Total Quality Management - CII Institute of Logistics

More documents

Recommendations

Info

Total Quality Management SPE C. .5623 .5622 .5621 .5620 .5619 .5618 .5617 Figure 4.7 Histogram Let's review each of these questions in terms of the histogram in Figure 4.7 1. Is the process producing parts to the bell-shaped curve? Because the histogram is roughly bell-shaped, then for practical applications we may say: a. The process appears "normal" and stable b. Variations are generally due to chance clauses. However when the histogram is not roughly bell-shaped: a. The process is not "normal" b. Assignable causes are influencing the variations. In special cases a frequency distribution or histogram of measurements from a stable process will not match the formal curve, but in most manufacturing operations, measurements from a stable process will make a good match to the normal curve. CII Institute of Logistics 76 Website: www.ciilogistics.com
Total Quality Management 2. Where is the process centered? The average of the histogram and the specification midpoint are close together; therefore, the process is well centered. When the average of the histogram and the specification midpoint are far apart, then the process setting needs some adjustment. 3. Is the process capable of meeting the engineering specification? The spread of the process falls within the specification limits; therefore, the process is estimated to be capable of meeting the engineering specification. Remember that the histogram is merely a snapshot of the process. If we take another set of data at another time, the picture may be different. However, the information we have on this process tells us that it is capable of meeting the engineering specification. People in manufacturing often form opinions about the quality of the parts they are making based on far less information than we had in the example above. It is much wiser and safer to use a statistical technique, as we did, to see what the job is doing before we decide how good or how bad the parts are. Remember the old saying, "One picture is worth a thousand words". Few people can look at a set of jumbled-up data and see the same things they can see after the data have been organized in pictorial form. Once you begin to think statistically about your job, you will find that you naturally want to get factual data and plot it in the appropriate form. With this method, you will make good decisions based on facts instead of giving opinions based on measurements from only a few pieces. The process spread is determined by the curve it forms. When this curve is compared to the specification, we can tell when the process is making parts outside the specification. When the spread of the curve is wider than the specification or when one tail of the curve goes outside of the specification, we can estimate the number of pieces that fall outside the specification. CII Institute of Logistics 77 Website: www.ciilogistics.com
Page 1 and 2:
Total Quality Management SEMESTER -
Page 3 and 4:
Total Quality Management Course Con
Page 5 and 6:
Total Quality Management Historical
Page 7 and 8:
Total Quality Management practition
Page 9 and 10:
Total Quality Management Deming’s
Page 11 and 12:
Total Quality Management numbers to
Page 13 and 14:
Total Quality Management measuring
Page 15 and 16:
Total Quality Management (6) People
Page 17 and 18:
Total Quality Management 1.7 Qualit
Page 19 and 20:
Total Quality Management 1.8 Compet
Page 21 and 22:
Total Quality Management Ident
Page 23 and 24:
Total Quality Management informatio
Page 25 and 26: Total Quality Management 4) Compete
Page 27 and 28: Total Quality Management Does s
Page 29 and 30: Total Quality Management Fig 3 The
Page 31 and 32: Total Quality Management mechanism
Page 33 and 34: Total Quality Management TQ focused
Page 35 and 36: Total Quality Management Every proc
Page 37 and 38: Total Quality Management Effectiven
Page 39 and 40: Total Quality Management 2. 6 Bench
Page 41 and 42: Total Quality Management 2.8 Key Re
Page 43 and 44: Total Quality Management Values: Va
Page 45 and 46: Total Quality Management Implementa
Page 47 and 48: Total Quality Management 3.3 The Im
Page 49 and 50: Total Quality Management Identify
Page 51 and 52: Total Quality Management The fact t
Page 53 and 54: Total Quality Management ii. iii. m
Page 55 and 56: Total Quality Management business n
Page 57 and 58: Total Quality Management Accounts :
Page 59 and 60: Total Quality Management By adoptin
Page 61 and 62: Total Quality Management iii. Someo
Page 63 and 64: Total Quality Management details of
Page 65 and 66: Total Quality Management Statistica
Page 67 and 68: Total Quality Management All the id
Page 69 and 70: Total Quality Management 12 x 11 x
Page 71 and 72: Total Quality Management CAUSES OF
Page 73 and 74: Total Quality Management good quali
Page 75: Total Quality Management x x x x x
Page 79 and 80: Total Quality Management Between di
Page 81 and 82: Total Quality Management On the cha
Page 83 and 84: Total Quality Management This chart
Page 85 and 86: Total Quality Management 20 16 12 8
Page 87 and 88: Total Quality Management If you int
Page 89 and 90: Total Quality Management The feelin
Page 91 and 92: Total Quality Management Bird’s E
Page 93 and 94: Total Quality Management Please not
Page 95 and 96: Total Quality Management Motorola
Page 97 and 98: Total Quality Management Table 5.1
Page 99 and 100: Total Quality Management Employees
Page 101 and 102: Total Quality Management Benchmarki
Page 103 and 104: Total Quality Management but amazin
Page 105 and 106: Total Quality Management 5.5 A Broa
Page 107 and 108: Total Quality Management Communica
Page 109 and 110: Total Quality Management Bird’s E
Page 111 and 112: Total Quality Management In additio
Page 113 and 114: Total Quality Management 6.5 WHEN S
Page 115 and 116: Total Quality Management QUALITY IN
Page 117 and 118: Total Quality Management SYSTEM M
show all

Total Quality Management - CII Institute of Logistics

Create successful ePaper yourself

Delete template?

Save as template?