{"id":18991,"date":"2023-08-25T17:27:32","date_gmt":"2023-08-25T11:57:32","guid":{"rendered":"https:\/\/smartfoodsafe.com\/clone\/?p=18991"},"modified":"2026-01-06T13:37:05","modified_gmt":"2026-01-06T08:07:05","slug":"establishing-statistical-process-control-in-quality-management-systems-qms","status":"publish","type":"post","link":"https:\/\/smartfoodsafe.com\/clone\/establishing-statistical-process-control-in-quality-management-systems-qms\/","title":{"rendered":"Establishing Statistical Process Control in Quality Management Systems (QMS)"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\r\n\r\n\r\n \r\n Smart HACCP Table of Contents<\/title>\r\n <style>\r\n .wrapper {\r\n display: flex;\r\n min-height: 90vh;\r\n margin: 0;\r\n padding: 0;\r\n}\r\n\r\n.toc {\r\n min-width: 300px !important;\r\n padding: 12px;\r\n background-color: #FEEEDB80 !important;\r\n position: sticky;\r\n top: 23% !important;\r\n height: fit-content;\r\n box-sizing: border-box;\r\n border-radius: 10px;\r\n}\r\n\r\n.toc h2 {\r\n font-size: 1.2rem;\r\n margin-top: 1rem;\r\n margin-bottom: 1rem;\r\n font-family: Plus Jakarta Sans;\r\n font-weight: 900;\r\n color: #0b163f;\r\n padding-left: 6px;\r\n}\r\n\r\n.toc ul {\r\n list-style: none;\r\n padding-left: 0;\r\n font-family: Plus Jakarta Sans;\r\n color: #0b163f;\r\n}\r\n.toc li.active {\r\n background-color: #f7d0a3; \/* Desired background color *\/\r\n color: #0B163F; \/* Desired text color *\/\r\n font-weight: bold;\r\n border-radius: 5px;\r\n}\r\n\r\n.toc li a {\r\n color: inherit;\r\n text-decoration: none;\r\n display: block; \/* Ensures the link fills the entire list item *\/\r\n width: 100%;\r\n height: 100%;\r\n padding-top: 7px;\r\n padding-bottom: 7px;\r\n padding-left: 10px;\r\n}\r\n\r\n.content {\r\n flex-grow: 1;\r\n padding: 0rem 1rem 0rem 1.5rem;\r\n box-sizing: border-box;\r\n}\r\n.content-img {\r\n width: 70%;\r\n height: auto;\r\n display: block;\r\n margin: 0 auto 1rem auto; \/* centers image and adds bottom spacing *\/\r\n border-radius: 0.75rem; \/* optional: slightly rounded corners *\/\r\n}\r\n.content-bar-img, .content-bar-heading\r\n {\r\n margin-bottom: 8px !important;\r\n}\r\n.section {\r\n margin-bottom: 3rem;\r\n margin-top: 1rem;\r\n}\r\n\r\n.section h2 {\r\n font-size: 1.5rem;\r\n margin-top: 0;\r\n color: #0b163f;\r\n font-family: Plus Jakarta Sans;\r\n font-weight: 900;\r\n}\r\n\r\n.section p {\r\n color: #333;\r\n line-height: 1.6;\r\n font-family: poppins;\r\n font-weight: 400;\r\n color: #0b163f;\r\n}\r\n\r\n.section ul {\r\n padding-left: 1.2rem;\r\n margin-top: 0.5rem;\r\n font-family: poppins;\r\n font-weight: 400;\r\n color: #0b163f;\r\n}\r\n\r\n.section ul li {\r\n margin-bottom: 0.5rem;\r\n}\r\n\r\nhtml {\r\n scroll-behavior: smooth;\r\n}\r\n\r\n.section-image {\r\n width: 100%;\r\n max-width: 600px;\r\n height: auto;\r\n margin-top: 1.5rem;\r\n border-radius: 10px !important;\r\n}\r\n\r\n@media (max-width: 768px) {\r\n .wrapper {\r\n flex-direction: column;\r\n }\r\n\r\n .toc ,\r\n .content {\r\n height: auto;\r\n }\r\n .toc {\r\n position: unset;\r\n width: fit-content !important;\r\n}\r\n}\r\n.section p a, .section ol li a,.section ul li a{\r\n color: #E88300;\r\n text-decoration: underline;\r\n}\r\n\r\n@media (min-width: 769px) and (max-width: 1024px) {\r\n nav.toc.list {\r\n min-width: 220px !important;\r\n height: fit-content !important;\r\n padding: 15px;\r\n }\r\n .toc li a{\r\n font-size: 14px;\r\n line-height: 1.5em;\r\n }\r\n}\r\n\r\n@media (max-width: 1024px) {\r\nnav.toc.list {\r\n margin-left: 25px;\r\n margin-bottom: 25px;\r\n margin-right: 20px;\r\n}\r\n.left_content_post_col {\r\n width: 100% !important;\r\n}\r\n}\r\n@media (min-width: 1025px) and (max-width: 1193px) {\r\n.content {\r\n padding: 1rem !important;\r\n}\r\n .toc, .sticky-right-bar {\r\n top: 22% !important;\r\n \/*height: 40vh !important;*\/\r\n \/*min-height: 40vh;*\/\r\n }\r\n.sticky-right-bar .elementor-heading-title {\r\n font-size: 16px !important;\r\n font-weight: 600;\r\n}\r\n.right-media-screen{\r\n width: 25% !important;\r\n}\r\n \/*.toc {*\/\r\n \/* height: 69vh !important;*\/\r\n \/* }*\/\r\n}\r\n\r\n@media (min-width: 2249px) and (max-width:4000px){\r\n \/*.wrapper .toc, .sticky-right-bar {*\/\r\n \/* height: 50vh !important;*\/\r\n \/* min-height: 40vh;*\/\r\n \/*}*\/\r\n}\r\n @media only screen and (min-width: 2250px) and (min-height: 1500px) {\r\n .wrapper .toc {\r\n min-width: 300px !important;\r\n padding: 1.5rem;\r\n background-color: #FEEEDB80 !important;\r\n position: sticky;\r\n top: 23% !important;\r\n height: 30vh !important;\r\n box-sizing: border-box;\r\n border-radius: 10px;\r\n}\r\n}\r\n\r\n\r\n <\/style>\r\n<\/head>\r\n<body>\r\n\r\n<div class=\"wrapper\">\r\n <!-- Left Side: TOC -->\r\n <nav class=\"toc list\">\r\n <h2>Table of Contents<\/h2>\r\n <ul>\r\n <li><a href=\"#section1\">1. Statistical Process Control As A Subset Tool of Six Sigma<\/a><\/li>\r\n <li><a href=\"#section2\">2. Examining the Concept of Statistical Process Control<\/a><\/li>\r\n <li><a href=\"#section3\">3. General Steps for Implementing SPC in the Process<\/a><\/li>\r\n <li><a href=\"#section4\">4. Application of Statistical Process Control in the Food Industry<\/a><\/li>\r\n <li><a href=\"#section5\">5. Employ Statistical Process Control for your Food Industry With Smart Food Safe<\/a><\/li>\r\n <\/ul>\r\n <\/nav>\r\n\r\n <!-- Right Side: Content -->\r\n <main class=\"content\">\r\n <img decoding=\"async\" src=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2023\/08\/General-steps-for-implementing.png\" alt=\"Help Icon\" class=\"sticky-img\" \/>\r\n<section class=\"section\">\r\n <p>\u201cQuality comes not from inspection but from the improvement of the production process,\u201d was notably stated by <a href=\"https:\/\/en.wikipedia.org\/wiki\/W._Edwards_Deming\" rel=\"nofollow\" target=\"_blank\"><strong>William Edwards Deming<\/strong><\/a>, widely acknowledged as the leading management thinker in the field of quality. This quote strongly resonates with the key objective of statistical process control (SPC), one of his pioneering quality management strategies.<\/p>\r\n\r\n<p>So, what is SPC? In a nutshell, it refers to a quality control approach that utilizes statistical techniques for the supervision and regulation of a process to guarantee its adherence to predefined specifications. SPC is utilized for the purpose of managing and enhancing production processes, with the goal of boosting both the efficiency of processes and the quality of the resulting products.<\/p>\r\n\r\n<p>As the times are changing with the sophistication and modernization of the food supply chain, SPC, as a significant aspect of the conventional paradigm of quality, gained momentum in food quality management over the past few years. Though primarily used as a quality management strategy, SPC has multiple functionalities in the food industry by collecting and analyzing real-time data during various stages of production to identify and address variations, deviations, or abnormalities that could impact product integrity and safety.<\/p>\r\n\r\n<p id=\"section1\">In this article, we will delve into the different elements for food processors to bear in mind for successfully establishing a statistical process control in their manufacturing unit as part of their food quality management efforts.<\/p>\r\n\r\n <h2>Statistical Process Control As A Subset Tool of Six Sigma<\/h2>\r\n <p>Six Sigma is a quality enhancement initiative with the prime purpose of minimizing defects to a level of 3.4 parts per million (PPM) through the reduction of process variations. The term \u201cSix Sigma\u201d is derived from a statistical concept where the goal is to achieve a process performance that is six standard deviations away from the mean, resulting in an extremely high level of quality and consistency. SPC is a subset tool of Six Sigma used to keep a close eye on production operations to spot any abnormalities and alert the company so they can fix it before it gets worse. <\/p>\r\n <img decoding=\"async\" src=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2023\/08\/Graph.png\">\r\n <p>Reference: <a href=\"https:\/\/asq.org\/quality-resources\/six-sigma\" rel=\"nofollow\" target=\"_blank\">https:\/\/asq.org\/quality-resources\/six-sigma<\/a><\/p>\r\n <p>At the heart of Six Sigma is a focus on measuring and analyzing the performance of processes in order to identify and eliminate variations that can lead to defects or errors. This is achieved through a structured methodology known as DMAIC, which stands for Define, Measure, Analyze, Improve, and Control. Each phase of DMAIC has specific goals and activities:<\/p>\r\n <ul>\r\n <li>Define: In this phase, the project goals, scope, and customer requirements are clearly defined. This helps ensure that the improvement efforts are aligned with the needs of both internal and external stakeholders.<\/li>\r\n <li>Measure: During this phase, relevant data is collected, and measurements are taken to assess the current performance of the process. This step helps quantify the extent of variability and identify key areas of concern.<\/li>\r\n <li>Analyze: The data collected in the previous phase is analyzed to identify the root causes of defects or inefficiencies. Statistical tools and techniques are employed to pinpoint the factors contributing to process variations.<\/li>\r\n <li>Improve: In this phase, solutions are developed and implemented to address the identified root causes. These solutions are tested and refined to ensure they have a positive impact on process performance.<\/li>\r\n <li id=\"section2\">Control: The improvements made in the previous phase are integrated into the process, and measures are put in place to monitor and sustain the enhanced performance over time. Control mechanisms are established to prevent the re-emergence of defects and variations.<\/li>\r\n<\/ul>\r\n\r\n<\/section>\r\n\r\n<section class=\"section py-12 px-6 md:px-16 bg-white text-gray-800 font-sans\">\r\n <div class=\"max-w-7xl mx-auto\">\r\n <h2 class=\"text-4xl font-bold text-[#097D7F] mb-6\">Examining the Concept of Statistical Process Control<\/h2>\r\n <h4 style=\"font-size: 17px;\">History<\/h4>\r\n<p>SPC gained its initial prominence after World War II, in 1950, within the rapid development of the Japanese economy and manufacturing sector by promoting statistical tools, thanks to William E. Deming, who expanded upon the concepts originally formulated by Walter A. Shewhart in 1920. Nevertheless, it wasn\u2019t until 1980 that the Western manufacturing industry swiftly embraced this method for its own specific purposes.<\/p>\r\n<p>With the growth of industries and technology, SPC became well-known and spread beyond manufacturing into various sectors, including food industries, shaping the way organizations manage and optimize processes to meet quality standards.<\/p>\r\n\r\n<h4 style=\"font-size: 17px;\">Objective<\/h4>\r\n<p>SPC seeks to take the data generated from the manufacturing processes and use it in a more productive way. The key objective is to harness statistical tools and analytical techniques to extract data for gaining information and insights into manufacturing processes. These insights are then utilized to implement and install effective controls within the processes.<\/p>\r\n<p>SPC aids in ensuring that the process operates efficiently, resulting in an increased production of products that align with specifications while minimizing waste, rework, or scrap, and it can be applied to any process where the \u201cconforming product\u201d output can be measured, such as manufacturing lines.<\/p>\r\n\r\n<h4 style=\"font-size: 17px;\">Importance of SPC for Food Manufacturers<\/h4>\r\n<p>Every aspect of the food supply chain generates data, and a significant portion of this data collected by food businesses is retained as evidence for compliance. It also presents an opportunity for food establishments to comprehend the processes or propel enhancement as this data embodies an ocean of insight, which, if effectively put into use, can help augment process productivity, conformance, and worker experience.<\/p>\r\n<p>Food safety and quality standards are evolving to emphasize a process-based approach to developing, implementing, and improving food quality management systems aligning with compliance and risk-based models. SPC simply helps companies move towards prevention-based quality control instead of detection-based quality controls. This is why several GFSI standards have begun to realize and integrate SPC within its guidelines.<\/p>\r\n<p>Recent revisions to multiple standards are compelling enterprises to acknowledge the necessity of identifying and implementing SPC and other statistical analyses. For instance, the widely recognized food quality management standard ISO 22000 encompasses the <a href=\"https:\/\/www.iso.org\/obp\/ui\/en\/#iso:std:iso:22000:ed-2:v1:en\" rel=\"nofollow\" target=\"_blank\"><strong>Plan-Do-Check-Act (PDCA) cycle<\/strong><\/a>, in which the \u201cCheck\u201d phase refers to monitoring, measuring, and verifying processes and the consequent products and services, followed by evaluating the information and data, and reporting the results. SPC becomes highly relevant in this context by being a tool to facilitate continuous monitoring of processes and analyzing operation data, enabling manufacturers to demonstrate compliance with ISO 22000 requirements effectively. SPC\u2019s data-driven methodology ensures that deviations are promptly detected and addressed, reducing the risk of food quality issues.<\/p>\r\n\r\n<h3 style=\"font-size: 17px;\">Key Components for Understanding SPC<\/h3>\r\n<p><strong>\u2022 Process Variation<\/strong><\/p>\r\n<p>The basic assumption of SPC is that all processes are generally subject to variation. <a href=\"https:\/\/sixsigmastudyguide.com\/statistical-process-control-spc\/\" rel=\"nofollow\" target=\"_blank\"><strong>Variation<\/strong><\/a> is the inherent tendency of a process to deviate, move, shift, or vary around a mean central point. It is a phenomenon that can be observed throughout nature as all processes have variations and are impossible to eliminate. Variations can belong to any of these two categories:<\/p>\r\n\r\n<ul>\r\n <li>Common Cause: This type of variation in a process arises by chance and cannot be attributed to any specific factor. It represents the inherent variability within the process. When a process is affected by common cause variation, it remains stable, and its behavior is predictable.<\/li>\r\n <li>Assignable Cause: Alternatively referred to as \u201cspecial cause,\u201d it is the type of variation in a process that is not due to chance. It can be identified and eliminated by identifying its root cause. When a process is influenced by an assignable cause, it becomes unstable, and its behavior cannot be easily predicted.<\/li>\r\n<\/ul>\r\n\r\n<p><strong>\u2022 Process Data<\/strong><\/p>\r\n<p>As Deming rightly stated, \u201cUncontrolled variation is the enemy of quality.\u201d So, how to control process variation? Every process data has a typical or normal distribution around the mean, and most readings will fall close to the mean while fewer readings will fall further away from the mean value. By understanding and knowing the degree and nature of variability, it is possible to control the variation. In order to break down the characteristics of variations, data collection is essential. Data comes in two types: variable data (numerical) and attributes (non-numerical). In food industries, data comes in many forms, namely, process parameters such as time, temperature, pressure, pH, etc., audit outcomes, non-conformance, critical control point monitoring data, and sanitation and cleaning results.<\/p>\r\n\r\n<p><strong>\u2022 Data Monitoring<\/strong><\/p>\r\n<p>Controlling process variations necessitates monitoring and measuring the collected data, and in food companies, the principle of Hazard Analysis Critical Control Point (HACCP) accomplishes this goal. Monitoring data in food quality management is based on two requirements:<\/p>\r\n\r\n<ol style=\"font-family: 'Poppins', sans-serif; color: #0b163f;\">\r\n <li>Compliance with regulations and customer demands<\/li>\r\n <li>Risk in the process or product<\/li>\r\n<\/ol>\r\n\r\n<p><strong>\u2022 Analyzing Data<\/strong><\/p>\r\n<p id=\"section3\">Measuring an ample amount of data can be followed by simply determining if the data point meets the specification or analyzing the data using SPC techniques to describe the process in terms of capability to meet the specification consistently. Even if process data yields compliance with the mandated specifications, slight shifts in the process could eventually lead to out-of-spec results over time. With the application of SPC, these variations can be revealed and rectified in time, thereby proactively preventing their occurrence in the long run.<\/p>\r\n\r\n <\/div>\r\n<\/section>\r\n\r\n\r\n<section class=\"section\">\r\n <h2>General Steps for Implementing SPC in the Process<\/h2>\r\n <ol style=\"font-family: 'Poppins', sans-serif; color: #0b163f;\">\r\n <li>Identify the Processes: To begin, specify the crucial processes that significantly influence the product\u2019s output or the process that holds paramount importance to the customer.<\/li>\r\n <li>Define Measurable Process Attributes: Recognize the specific attributes that have to be measured throughout the production phase.<\/li>\r\n <li>Determine the Measurement Method: Formulate instructions or a procedure for the measurement method, including the instruments employed for measurement.<\/li>\r\n <li>Develop Subgroup Strategy and Sampling Plan: Moving forward, strategize the size of subgroups and frequency of the samples to be collected based on the product\u2019s critical nature.<\/li>\r\n <li>Collect Data and Plot the SPC Chart: Proceed to collect data according to the defined sample size. Choose an appropriate SPC chart considering the type of data (Continuous or Discrete) and the size of subgroups.<\/li>\r\n <li>Describe the Natural Variation of Attributes: Calculate the control limits. Derive the upper control limit (UCL) and the lower control limit (LCL) for both X-Bar and Range. For the X-Bar chart, the UCL and LCL help indicate if the process mean is consistent and within acceptable bounds. On the other hand, for the Range chart, the UCL and LCL help gauge the variability or spread of data within subgroups.<\/li>\r\n <li>Monitor Process Variation: Interpret the control chart, scrutinizing any out-of-control points and discerning patterns. Given that this is a continuous endeavor, consistently oversee process variability.<\/li>\r\n <li>Execute Corrective Actions: If any out-of-control points or patterns are identified, implement appropriate corrective actions to bring the process back under control and prevent further deviations.<\/li>\r\n <li id=\"section4\">Ensure Continuous Enhancement: Continuously analyze the gathered data and insights from the control chart to identify opportunities for process improvement and refinement, aiming for ongoing enhancement of the production process.<\/li>\r\n<\/ol>\r\n\r\n<\/section>\r\n \r\n \r\n<section class=\"section\">\r\n <h2>Application of Statistical Process Control in the Food Industry<\/h2>\r\n <p>With the general steps of SPC application in consideration, let\u2019s delve into the application of SPC in the food industry:<\/p>\r\n <h3 style=\"font-size: 17px;\">Data Collection<\/h3>\r\n<p>Relevant data points are collected during the production process. These data points could include measurements of temperature, pressure, pH, moisture content, weight, and other critical factors that influence the quality of the final product.<\/p>\r\n\r\n<h3 style=\"font-size: 17px;\">Control Charts<\/h3>\r\n<p>A control chart is one of the primary statistical process control techniques (SPC). The control chart is a process\u2010behavior chart for the graphical display of quality characteristics that are measured or computed from a sample versus the sample number or time. Furthermore, the control chart contains a center line that represents the average value of the quality characteristics and two other horizontal lines known as the upper control limit (UCL) and lower control limit (LCL). These charts help visualize the data collected over time and provide insights into the stability and variability of the process.<\/p>\r\n<p>X-Bar and R-Chart \u2013 The X-Bar and R-chart are quality control charts utilized to track the mean and the variation of a process through samples collected within a specified timeframe. They are employed to oversee the process performance of continuous data, such as weight or volume, gathered in subgroups at predetermined intervals. It monitors the average (X-Bar) and range (R) of a sample taken at regular intervals.<\/p>\r\n<img decoding=\"async\" src=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2023\/08\/weight-chart.png\" style=\"border-radius: 10px;\" \/>\r\n\r\n<p>Reference: <a href=\"https:\/\/support.minitab.com\/en-us\/minitab\/21\/help-and-how-to\/quality-and-process-improvement\/control-charts\/how-to\/variables-charts-for-subgroups\/xbar-r-chart\/interpret-the-results\/key-results\/\" rel=\"nofollow\" target=\"_blank\">https:\/\/support.minitab.com\/en-us\/minitab\/21\/help-and-how-to\/quality-and-process-improvement\/control-charts\/how-to\/variables-charts-for-subgroups\/xbar-r-chart\/interpret-the-results\/key-results\/<\/a><\/p>\r\n<p>X bar \u2013 S Charts \u2013 X Bar \u2013 S charts are frequently used control charts for evaluating both the process mean and standard deviation over time. They come into play when subgroups have large sample sizes, and the S chart provides a better understanding of the spread of subgroup data than the range. <\/p>\r\n<img decoding=\"async\" src=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2023\/08\/x-bar-chart.png\" style=\"border-radius: 10px;\" \/>\r\n<p>Reference: <a href=\"https:\/\/support.minitab.com\/en-us\/minitab\/21\/help-and-how-to\/quality-and-process-improvement\/control-charts\/how-to\/variables-charts-for-subgroups\/xbar-s-chart\/before-you-start\/overview\/\" rel=\"nofollow\" target=\"_blank\">https:\/\/support.minitab.com\/en-us\/minitab\/21\/help-and-how-to\/quality-and-process-improvement\/control-charts\/how-to\/variables-charts-for-subgroups\/xbar-s-chart\/before-you-start\/overview\/<\/a><\/p>\r\n<p>X-MR Charts (Individual-Moving Range Charts) \u2013 An Individual-Moving Range (I-MR) chart is the same as an X-MR chart. This chart is employed when collecting one observation at a time and not as continuous data in groups.<\/p>\r\n\r\n<img decoding=\"async\" src=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2023\/08\/I-mar-chart.png\" style=\"border-radius: 10px;\" \/>\r\n<p>Reference: <a href=\"https:\/\/support.minitab.com\/en-us\/minitab\/21\/help-and-how-to\/quality-and-process-improvement\/control-charts\/how-to\/variables-charts-for-individuals\/i-mr-chart\/before-you-start\/overview\/\" rel=\"nofollow\" target=\"_blank\">https:\/\/support.minitab.com\/en-us\/minitab\/21\/help-and-how-to\/quality-and-process-improvement\/control-charts\/how-to\/variables-charts-for-individuals\/i-mr-chart\/before-you-start\/overview\/<\/a><\/p>\r\n<p>Run Charts \u2013 A run chart displays observed data as they evolve over time. This basic graph arranges data values in sequential order, serving as a tool to test for randomness in the process. It is limited to single-point data.<\/p>\r\n\r\n<p>P Chart and NP Chart \u2013 This chart is used for monitoring the proportion of defective items in a sample (P) or the count of defective items (NP). These charts are suitable for categorical data. Opt for a P chart when the data displays a percentage of defects or non-confirming units in a given number of samples. Opt for an NP chart when the data is collected in same-sized subgroups, and they demonstrate how the process changes over time, measured by the number of nonconforming items (defectives) produced.<\/p>\r\n\r\n<img decoding=\"async\" src=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2023\/08\/P-chart.png\" style=\"border-radius: 10px; width: 100%;\" \/>\r\n<p>Reference: <a href=\"https:\/\/support.minitab.com\/en-us\/minitab\/21\/help-and-how-to\/quality-and-process-improvement\/control-charts\/how-to\/attributes-charts\/p-chart\/before-you-start\/overview\/\" rel=\"nofollow\" target=\"_blank\">https:\/\/support.minitab.com\/en-us\/minitab\/21\/help-and-how-to\/quality-and-process-improvement\/control-charts\/how-to\/attributes-charts\/p-chart\/before-you-start\/overview\/<\/a><\/p>\r\n<img decoding=\"async\" src=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2023\/08\/N-chart.png\" style=\"border-radius: 10px; width: 100%;\" \/>\r\n<p>Reference: <a href=\"https:\/\/support.minitab.com\/en-us\/minitab\/21\/help-and-how-to\/quality-and-process-improvement\/control-charts\/how-to\/attributes-charts\/np-chart\/before-you-start\/example\/\" rel=\"nofollow\" target=\"_blank\">https:\/\/support.minitab.com\/en-us\/minitab\/21\/help-and-how-to\/quality-and-process-improvement\/control-charts\/how-to\/attributes-charts\/np-chart\/before-you-start\/example\/<\/a><\/p>\r\n<p>C Chart and U Chart \u2013 Similar to the P and NP charts, these charts are used for monitoring the count (C) or rate (U) of defects in a sample.<\/p>\r\n<img decoding=\"async\" src=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2023\/08\/U-chart.png\" style=\"border-radius: 10px; width: 100%;\" \/>\r\n<p>Reference: <a href=\"https:\/\/support.minitab.com\/en-us\/minitab\/21\/help-and-how-to\/quality-and-process-improvement\/control-charts\/how-to\/attributes-charts\/u-chart\/before-you-start\/overview\/\" rel=\"nofollow\" target=\"_blank\">https:\/\/support.minitab.com\/en-us\/minitab\/21\/help-and-how-to\/quality-and-process-improvement\/control-charts\/how-to\/attributes-charts\/u-chart\/before-you-start\/overview\/<\/a><\/p>\r\n<p>The type of control chart depends on the following factors:<\/p>\r\n<ul>\r\n <li>Type of data being charted (continuous or attribute)<\/li>\r\n <li>Required sensitivity (size of the change to be detected) of the chart<\/li>\r\n <li>Whether the chart includes data from multiple locations or not<\/li>\r\n <li>Ease and cost of sampling<\/li>\r\n <li>Production volumes<\/li>\r\n<\/ul>\r\n<img decoding=\"async\" src=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2023\/08\/Graph-tree.png\" style=\"border-radius: 10px; width: 100%;\" \/>\r\n<p>Reference: <a href=\"https:\/\/www.qualitygurus.com\/seven-quality-tools-control-charts\/\" rel=\"nofollow\" target=\"_blank\">https:\/\/www.qualitygurus.com\/seven-quality-tools-control-charts\/<\/a><\/p>\r\n<h3 style=\"font-size:17px; font-weight:bold;\">Establishing Control Limits<\/h3>\r\n<p>Control limits are set on the control charts to define the acceptable range of variation. These limits are usually based on historical data or industry standards. Data points falling within the control limits are considered acceptable, while those falling outside the limits might indicate an issue with the process.<\/p>\r\n<p>Here is how you can calculate the control units:<\/p>\r\n<p>Estimate the standard deviation (\u03c3) of the sample data<\/p>\r\n<p>To calculate Upper Control Limit, UCL = Average + 3 x \u03c3<\/p>\r\n<p>To calculate Lower Control Limit, LCL = Average \u2013 3 x \u03c3<\/p>\r\n<img decoding=\"async\" src=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2023\/08\/Process-output.png\" style=\"border-radius: 10px; width: 100%;\" \/>\r\n<p>Reference: <a href=\"https:\/\/opexresources.com\/introduction-statistical-process-control\/\" rel=\"nofollow\" target=\"_blank\">https:\/\/opexresources.com\/introduction-statistical-process-control\/<\/a><\/p>\r\n<p>Other key formulas needed in the SPC process are for calculating Cp (Capability) and Cpk (Performance) to determine whether the process is able to meet specifications.<\/p>\r\n\r\n<p>Cp = (USL \u2013 LSL) \/ (6 \u03c3)<\/p>\r\n\r\n<p>Cpk = min[(USL \u2013 X) \/ (3 \u03c3), (X \u2013 LSL) \/ (3 \u03c3)]<\/p>\r\n\r\n<p>where, X = process average<\/p>\r\n<p>LSL = Lower Specification Limit<\/p>\r\n<p>USL = Upper Specification Limit<\/p>\r\n<p>\u03c3est = Process Standard Deviation<\/p>\r\n<h4 style=\"font-size: 17px;\">Monitoring and Analysis<\/h4>\r\n<p>Regularly analyzing the control charts helps identify trends, shifts, or patterns in the process. Any points beyond the control limits, repeated patterns, or other unusual occurrences can signal the need for investigation and corrective action.<\/p>\r\n\r\n<h4 style=\"font-size: 17px;\">Corrective Actions<\/h4>\r\n<p>When the process goes out of control or shows signs of potential issues, corrective actions are taken. These actions could involve adjusting process parameters, troubleshooting equipment, modifying procedures, or implementing process improvements.<\/p>\r\n\r\n<h4 style=\"font-size: 17px;\">Continuous Improvement<\/h4>\r\n<p id=\"section5\">SPC is not a one-time activity but a continuous process. The insights gained from the control charts and the corrective actions taken contribute to process improvement efforts over time as they equip food businesses to understand how capable their food industry processes are for producing spec-conforming products.<\/p>\r\n<\/section>\r\n \r\n<section class=\"section\">\r\n <h2>Employ Statistical Process Control for your Food Industry With Smart Food Safe<\/h2>\r\n <p>With the tech-enabled features of <strong><a href=\"https:\/\/smartfoodsafe.com\/clone\/\">Smart Food Safe<\/a><\/strong>\u2018s multitude of digital modules, food establishments can implement statistical process control (SPC) for a robust quality control system that facilitates the detection of variations and deviations in food production processes, ensuring consistent product quality while limiting waste and risks.<\/p>\r\n <p>Our custom reporting feature empowers businesses in the food industry by serving as a valuable tool for enabling the creation of specialized process control charts and identifying and addressing compliance gaps within the food production processes. Intuitive dashboards provide a consolidated place for presenting these charts, offering a visual representation of key performance indicators and critical control points, aiding businesses in closely monitoring their operations. In this way, businesses can proactively pinpoint areas for refinement, optimize their processes, and ultimately bolster the overall safety and quality of their food products.<\/p>\r\n<\/section> \r\n \r\n <\/main>\r\n<\/div>\r\n<script>\r\n document.addEventListener('DOMContentLoaded', function () {\r\n const tocItems = document.querySelectorAll('.toc li');\r\n\r\n tocItems.forEach(function (item) {\r\n item.addEventListener('click', function () {\r\n \/\/ Remove 'active' class from all TOC items\r\n tocItems.forEach(function (el) {\r\n el.classList.remove('active');\r\n });\r\n\r\n \/\/ Add 'active' class to the clicked TOC item\r\n this.classList.add('active');\r\n });\r\n });\r\n });\r\n<\/script>\r\n\r\n\r\n<\/body>\r\n<\/html>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-401f28e elementor-widget elementor-widget-accordion\" data-id=\"401f28e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-accordion\">\n\t\t\t\t\t\t\t<div class=\"elementor-accordion-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-6721\" class=\"elementor-tab-title\" data-tab=\"1\" role=\"button\" aria-controls=\"elementor-tab-content-6721\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-accordion-title\" tabindex=\"0\">What is Statistical Process Control (SPC) and how does it relate to quality management in the food industry?<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-6721\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"1\" role=\"region\" aria-labelledby=\"elementor-tab-title-6721\"><p>Statistical Process Control (SPC) is a method that employs statistical techniques to monitor and regulate processes, ensuring they adhere to predefined specifications. In the food industry, SPC enhances quality management by detecting variations, preventing issues, and maintaining compliance with safety standards.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-accordion-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-6722\" class=\"elementor-tab-title\" data-tab=\"2\" role=\"button\" aria-controls=\"elementor-tab-content-6722\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-accordion-title\" tabindex=\"0\">What are control charts, and how are they used in SPC?<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-6722\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"2\" role=\"region\" aria-labelledby=\"elementor-tab-title-6722\"><p>SPC integrates with QMS by providing real-time data insights. It aids in continuous monitoring, identifying process variations, and ensuring compliance with quality standards. SPC\u2019s proactive approach aligns with QMS principles, fostering consistent quality and efficient processes in various industries, including the food sector.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-accordion-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-6723\" class=\"elementor-tab-title\" data-tab=\"3\" role=\"button\" aria-controls=\"elementor-tab-content-6723\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-accordion-title\" tabindex=\"0\">What is the relationship between SPC and Six Sigma?<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-6723\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"3\" role=\"region\" aria-labelledby=\"elementor-tab-title-6723\"><p>SPC is a subset of Six Sigma, a quality management methodology. While Six Sigma aims to minimize defects through variation reduction, SPC is a tool within Six Sigma that monitors processes in real-time. SPC helps identify anomalies and deviations, contributing to Six Sigma\u2019s goal of process improvement and defect reduction.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-accordion-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-6724\" class=\"elementor-tab-title\" data-tab=\"4\" role=\"button\" aria-controls=\"elementor-tab-content-6724\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-accordion-title\" tabindex=\"0\">Industrial and Manufacturing LMS<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-6724\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"4\" role=\"region\" aria-labelledby=\"elementor-tab-title-6724\"><p>In the industrial and manufacturing sector, a specialized Learning Management System (LMS) is instrumental in fostering workforce development. It equips employees with the skills needed to operate complex machinery safely and efficiently.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<script type=\"application\/ld+json\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What is Statistical Process Control (SPC) and how does it relate to quality management in the food industry?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"<p>Statistical Process Control (SPC) is a method that employs statistical techniques to monitor and regulate processes, ensuring they adhere to predefined specifications. 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While Six Sigma aims to minimize defects through variation reduction, SPC is a tool within Six Sigma that monitors processes in real-time. SPC helps identify anomalies and deviations, contributing to Six Sigma\\u2019s goal of process improvement and defect reduction.<\\\/p>\"}},{\"@type\":\"Question\",\"name\":\"Industrial and Manufacturing LMS\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"<p>In the industrial and manufacturing sector, a specialized Learning Management System (LMS) is instrumental in fostering workforce development. It equips employees with the skills needed to operate complex machinery safely and efficiently.<\\\/p>\"}}]}<\/script>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-19b7bce6 elementor-hidden-tablet right_content_post_col\" data-id=\"19b7bce6\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-7140dd76 sticky-right-bar elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7140dd76\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{"background_background":"classic"}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-114bc20e content-bar\" data-id=\"114bc20e\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-26ac1cfa content-bar-img elementor-widget elementor-widget-image\" data-id=\"26ac1cfa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"512\" height=\"380\" src=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2025\/05\/Management_Software_img.png\" class=\"attachment-large size-large wp-image-61364\" alt=\"\" srcset=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2025\/05\/Management_Software_img.png 512w, https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2025\/05\/Management_Software_img-300x223.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4dbc5a0a content-bar-heading elementor-widget elementor-widget-heading\" data-id=\"4dbc5a0a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Quality and Food Safety Management Software\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-efea49 elementor-widget elementor-widget-text-editor\" data-id=\"efea49\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tFood Safety and Quality Management Software to streamline processes, track compliance, ensure traceability and maintain audit readiness with global quality and food safety standards\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-404c5fdb elementor-align-center elementor-widget elementor-widget-button\" data-id=\"404c5fdb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/calendly.com\/smartfoodsafe\/60-minutes\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Book Demo<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<div class=\"elementor-element elementor-element-4e3a4331 elementor-widget elementor-widget-spacer\" data-id=\"4e3a4331\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6c214850 elementor-hidden-widescreen elementor-hidden-desktop elementor-hidden-tablet_extra elementor-hidden-mobile elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6c214850\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{"background_background":"classic"}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1c64d39b\" data-id=\"1c64d39b\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-3a864ecb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3a864ecb\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{"background_background":"classic"}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-9d7c3e3\" data-id=\"9d7c3e3\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6e67de06 elementor-widget elementor-widget-heading\" data-id=\"6e67de06\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Quality and Food Safety Management Software\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-109459a8 elementor-widget elementor-widget-text-editor\" data-id=\"109459a8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tFood Safety and Quality Management Software to streamline processes, track compliance, ensure traceability and maintain audit readiness with global quality and food safety standards\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6f84c5fb elementor-align-center elementor-tablet-align-center elementor-widget elementor-widget-button\" data-id=\"6f84c5fb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/calendly.com\/smartfoodsafe\/60-minutes\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Book Demo<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-76c47c61 elementor-hidden-widescreen elementor-hidden-desktop\" data-id=\"76c47c61\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-45fa0fc0 elementor-widget elementor-widget-image\" data-id=\"45fa0fc0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"512\" height=\"380\" src=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2025\/05\/Management_Software_img.png\" class=\"attachment-large size-large wp-image-61364\" alt=\"\" srcset=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2025\/05\/Management_Software_img.png 512w, https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2025\/05\/Management_Software_img-300x223.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>\u201cQuality comes not from inspection but from the improvement of the production process,\u201d was notably stated by William Edwards Deming, widely acknowledged as the leading management thinker in the field of quality. This quote strongly resonates with the key objective of statistical process control (SPC), one of his pioneering quality management strategies.<\/p>\n","protected":false},"author":5,"featured_media":45248,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"_joinchat":[],"footnotes":""},"categories":[1],"tags":[],"class_list":["post-18991","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Establishing Statistical Process Control in Quality Management Systems (QMS)<\/title>\n<meta name=\"description\" content=\"Pivotal role of Statistical Process Control (SPC) in elevating food quality. 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Process-based quality management systems to the world of food quality control.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/smartfoodsafe.com\/clone\/establishing-statistical-process-control-in-quality-management-systems-qms\/\" \/>\n<meta property=\"og:site_name\" content=\"Smart Food Safe\" \/>\n<meta property=\"article:published_time\" content=\"2023-08-25T11:57:32+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-01-06T08:07:05+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2023\/08\/General-steps-for-implementing.png\" \/>\n\t<meta property=\"og:image:width\" content=\"5638\" \/>\n\t<meta property=\"og:image:height\" content=\"3601\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Manu R\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Manu R\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"16 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/smartfoodsafe.com\/clone\/establishing-statistical-process-control-in-quality-management-systems-qms\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/smartfoodsafe.com\/clone\/establishing-statistical-process-control-in-quality-management-systems-qms\/\"},\"author\":{\"name\":\"Manu R\",\"@id\":\"https:\/\/smartfoodsafe.com\/clone\/#\/schema\/person\/befcd77b7bed8b6ecc769bc73ced15c9\"},\"headline\":\"Establishing Statistical Process Control in Quality Management Systems (QMS)\",\"datePublished\":\"2023-08-25T11:57:32+00:00\",\"dateModified\":\"2026-01-06T08:07:05+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/smartfoodsafe.com\/clone\/establishing-statistical-process-control-in-quality-management-systems-qms\/\"},\"wordCount\":3116,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/smartfoodsafe.com\/clone\/#organization\"},\"image\":{\"@id\":\"https:\/\/smartfoodsafe.com\/clone\/establishing-statistical-process-control-in-quality-management-systems-qms\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2023\/08\/General-steps-for-implementing.png\",\"articleSection\":[\"Technology\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/smartfoodsafe.com\/clone\/establishing-statistical-process-control-in-quality-management-systems-qms\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/smartfoodsafe.com\/clone\/establishing-statistical-process-control-in-quality-management-systems-qms\/\",\"url\":\"https:\/\/smartfoodsafe.com\/clone\/establishing-statistical-process-control-in-quality-management-systems-qms\/\",\"name\":\"Establishing Statistical Process Control in Quality Management Systems (QMS)\",\"isPartOf\":{\"@id\":\"https:\/\/smartfoodsafe.com\/clone\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/smartfoodsafe.com\/clone\/establishing-statistical-process-control-in-quality-management-systems-qms\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/smartfoodsafe.com\/clone\/establishing-statistical-process-control-in-quality-management-systems-qms\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/smartfoodsafe.com\/clone\/wp-content\/uploads\/2023\/08\/General-steps-for-implementing.png\",\"datePublished\":\"2023-08-25T11:57:32+00:00\",\"dateModified\":\"2026-01-06T08:07:05+00:00\",\"description\":\"Pivotal role of Statistical Process Control (SPC) in elevating food quality. 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