3 - Statistical process control (SPC).Physics teacher Azerbaijan, Baku, Telman Askeraliyev

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1. PHYSICS TEACHER AZERBAIJAN

2. STATISTICAL PROCESS CONTROL Instructor: M. Sci. Telman Askeraliyev ( Politecnico di Torino, Italy ) Azerbaijan – Baku – ADNSU - 2017 https://www.linkedin.com/in/physics-teacher-azerbaijan/ https://www.instagram.com/physics_teacher_azerbaijan/ [phone]

3. DEFINITION https://www.linkedin.com/in/physics-teacher-azerbaijan/ https://www.instagram.com/physics_teacher_azerbaijan/ [phone] #gcse #tutor #digitalsat #physicsteacher #azerbaijan #physics #muellim #teacher #xaricdetehsil #baghdad #iraq #kuwait #georgia #digitalsat #satmath #maths #mathteacher #ada #ibdp #igcse #satmath #english #adaschool #gcse #internationalschool #physicsteacher #azerbaijan #physics #kazakhstan #azerbaijan #physics #physicsteacher #tutor #school #science #moscow #almaty #kazakhstan #tutor #fizika #muellim #teacher #xaricdetehsil #baghdad #iraq #kuwait #georgia #digitalsat #satmath #maths #mathteacher #ada #ibdp #igcse #satmath #english #adaschool #gcse #internationalschool #physicsteacher #azerbaijan #physics #physicsteacher #azerbaijan #physics #exams #revision #bakuoxfordschool #bakumodernschool #zirveliseyi #idrakliseyi#kaspiliseyi#zengiliseyi #muasirtehsilkompleksi #avropaazerbaycanmektebi #sabisinternationalschool #physicstutor #physics #physicsteacher #fyi #almaty #astana

4. QUALITY ENGINEERING Instructor: M. Sci. Telman Askeraliyev ( Politecnico di Torino, Italy ) Azerbaijan – Baku – ADNSU - 2017

5. STATISTICAL PROCESS CONTROL

6. QUALITY ENGINEERING Statistical process control  ( SPC ) is a method of quality control which uses statistical methods. SPC is applied in order to monitor and control a process. Monitoring and controlling the process ensures that it operates at its full potential. At its full potential, the process can make as much conforming product as possible with a minimum (if not an elimination) of waste . SPC can be applied to any process where the "conforming product" (product meeting specifications) output can be measured. Key tools used in SPC include control charts, a focus on continuous improvement; and the design of experiments. An example of a process where SPC is applied is manufacturing lines.

7. CONTROL CHART Control charts or  process- behavior charts , are a statistical process control tool used to determine if a manufacturing or business process is in a state of control.

8. CONTROL CHART 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.

9. CONTROL CHART When we have plotted the process, if the variable oscillates within the upper and lower limits, the process is said to be in control. If the oscillation goes beyond the limits, or if a series of points follows certain well-defined rules, the process is said to be out of control, or unstable

10. OUT OF CONTROL

11. IN CONTROL PROCESS

12. APPLICATION OF CONTROL CHARTS When controlling ongoing processes by finding and correcting problems as they occur.  When predicting the expected range of outcomes from a process.  When determining whether a process is stable (in statistical control).  When analyzing patterns of process variation from special causes (non-routine events) or common causes (built into the process).  When determining whether your quality improvement project should aim to prevent specific problems or to make fundamental changes to the process.

13. VARIABILITY Variability is inherent in every process Natural or common causes Special or assignable causes Provides a statistical signal when assignable causes are present Detect and eliminate assignable causes of variation

14. VARIATION All natural processes are affected by intrinsic variation. In nature, no matter how hard we try, there can never be two identical actions that generate exactly the same result. This simple statement contains a deeper truth that is connected with change and entropy (a measure of disorder in the environment). Change is a constant in nature that is not only necessary but literally vital. There would be no life without change. How does this intrinsic characteristic of nature affect the activities of an organization? By definition, an organization (system) is a set of interdependent individuals and activities that work together to achieve a well-defined goal. Each of the activities that make up the organization and every individual are affected by variation, and this variation influences the possibility of achieving the goal. [1]

15. Common cause and special cause “Common causes", also called  Natural patterns , are the usual, historical, quantifiable variation in a system, while "special causes" are unusual, not previously observed, non-quantifiable variation.

16. Common causes Inappropriate procedures Poor design Poor maintenance of machines Lack of clearly defined standard operating procedures Poor working conditions, e.g. lighting, noise, dirt, temperature, ventilation Substandard raw materials Measurement error Quality control error Vibration in industrial processes Ambient temperature and humidity Normal wear and tear Variability in settings Computer response time

17. Common causes Natural variations in the production process These are to be expected

18. Special causes/assignable causes Poor adjustment of equipment... Operator falls asleep Faulty controllers Machine malfunction Fall of ground Computer crash Poor batch of raw material Power surges High healthcare demand from elderly people Broken part Abnormal traffic on web ads Extremely long lab testing turnover time due to switching to a new computer system Operator absent

19. Special causes/assignable causes Variations that can be traced to a specific reason (machine wear, misadjusted equipment, fatigued or untrained workers) The objective is to discover when assignable causes are present and eliminate them

20. SAMPLES To measure the process, we take samples and analyze the sample statistics following these steps (a) Samples of the product, say five boxes of cereal taken off the filling machine line, vary from each other in weight Frequency Weight # # # # # # # # # # # # # # # # # # # # # # # # # # Each of these represents one sample of five boxes of cereal

21. FREQUENCY DISTRIBUTION

22. FREQUENCY DISTRIBUTION You can tell quite a bit about a variable by looking at a chart of its frequency distribution. For example, Figure above shows the weights of a sample of 100 people. Most of them are between 140 and 180 pounds. In this sample, there are about as many people who weigh a lot (say, over 175 pounds) as there are whose weight is relatively low (say, up to 130). The range of weights—that is, the difference between the lightest and the heaviest weights—is about 85 pounds, from 116 to 200.

23. FREQUENCY DISTRIBUTION A quality control engineer might sample 100 ceramic tiles from a production run of 10,000 and count the number of defects on each tile. Most would have zero, one, or two defects, several would have three or four, and a very few would have five or six. This is another positively skewed distribution—quite a common situation in manufacturing process control.

24. FREQUENCY DISTRIBUTION It’s helpful to use frequency distributions in statistical analysis for two broad reasons. One concerns visualizing how a variable is distributed across people or objects. The other concerns how to make inferences about a population of people or objects on the basis of a sample. Those two reasons help define the two general branches of statistics:  descriptive  statistics and  inferential  statistics. Along with descriptive statistics such as averages, ranges of values, and percentages or counts, the chart of a frequency distribution puts you in a stronger position to understand a set of people or things because it helps you visualize how a variable behaves across its range of possible values.

25. SAMPLES (b) After enough samples are taken from a stable process, they form a pattern called a distribution The solid line represents the distribution Frequency Weight

26. SAMPLES (c) There are many types of distributions, including the normal (bell-shaped) distribution, but distributions do differ in terms of central tendency (mean), standard deviation or variance, and shape Weight Central tendency Weight Variation Weight Shape Frequency

27. REFERENCES 1)http://www.intelligentmanagement.ws/learningcentre/what-is-a-systemic-organization/variation/

28. Thank you! Thank you!