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It addresses the consequences of choosing an inappropriate chart type and focuses on variable (continuous) data rather than attribute (discrete) data. The guide includes a decision-tree approach and describes how tools like InfinityQS ProFicient support setup, display, sampling, and real-time alerting for process improvement.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/a-practical-guide-to-selecting-the-right-control-chart-part-1-control-charts-and-basic-considerations/563313/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/a-practical-guide-to-selecting-the-right-control-chart-part-1-control-charts-and-basic-considerations/563313.png","ImageObject",442,249,{"name":88,"@type":89},"Quinn Holloway","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-20","2026-10-12",true,{"@type":98,"interactionType":99,"userInteractionCount":47},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What is the purpose of a control chart in manufacturing?","Question",{"text":108,"@type":109},"A control chart is a real-time, time-ordered graphical feedback tool that indicates when significant changes occur in a manufacturing process. It helps operators decide when to act and when to do nothing based on process behavior.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How do variables data and attribute data differ for control chart selection?",{"text":113,"@type":109},"Variables data are continuous measurements (often with decimals), such as time, weight, length, or temperature. Attribute data are discrete counts or presence/absence measurements (such as defects), cannot use decimals, and cannot be used to calculate averages.",{"name":115,"@type":106,"acceptedAnswer":116},"Does an in-control process guarantee outputs meet specification limits?",{"text":117,"@type":109},"No. A process can be stable and in-control while still producing outputs outside specification limits, so statistical control should be confirmed before relying on capability analysis results.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},563313,1792382664,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":47,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":141},2336474466712,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","A Practical Guide to Selecting the Right Control Chart  \nInfinityQS International, Inc. | 12601 Fair Lakes Circle | Suite 250 | Fairfax, VA 22033 | [www.infinityqs.com](www.infinityqs.com)  \nIntroduction  \nControl charts were invented in the 1920’s by Dr. Walter Shewhart as a visual tool to determine if a manufacturing process is in statistical control. If the control chart indicates the manufacturing process is not in control, then corrections or changes should be made to the process parameters to ensure process and product consistency. For manufacturers, control charts are typically the first indication that something can be improved and warrants a root cause analysis or other process improvement investigation. Today, control charts are a key tool for quality control and figure prominently in lean manufacturing and Six Sigma efforts.  \nWith over 300 types of control charts available, selecting the most appropriate one for a given situation can be overwhelming. You may be using only one or two types of charts for all your manufacturing process data simply because you haven’t explored further possibilities or aren’t sure when to use others. Choosing the wrong type of control chart may result in “false positives” because the chart may not be sensitive enough for your process. Or there may be ways to analyze parts and processes you thought weren’t possible, resulting in new insights for possible process improvements.  \nThis guide leads quality practitioners through a simple decision tree to select the right control chart to improve manufacturing processes and product quality. This guide focuses on variables data , not attribute data , and highlights powerful charting functionality that users often overlook. You will learn which control chart is best fora given situation. InfinityQS’ ProFicient software offers easy setup and display of a wide variety of control charts including the ones highlighted throughout this guide. In addition, ProFicient’s quality hub gathers data from disparate sources, across multiple plants or production lines, using automated or manual sampling to present control charts in real time and alerting operators and quality engineers to take samples and initiate process improvements.  \nVariables Data – Measurements taken on a continuous scale such as time, weight, length, height, temperature, pressure, etc. These measurements can have decimals.  \nAttribute Data – Measurements taken in discrete units which indicates the presence or absence of something such as number of defects, injuries, errors, etc. This data cannot have decimalsand cannot be used to calculate other information such as averages.  \nTable of Contents  \nPart 1. Control Charts and Basic Considerations . . . . . . . . . . . . . . . 3  \nPart 2. The Three Core Variables Charts: Using Sample Size to Determine Core Chart Type. 6  \nPart 3. Special Processing Options . . . . . . . . . . . . . . . . . . . 10  \nSidebar-SPC for Very High Sampling Rates . . . . . . . . . . . . . . . .19  \nInfinityQS International, Inc. | 12601 Fair Lakes Circle | Suite 250 | Fairfax, VA 22033 | [www.infinityqs.com](www.infinityqs.com) 2  \nPart 1. Control Charts and Basic Considerations  \nWhat IS and is NOT a Control Chart?  \nJust to make sure we’re on the same page, let’s first clarify what a control chart is. A control chart is a real-time , time-ordered, graphical process feedback tool designed to tell an operator when significant changes have occurred in the manufacturing process. Control charts tell the operator when to do something and when to do nothing. A control chart illustrates process behaviors by detecting changes ina process output’s mean and/or standard deviation about the mean. Every process exhibits some normal levels of variation, but a control chart is designed to  \nseparate this normal or “common cause” variation from special cause variations. Control charts indicate visually whether a process is incontrol (stable and predictable), or if it is out of con","cbCaibpCe3oetjtQ","https://ap.wps.com/l/cbCaibpCe3oetjtQ","pdf",3530033,20,"English","# Introduction\n# Part 1. Control Charts and Basic Considerations\n## What IS and is NOT a Control Chart?\n## Standard Deviation\n## Mean\n## Specification Limits","[{\"question\":\"What is the purpose of a control chart in manufacturing?\",\"answer\":\"A control chart is a real-time, time-ordered graphical feedback tool that indicates when significant changes occur in a manufacturing process. It helps operators decide when to act and when to do nothing based on process behavior.\"},{\"question\":\"How do variables data and attribute data differ for control chart selection?\",\"answer\":\"Variables data are continuous measurements (often with decimals), such as time, weight, length, or temperature. Attribute data are discrete counts or presence/absence measurements (such as defects), cannot use decimals, and cannot be used to calculate averages.\"},{\"question\":\"Does an in-control process guarantee outputs meet specification limits?\",\"answer\":\"No. A process can be stable and in-control while still producing outputs outside specification limits, so statistical control should be confirmed before relying on capability analysis results.\"}]","A Practical Guide to Selecting the Right Control Chart - Part 1 - Control Charts and Basic Considerations | PDF",1791828616,7]