Supply statistical control

If the subcontractor supplies statistical data from the manufacturing process that indicates that quality is being controlled, then an analysis of this data based on assurances you have obtained through site evaluation can provide sufficient confidence in part quality to permit release into the organization. 

It is only possible to supply parts with identical characteristics if the measurement system as well as the production processes are under statistical control. In an environment in which daily production quantities are in the range of 1,000 to 10,000 units, inaccuracies in the measurement system that go undetected can have a disastrous impact on customer satisfaction and hence profits. 

If there is just one point outside the action limits, there should be an immediate evaluation. If there are two points or more in a brief period of time, the system should be shut down and analyzed for the cause. An example is Figure 5.13. As stated there, one possibility is a temporary contamination in the distilled water supply, which is certainly a concern even for a short period of time. A system out of statistical control, even for a short period of time, diminishes the reliability of any results. 

Statistical Control for a New Method To implement a new method, a laboratory must produce a preliminary track record of its success so that quality control charts can be established and then maintained. Aside from acquiring the space, supplies, equipment, instrumentation, and manpower required, the method must be tested, modified, tested again, etc., until it is ready to go "online." Gillis and Callio (listed in Bibliography) recommend the following sequence for preparing an instrumental method for routine use. 

Around the CL are the control limits, set at 3 SE of the statistic being plotted. If the statistic value falls outside the control limits, this is a signal that the process is not in a state of statistical control. Because the standard errors are functions of the process standard deviation a, an estimate of this quantity is necessary. This can be supplied by the average range. The lower control limit (LCL) and upper control limit (UCL) are calculated as follows ... 

Abstract. Functional data appear in a multitude of industrial applications and processes. However, in many cases at present, such data continue to be studied from the conventional standpoint based on Statistical Process Control (SPC), losing the capacity of analyzing different aspects over the time. In this study is presented a Statistical Control Process based on functional data analysis to identify outliers or special causes of variability of harmonics appearing in power systems which can negatively impact on quality of electricity supply. The results obtained from the functional approach are compared with those obtained with conventional Statistical Process Control that has been done firstly. 

One of the Office of the Chiefs earliest administrative studies was aimed at eliminating excessive paper work throughout the CWS. In the summer of 1942 the Administration and Management Branch, Control Division, in conjunction with the Executive Office, Industrial Division, made a survey of the forms and records maintained in the Washington headquarters and at the installations. They found that the installations had independently developed their own forms, 90 percent of which could have been eliminated without loss of efficiency, and that there was a staggering duplication of records between the chiefs office and the installations. Yet, despite all the record keeping, or probably because of it, no one in the CWS could tell just what was in the supply system. What was needed was an improved method of statistical control. 

Recent trends in the paper industry have had an impact on starch supplies. The implementation of statistical process control has led to more awareness that the production of high-quality paper grades depends on the use of high-quality materials. As a consequence, there is increased emphasis on acceptance specifications for starch supplies according to ISO guidelines and more rigorous testing in order to ensure consistently uniform quality. 

The production of material of a consistent quality is one of the major goals of development work. Quality problems in a product are identified by the constant monitoring and analysis of the output from the plant, using statistical process control techniques [D-4]. Some of these methods have already been mentioned in Section B, 3.4.2. The avoidance of product quality problems results in direct cost benefits and also brings about a reduction in the environmental impact of its manufacture. This is because material does not need to be reworked, recycled or sent for disposal. A reduction in the number of inferior quality batches of material leads to an increase in output from the plant. More material is produced for the same effort, with the added benefit that it can be consistently supplied to the sales warehouse or be used in consuming processes. 

To prevent material loss due to excursions such as the previous example, robust process control systems are required throughout the supply chain from the raw materials manufacturer to the pad manufacturer and the CMP module. Invariably, incident reviews of such excursions reveal that the excursion could have been prevented or limited to only a small amount of material lost if the proper statistical process control systems had been in place. Invariably, the excursion could have been detected by careful scrutiny of an in-process parameter that was either monitored or should have been monitored by the subsupplier, pad manufacturer, and/or the CMP operation. 

Another problem which obscures the analogy between different phase transitions is the fact that one does not always wish to work with the corresponding statistical ensembles. Consider, for example, a first-order transition where from a disordered lattice gas islands of ordered c(2x2) structure form. If we consider a physisorbed layer in full thermal equilibrium with the surrounding gas, then the chemical potential of the gas and the temperature would be the independent control variables. In equilibrium, of course, the chemical potential jx of subsystems is the same, and so the chemical potential of the lattice gas and that of the ordered islands would be the same, while the surface density (or coverage 9) in the islands will differ from that of the lattice gas. The three-dimensional gas acts as a reservoir which supplies adsorbate atoms to maintain the equilibrium value of the coverage in the ordered islands when one cools the adsorbed layer through the order-disorder transition. However, one often considers such a transition at... 

Quality Control Acceptance Criteria Commercial assay kits that also have QC samples supplied will normally contain data on expected target value for each QC sample and usually also expected or acceptable ranges. The procedures used to produce these figures and reasoning behind them can vary widely between different manufacturers and so great caution is required, particularly with R D type kits. It is common to find that these ranges do not have any connection to the actual performance of the assay from a statistical standpoint, and also that they are usually much wider than one may want to accept (or achieve). 

Following ROS production and [Ca2+]c increase, we observed that cell death was also enhanced (examined 2 h after addition of water), suggesting that the oxidatively conditioned water can be used in the control of cell viability (Fig. 3, right). Fig. 4 is the statistical confirmation of the actions of oxidatively conditioned waters on calcium influx and superoxide production through repeated experiments. UV, USW and oxygenation (supply of 02 gas) may be synergistically stimulating the responses. 

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