidate oil sands projects could be rapidly and easily obtained.

Spatio-temporal feature extraction network based multi-performance indicators synergetic monitoring method for complex industrial processes

In the context of smart manufacturing, modern industrial processes are becoming increasingly complex in terms of process flows, product varieties, and performance indicators (PIs). Performance-driven process monitoring attracts extensive attentions in recent years. However, most methods require spatio-temporal correspondence between process variables and PIs, and rarely consider the correlation among various PIs. In this paper, a spatio-temporal feature extraction network-based multi-performance indicators synergetic monitoring framework is presented. Firstly, considering the missing data in PI measurements, a weighted sum of tensor nuclear norm (WSTNN) based batch-process data completion approach is developed, which can adeptly handle local missing and incomplete data issues and establish the spatio-temporal correspondence for subsequent modeling. Secondly, for a specific PI, a new canonical variate analysis embedded spatio-temporal convolutional network (CVA-STCN) is designed to extract the PI-related features with spatio-temporal dependency. Thirdly, considering the dynamic interaction of multiple PIs, a third-order feature tensor is established to perform the future fusion, and the correlations among various PI-related features are explored via tensor decomposition. Finally, a hierarchical multi-performance indicators synergetic monitoring model is developed over several subspaces. The proposed method is verified on Tennessee Eastman process and an actual hot strip mill process. Overall, the monitoring performance of the proposed method outperforms the traditional ones, in terms of higher fault detection rates and lower false alarm rates. Moreover, the information provided by the multi-subspace synergetic monitoring charts can offer valuable guidance to field engineers.

Pork meat production: Proposal for monitoring indicators based on life cycle assessment

This study carried out a Life Cycle Assessment (LCA) of the pork production process based on a detailed Life Cycle Inventory (LCI) of inputs and outputs of 3 years of the process, from the entrance of the live animal to the final packaged products. The Life Cycle Impact Analysis (LCIA) was carried out using the Environmental Footprint method to observe which impact categories are most relevant in environmental terms, excluding impacts related to production of the living animal. The results of the LCIA made it possible to identify the most relevant impact categories, such as Acidification, Climate change, Freshwater ecotoxicity, Freshwater and Marine eutrophication, Resource and Water use. The impacts detected supported the definition of critical Key Performance Indicators (KPI) and Process Performance Indicators (PPIs) in environmental aspects, establishing a cause-and-effect relationship with the organization's strategic objectives. A model for monitoring environmental impacts with established indicators was developed based on the LCA study of the pork processing. The proposed monitoring indicators were identified based on the environmental impacts and this industrial sector can utilize these findings to adopt a more sustainable process.

Influence of inoculum on process parameters and microbial communities during anaerobic digestion of cattle manure: Insights from metabarcoding analysis

Linking the variety of microbial ecology attributes to system functioning is critical to improve operational strategies and to support microbial pathways, in order to conceive efficient and reliable anaerobic digestion process. In the present study, we conducted a direct comparison between two digesters regarding the process parameters and microbial community dynamics between two digesters: one containing only the substrate (cattle manure) (Digester S), and the other (Digester F) enriched with an inoculum. This study aimed to investigate taxonomic and functional changes in the digester microbiome using a 16S rRNA sequencing approach. Results exhibit that the bacterial richness, diversity, and regularity were higher in Digester F. This later showed also a higher biogas yields (BY) and the process performance indicators compared to Digester S. The BY obtained in digester F was 2.17 times higher than that obtained in digester S. Furthermore, significant correlations were obtained between biogas yields (BY) and microbial communities with Firmicutes/Bacillota (Clostridium, Enterococcus, Streptococcus, Ruminococcaceae, Mogibacterium, and Turicibacter), Actinobacteria (Corynebacterium), and Bacteroidetes (Bacteroides, Paludibacter) in the AD systems. Conversely, in Digester S, biogas yield (BY) was highly significant and positively correlated with the abundance of Actinobacteria (Rhodococcus) and Proteobacteria (Moraxella, Oxalobacter). Moreover, the inoculum in digester F resulted in the enrichment of main bacterial functions involved in biogas production. This study provides results that justify the changes in the structure of the microbial community and the functional strategy used by the most representative micro-organisms in the consortia that carried out this process.

Evaluating the impact of periodic product review on pharmaceutical production process improvement: A case study

The aim of this study was to evaluate the impact of Periodic Product Review (PPR) on improvements in the production process of pharmaceuticals by statistically analyzing the in-process control of the parameters average weight by coating and hardness. Methods. For this purpose, a case study was conducted in a pharmaceutical industry analyzing the statistical PPR data of a coated tablet from 2021 and 2022. Diagrams and tables based on probability and statistical theory were used for the analysis. Results. The data analysis revealed that PPR was effective in helping to identify problems in the production process, which were then resolved. As a result, the production performance of this drug product was significantly improved, as evidenced by the reduction in the variability of analytical results and process performance indicators within the acceptable range. Conclusion. The results of this work show that PPR is an important tool for ensuring the quality of pharmaceutical products. Statistical analysis of PPR data makes it possible to identify trends and patterns that may indicate the need for improvements in the production process.

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.

Optimizing Metformin HCl manufacturing: A Six Sigma approach to assess process capability

A process capability analysis uses indices such as process capability, process capability index, process performance index, and process performance to evaluate the output of an in-control process in relation to stated restrictions. In the instance of Metformin HCl tablets, the goal of a Six Sigma study is to enhance the method for creating perfect pills while ensuring maximum customer satisfaction. Process capability is critical for meeting industry standards, reducing variation, and ensuring product quality before commercialization, consequently streamlining and cost-effectively streamlining the inspection process. The control and capacities of antidiabetic pills are described in this study, with a focus on data collection and process capability analysis utilizing statistical tools such as MINITAB 21.2. The results show that the process capabilities for several tests-hardness, thickness, disintegration, dissolution, and friability exceed the 3.64 threshold. Finally, based on this analysis, the current process capability is rated appropriate, showing its capability to fulfill industry standards effectively.

Methods for evaluating and improving manual process data acquisition in one-of-a-kind productions

The One-of-a-kind production is characterized by manual and specialized manufacturing processes. Human adaptability is essential here. As a result, production data acquisition is mainly manual. In many cases, the recorded data are inaccurate or even subject to errors. Consequently, the opportunities for process optimization are impeded. This article therefore analyzes data quality problems and their causes. Subsequently, methods for the evaluation of process data such as process mining and performance indicators are considered and applied to production data sets. The comparison enables the derivation of organizational measures. The result is a practice-oriented method for continuously checking the data quality of manual production data acquisition.

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.