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.

Statistical Process Control Software Market Outlook

According to our latest research, the global statistical process control software market size reached USD 1.57 billion in 2024, supported by a robust demand for advanced quality management solutions across industries. The market is projected to grow at a CAGR of 10.4% during the forecast period, reaching USD 4.19 billion by 2033. This expansion is primarily driven by the increasing adoption of automation, digital transformation initiatives, and the rising need for real-time data analytics to enhance operational efficiency and product quality.

One of the key growth factors fueling the statistical process control software market is the escalating focus on quality assurance and compliance in highly regulated sectors. Industries such as pharmaceuticals, food & beverage, and automotive are under mounting pressure to adhere to stringent quality standards and regulatory requirements. As a result, organizations are increasingly investing in statistical process control (SPC) software to monitor production processes, identify deviations, and ensure consistent product quality. The integration of SPC solutions with enterprise resource planning (ERP) and manufacturing execution systems (MES) further enhances their utility, enabling companies to leverage real-time data for informed decision-making and proactive process optimization.

Another significant driver for the statistical process control software market is the rapid advancement in digital technologies, including the Industrial Internet of Things (IIoT), artificial intelligence, and machine learning. These technologies are being seamlessly integrated into SPC platforms, empowering manufacturers to collect and analyze vast volumes of process data in real time. The ability to detect anomalies, predict equipment failures, and implement corrective actions swiftly has become a critical differentiator for organizations striving for operational excellence. Moreover, the shift toward smart factories and Industry 4.0 initiatives is amplifying the demand for sophisticated SPC software capable of supporting predictive analytics, automated reporting, and continuous process improvement.

The growing trend of cloud adoption across enterprises is also significantly contributing to the market’s growth. Cloud-based statistical process control software offers scalability, flexibility, and cost-effectiveness, making it an attractive solution for organizations of all sizes, particularly small and medium enterprises (SMEs). The ease of deployment, reduced IT infrastructure costs, and the ability to access real-time insights from any location are compelling advantages that are accelerating the shift from traditional on-premises solutions to cloud-based platforms. This trend is expected to intensify as organizations seek to enhance their digital capabilities and support remote operations in an increasingly dynamic business environment.

Regionally, North America continues to dominate the statistical process control software market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of advanced manufacturing industries, high digitalization rates, and a strong focus on quality management and regulatory compliance. However, the Asia Pacific region is witnessing the fastest growth, propelled by rapid industrialization, increasing investments in smart manufacturing technologies, and the expansion of the automotive and electronics sectors. Europe also remains a significant market, driven by stringent quality standards and the widespread adoption of automation in the manufacturing sector.

Component Analysis

The statistical process control software market by component is segmented into software and services. The software segment holds a substantial share of the market, as organizations across industries increasingly rely on advanced SPC software solutions to automate quality control processes and ensure data-driven decision-making. These software platforms offer a wide a

Find detailed analysis in Market Research Intellect's Statistical Process Control Software Market Report, estimated at USD 1.2 billion in 2024 and forecasted to climb to USD 2.5 billion by 2033, reflecting a CAGR of 8.5%.Stay informed about adoption trends, evolving technologies, and key market participants.

Statistical Process Control Software for Food Industry Market Outlook

According to our latest research, the global Statistical Process Control (SPC) Software for Food Industry market size reached USD 1.26 billion in 2024, reflecting robust adoption across manufacturing and processing sectors. The market is projected to expand at a CAGR of 8.2% from 2025 to 2033, with the forecasted market size expected to reach USD 2.52 billion by 2033. This sustained growth is primarily fueled by increasing regulatory scrutiny, the rising need for quality assurance, and the accelerating digital transformation within the food industry.

The growth trajectory of the Statistical Process Control Software for Food Industry market is underpinned by the sector’s urgent need for real-time quality monitoring and data-driven decision-making. As food safety regulations become more stringent globally, food manufacturers and processors are compelled to adopt advanced SPC software solutions to ensure compliance and minimize the risk of costly recalls. The integration of SPC software enables companies to systematically monitor production processes, identify deviations, and implement corrective actions promptly. This not only enhances product quality but also significantly reduces waste and operational inefficiencies, which is crucial in a highly competitive market landscape. The growing consumer demand for transparency and traceability in food production further amplifies the adoption of SPC software, as it provides a robust framework for documenting and analyzing process data.

Another major growth factor for the SPC software market in the food industry is the rapid digitalization and automation of manufacturing processes. The proliferation of Industry 4.0 technologies, such as IoT-enabled sensors and machine learning algorithms, has revolutionized how food companies monitor and control their operations. By integrating SPC software with these advanced technologies, organizations can achieve a higher level of process automation, predictive analytics, and proactive quality management. This digital transformation not only streamlines compliance with food safety standards but also empowers companies to respond swiftly to market demands and supply chain disruptions. As a result, the value proposition of SPC software extends beyond compliance, offering strategic advantages in operational agility and cost competitiveness.

Additionally, the market is benefiting from the increasing awareness and adoption of cloud-based SPC solutions. Cloud deployment models offer significant advantages in terms of scalability, remote accessibility, and cost-effectiveness, making them particularly attractive to small and medium enterprises (SMEs) in the food sector. With cloud-based SPC software, companies can centralize data management, facilitate real-time collaboration across geographically dispersed teams, and leverage advanced analytics without the need for substantial upfront investments in IT infrastructure. This democratization of technology is accelerating the penetration of SPC software across all tiers of the food industry, from large multinational corporations to emerging local players. Consequently, the market is witnessing a surge in demand for flexible, user-friendly, and customizable SPC solutions tailored to the unique requirements of the food industry.

From a regional perspective, North America remains the dominant market for SPC software in the food industry, driven by a mature regulatory environment, high levels of technological adoption, and the presence of major food manufacturing companies. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid industrialization, rising food safety concerns, and government initiatives to modernize food processing infrastructure. Europe also represents a significant share of the market, owing to stringent food safety regulations and a strong emphasis on quality control. The Middle East & Africa and Latin America are gradually adopting SPC software, supported by increasing investments in food processing and export-oriented growth strategies. Overall, the global market is characterized by dynamic regional trends and evolving customer needs, which are shaping the future landscape of SPC software adoption in the food industry.

The global Statistical Process Control System (SPC) market is projected to expand significantly from 2025 to 2033, reaching a projected market value of XXX million USD by 2033, growing at a CAGR of XX% over the forecast period. The increasing adoption of SPC in various industries, including manufacturing, pharmaceuticals, and food processing, to improve quality and reduce costs, is driving the market growth. The market is segmented by type (cloud-based, on-premises), application (industrial intelligent manufacturing, digital finance, food industrial, pharmaceutical production, others), and region (North America, Europe, Asia Pacific, South America, Middle East & Africa). Key players in the market include SAP, Oracle, Schneider Electric, Honeywell, Chinasoft International, BCN Group, Blulink, Delta Electronics, Taiyou Tech, OrBit Systems, Yonyou Network Technology, Advantive, Iconics, Hertzler Systems, Shenzhen Pinguan Technology, Wisdom, and others. Technological advancements, such as the integration of AI and machine learning into SPC systems, are expected to drive innovation and further market expansion.

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.

The SPC Software market is booming, projected to reach $7 billion by 2033 with a 12% CAGR. This in-depth analysis explores market size, key drivers (Industry 4.0, data-driven decisions), trends (cloud-based solutions), restraints, and leading companies. Discover growth opportunities in this vital sector.

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.

We present a new method for statistical process control (SPC) of a discrete part manufacturing system based on intrinsic geometrical properties of the parts, estimated from three-dimensional sensor data. An intrinsic method has the computational advantage of avoiding the difficult part registration problem, necessary in previous SPC approaches of three-dimensional geometrical data, but inadequate if noncontact sensors are used. The approach estimates the spectrum of the Laplace–Beltrami (LB) operator of the scanned parts and uses a multivariate nonparametric control chart for online process control. Our proposal brings SPC closer to computer vision and computer graphics methods aimed to detect large differences in shape (but not in size). However, the SPC problem differs in that small changes in either shape or size of the parts need to be detected, keeping a controllable false alarm rate and without completely filtering noise. An online or “Phase II” method and a scheme for starting up in the absence of prior data (“Phase I”) are presented. Comparison with earlier approaches that require registration shows the LB spectrum method to be more sensitive to rapidly detect small changes in shape and size, including the practical case when the sequence of part datasets is in the form of large, unequal size meshes. A post-alarm diagnostic method to investigate the location of defects on the surface of a part is also presented. While we focus in this article on surface (triangulation) data, the methods can also be applied to point cloud and voxel metrology data.

ABSTRACT For the release of pharmaceutical products into the drug market; most of the pharmaceutical companies depend on acceptance criteria - that are set internally, regulatory and/or pharmacopeially. However, statistical process control monitoring is underestimated in most quality control in cases; although it is important not only for process stability and efficiency assessment but also for compliance with all appropriate pharmaceutical practices such as good manufacturing practice and good laboratory practice, known collectively as GXP. The current work aims to investigate two tablet inspection characteristics monitored during in-process control viz. tablet average weight and hardness. Both properties were assessed during the compression phase of the tablet and before the coating stage. Data gathering was performed by the Quality Assurance Team and processed by Commercial Statistical Software packages. Screening of collected results of 31 batches of an antibacterial tablet - based on Fluoroquinolone -showed that all the tested lots met the release specifications, although the process mean has been unstable which could be strongly evident in the variable control chart. Accordingly, the two inspected processes were not in the state of control and require strong actions to correct for the non-compliance to GXP. What is not controlled cannot be predicted in the future and thus the capability analysis would be of no value except to show the process capability retrospectively only. Setting the rules for the application of Statistical Process Control (SPC) should be mandated by Regulatory Agencies.

The dataset refers to the measurement of axes of Ti-6Al-4V cylindrical surfaces obtained by lathe turning. The machined surfaces were measured using a Coordinate Measuring Machine (CMM) and the axis of each cylinder was derived from the CMM measures.

The dataset consists of a MAT-file including the CMM measurements and a Matlab function “LoadData.m” to extract and convert the data into Cartesian coordinates.

All the details about the dataset can be found in:

Colosimo, B.M., Pacella, M. Analyzing the effect of process parameters on the shape of 3D profiles (2011) Journal of Quality Technology, 43 (3), pp. 169-195.DOI: 10.1080/00224065.2011.11917856 Pacella, M., Colosimo, B.M. Multilinear principal component analysis for statistical modeling of cylindrical surfaces: a case study (2018) Quality Technology and Quantitative Management, 15 (4), pp. 507-525.DOI: 10.1080/16843703.2016.1226710

Discover the booming Real-Time SPC Software market! Explore market size, growth trends, key players (Minitab, P3 Adaptive, etc.), and regional insights. Learn how real-time data analysis is revolutionizing manufacturing efficiency and quality control. Get the latest market forecast to 2033.

Access Market Research Intellect's Statistical Process Control System (SPC) Market Report for insights on a market worth USD 1.5 billion in 2024, expanding to USD 3.2 billion by 2033, driven by a CAGR of 9.5%.Learn about growth opportunities, disruptive technologies, and leading market participants.

The Real-Time Statistical Process Control (SPC) Software market is experiencing robust growth, driven by the increasing need for enhanced quality control and operational efficiency across various industries. The market, estimated at $500 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $1.5 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of Industry 4.0 technologies and the Internet of Things (IoT) is generating vast amounts of data, demanding sophisticated real-time analysis capabilities offered by SPC software. Secondly, the growing pressure to minimize production defects and improve product quality is compelling manufacturers to adopt advanced quality management systems. Thirdly, cloud-based SPC solutions are gaining significant traction, offering scalability, accessibility, and reduced infrastructure costs, thereby driving market growth across both large enterprises and SMEs. The segmentation shows strong adoption in both large enterprises and SMEs, with cloud-based solutions steadily outpacing locally-installed software. Geographically, North America and Europe currently hold the largest market share, but the Asia-Pacific region is poised for rapid expansion, driven by industrial growth and technological advancements. The competitive landscape is characterized by a mix of established players and emerging companies. Established vendors like Minitab and Hexagon leverage their brand recognition and extensive customer base to maintain market leadership. However, smaller, agile companies are innovating with cutting-edge features and cloud-based offerings, creating a dynamic market environment. While factors such as high initial investment costs and the need for specialized expertise can restrain adoption to some extent, the overall long-term growth prospects for the Real-Time SPC Software market remain extremely positive. Continued technological advancements, increasing awareness of the benefits of real-time quality control, and the expanding adoption of Industry 4.0 will continue to fuel market expansion in the coming years.

Abstract–Statistical process control (SPC) charts are critically important for quality control and management in manufacturing industries, environmental monitoring, disease surveillance, and many other applications. Conventional SPC charts are designed for cases when process observations are independent at different observation times. In practice, however, serial data correlation almost always exists in sequential data. It has been well demonstrated in the literature that control charts designed for independent data are unstable for monitoring serially correlated data. Thus, it is important to develop control charts specifically for monitoring serially correlated data. To this end, there is some existing discussion in the SPC literature. Most existing methods are based on parametric time series modeling and residual monitoring, where the data are often assumed to be normally distributed. In applications, however, the assumed parametric time series model with a given order and the normality assumption are often invalid, resulting in unstable process monitoring. Although there is some nice discussion on robust design of such residual monitoring control charts, the suggested designs can only handle certain special cases well. In this article, we try to make another effort by proposing a novel control chart that makes use of the restarting mechanism of a CUSUM chart and the related spring length concept. Our proposed chart uses observations within the spring length of the current time point and ignores all history data that are beyond the spring length. It does not require any parametric time series model and/or a parametric process distribution. It only requires the assumption that process observation at a given time point is associated with nearby observations and independent of observations that are far away in observation times, which should be reasonable for many applications. Numerical studies show that it performs well in different cases.

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.