data processing control

introducing a new option for customers to control their windows 10 diagnostic data

introducing a new option for customers to control their windows 10 diagnostic data

Windows diagnostic data is used to help Microsoft keep the operating system secure, up to date and operating properly, troubleshoot problems, and make product improvements. More information about Windows diagnostic data is available here.

Previously, enterprise customers had two options in managing their Windows 10 diagnostic data: 1) allow Microsoft to be the controller of that data and responsible for determining the purposes and means of the processing of Windows diagnostic data in order to improve the Windows 10 operating system and deliver analytical services, or 2) turn off diagnostic data flows altogether.

Now, customers will have a third option that allows them to be the controller for their Windows 10 diagnostic data, while still benefiting from the purposes that this data serves, such as quality of updates and device drivers. Under this approach, Microsoft will act as a data processor, processing Windows diagnostic data on behalf of the controller.

This new option will enable customers to use familiar tools to manage, export, or delete data to help them meet their compliance obligations. For example, using the Microsoft Azure portal, customers will have the means to respond to their own users requests, such as delete and export diagnostic data. Admins can easily add or remove Windows devices to the service using group policy or mobile device management (MDM).

User control and transparency are among our key privacy principles and central to Microsofts mission to empower every person and every organization on the planet to achieve more. For those customers who are interested, this option makes it even easier for them to enable and get the most out of core Microsoft 365 experiences, while addressing their compliance efforts.

May 3, 2021 | Rima Alaily, Vice President and Deputy General Counsel and Casper Klynge, Vice President of European Government Affairs

data processing | definition & facts | britannica

data processing | definition & facts | britannica

Data processing, manipulation of data by a computer. It includes the conversion of raw data to machine-readable form, flow of data through the CPU and memory to output devices, and formatting or transformation of output. Any use of computers to perform defined operations on data can be included under data processing. In the commercial world, data processing refers to the processing of data required to run organizations and businesses.

data processing policy | format

data processing policy | format

This Ideaform Data Processing Policy forms part of, and is subject to the provisions of, the Ideaform Terms of Service. Capitalized terms that are not defined in this Data Processing Policy have the meanings set forth in the Terms of Service.

This Data Processing Policy only applies to you if you or your End Users are data subjects located within the EEA or Switzerland and only applies in respect of Your Controlled Data. You agree that Ideaform is not responsible for personal data that you have elected to process through Third Party Services or outside of the Services, including the systems of any other third-party cloud services, offline or on-premises storage.

a. First, submit a request for additional information in writing to Ideaform, specifying all details required to enable Ideaform to review this request effectively, including without limitation the information being requested, what form you need to obtain it in and the underlying legal requirement for the request (the Request). You agree that the Request will be limited to information regarding our Security Measures.

b. Within a reasonable time after we have received and reviewed the Request, you and we will discuss and work in good faith towards agreeing on a plan to determine the details of how the Request can be addressed. You and we agree to use the least intrusive means for Ideaform to verify Ideaforms compliance with the Security Measures in order to address the Request, taking into account applicable legal requirements, information available to or that may be provided to you, the urgency of the matter and the need for Ideaform to maintain uninterrupted business operations and the security of its facilities and protect itself and its customers from risk and to prevent disclosure of information that could jeopardize the confidentiality of Ideaform or our users information.

You authorize us to transfer Your Controlled Data away from the country in which such data was originally collected. In particular, you authorize us to transfer Your Controlled Data to the US and to Canada. We will transfer Your Controlled Data to outside the EEA using the Swiss-U.S. and EU-U.S. Privacy Shield Frameworks or another lawful data transfer mechanism that is recognized under EU Data Protection Law as providing an adequate level of protection for such data transfers.

The liability of each party under this Data Processing Policy is subject to the exclusions and limitations of liability set out in the Agreement. You agree that any regulatory penalties or claims by data subjects or others incurred by Ideaform in relation to Your Controlled Data that arise as a result of, or in connection with, your failure to comply with your obligations under this Data Processing Policy or EU Data Protection Law shall reduce Ideaforms maximum aggregate liability to you under the Agreement in the same amount as the fine and/or liability incurred by us as a result.

You are responsible for any costs and expenses arising from Ideaforms compliance with your instructions or requests pursuant to the Agreement (including this Data Processing Policy) which fall outside the standard functionality made available by Ideaform generally through the Services.

process control: meaning, objectives, types, importance, and advantages

process control: meaning, objectives, types, importance, and advantages

The system is automated and deals with analog signals transmitted from meters and sensors to specialized computers that enable continuous adjustment of flow, pressure, and temperature. Remember, it is an important tool that uses digital/analog and analog/digital conversion extensively.

In this type of application, exact quantities of particular raw materials are mixed for a precise period in a particular manner to achieve a specific product. An important example is the production of medicines which requires specific directions for the exact result.

It is all about maintaining the output of a particular process within the desired range. The temperature, proportion of ingredients, and how they are mixed has an impact on the quality of the finished product.

The solutions are very important as technology enables a manufacturer to run his operations within particular limits. It also allows him to set further limits so that he can ensure quality as well as maximize the profit margins. The importance of process control is as follows-

Process control is a must for mass production as its precise nature helps in the automation of industrial processes. It is easy to operate complex processes with exact equipment and data from the central control room.

Producing a product is not an easy feat, especially if a company has to manipulate substances. Sometimes it is just a demanding process and at other times dangerous. Remember the smallest change can cause serious repercussions and make a difference in the finished product.

Consumption of energy is at a fast rate as manufacturers want to deliver the highest output with minimum production cost via their equipment. The process control is a method that allows overseeing the production process.

The managers can measure and monitor the workings in their plants and make viable guesses as per their data and information. It becomes quite easy for them to place a viable strategy so that the machinery can be used at maximum levels.

The best thing about it is that the organization can maintain its quality levels by reducing the production of defective goods. The process control system proves itself as a smart tool that is also energy efficient.

statistical process control (spc) and data analysis course | udemy

statistical process control (spc) and data analysis course | udemy

Statistical Process Control (SPC) refers to the use of statistical techniques to control a process, production or manufacturing method through monitoring of process behavior, as a result discovering issues related to internal systems, and allowing for corrective actions to be taken before failure occurs. The best decisions are made using facts and data. The collection and interpretation of data is equally important in manufacturing and service environments.

One major goal of this course is to enable you understand how to use SPC to prevent defects from occurring and to drive process improvement. In this course, you will learn how the process champion can utilize SPC in taking action to adjust or investigate process deviation. You will learn how to prevent inappropriate or unnecessary process errors and adjustments. With the help of this course, you will learn the different causes of variation and what actions to take when a process is drifting out of control. This course will enable you to detect issues relating to machine wear, operator setup issues, raw material changes, and differences between similar machines that can affect the quality of your final products.

At the end of this course, you would be able to describe key concepts in SPC, different types of control charts, explain the concepts of process control, control limits and process capability. You would be able to apply different kinds of control charts for process monitoring. You would gain thorough understanding of key techniques for capturing data in quality and state the different guidelines and methods for data collection. The course contains a bonus section of statistical tools for product evaluation.

OLANAB is a consulting firm operating in the knowledge and professional servicing industry, dedicated to providing excellent delivery in the areas of professional development, quality assurance and related activities across organizations in Nigeria and beyond. We support professionals throughout their career journey through our E-Learning platform, live training, ebooks and articles.

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Our goal is to partner with our clients, helping to improve their business processes in the areas of training, business and manufacturing process improvement and consultation, thereby making them truly world class organizations.

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what is a dpu? | nvidia blog

what is a dpu? | nvidia blog

More recently the GPU, or graphics processing unit, has taken a central role. Originally used to deliver rich, real-time graphics, their parallel processing capabilities make them ideal for accelerated computing tasks of all kinds. Thanks to these capabilities, GPUs are essential to artificial intelligence, deep learning and big data analytics applications.

That approach isnt competitive and doesnt scale, because trying to beat the traditional x86 CPU with a brute force performance attack is a losing battle. If 100 Gigabit/sec packet processing brings an x86 to its knees, why would an embedded CPU perform better?

Instead, the network interface needs to be powerful and flexible enough to handle all network data path processing. The embedded CPU should be used for control path initialization and exception processing, nothing more.

supervisory control and data acquisition system - an overview | sciencedirect topics

supervisory control and data acquisition system - an overview | sciencedirect topics

All SCADA systems collect real-time and historical information and then provide operational personnel with a wide range of modes in which this information can be displayed and accessed. Most SCADA systems offer process-related (operational) displays and system-related (diagnostic) displays. There are a wide range of operating system and SCADA system utility programs that are used to initially configure the SCADA system to perform the necessary tasks. The displays and presentation modes discussed in this section have been created using those utility programs or automatically created using the data provided during the configuration process.

SCADA systems are responsible for coordinating communication and control actions between substations and control rooms. In the 1960s, first-generation SCADA systems were used in state-change alarming and manned substations. In the 1970s, the second generation of SCADA system adopted RTU technology with polling mode to provide information for the control room. The third generation of SCADA systems began in the 1980s with more advanced technologies that provide better bandwidth and efficiency than the second-generation systems.

The SCADA system is mainly used to control the power grid based on a local area network (LAN) with voltage levels higher than 6.6kV, which is usually located in remote locations. System functions of SCADA system include data acquisition and processing, remote control, alarm processing, historical data, graphical human-machine interface (HMI), emergency control switch, demand-side management, etc. There are usually two control schemes for SCADA system, that is, centralized control and distributed control.

Some functions of the SCADA system require centralized control, such as scheduling of low-frequency load shedding and load side management, and the problem of the centralized control is the lack of reliable basic communication facilities. As some substations lack basic communication facilities such as RTUs, this results in a slower detection rate of switch changes in SCADA systems. Centralized SCADA systems have functions such as network topology management, asset databases, maintenance of hardware and software, and centralized configuration management. However, such systems tend to have some disadvantages such as the lack of economical communication infrastructure, slow operation, physical distance test bottlenecks, and single point of failure. Distributed SCADA consists of small SCADA systems located in different substations. Compared with centralized systems, distributed SCADA has some advantages such as modular repeatable logic function, low-cost wireless communication, and better switching response time. However, there are also some difficulties and challenges with this type of system: Distributed SCADA systems require additional maintenance facilities and management tools that are compatible with multiple distributed operating systems.

Supervisory Control and Data Acquisition (SCADA) systems are typically used for systems such as long-distance pipelines that are geographically spread out. They can include multiple sites and generally operate over large distances. Standard communication protocols are used, thus allowing equipment and instrumentation from different manufactures to work together in the same system.

SCADA systems have many similarities to DCS. In particular the operators are able to monitor and control the overall operation from a central control station with many of the calculations and decisions being made by peripheral devices.

When used with pipelines the SCADA system monitors flows, pressures, and temperatures across the entire system and applies control through the management of pumping, compressor, and valve stations. The SCADA system will also help identify leaks or breaks in the pipeline.

SCADA systems can regulate all kinds of industrial processes automatically. For example, if too much pressure is building up in a gas pipeline, a SCADA system could automatically open a release valve. Electricity production can be adjusted to meet demands on the power grid. Even these real-world examples are simplified; a full-scale SCADA system can adjust a managed system in response to multiple inputs.

SCADA systems generally cover large geographic areas with the controller application housed in the appropriate terminal that is controlled by an operator working centrally. Reliable communication links between the SCADA central host and the field-level devices are therefore crucial to the efficient operation of such systems. Where a critical control algorithm is required and the controller must be located remotely, the communication link must be designed to contribute effectively to the reliability of the entire system. The cost associated with this requirement may be high enough to warrant placing the automatic control function at the site.

SCADA systems are coming in line with standard networking technologies, with Ethernet and TCP/IP-based protocols replacing the older proprietary standards. Although certain characteristics of frame-based network communication technology (determinism, synchronization, protocol selection, environment suitability) have restricted the adoption of Ethernet in a few specialized applications, it is now broadly accepted in the majority of situations. With the emergence of software as a service in the broader software industry, some vendors have begun offering application-specific SCADA systems hosted on remote platforms over the Internet. This removes the need to install and commission systems at the end-users facility and takes advantage of security features already available in Internet technology. Some concerns inherent in this approach include security, Internet connection reliability, and latency.

SCADA systems run on a PC (usually a standard Windows-based desktop computer vulnerable to Windows viruses, connected to the internet). They can receive signals from one or more PLCs, or from remote telemetry outstations (RTUs) which convert 420mA signals from field instruments into digital data.

The SCADA system has an HMIusually in the form of simulation screens which look rather like animated PFDs, alarm-handling screens, trends screens which allow variation in parameters to be seen as a graph against time, and input screens which allow process parameters to be changed (ideally only by authorized users).

An early generation of SCADA systems, based on mainframe systems (the available concept of computing of those days), without no availability of networking and as almost stand-alone systems, i.e., no direct connectivity to the other systems. There was a practice of installing two mainframe computers with duplicate features at bus level, as primary and the backup systems. The purpose of this installation was to retain the system redundant. The backup systems were to monitor the primary system (or an additional monitoring of the plant sensors sometimes), unless any failure occurs. The communication protocols (as usually developed by the RTU/PLC manufacturers) were designed to perform very limited tasks, mainly the information and data transfer between the mainframe MTU and plant devices and the sensors etc. These protocols were not standardized till that time [42] (Fig. 28).

An improved version with the advent of comparatively small sized MTU computers, and mainly with a revolutionary change of networking, the LANs. The single SCADA system tasks were distributed among various small control systems, each was responsible to perform a specific task as a part of a complete process. Each station, (responsible for an individual task) was connected via LAN to perform information transfer among each other via LAN and the communication server in real time. This configuration helped in improving the system reliability (redundancy) as most of the time of the process all operating stations were remained online. These subsystems (local stations) were connected to master SCADA by means of WAN but still the limitations of second generation were due to no improvement in the standardization of communication protocols.

Much like second generation distributed SCADA, with a revolutionary change of standardized communication protocols. With the integration of many LAN networks, for example, a process control network (PCN) and the SCADA control made possible over a wide spread separated geographical location, where several distributed SCADA systems worked under the supervision of a single and centralized master SCADA system [23]. Because of standardized protocols, it was possible to design a SCADA network with COTS (commercial off the shelf) components [40]. Use of WAN protocols (TCP/IP) for the communication among master computer, HMI, RTUs and field devices is an example of such advancement in the same generation.

The latest available generation of SCADA-based system, much like third generation, with a use of cloud computing termed as internet of things technology. The concept of cloud computing is very popular these days at all levels of users, from professional to home individuals, the concept of using it with SCADA is relatively new to the field. Transferring complete or some portion of SCADA system to the cloud, is cost-effective, and with proper security measures can enhance the system coverage (scalability) to a considerable extent [43], i.e., the use of internet and cloud computing the infrastructure cost of SCADA is lesser as compared with past generations, while these systems also offer easy maintenance and high level of integration of wide spread networks with a capability of implementation of more complex control algorithms. An end user cost reduction is estimated up to 90% compared to a conventional SCADA system [43]. Another huge advantage is the data backup over cloud space, along with the instant accessibility to real-time, and backed up data (e.g., historical information) anytime from anywhere. This indeed enhances the systems overall redundancy [43].

With cloud computing it is possible to get on-demand access of shared cloud computing provides convenient, on-demand network access to shared devices and resources of a SCADA network with ease. Network resources may be servers, networks, storage spaces, application software, and system services. The speedy access to these and many other resources, offers less managerial work with less transportation of physical resources and human workers [43]. Networking of many SCADA systems can bring different but related systems in synchronization that can increase the reliability of overall system, with real time information transfer. Some organizations use cloud computing for their temporary data handing needs, that is known as cloud bursting [44].

Public: under the ownership and administration of a typical organization, a public network can be accessed for an organization using it on its SCADA system, on purchased lease bases [43]. Apparently, these cloud networks use the same (or mostly similar) architecture, but the security concerns are reasonably different [45]. There is a possibility that this network is not dedicated for SCADA system, or to a single organization, and other internet-based services are also available on the network for other users, those have nothing to do with SCADA networks. In some cases, cloud services can be used free of cost [45].

Private: this network arrangement is distinct for an organization for their SCADA system (or may be some other systems under the same organization). The network management (and hosting) responsibilities can be performed by the same organization or some third-party service providers [43,45]. Private data centers are generally expensive because of the requirements of space, hardware, and environmental controls, management and maintenance [45].

Hybrid: these network services are a combination of public and private arrangements. The main concern in this kind of network design and implementation is standardization of different networks and their protocols both for communication links and involved devices [43]. The configurations can be combination even from different service providers [46]. The cloud bursting is an example of hybrid cloud networking, where a SCADA system company will be charged only for additional cloud computing usage [47].

On site application: the SCADA system normally operated on system location, the real-time information is reported, shared, circulated, and stored to the network via direct access connection to the cloud. The cloud is remotely connected to the site system, and control unit.

Remote application: the SCADA system is distantly controlled by the cloud, provided the application is controlling the system from the cloud. The other storage and information sharing remains much like the other configuration as does in standard cloud computing. These configurations are generally termed as distributed SCADA [43] (Figs. 30 and 31).

Fig. 30. Public cloud supervisory control and data acquisition (SCADA), public cloud formation in which the SCADA system is running onsite and delivers data via the cloud. PDA, personal digital assistants; PLC, programmable logic controller.

Fig. 31. Hybrid supervisory control and data acquisition (SCADA), a private/hybrid cloud in which the controllers are connected via wide area network (WAN) links to the SCADA application running entirely in the cloud.

Despite of the advantages of cloud computing-based SCADA generation systems, a huge concern of these generations with use of internet and larger network connectivity, is their security risk [19]. Although with the advent of this level of control, secure protocols are available and are in progress for future use. Common security threats are hackers, foreign intelligences, insiders, worms, viruses, and industrial spies. The associated solutions to SCADA security include the use of traditional IT security methods. In this view, the difference of SCADA IT security systems and conventional IT security systems should be kept in consideration [40]. A table of a comparison is given (Table 2).

Supervisory Control and Data Acquisition (SCADA) systems were developed to allow operators to supervise the operation of pipelines, tank farms, water and waste treatment, and electric power distribution networks, where the respective pumps or compressors, tanks, and substations are located at a substantial distance from the operator. The major elements of a SCADA system are the remote termination unit (RTU) that converts local sensor and actuator data to digital values and is located at a distance from the operator, the master station where the operator and an HMI are located, and a wired or wireless communications network to join the two.

The Control part of SCADA is operator manual operation of remote devices such as pump and compressor motors, instrumentation or other remote control loops, opening or closing valves, and the selection of switch settings for substations. In all cases, the system validates the present status of the device being operated, reports it on the SCADA HMI, and allows the operator to check that status before operating it (check before operate).

Each network SCADA system provides integrated parameter monitoring, data processing, and automatic control of circuit switching, load smoothing, and regulation of voltage and frequency throughout the network. The SCADA system also provides status displays for all major equipment and transmission lines, parametric trends, alarms, and a manual control interface for the load-control center operators.

The SCADA system oversees the network and responds to changing conditions. For example, if generating station G1 trips off line, the consequential voltage and frequency fluctuations may require rapid, active circuit switching to route additional power to substation S1. The SCADA system automatically controls energy transfers by switching the appropriate circuit breakers, and it increases output from the remaining generators to compensate for the lost generating capacity. If the fluctuations cannot be stabilized, the SCADA system implements preprogrammed automatic protection protocols to open the connections to selected substations and restores stable conditions throughout the remainder of the network.

SCADA systems can provide solutions for the monitoring and control of their FC systems. The solution includes HMI/SCADA application; real-time automation software; and the alarm and events management system. An FC power system requires a user-friendly, robust HMI, secure to allow monitoring and remote-site control of the FC power stations as standalone/grid-connected systems. The SCADA system also can provide the FC power system with alarm management and real-time functionality. The dispatchers at the control room can acknowledge or take action when they alerted by an event or alarm. The Internet-enabled HMI control promotes ease of use and remote control/monitoring of that kind of power system. Another benefit is the integration into existing microgrid systems. SCADA delivers the facilitated Internet-based remote access HMI. The SCADA delivers also an event/alarm management that is required for remote monitoring/control of a standby FC power station working in either standalone or hybrid modes.

Because of the specific nature of FC systems, measurement, supervision, and control devices are substantial. It is interesting to find a system enable the real-time monitoring and control the performance of a PEM FC system. The control process occurs according to the instructions recommended by the FC stack manufacturers. The automation of FC station is based on an SCADA system that acquires data and monitors the input/output measurements and control signals [32,33]. The SCADA system stores data in an organized process database. The SCADA platform consist of three main elements: an SCADA server, the FC system controller, and memory storage device. The SCADA server provides access to the system's electrical parameters and configures interface through serial communication. The SCADA user can access the data for analyzing, reporting, or making engineering tasks for system expansion. In the beginning, the main screen of the SCADA system enables users to select the role in which they wish to work (operator, engineer, or administrator). The software tool has a detailed description of the FC plant and rated input/output values, rated operating parameters, proper control interface, and station operation. The SCADA software traces the dynamic changes in the site and sends data to the master station control room, which supervises the whole system.

The FC current and voltage transducers are being connected to analog input terminals of RTU, and thus monitoring voltage and current, as shown in Fig. 9. If an FC is run above its rated current for long periods of time, it can contribute to equipment degradation, so monitoring the fuel voltage guarantees that increases in FC stack current doesnt drive the cells into reversal. Cell reversal results in heat, and can result in a cell-to-cell short circuit and an electrical arcing. A blocking diode is placed in series to prevent reversal currents, and a protective relay is added to protect the FC system from external faults. The SCADA is responsible for receiving information from all the subsystems and enable them to work simultaneously. An external power source is required to supply all the devices in the balance of plant, including the FC. This external power source is necessary because the balance of plant devices should be powered even before the FC system begins working, and because of the extra time required for the FC system to reach steady-state operation and deliver rated electrical power [32].

statistical process control (spc) tutorial

statistical process control (spc) tutorial

MoreSteam Hint: As a pre-requisite to improve your understanding of the following content, we recommend that you review the Histogram module and its discussion of frequency distributions.

The concepts of Statistical Process Control (SPC) were initially developed by Dr. Walter Shewhart of Bell Laboratories in the 1920's, and were expanded upon by Dr. W. Edwards Deming, who introduced SPC to Japanese industry after WWII. After early successful adoption by Japanese firms, Statistical Process Control has now been incorporated by organizations around the world as a primary tool to improve product quality by reducing process variation.

Dr. Shewhart identified two sources of process variation: Chance variation that is inherent in process, and stable over time, and Assignable, or Uncontrolled variation, which is unstable over time - the result of specific events outside the system. Dr. Deming relabeled chance variation as Common Cause variation, and assignable variation as Special Cause variation.

Based on experience with many types of process data, and supported by the laws of statistics and probability, Dr. Shewhart devised control charts used to plot data over time and identify both Common Cause variation and Special Cause variation.

If you have reviewed the discussion of frequency distributions in the Histogram module, you will recall that many histograms will approximate a Normal Distribution, as shown below (please note that control charts do not require normally distributed data in order to work - they will work with any process distribution - we use a normal distribution in this example for ease of representation):

In order to work with any distribution, it is important to have a measure of the data dispersion, or spread. This can be expressed by the range (highest less lowest), but is better captured by the standard deviation (sigma). The standard deviation can be easily calculated from a group of numbers using many calculators, or a spreadsheet or statistics program.

Statistical tables have been developed for various types of distributions that quantify the area under the curve for a given number of standard deviations from the mean (the normal distribution is shown in this example). These can be used as probability tables to calculate the odds that a given value (measurement) is part of the same group of data used to construct the histogram.

Shewhart found that control limits placed at three standard deviations from the mean in either direction provide an economical tradeoff between the risk of reacting to a false signal and the risk of not reacting to a true signal - regardless the shape of the underlying process distribution.

If the process has a normal distribution, \$$99.7\%\$$ of the population is captured by the curve at three standard deviations from the mean. Stated another way, there is only a \$$1-99.7\%\$$, or \$$0.3\%\$$ chance of finding a value beyond \$$3\$$ standard deviations. Therefore, a measurement value beyond \$$3\$$ standard deviations indicates that the process has either shifted or become unstable (more variability).

The illustration below shows a normal curve for a distribution with a mean of 69, a mean less \$$3\$$ standard deviations value of \$$63.4\$$, and a mean plus \$$3\$$ standard deviations value of \$$74.6\$$. Values, or measurements, less than \$$63.4\$$ or greater than \$$74.6\$$ are extremely unlikely. These laws of probability are the foundation of the control chart.

Now, consider that the distribution is turned sideways, and the lines denoting the mean and \$$\pm 3\$$ standard deviations are extended. This construction forms the basis of the Control chart. Time series data plotted on this chart can be compared to the lines, which now become control limits for the process. Comparing the plot points to the control limits allows a simple probability assessment.

We know from our previous discussion that a point plotted above the upper control limit has a very low probability of coming from the same population that was used to construct the chart - this indicates that there is a Special Cause - a source of variation beyond the normal chance variation of the process.

Deploying Statistical Process Control is a process in itself, requiring organizational commitment across functional boundaries. The flow-chart below outlines the major components of an effective SPC effort. The process steps are numbered for reference.

Statistical Process Control is based on the analysis of data, so the first step is to decide what data to collect. There are two categories of control chart distinguished by the type of data used: Variable or Attribute.

Variable data comes from measurements on a continuous scale, such as: temperature, time, distance, weight. Attribute data is based on upon discrete distinctions such as good/bad, percentage defective, or number defective per hundred.

A critical but often overlooked step in the process is to qualify the measurement system. No measurement system is without measurement error. If that error exceeds an acceptable level, the data cannot be acted upon reliably. For example: a Midwest building products manufacturer found that many important measurements of its most critical processes had error in excess of \$$200\%\$$ of the process tolerance. Using this erroneous data, the process was often adjusted in the wrong direction - adding to instability rather than reducing variability. See the Measurement Systems Analysis section of the Toolbox for additional help with this subject.

Develop a sampling plan to collect data (subgroups) in a random fashion at a determined frequency. Be sure to train the data collectors in proper measurement and charting techniques. Establish subgroups following a rational subgrouping strategy so that process variation is captured BETWEEN subgroups rather than WITHIN subgroups. If process variation (e.g. from two different shifts) is captured within one subgroup, the resulting control limits will be wider, and the chart will be insensitive to process shifts.

The type of chart used will be dependent upon the type of data collected as well as the subgroup size, as shown by the table below. A bar, or line, above a letter denotes the average value for that subgroup. Likewise, a double bar denotes an average of averages.

Consider the example of two subgroups, each with \$$5\$$ observations. The first subgroup's values are: \$$3, 4, 5, 4, 4\$$ - yielding a subgroup average of \$$4 (\bar{X}_1)\$$. The second subgroup has the following values: \$$5, 4, 5, 6, 5\$$ - yielding an average of \$$5 (\bar{X}_2)\$$. The average of the two subgroup averages is \$$\frac{4 + 5}{2} = 4.5\$$, which is called X double-bar \$$(\bar{\bar{X}})\$$, because it is the average of the averages.

Each process charted should have a defined reaction plan to guide the actions to those using the chart in the event of an out-of-control or out-of-specification condition. Read Section 10 below to understand how to detect out-of-control conditions.

One simple way to express the reaction plan is to create a flow chart with a reference number, and reference the flow chart on the SPC chart. Many reaction plans will be similar, or even identical for various processes. Following is an example of a reaction plan flow chart:

MoreSteam Note: Specifications should NEVER be expressed as lines on control charts because the plot point is an average, not an individual. The only exception is the moving range chart, which is based on a subgroup size of one.Consider the case of a subgroup of three data points: \$$13, 15, 17\$$. Suppose the upper specification limit is 16. The average of the subgroup is only \$$15\$$, so the plot point looks like it is within the specification, even though one of the measurements was out of spec.! However, specifications should be printed on the side, top, or bottom of the chart for comparing individual readings.

After establishing control limits, the next step is to assess whether or not the process is in control (statistically stable over time). This determination is made by observing the plot point patterns and applying six simple rules to identify an out-of-control condition.

MoreSteam Hint: Control charts offer a powerful medium for communication. Process shifts, out-of-control conditions, and corrective actions should be noted on the chart to help connect cause and effect in the minds of all who use the chart. The best charts are often the most cluttered with notes!

If an out-of-control condition is noted, the next step is to collect and analyze data to identify the root cause. Several tools are available through the MoreSteam.com Toolbox function to assist this effort - see the Toolbox Home Page. You can use MoreSteam.com's Traction to manage projects using the Six Sigma DMAIC and DFSS processes.

After identifying the root cause, you will want to design and implement actions to eliminate special causes and improve the stability of the process. You can use the Corrective Action Matrix to help organize and track the actions by identifying responsibilities and target dates.

The ability of a process to meet specifications (customer expectations) is defined as Process Capability, which is measured by indexes that compare the spread (variability) and centering of the process to the upper and lower specifications. The difference between the upper and lower specification is know as the tolerance.

After establishing stability - a process in control - the process can be compared to the tolerance to see how much of the process falls inside or outside of the specifications. Note: this analysis requires that the process be normally distributed. Distributions with other shapes are beyond the scope of this material.

MoreSteam Reminder: Specifications are not related to control limits - they are completely separate. Specifications reflect "what the customer wants", while control limits tell us "what the process can deliver".

Cp is often referred to as "Process Potential" because it describes how capable the process could be if it were centered precisely between the specifications. A process can have a Cp in excess of one but still fail to consistently meet customer expectations, as shown by the illustration below:

The measurement that assesses process centering in addition to spread, or variability, is Cpk. Think of Cpk as a Cp calculation that is handicapped by considering only the half of the distribution that is closest to the specification. Cpk is calculated as follows:

So Cpk is \$$0.67\$$, indicating that a small percentage of the process output is defective (about \$$2.3\%\$$). Without reducing variability, the Cpk could be improved to a maximum \$$1.33\$$, the Cp value, by centering the process. Further improvements beyond that level will require actions to reduce process variability.

MoreSteam Hint: Statistical Process Control requires support from the top, like any program. The process will be most effective if senior managers make it part of their daily routine to review charts and make comments. Some practitioners initial charts when they review them to provide visual support. Charts that are posted on the floor make the best working tools - they are visible to operators, and are accessible to problem-solving teams.

While the initial resource cost of statistical process control can be substantial the return on investment gained from the information and knowledge the tool creates proves to be a successful activity time and time again. This tool requires a great deal of coordination and if done successfully can greatly improve a processes ability to be controlled and analyzed during process improvement projects.

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data processing technician: job description and requirements

data processing technician: job description and requirements

A data processing technician is mainly responsible for organizing and cataloging data. The job requires a technician to be very detail-oriented and to maintain focus while performing repetitive duties. Job candidates usually need a high school diploma for this position, and they often receive on-the-job training.

A data processing technician works with computers and equipment to process large amounts of data. A high school diploma or on-the-job training prepares technicians for this detail-oriented job, which may include repetitive and often technical work.

Data processing technicians operate computers and other communications equipment and may perform data entry work as part of a larger data processing system. Being a technician is a very detail-oriented job involving many repetitive duties. In addition to inputting, organizing, and cataloging data, a technician may also be responsible for performing quality control checks, identifying job problems, and ensuring that job orders are completed according to schedule.

The technician's work is often performed at a data center or at another location that supports the running of data center operations. After a certain level of experience, data processing technicians may move on to supervising the work of other technicians.

Most data processing technician jobs require at most a high school diploma. For those who start work immediately after high school, training is usually provided on the job. For those positions that do not require a high school education, experience in the data processing and entry field is usually cited as a minimum qualification.

A good grasp of basic English skills is important for technician positions. Solid proficiency in math and science may also be useful for jobs where technical material or statistical reports are being processed.

There are a variety of ways to gain the work skills for a data processing job. High schools, vocational schools, and community colleges have classes that teach basic clerical and administrative skills such as word processing and database management, in addition to the traditional English, math, and science courses that are usually offered. There are also at-home tutorials online and in books and tapes that teach similar skills.

Technicians need to possess good communication skills, be able to function in a fast-paced environment and adapt to new technologies quickly. Their work may involve being able to work well with team members as well as independently and serving customer needs.

According to the U.S. Bureau of Labor Statistics (BLS, www.bls.gov), the employment of data entry keyers, a different title for data processing technicians, is expected to decline by 25% during the decade from 2019 to 2029. The BLS published the median annual salary earned by data entry keyers as $34,440 in May 2020.

A high school diploma and on-the-job training is often sufficient for a data processing job. It is important for these technicians to have basic English skills and, in situations where statistical data is being processed, proficiency in math could be beneficial. Jobs in this profession are projected to decline through 2029, and the median salary in 2020 was about $34,000.

Study.com / Data Processing Technician: Job Description and Requirements. (2021, May 8 of publication). Retrieved from https://study.com/articles/Data_Processing_Technician_Job_Description_and_Requirements.html

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