Abstract In this study, the manufacturing process of lamivudine (3TC) and zidovudine (AZT) tablets (150+300 mg respectively) was evaluated using statistical process control (SPC) tools. These medicines are manufactured by the Fundação para o Remédio Popular “Chopin Tavares de Lima” (FURP) laboratory, and are distributed free of charge to patients infected with HIV by the Ministry of Health DST/AIDS national program. Data of 529 batches manufactured from 2012 to 2015 were collected. The critical quality attributes of weight variation, uniformity of dosage units, and dissolution were evaluated. Process stability was assessed using control charts, and the capability indices Cp, Cpk, Pp, and Ppk (process capability; process capability adjusted for non-centered distribution; potential or global capability of the process; and potential process capability adjusted for non-centered distribution, respectively) were evaluated. 3TC dissolution data from 2013 revealed a non-centered process and lack of consistency compared to the other years, showing Cpk and Ppk lower than 1.0 and the chance of failure of 2,483 in 1,000,000 tablets. Dissolution data from 2015 showed process improvement, revealed by Cpk and Ppk equal to 2.19 and 1.99, respectively. Overall, the control charts and capability indices showed the variability of the process and special causes. Additionally, it was possible to point out the opportunities for process changes, which are fundamental for understanding and supporting a continuous improvement environment.

Statistical Process Control Software Market size was valued at USD 943.25 Million in 2024 and is projected to reach USD 2151.93 Million by 2031, growing at a CAGR of 11.98% from 2024 to 2031.

Statistical Process Control Software Market Drivers

Quality Assurance and Improvement: Increasing emphasis on quality control and continuous improvement in manufacturing and production processes drives the demand for SPC software. Organizations use SPC to monitor and control process variations, ensuring consistent product quality and reducing defects.

Regulatory Compliance: Many industries, such as pharmaceuticals, automotive, aerospace, and food and beverage, are subject to strict regulatory standards and quality requirements. SPC software helps organizations comply with these regulations by providing tools for monitoring and documenting process performance.

Industrial Automation and Industry 4.0: The rise of industrial automation and the implementation of Industry 4.0 technologies have increased the adoption of SPC software. These technologies rely on real-time data analysis and process control to optimize manufacturing operations and improve efficiency.

In this paper, we show that concept of Statistical Process Control tools was thoroughly examined and the definitions of quality control concepts were presented. This is significant because of it is anticipated that this study will contribute to the literature as an exemplary application that demonstrates the role of statistical process control (SPC) tools in quality improvement in the evaluation and decision-making phase.

This is significant because of this study is to investigate applications of quality control, to clarify statistical control methods and problem-solving procedures, to generate proposals for problem-solving approaches, and to disseminate improvement studies in the ready-to-wear industry. The basic Statistical Process Control tools used in the study, the most repetitive faults were detected and these faults were divided into sub-headings for more detailed analysis. In this way, it was tried to prevent the repetition of faults by going down to the root causes of any detected fault. With this different perspective, it is expected that the study will contribute to other fields.

We give consent for the publication of identifiable details, which can include photograph(s) and case history and details within the text (“Material”) to be published in the Journal of Quality Technology. We confirm that have seen and been given the opportunity to read both the Material and the Article (as attached) to be published by Taylor & Francis.

Data for benchmarking SPC against other process monitoring methods. The data consist of a one-dimensional timeseries of floats (x.csv). Addititionally information whether the data are within the specifications are provided as another time series (y.csv). The data are generated by solving an optimization problem for each time to generate a mixture distribution of different probability distributions. Then for each timestep one record is sampled. Inputs for the optimization problem are the given probability distributions, the lower and upper limit of the tolerance interval as well as the desired median of the data. Additionally weights of the different probability distributions can be given as boundary condions for the different time steps. Metadata generated from the solving are stored in k_matrix.csv (wheights at each time step) and distribs (probability distribution objects). The data consists of phases with data from a stable mixture distribution and phases with data from a mixture distribution that do not fulfill the stability criteria.

Datasets to the planned publication "Generalized Statistical Process Control via 1D-ResNet Pretraining" by Tobias Schulze, Louis Huebser, Sebastian Beckschulte and Robert H. Schmitt (Chair for Intelligence in Quality Sensing, Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aachen University)

Data for benchmarking SPC against other process monitoring methods. The data consist of a one-dimensional timeseries of floats (x.csv). Addititionally information whether the data are within the specifications are provided as another time series (y.csv). The data are generated by solving an optimization problem for each time to generate a mixture distribution of different probability distributions. Then for each timestep one record is sampled. Inputs for the optimization problem are the given probability distributions, the lower and upper limit of the tolerance interval as well as the desired median of the data. Additionally weights of the different probability distributions can be given as boundary condions for the different time steps. Metadata generated from the solving are stored in k_matrix.csv (wheights at each time step) and distribs (probability distribution objects according to https://doi.org/10.5281/zenodo.8249487). The data consists of phases with data from a stable mixture distribution and phases with data from a mixture distribution that do not fulfill the stability criteria.

The train data were used to train the G-SPC model. The test data were used for benchmarking purposes

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2023 Internet of Production – 390621612.

ABSTRACT In this paper, a new software for Statistical Process Control (SPC) is proposed. The system, the so-called CEP Online, was developed based on statistical computing resources of well-known free softwares, such as HTML, PHP, R and MySQL under an online server with operating system Linux Ubuntu. The main uni and multivariate SPC tools are available for monitoring and evaluation of manufacturing and non-manufacturing production processes over time. Some advantages of the new software are: (i) low operational cost, since it is cloud-based, only needing a computer connected to the Internet; (ii) easy to use with great interaction with the user; (iii) it does not require investment in any specific hardware or software; (iv) real time reports generation on process condition monitoring and process capability. Thus, the CEP Online offers for SPC practitioners fast, efficient and accurate SPC procedures. Therefore, CEP Online becomes an important resource for those who have no access to non-free softwares, such as SAS, SPSS, Minitab and STATISTICA. To the best of our knowledge, the CEP Online is unique with respect to its characteristics.

Statistical Process Control for Aerospace Manufacturing Market Outlook

According to our latest research, the global Statistical Process Control (SPC) for Aerospace Manufacturing market size reached USD 1.43 billion in 2024, reflecting the increasing adoption of advanced quality management solutions across the aerospace sector. The market is projected to expand at a robust CAGR of 8.7% from 2025 to 2033, culminating in a forecasted market value of USD 3.09 billion by 2033. This growth is primarily driven by the escalating need for precision, regulatory compliance, and operational efficiency in aerospace manufacturing environments, as companies seek to minimize defects, reduce costs, and enhance product reliability.

The growth trajectory of the SPC for Aerospace Manufacturing market is significantly influenced by the aerospace industry’s relentless pursuit of quality and safety. As aircraft components become increasingly complex and regulatory bodies enforce stricter standards, manufacturers are compelled to implement robust process control methodologies. Statistical Process Control enables real-time monitoring and analysis of manufacturing processes, allowing for immediate identification and correction of deviations. This proactive approach reduces the risk of costly recalls and ensures that products consistently meet both customer and regulatory expectations. The integration of SPC with Industry 4.0 technologies, such as the Industrial Internet of Things (IIoT) and artificial intelligence, further enhances its value proposition by providing predictive insights and automating quality assurance tasks.

Another critical growth factor is the rising adoption of digital transformation initiatives across aerospace manufacturing facilities. Companies are investing heavily in digital SPC solutions to streamline data collection, facilitate advanced analytics, and enable remote monitoring. This digital shift is not only improving process visibility and traceability but is also fostering a culture of continuous improvement. As the aerospace sector faces mounting pressure to accelerate production cycles and reduce time-to-market, the ability to quickly identify process inefficiencies and implement corrective actions becomes a key competitive differentiator. In addition, the growing prevalence of multi-site manufacturing operations necessitates standardized quality control systems, further fueling demand for scalable SPC platforms.

The market’s expansion is also supported by the increasing complexity of aerospace supply chains. With the proliferation of global sourcing and the involvement of numerous suppliers, maintaining consistent quality standards has become more challenging. OEMs and Tier 1 suppliers are mandating the use of SPC tools among their supply chain partners to ensure uniformity and compliance with stringent aerospace standards, such as AS9100 and ISO 9001. This trend is particularly pronounced in regions with rapidly growing aerospace sectors, such as Asia Pacific and Europe, where local manufacturers are striving to meet international benchmarks. Furthermore, the ongoing advancements in SPC software, including cloud-based deployment and real-time data integration, are making these solutions more accessible and cost-effective for organizations of all sizes.

Regionally, North America continues to dominate the SPC for Aerospace Manufacturing market, owing to the presence of major aerospace OEMs, a mature regulatory environment, and early adoption of advanced manufacturing technologies. However, Asia Pacific is emerging as the fastest-growing region, driven by substantial investments in aerospace infrastructure, expanding manufacturing capabilities, and increasing focus on quality management. European manufacturers are also prioritizing SPC adoption to maintain their competitive edge and comply with evolving regulatory frameworks. As the global aerospace industry becomes more interconnected, cross-regional collaborations and harmonization of quality standards are expected to further accelerate the adoption of SPC solutions worldwide.

The Statistical Process Control System (SPC) market has emerged as a critical component in quality management and process optimization across various industries, significantly enhancing operational efficiency and product quality. SPC utilizes statistical methods and tools to monitor and control manufacturing process

Unlock the power of precise measurement! Explore the booming Measurement System Analysis (MSA) Tools market, projected to reach $2.725 billion by 2033. Discover key trends, leading companies, and regional insights driving this 8% CAGR growth. Learn how MSA tools enhance product quality and process efficiency.

This article presents the challenges faced when applying statistical process control (SPC) in today’s evolving industrial landscape. The increased adoption by manufacturers of remote condition monitoring of their products in the hands of customers, driven by enabling technologies like the Internet of Things, makes statistical process control a pivotal tool. However, its implementation requires a careful consideration of the peculiarities imposed by these new usages. All available equipment data and their nuances, such as customer usage conditions and unmeasurable yet impactful variables on product performance, must be considered and used in some way. It is also not possible in this new environment to distinguish between standard phases I and II of SPC. Thus, a model able to automatically identify out of control behavior is required to both signal potential issues and keep out of control observations from contaminating the on-going model estimated parameters. To address these challenges, the authors propose a Bayesian hierarchical model with built-in outlier detection, demonstrated through its application to a real-world remote monitoring system for industrial printers deployed at customer sites. The primary goal extends beyond motivation and aims to deepen the understanding of potential new usages of SPC and their challenges in modern industrial settings.

Discover the booming Measurement System Analysis (MSA) Tools market. This in-depth analysis reveals a $2.5B market in 2025, projected to reach $4.8B by 2033, driven by Industry 4.0 and regulatory compliance. Explore key trends, regional insights, and leading companies shaping this dynamic landscape.

ABSTRACT The aim of this study was to evaluate drip irrigation as a process, by monitoring the average flow applied by the emitter using tools of statistical quality control. Four kinds of drippers were selected, two inline labyrinth type and two online where one of the inline emitters was not self-compensating and the other, self-compensating emitter. The system was installed in the field and tested for 85 hours, using three kinds of treated domestic sewage effluents and tap water. The system was under statistical control when the emitters were new, however none of the drippers reaches the manufacturer's specification for average flow. The online drippers showed more dispersion for individual flow measurements and the non-self-compensating inline dripper was more accurately for this variable. After the end of experiment, irrigation process was not under statistical control for any kind of emitter. When using treated wastewater effluents for irrigation we recommend a first evaluation before 7 working hours, to implement appropriated correcting procedures to reduce clogging and as a result, maintain the process quality.

Parameter values for scenarios considered in the simulation, when the true data-generating process is UL distributed (in-control condition).

Parameter values for scenarios considered in the simulation, when the true data-generating process is beta distributed (in-control condition).

Real‑Time SPC Dashboard Market Outlook

According to our latest research, the global Real‑Time SPC Dashboard market size reached USD 1.98 billion in 2024, and is anticipated to grow at a robust CAGR of 11.2% through the forecast period, reaching a projected market value of USD 5.07 billion by 2033. The primary growth factor driving this expansion is the increasing demand for advanced quality control and process optimization tools across manufacturing and other process-driven industries, as organizations strive to enhance operational efficiency and product quality in real time.

The growth trajectory of the Real‑Time SPC Dashboard market is being significantly influenced by the rapid adoption of Industry 4.0 principles, particularly in manufacturing and process industries. As organizations seek to digitize their operations, the integration of real-time data analytics and statistical process control (SPC) dashboards has become essential for ensuring consistent product quality, minimizing process variations, and reducing operational costs. The proliferation of IoT devices and smart sensors has further enabled seamless data collection and analysis, empowering enterprises to make informed decisions instantaneously. This digital transformation trend, coupled with the increasing focus on regulatory compliance and quality certifications, is expected to sustain market growth over the coming years.

Another substantial driver is the growing need for predictive analytics and proactive quality management in sectors such as automotive, food & beverage, pharmaceuticals, and electronics. Real‑Time SPC Dashboards enable organizations to monitor critical parameters continuously, detect anomalies, and implement corrective actions before defects escalate. This capability not only minimizes waste and rework costs but also enhances customer satisfaction and brand reputation. Furthermore, the integration of artificial intelligence and machine learning algorithms into SPC dashboards is unlocking new opportunities for predictive maintenance, process optimization, and root cause analysis, thereby amplifying the value proposition of these solutions for end-users.

Moreover, the shift towards cloud-based deployment models is accelerating the adoption of Real‑Time SPC Dashboards among small and medium enterprises (SMEs) and large organizations alike. Cloud-based solutions offer scalability, flexibility, and cost-effectiveness, enabling businesses to access advanced analytics tools without significant upfront investments in hardware or IT infrastructure. This democratization of technology is fostering the widespread implementation of SPC dashboards across diverse industries, including those with traditionally limited access to sophisticated quality management tools. The continuous evolution of user-friendly interfaces and customizable dashboard features is further enhancing user adoption and engagement, contributing to sustained market expansion.

Regionally, North America and Asia Pacific are at the forefront of market growth, driven by high technology adoption rates, strong manufacturing bases, and the presence of leading industry players. Europe follows closely, supported by stringent quality regulations and a robust industrial sector. Latin America and the Middle East & Africa are also witnessing steady growth, propelled by increasing investments in industrial automation and digital transformation initiatives. The regional dynamics are expected to evolve further as emerging economies prioritize quality improvement and operational excellence across key industries.

Component Analysis

The Real‑Time SPC Dashboard market by component is segmented into Software, Hardware, and Services. The software segment dominates the market, accounting for the largest revenue share in 2024, owing to the surging demand for advanced analytics platforms that enable real-time data visualization, statistical analysis, and process monitoring. Modern SPC software solutions are increasingly equipped with intuitive interfaces, customizable dashboards, and integration capabilities with enterprise resource planning (ERP) and m

Parameter values for scenarios considered in the simulation (out-of-control condition).

No-Code SPC App Builder Market Outlook

According to our latest research, the global No-Code SPC App Builder market size reached USD 1.26 billion in 2024, demonstrating robust momentum driven by digital transformation initiatives and the increasing demand for streamlined quality management solutions. The market is projected to grow at a CAGR of 22.3% from 2025 to 2033, reaching an estimated value of USD 9.91 billion by the end of the forecast period. This remarkable growth is fueled by the rising adoption of no-code platforms across industries, enabling organizations to efficiently deploy Statistical Process Control (SPC) applications without the need for extensive coding expertise.

One of the primary growth factors for the No-Code SPC App Builder market is the accelerated pace of digitalization in manufacturing and allied sectors. Organizations are increasingly seeking agile, scalable, and user-friendly platforms to facilitate real-time process monitoring and quality control. The no-code approach empowers process engineers, quality managers, and even non-technical users to rapidly configure SPC applications tailored to their unique workflows, eliminating bottlenecks associated with traditional software development cycles. This democratization of app development not only reduces costs but also significantly shortens deployment timelines, resulting in improved operational efficiency and faster time-to-value for enterprises.

Another critical driver is the mounting regulatory pressure and need for stringent compliance across industries such as healthcare, pharmaceuticals, and food & beverage. With evolving quality standards and regulatory frameworks, businesses require flexible solutions that can be quickly adapted to new requirements. No-Code SPC App Builder platforms allow organizations to seamlessly update and customize quality control processes, ensuring ongoing compliance without incurring heavy IT overheads. This adaptability is particularly vital in sectors where traceability, auditability, and data integrity are paramount, further accelerating the adoption of no-code SPC solutions.

The proliferation of cloud computing and the surge in remote operations post-pandemic have also played a pivotal role in shaping the market landscape. Cloud-based no-code SPC app builders provide unparalleled accessibility, scalability, and collaboration capabilities, enabling global teams to monitor and optimize processes from anywhere. This shift has not only enhanced business continuity but also opened new avenues for small and medium enterprises (SMEs) to leverage advanced SPC tools without significant capital investment. As organizations continue to prioritize digital resilience and flexibility, the demand for cloud-native, no-code SPC platforms is expected to witness sustained growth.

From a regional perspective, North America currently dominates the No-Code SPC App Builder market, accounting for the largest revenue share in 2024. This leadership is attributed to the early adoption of digital technologies, a strong presence of manufacturing and high-tech industries, and a mature ecosystem of software vendors. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid industrialization, government initiatives promoting smart manufacturing, and a burgeoning SME sector. Europe and Latin America are also experiencing steady growth, fueled by increasing investments in quality management and process automation. The Middle East & Africa region, while still nascent, is witnessing rising interest as industries seek to modernize their operations and enhance competitiveness.

Component Analysis

The No-Code SPC App Builder market by component is segmented into platform and services. The platform segment encompasses the core no-code development environments that enable users to design, deploy, and manage SPC applications without writing code. This segment is witnessing robust growth as organizations prioritize platforms that offer intuitive drag-and-drop interfaces, pre-built S

Computational cost (time and memory space) required for each regression model (Phase I data).

Real-Time SPC for Fill-Weight Distribution Market Outlook

According to our latest research, the global market size for Real-Time SPC for Fill-Weight Distribution reached USD 1.46 billion in 2024, supported by a robust adoption across manufacturing and quality control sectors. The market is experiencing a strong growth trajectory, with a CAGR of 8.1% projected from 2025 to 2033. By 2033, the Real-Time SPC for Fill-Weight Distribution market is forecasted to attain a value of USD 2.87 billion. This impressive growth is primarily fueled by the increasing demand for automation, regulatory compliance, and the drive for enhanced operational efficiency in high-volume production environments.

One of the most significant growth drivers for the Real-Time SPC for Fill-Weight Distribution market is the rising focus on product quality and consistency, especially in industries like food & beverage and pharmaceuticals. Manufacturers are under constant pressure to ensure that every product meets stringent fill-weight requirements, both to comply with regulatory standards and to maintain brand reputation. Real-Time Statistical Process Control (SPC) systems provide a powerful solution by enabling continuous monitoring and immediate feedback, allowing for rapid adjustments to production processes. This minimizes the risk of overfilling or underfilling, which can lead to significant cost savings and reduced product recalls. The integration of advanced analytics and machine learning within SPC solutions further enhances their ability to detect anomalies and predict potential issues, making them indispensable for modern production lines.

Another key factor propelling market growth is the increasing adoption of Industry 4.0 principles across manufacturing sectors. The convergence of IoT, cloud computing, and real-time data analytics is transforming traditional quality control processes. Real-Time SPC systems are now capable of aggregating data from diverse sources, providing granular insights into fill-weight distribution trends and enabling proactive decision-making. As manufacturers invest in smart factories and digital transformation initiatives, the demand for sophisticated SPC solutions is expected to surge. Furthermore, the growing complexity of supply chains and the need for traceability are compelling organizations to deploy real-time monitoring tools to ensure compliance and maintain operational agility.

The expansion of the Real-Time SPC for Fill-Weight Distribution market is also being driven by the increasing regulatory scrutiny across various industries. Regulatory bodies worldwide are implementing stricter guidelines regarding product labeling, weight accuracy, and consumer safety. Failure to comply can result in severe penalties, product recalls, and reputational damage. As a result, companies are prioritizing investments in real-time quality control systems to mitigate risks and demonstrate compliance. Moreover, the growing trend towards sustainability and waste reduction is encouraging manufacturers to optimize their fill-weight processes, further boosting the adoption of SPC technologies.

Regionally, North America and Europe are leading the adoption of Real-Time SPC for Fill-Weight Distribution solutions, driven by advanced manufacturing infrastructure and strict regulatory frameworks. The Asia Pacific region, however, is emerging as the fastest-growing market, fueled by rapid industrialization, expanding manufacturing bases, and increasing investments in automation. Countries such as China, India, and Japan are witnessing significant uptake of SPC solutions as they strive to enhance production efficiency and meet international quality standards. Latin America and the Middle East & Africa are also showing promising growth, albeit from a smaller base, as local industries modernize and embrace digital transformation.

Component Analysis

The Real-Time SPC for Fill-Weight Distribution market is segmented by component into software, hardware, and services, each playing a critical rol

Statistical Tolerance Analysis Software Market Outlook

According to our latest research, the global Statistical Tolerance Analysis Software market size reached USD 1.13 billion in 2024, driven by the increasing demand for precision engineering and digital transformation across manufacturing sectors. The market is expected to grow at a robust CAGR of 8.7% from 2025 to 2033, reaching a projected value of USD 2.41 billion by 2033. This growth is primarily fueled by the adoption of advanced simulation and modeling tools in industries such as automotive, aerospace, and electronics, where stringent quality standards and tighter tolerances are essential for competitive advantage and regulatory compliance.

One of the primary growth factors for the Statistical Tolerance Analysis Software market is the accelerating shift toward digital manufacturing and Industry 4.0 initiatives worldwide. As manufacturers strive to reduce costs, minimize errors, and improve product quality, the integration of robust statistical tolerance analysis tools within product lifecycle management (PLM) and computer-aided design (CAD) systems has become indispensable. These software solutions enable engineers and designers to predict the impact of dimensional variations on product performance, thus reducing the need for costly physical prototypes and rework. The increasing complexity of products, especially in the automotive and aerospace sectors, further necessitates the adoption of advanced tolerance analysis, as even minor deviations can lead to significant failures or compliance issues.

Another significant driver is the growing regulatory pressure and emphasis on quality assurance across global supply chains. Industries such as healthcare, electronics, and defense are subject to stringent quality and safety standards, making statistical tolerance analysis a critical component of their design and manufacturing processes. The software not only facilitates compliance with international standards but also enhances traceability and documentation, which are crucial for audits and certifications. Additionally, the rise of additive manufacturing and the use of new materials have introduced new sources of variability, further underscoring the need for sophisticated tolerance analysis tools that can handle complex geometries and multi-physics simulations.

The proliferation of cloud computing and the increasing availability of software-as-a-service (SaaS) models have also played a pivotal role in expanding the reach of statistical tolerance analysis software. Cloud-based solutions offer scalability, flexibility, and cost-effectiveness, making them particularly attractive to small and medium enterprises (SMEs) that may not have the resources for extensive on-premises infrastructure. Furthermore, the integration of artificial intelligence and machine learning capabilities into these platforms is enabling more accurate predictions, automated optimization, and real-time analytics, thereby delivering enhanced value to end-users. This technological evolution is expected to continue reshaping the competitive landscape and driving market growth well into the next decade.

Regionally, North America currently dominates the Statistical Tolerance Analysis Software market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The presence of leading manufacturing hubs, strong R&D investments, and early adoption of digital technologies have contributed to this leadership. However, the Asia Pacific region is poised for the fastest growth over the forecast period, propelled by rapid industrialization, expanding automotive and electronics sectors, and increasing government support for smart manufacturing initiatives. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as local manufacturers increasingly recognize the benefits of advanced tolerance analysis for improving competitiveness and export potential.

In the realm of quality management, Statistical Process Control Software plays a crucial role by providing manufacturers with the tools to monitor and control production processes. This software enables organizations to maintain consistent quality levels by using statistical methods to identify and correct deviations from desired performance. By integr