sharepoint quality management system.docx

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I. Contents of sharepoint quality management system ================== I recently have been engaged with a medical device manufacturer who lives and breathes according to FDA regulations and GMP and Quality Management. The environment presently is a mix of XP computers, traditional shared network drives, and a bit of visual source safe for document storage and control. While I could espouse how valuable Sharepoint could be to help them regarding their general needs of collaboration, team work, knowledge management, business process management, etc., I'm hoping there may be someone who reads this board and may be able to express more specifically how Sharepoint can fit in to this FDA regulated environment. I tried googling to see if there was any success stories of using Sharepoint 2007/2010 in a similar environment such as ISO 9001 or other related medical device manufacturing environment. But I really didn't hit the mark. So i'm posting this message to see if any of you know of any success stories about how Sharepoint 2010 can be an effective platform in an FDA-regulated, QMS manufacturing environment. I know Sharepoint could handle much of what we need to do. BUt i'm hoping that I can either 1) obtain a case study showing how some other leading organization adopted Sharepoint 2007/2010

in this very specific, non-generic environment, and 2) if there are any products or consultants who specialize in fitting Sharepoint into such an environment. In my background, I've used all kinds of general business uses and find the solution to be incredibly powerful. I know to some extent that the off the shelf features would help our team in general. But they are looking for a platform that is very specific to QMS controls, in addition to general collaboration, business process, configuration management, issue tracking, incident management, and document management revolving around this FDA-regulated need. I will lose the recommendation war if I can't show other case studies of how others have put sharepoint to use in this type of environment. If I can find such supporting information, I will be in a position to help my organization adopt the platform and that would be very exciting. Truth be told, this is the first organization i've worked for in about a decade that didn't have Sharepoint. I'm professionally dying without it here in my role, but the organization also is not getting the benefits of Sharepoint either. (you can't use what you don't have). ==================

III. Quality management tools

1. Check sheet The check sheet is a form (document) used to collect data in real time at the location where the data is generated. The data it captures can be quantitative or qualitative. When the information is quantitative, the check sheet is sometimes called a tally sheet. The defining characteristic of a check sheet is that data are recorded by making marks ("checks") on it. A typical check sheet is divided into regions, and marks made in different regions have different significance. Data are read by observing the location and number of marks on the sheet. Check sheets typically employ a heading that answers the Five Ws: 

Who filled out the check sheet

   

What was collected (what each check represents, an identifying batch or lot number) Where the collection took place (facility, room, apparatus) When the collection took place (hour, shift, day of the week) Why the data were collected

2. Control chart Control charts, also known as Shewhart charts (after Walter A. Shewhart) or process-behavior charts, in statistical process control are tools used to determine if a manufacturing or business process is in a state of statistical control. If analysis of the control chart indicates that the process is currently under control (i.e., is stable, with variation only coming from sources common to the process), then no corrections or changes to process control parameters are needed or desired. In addition, data from the process can be used to predict the future performance of the process. If the chart indicates that the monitored process is not in control, analysis of the chart can help determine the sources of variation, as this will result in degraded process performance.[1] A process that is stable but operating outside of desired (specification) limits (e.g., scrap rates may be in statistical control but above desired limits) needs to be improved through a deliberate effort to understand the causes of current performance and fundamentally improve the process. The control chart is one of the seven basic tools of quality control.[3] Typically control charts are used for time-series data, though they can be used

for data that have logical comparability (i.e. you want to compare samples that were taken all at the same time, or the performance of different individuals), however the type of chart used to do this requires consideration.

3. Pareto chart A Pareto chart, named after Vilfredo Pareto, is a type of chart that contains both bars and a line graph, where individual values are represented in descending order by bars, and the cumulative total is represented by the line. The left vertical axis is the frequency of occurrence, but it can alternatively represent cost or another important unit of measure. The right vertical axis is the cumulative percentage of the total number of occurrences, total cost, or total of the particular unit of measure. Because the reasons are in decreasing order, the cumulative function is a concave function. To take the example above, in order to lower the amount of late arrivals by 78%, it is sufficient to solve the first three issues. The purpose of the Pareto chart is to highlight the most important among a (typically large) set of factors. In quality control, it often represents the most common sources of defects, the highest occurring type of defect, or the most frequent reasons for customer complaints, and so on. Wilkinson (2006) devised an algorithm for producing statistically based acceptance limits (similar to confidence intervals) for each bar in the Pareto chart.

4. Scatter plot Method A scatter plot, scatterplot, or scattergraph is a type of mathematical diagram using Cartesian coordinates to display values for two variables for a set of data. The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis.[2] This kind of plot is also called a scatter chart, scattergram, scatter diagram,[3] or scatter graph. A scatter plot is used when a variable exists that is under the control of the experimenter. If a parameter exists that is systematically incremented and/or decremented by the other, it is called the control parameter or independent variable and is customarily plotted along the horizontal axis. The measured or dependent variable is customarily plotted along the vertical axis. If no dependent variable exists, either type of variable can be plotted on either axis and a scatter plot will illustrate only the degree of correlation (not causation) between two variables. A scatter plot can suggest various kinds of correlations between variables with a certain confidence interval. For example, weight and height, weight would be on x axis and height would be on the y axis. Correlations may be positive (rising), negative (falling), or null (uncorrelated). If the pattern of dots slopes from lower left to upper right, it suggests a positive correlation between the variables being studied. If the pattern of dots slopes from upper left to lower right, it suggests a negative correlation. A line of best fit (alternatively called 'trendline') can be drawn in order to study the correlation between the variables. An equation for the correlation between the variables can be determined by established best-fit procedures. For a linear correlation, the best-fit procedure is known as linear regression and is guaranteed to generate a correct solution in a finite time. No universal best-fit procedure is

guaranteed to generate a correct solution for arbitrary relationships. A scatter plot is also very useful when we wish to see how two comparable data sets agree with each other. In this case, an identity line, i.e., a y=x line, or an 1:1 line, is often drawn as a reference. The more the two data sets agree, the more the scatters tend to concentrate in the vicinity of the identity line; if the two data sets are numerically identical, the scatters fall on the identity line exactly.

5.Ishikawa diagram Ishikawa diagrams (also called fishbone diagrams, herringbone diagrams, cause-and-effect diagrams, or Fishikawa) are causal diagrams created by Kaoru Ishikawa (1968) that show the causes of a specific event. [1][2] Common uses of the Ishikawa diagram are product design and quality defect prevention, to identify potential factors causing an overall effect. Each cause or reason for imperfection is a source of variation. Causes are usually grouped into major categories to identify these sources of variation. The categories typically include  People: Anyone involved with the process  Methods: How the process is performed and the specific requirements for doing it, such as policies, procedures, rules, regulations and laws  Machines: Any equipment, computers, tools, etc. required to accomplish the job  Materials: Raw materials, parts, pens, paper, etc. used to produce the final product  Measurements: Data generated from the process that are used to evaluate its quality  Environment: The conditions, such as location, time, temperature, and culture in which the process operates

6. Histogram method A histogram is a graphical representation of the distribution of data. It is an estimate of the probability distribution of a continuous variable (quantitative variable) and was first introduced by Karl Pearson.[1] To construct a histogram, the first step is to "bin" the range of values -- that is, divide the entire range of values into a series of small intervals -- and then count how many values fall into each interval. A rectangle is drawn with height proportional to the count and width equal to the bin size, so that rectangles abut each other. A histogram may also be normalized displaying relative frequencies. It then shows the proportion of cases that fall into each of several categories, with the sum of the heights equaling 1. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) must be adjacent, and usually equal size.[2] The rectangles of a histogram are drawn so that they touch each other to indicate that the original variable is continuous.[3]

III. Other topics related to sharepoint quality management system (pdf download) quality management systems quality management courses quality management tools iso 9001 quality management system quality management process quality management system example quality system management quality management techniques quality management standards quality management policy quality management strategy

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