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.

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

Because of the “curse of dimensionality,” high-dimensional processes present challenges to traditional multivariate statistical process monitoring (SPM) techniques. In addition, the unknown underlying distribution of and complicated dependency among variables such as heteroscedasticity increase the uncertainty of estimated parameters and decrease the effectiveness of control charts. In addition, the requirement of sufficient reference samples limits the application of traditional charts in high-dimension, low-sample-size scenarios (small n, large p). More difficulties appear when detecting and diagnosing abnormal behaviors caused by a small set of variables (i.e., sparse changes). In this article, we propose two change-point–based control charts to detect sparse shifts in the mean vector of high-dimensional heteroscedastic processes. Our proposed methods can start monitoring when the number of observations is a lot smaller than the dimensionality. The simulation results show that the proposed methods are robust to nonnormality and heteroscedasticity. Two real data examples are used to illustrate the effectiveness of the proposed control charts in high-dimensional applications. The R codes are provided online.

Synchronization of variable trajectories from batch process data is a delicate operation that can induce artifacts in the definition of multivariate statistical process control (MSPC) models for real-time monitoring of batch processes. The current paper introduces a new synchronization-free approach for online batch MSPC. This approach is based on the use of local MSPC models that cover a normal operating conditions (NOC) trajectory defined from principal component analysis (PCA) modeling of non-synchronized historical batches. The rationale behind is that, although non-synchronized NOC batches are used, an overall NOC trajectory with a consistent evolution pattern can be described, even if batch-to-batch natural delays and differences between process starting and end points exist. Afterwards, the local MSPC models are used to monitor the evolution of new batches and derive the related MSPC chart. During the real-time monitoring of a new batch, this strategy allows testing whether every new observation is following or not the NOC trajectory. For a NOC observation, an additional indication of the batch process progress is provided based on the identification of the local MSPC model that provides the lowest residuals. When an observation deviates from the NOC behavior, contribution plots based on the projection of the observation to the best local MSPC model identified in the last NOC observation are used to diagnose the variables related to the fault. This methodology is illustrated using two real examples of NIR-monitored batch processes: a fluidized bed drying process and a batch distillation of gasoline blends with ethanol.

Del Castillo and Zhao have recently proposed a new methodology for the Statistical Process Control (SPC) of discrete parts whose 3-dimensional (3D) geometrical data are acquired with non-contact sensors. The approach is based on monitoring the spectrum of the Laplace–Beltrami (LB) operator of each scanned part estimated using finite element methods (FEM). The spectrum of the LB operator is an intrinsic summary of the geometry of a part, independent of the ambient space. Hence, registration of scanned parts is unnecessary when comparing them. The primary goal of this case study paper is to demonstrate the practical implementation of the spectral SPC methodology through multiple examples using real scanned parts acquired with an industrial-grade laser scanner, including 3D-printed parts and commercial parts. We discuss the scanned mesh preprocessing needed in practice, including the type of remeshing found to be most beneficial for the FEM computations. For each part type, both the “phase I” and “phase II” stages of the spectral SPC methodology are showcased. In addition, we provide a new principled method to determine the number of eigenvalues of the LB operator to consider for efficient SPC of a given part geometry, and present an improved algorithm to automatically define a region of interest, particularly useful for large meshes. Computer codes that implement every method discussed in this paper, as well as all scanned part datasets used in the case studies, are made available and explained in the supplementary materials.

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.

corrMatrix.m: This Matlab function computes the correlation matrix of w-test statistics.

KMC.m: This Matlab function computes the critical values for max-w test statistic based on Monte Carlo method. It is needed to run corrMatrix.m before use it.

kNN.m: This Matlab function based on neural networks allows anyone to obtain the desired critical value with good control of type I error. In that case, you need to download file SBPNN.mat and save it in your folder. It is needed to run corrMatrix.m before use it.

SBPNN.mat: MATLAB's flexible network object type (called SBPNN.mat) that allows anyone to obtain the desired critical value with good control of type I error.

Examples.txt: File containing examples of both design and covariance matrices in adjustment problems of geodetic networks.

rawMC.txt: Monte-Carlo-based critical values for the following signifiance levels: α′= 0.001, α′= 0.01, α′= 0.05, α′= 0.1 and α′= 0.5. The number of the observations (n) were fixed for each α ′with n = 5 to n= 100 by a increment of 5. For each "n" the correlation between the w-tests (ρwi,wj) were also fixed from ρwi,wj = 0.00 to ρwi,wj = 1.00, by increment of 0.1, considering also taking into account the correlation ρwi,wj = 0.999. For each combination of α′,"n" and ρwi,wj, m= 5,000,000 Monte Carlo experiments were run.

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

In monitoring an ordered stream of discrete items from a repetitive process, the geometric CUSUM chart may be used to detect sudden shifts from an acceptable level for a process-proportion (p) such as fraction nonconforming. Much of the investigative effort for this CUSUM scheme has been concentrated on the detection of upward shifts, and a recent paper has provided guidance to quality engineers in choosing the parameters (k, h) of such a scheme. In this article, the corresponding task of aiding the choice of parameters for detecting a downward shift is addressed. It is shown, using extensive numerical investigations, that the use of a value for the parameter k based on the Sequential Probability Ratio is not optimal when one is using steady-state evaluation of the detection performance of the CUSUM scheme. Tables are presented listing recommended values of parameters for detection of five sizes of downward shift, for each of 27 in-control levels for p in the range 0.20 to 0.001. Interpolation and extrapolation to find parameter values for other in-control levels of p are also considered, and a range of examples presented. There is an equivalence between a geometric CUSUM scheme and a Bernoulli CUSUM scheme, so that the results of this investigation may also be used in choosing parameter values for a Bernoulli CUSUM chart.

In statistical process monitoring, control charts typically depend on a set of tuning parameters besides its control limit(s). Proper selection of these tuning parameters is crucial to their performance. In a specific application, a control chart is often designed for detecting a target process distributional shift. In such cases, the tuning parameters should be chosen such that some characteristic of the out-of-control (OC) run length of the chart, such as its average, is minimized for detecting the target shift, while the control limit is set to maintain a desired in-control (IC) performance. However, explicit solutions for such a design are unavailable for most control charts, and thus numerical optimization methods are needed. In such cases, Monte Carlo-based methods are often a viable alternative for finding suitable design constants. The computational cost associated with such scenarios is often substantial, and thus computational efficiency is a key requirement. To address this problem, a two-step design based on stochastic approximations is presented in this paper, which is shown to be much more computationally efficient than some representative existing methods. A detailed discussion about the new algorithm’s implementation along with some examples are provided to demonstrate the broad applicability of the proposed methodology for the optimal design of univariate and multivariate control charts. Computer codes in the Julia programming language are also provided in the supplemental material.

Industrial SPC Analytics Market Outlook

According to our latest research, the global Industrial SPC Analytics market size reached USD 2.14 billion in 2024, demonstrating robust adoption across diverse industrial sectors. The market is projected to expand at a CAGR of 9.8% from 2025 to 2033, reaching a forecasted value of USD 4.88 billion by 2033. This dynamic growth is primarily driven by the escalating need for real-time quality monitoring, the proliferation of Industry 4.0 initiatives, and the growing integration of advanced analytics in manufacturing environments. As per our latest research, industries are increasingly recognizing the value of Statistical Process Control (SPC) analytics to optimize production processes, minimize defects, and enhance overall operational efficiency.

The surge in demand for Industrial SPC Analytics solutions is largely attributed to the rising emphasis on quality assurance and regulatory compliance across various sectors. Manufacturers are under constant pressure to maintain stringent quality standards while simultaneously reducing operational costs. SPC analytics empowers organizations to proactively identify process variations, predict potential failures, and implement corrective actions before defects occur. The integration of SPC analytics with IoT devices and smart sensors enables real-time data collection and analysis, facilitating immediate responses to process deviations. This proactive approach not only ensures product consistency but also significantly reduces wastage, rework, and customer complaints, thus driving the market’s upward trajectory.

Another critical growth factor for the Industrial SPC Analytics market is the rapid digital transformation witnessed across industries. The adoption of cloud computing, big data analytics, and artificial intelligence has revolutionized traditional manufacturing processes. Modern SPC analytics platforms leverage these technologies to deliver advanced statistical insights, automated reporting, and predictive analytics capabilities. This technological evolution has made SPC analytics more accessible, scalable, and cost-effective for organizations of all sizes, including small and medium enterprises (SMEs). Furthermore, the increasing prevalence of connected factories and smart manufacturing ecosystems is accelerating the deployment of SPC analytics solutions, enabling manufacturers to achieve higher levels of process optimization and competitive advantage.

The growing complexity of supply chains and the need for end-to-end visibility are also fueling the demand for Industrial SPC Analytics. As manufacturers expand their operations globally, they face challenges related to process standardization, quality control across multiple sites, and compliance with diverse regulatory frameworks. SPC analytics provides a unified platform for monitoring and analyzing quality metrics across geographically dispersed facilities, ensuring consistency and traceability. The ability to aggregate and analyze data from multiple sources empowers organizations to make data-driven decisions, streamline operations, and respond swiftly to market demands. These factors collectively contribute to the sustained growth and adoption of SPC analytics in the industrial sector.

From a regional perspective, Asia Pacific continues to dominate the Industrial SPC Analytics market, accounting for the largest share in 2024. The region’s rapid industrialization, robust manufacturing base, and strong government support for digital transformation initiatives are key drivers of market growth. North America and Europe also represent significant markets, fueled by the presence of advanced manufacturing industries and early adoption of cutting-edge technologies. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, supported by increasing investments in industrial automation and quality management. Regional dynamics, such as regulatory requirements and industry-specific standards, play a crucial role in shaping market trends and adoption rates across different geographies.

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

This dataset provides analytical and other data in support of an analysis of lead and manganese in untreated drinking water from Atlantic and Gulf Coastal Plain aquifers, eastern United States. The occurrence of dissolved lead and manganese in sampled groundwater, prior to its distribution or treatment, is related to the potential presence of source minerals and specific environmental factors including hydrologic position along the flow path, water-rock interactions, and associated geochemical conditions such as pH and dissolved oxygen (DO) concentrations. A DO/pH framework is proposed as a screening tool for evaluating risk of elevated lead or manganese, based on the occurrence of elevated lead and manganese concentrations and the corresponding distributions of DO and pH in 258 wells screened in the Atlantic and Gulf Coastal Plain aquifers. Included in this data release are the Supplementary Information Tables that also accompany the Applied Geochemistry journal article: Table of details on construction and hydrologic position of wells (percent distance from outcrop and percent depth to well centroid) sampled in Atlantic and Gulf Coastal Plain aquifers, 2012 and 2013. Table of general chemical characteristic and concentrations of major and trace elements and calculated parameters for groundwater samples from wells in Atlantic and Gulf Coastal Plain aquifers, 2012 and 2013. Table of mineral saturation indices (SI) and partial pressures of CO2 (PCO2 ) and O2 (PO2) computed with PHREEQC (Parkhurst and Appelo, 2013) for 258 groundwater samples from wells in Atlantic and Gulf Coastal Plain aquifers, 2012 and 2013. Table of spearman's rank correlation coefficient (r) matrix of principal components (PC1-PC6) and chemical data for 258 groundwater samples from wells in Atlantic and Gulf Coastal Plain aquifers, 2012 and 2013. Table of results of blank analysis for major and trace elements analyzed for 258 groundwater samples from wells in Atlantic and Gulf Coastal Plain aquifers, 2012 and 2013. Table of criteria and threshold concentrations for identifying redox processes in groundwater (after McMahon and Chapelle, 2008). Table of principal components analysis model of major factors affecting the chemistry of groundwater samples from wells in Atlantic and Gulf Coastal Plain aquifers, 2012 and 2013.

A comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework consists of three major phases: Phase 1—Preliminary raw data sets exploration, including time formatting and combining datasets of different lengths and different time intervals; Phase 2—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme values; and Phase 3—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado) were analyzed. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.The files that are in this data package include one excel file, converted to CSV format (Billy_Barr_raw_qaqc.csv) that contains the raw meteorological data, i.e., input data used for the QA/QC analysis. The second CSV file (Billy_Barr_1hr.csv) is the QA/QC and flagged meteorological data, i.e., output data from the QA/QC analysis. The last file (QAQC_Billy_Barr_2021-03-22.R) is a script written in R that implements the QA/QC and flagging process. The purpose of the CSV data files included in this package is to provide input and output files implemented in the R script.

Batch effects are technical sources of variation introduced by the necessity of conducting gene expression analyses on different dates due to the large number of biological samples in population-based studies. The aim of this study is to evaluate the performances of linear mixed models (LMM) and Combat in batch effect removal. We also assessed the utility of adding quality control samples in the study design as technical replicates. In order to do so, we simulated gene expression data by adding “treatment” and batch effects to a real gene expression dataset. The performances of LMM and Combat, with and without quality control samples, are assessed in terms of sensitivity and specificity while correcting for the batch effect using a wide range of effect sizes, statistical noise, sample sizes and level of balanced/unbalanced designs. The simulations showed small differences among LMM and Combat. LMM identifies stronger relationships between big effect sizes and gene expression than Combat, while Combat identifies in general more true and false positives than LMM. However, these small differences can still be relevant depending on the research goal. When any of these methods are applied, quality control samples did not reduce the batch effect, showing no added value for including them in the study design.

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

From October 2017 through September 2022, the National Water Quality Network (NWQN) monitored 110 surface-water river and stream sites and more than 1,800 groundwater wells for a large number of water-quality analytes, for which associated quality-control data and corresponding statistical summaries are included in this data release. The quality-control data—for samples that were collected in the field (at all 110 surface-water sites, 350 groundwater wells, and 16 quality-control-only sites), prepared in the laboratory, or prepared by a third party—can be used to assess the quality of environmental data collected by the NWQN through the estimation of bias and variability in reported results. The general analyte groups that were monitored at NWQN surface-water and (or) groundwater sites and have associated quality-control data in this data release include major ions, nutrients, trace elements, pesticides, volatile organic compounds, hormones, pharmaceuticals, radionuclides, microbial indicators, sediment, and environmental tracers. For each analyte group, the data tables contain results for one or more of the following types of quality-control samples, where relevant: blanks, matrix spikes, and replicates collected at field sites; laboratory blanks, reagent spikes, and matrix spikes prepared by the USGS National Water Quality Laboratory (NWQL) (quality-control samples prepared by other analyzing laboratories are not included in the current data release); and third-party blanks, spikes, and reference samples prepared by the USGS Quality Systems Branch (QSB). For each relevant analyte, tables of summary statistics characterize the frequency and concentrations of blank detections, the typical magnitude of and variability in spike and reference-sample recoveries, and the typical variability between replicate concentrations. Tables included in this data release: Table1_SiteList.txt: Information about National Water Quality Network sites that have associated quality-control data. Table2_AnalyteList.txt: Information about National Water Quality Network analytes that have associated quality-control data, including available aquatic-life and (or) human-health benchmarks and selected information regarding analytical methods. Table3_BlankData.txt: For all relevant analytes, results for blanks collected at field sites, prepared in the laboratory, or prepared by a third party. Table4_SpikeData.txt: For all relevant analytes, results for matrix spikes prepared in the field, matrix spikes prepared in the laboratory, reagent spikes prepared in the laboratory, or reagent spikes prepared by a third party. For matrix spikes, results of paired environmental samples are included. Table5_ReplicateData.txt: For all relevant analytes, results for field replicates and paired environmental samples. Table 6_ReferenceData.txt: For all relevant analytes, results for third-party reference samples. Table7_BlankStats.txt: For all relevant analytes, summary statistics for each type of available blank sample. Table8_SpikeStats.txt: For all relevant analytes, summary statistics for each type of available spike sample. Table9_ReplicateStats.txt: For all relevant analytes, summary statistics for field replicates. Table10_ReferenceStats.txt: For all relevant analytes, summary statistics for reference samples.

Control charts are popular tools in the statistical process control toolkit and the exponentially weighted moving average (EWMA) chart is one of its essential component for efficient process monitoring. In the present study, a new Bayesian Modified-EWMA chart is proposed for the monitoring of the location parameter in a process. Four various loss functions and a conjugate prior distribution are used in this study. The average run length is used as a performance evaluation tool for the proposed chart and its counterparts. The results advocate that the proposed chart performs very well for the monitoring of small to moderate shifts in the process and beats the existing counterparts. The significance of the proposed scheme has proved through two real-life examples: (1) For the monitoring of the reaming process which is used in the mechanical industry. (2) For the monitoring of golf ball performance in the sports industry.

The CUSUM chart is good enough to detect small-to-moderate shifts in the process parameter(s) as it can be optimally designed to detect a particular shift size. The adaptive CUSUM (ACUSUM) chart provides good detection over a range of shift sizes because of its ability to update the reference parameter using the estimated process shift. In this paper, we propose auxiliary-information-based (AIB) optimal CUSUM (OCUSUM) and ACUSUM charts, named AIB-OCUSUM and AIB-ACUSUM charts, using a difference estimator of the process mean. The performance comparisons between existing and proposed charts are made in terms of the average run length (ARL), extra quadratic loss and integral relative ARL measures. It is found that the AIB-OCUSUM and AIB-ACUSUM charts are more sensitive than the AIB-CUSUM and ACUSUM charts, respectively. Moreover, the AIB-ACUSUM chart surpasses the AIB-OCUSUM chart when detecting a range of mean shift sizes. Illustrative examples are given to support the theory.

Abstract

Within the frame of PCBS' efforts in providing official Palestinian statistics in the different life aspects of Palestinian society and because the wide spread of Computer, Internet and Mobile Phone among the Palestinian people, and the important role they may play in spreading knowledge and culture and contribution in formulating the public opinion, PCBS conducted the Household Survey on Information and Communications Technology, 2014.

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestine in addition to providing data on the following: -

· Prevalence of computers and access to the Internet. · Study the penetration and purpose of Technology use.

Geographic coverage

Palestine (West Bank and Gaza Strip) , type of locality (Urban, Rural, Refugee Camps) and governorate

Analysis unit

Household. Person 10 years and over .

Universe

All Palestinian households and individuals whose usual place of residence in Palestine with focus on persons aged 10 years and over in year 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of a list of enumeration areas adopted in the Population, Housing and Establishments Census of 2007. Each enumeration area has an average size of about 124 households. These were used in the first phase as Preliminary Sampling Units in the process of selecting the survey sample.

Sample Size The total sample size of the survey was 7,268 households, of which 6,000 responded.

Sample Design The sample is a stratified clustered systematic random sample. The design comprised three phases:

Phase I: Random sample of 240 enumeration areas. Phase II: Selection of 25 households from each enumeration area selected in phase one using systematic random selection. Phase III: Selection of an individual (10 years or more) in the field from the selected households; KISH TABLES were used to ensure indiscriminate selection.

Sample Strata Distribution of the sample was stratified by: 1- Governorate (16 governorates, J1). 2- Type of locality (urban, rural and camps).

Sampling deviation

-

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on persons (aged 10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Preparation of Data Entry Program: This stage included preparation of the data entry programs using an ACCESS package and defining data entry control rules to avoid errors, plus validation inquiries to examine the data after it had been captured electronically.

Data Entry: The data entry process started on 8 May 2014 and ended on 23 June 2014. The data entry took place at the main PCBS office and in field offices using 28 data clerks.

Editing and Cleaning procedures: Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

Response rate

Response Rates= 79%

Sampling error estimates

There are many aspects of the concept of data quality; this includes the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are three components to the quality of statistics: accuracy, comparability, and quality control procedures.

Checks on data accuracy cover many aspects of the survey and include statistical errors due to the use of a sample, non-statistical errors resulting from field workers or survey tools, and response rates and their effect on estimations. This section includes:

Statistical Errors Data of this survey may be affected by statistical errors due to the use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators.

Variance calculations revealed that there is no problem in disseminating results nationally or regionally (the West Bank, Gaza Strip), but some indicators show high variance by governorate, as noted in the tables of the main report.

Non-Statistical Errors Non-statistical errors are possible at all stages of the project, during data collection or processing. These are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, and practical and theoretical training took place during the training course. Training manuals were provided for each section of the questionnaire, along with practical exercises in class and instructions on how to approach respondents to reduce refused cases. Data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

The sources of non-statistical errors can be summarized as: 1. Some of the households were not at home and could not be interviewed, and some households refused to be interviewed. 2. In unique cases, errors occurred due to the way the questions were asked by interviewers and respondents misunderstood some of the questions.