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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 1 IES 303 Chapter 5: Process Performance and Quality Objectives: Understand.

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Presentation on theme: "IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 1 IES 303 Chapter 5: Process Performance and Quality Objectives: Understand."— Presentation transcript:

1 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 1 IES 303 Chapter 5: Process Performance and Quality Objectives: Understand the quality from customer’s and producer’s perspectives Understand how to construct control charts Understand how to determine if a process is capable of producing service or product to specification Week 5-6 December 8-15, 2005

2 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 2 What is Quality? Which one has a higher quality? Source: Russell and TaylorIII (2005)

3 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 3 Meaning of Quality: Consumer’s Perspective  Fitness for use  ________________________ __________________________  Quality of design  ___________________________ __________________________  A Mercedes and a Ford are equally “fit for use,” but with different design dimensions Source: Russell and TaylorIII (2005)

4 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 4 Quality Measure in Manufacturing Industry

5 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 5 Quality Measure in service industry  Nature of defect is different in services  Service defect is a failure to meet customer requirements  Example of Quality Measure  ________________________________ “quickest, friendliest, most accurate service available.”

6 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 6 Costs of Poor Process Performance and Quality 1. ________________  Preventing defects before they happen  Ex: redesigning process/product/service, training employees, working with suppliers 2. ________________  Costs incurred in assessing the level of performance attained by the firm’s processes  As preventive measure improve performance, appraisal costs decrease because fewer resources and efforts are needed 3. ________________  Costs resulting from defects discovered during the production of a service / product 4. ________________  Cost that arise when a defect is discover after the customer has receive the service / product

7 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 7 Total Quality Management (TQM) Customer satisfaction Figure 5.2

8 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 8 Problem- Solving Process Plan DoDoDoDo Check Act Deming Wheel (PDCA)

9 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 9 Variation of Output Mean Standard Deviation/ Spread ____________________ More consistent process ____________________

10 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 10 Causes of Variations 1. ___________________  Variation inherent in a process  Unavoidable variation but can be reduced through improvements in the system 2. ___________________  Variation due to identifiable factors or unusual incidents  Ex: ______________________________________________  A process that is operating in the presence of assignable causes is said to be out of control  Can be modified through operator or management action  If ignored, tend to produce poor quality products or services

11 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 11 Basics of Control Charts  Control charts : _________________________________ _______________________________________________  Control limits : _________________________________ 12345678910 Sample number Upper control limit Process average Lower control limit Out of control A process is generally considered to be in control if No sample points outside the control limits Most points are near the process average, without too many close to the control limits Approximately equal number of sample points above and below the center line (process average) Randomly distributed around the centerline (no pattern)

12 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 12 Control Chart ExamplesUCLNominal LCL Samples Figure 5.6 Assignable causes likely 123

13 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 13 Control Chart Examples Nominal UCL LCL Sample number Variations Figure 5.7 Nominal UCL LCL Sample number Variations Figure 5.7 (b) _____________________ Figure 5.7 (a) _____________________

14 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 14 Control Chart Examples Nominal UCL LCL Sample number Variations Nominal UCL LCL Variations Figure 5.7 (d) _____________________ Figure 5.7 (c) _____________________

15 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 15 Control Chart Examples Nominal UCL LCL Sample number Variations Figure 5.7 (e) _____________________

16 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 16 Two Types of Error  ___________________ Occurs when the employee concludes that the process is out of control based on a sample result that falls outside control limits, when in fact it was due to randomness False Alarm Producer’s risk  ___________________ Occurs when the employee concludes that the process is in control and only randomness is present, when actually the process is out of statistical control Consumer’s risk

17 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 17 Types of Control Charts  Charts for variables Continuous scale measure.  Ex: length, weight, dimensions, time 1. ________________________ 2. ________________________  Charts for attributes  Discrete responses.  Ex: counts; good / bad; pass / fail; on-time / late 1. ________________________ 2. ________________________

18 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 18 Variable Control Charts x-bar and R-Charts In control process: BOTH process average and variability must be in control Possible that small range/variability but average is out of limit, or In limit average, but large variability  A 2, D 3, D 4 are pre-calculated from sample size (n) See Table 5.1 page 210 R-Chart R = range of each sample k = number of samples x-bar Chart

19 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 19 Ex 1: Slip-Ring Diameter adapted from Russell and Taylor (2003) (see also example 5.1) OBSERVATIONS (SLIP-RING DIAMETER, CM) SAMPLE k 12345xR 15.025.014.944.994.964.980.08 25.015.035.074.954.965.000.12 34.995.004.934.924.994.970.08 45.034.915.014.984.894.960.14 54.954.925.035.055.014.990.13 64.975.065.064.965.035.010.10 75.055.015.104.964.995.020.14 85.095.105.004.995.085.050.11 95.145.104.995.085.095.080.15 105.014.985.085.074.995.030.10 50.091.15 Construct x-bar and R chart and conclude

20 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 20 Ex 2: Light Bulb The Watson Electric Company produces light bulbs. The following data on the number of lumens for 40-watt light bulb were collected when the process is in control. Sample Observation 1234 1234512345 604 597 581 620 590 612 601 570 605 614 588 607 585 595 608 600 603 592 588 604 a.Calculate control limits for R and x-bar charts b.A new sample is obtained: 570, 603, 623, and 583. Is the process still in control?

21 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 21 Attribute Control Charts p- and c-charts p-chart  Proportion defective items in the sample  __________________ c-chart  Number of defects  __________________

22 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 22 Ex 3: Western Jeans Company adapted from Russell and Taylor (2003) Also see example 5.3 (pg 215)  The Western Jeans Company wants to establish a p- chart to monitor the production process. The company believes that approximately 99.74% of the variability in the production process (corresponding to 3-sigma limits) is random and should be within control limits, whereas.26% of the process variability is not random and suggests that the process is out of control  The company has taken 20 samples (one per day for 20 days), each containing 100 pairs of jeans (n = 100) and inspect them for defects. The results show in the table  Construct a p-chart to determine when the production process might be out of control

23 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 23 Ex 4: Housekeeping service adapted from Russell and Taylor (2003) Also see example 5.4 (pg 216)  Housekeeping service  Measure of, for example, dirty sheets, bedcovers, pillow, missing room and toilet supplies, and etc.  Data in the table are the results from 15 inspection samples (rooms) conducted at random during 1-month period  Use 3-sigma limit and construct c- chart

24 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 24 Ex 5: Highway Accident The AA County Highway Safety Department monitors accidents at the intersection B. There are 3 accidents on average per month. a. Construct an appropriate control chart with 3-sigma control limits b. Last month, 7 accidents occurred at the intersection. Is it sufficient evidence to justify a claim that something has changed in the intersection?

25 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 25 Process Capability  Range of natural variability in process  Measured with control charts.  Process cannot meet specifications if natural variability exceeds tolerances  3-sigma quality  Specifications equal the process control limits.  6-sigma quality  Specifications twice as large as control limits Lower specification Mean Upper specification Nominal value Six sigma Four sigma Two sigma Figure 5.13 To determine whether the process is capable of producing non-defective unit

26 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 26 Process Capability adapted from Russell and Taylor (2003) (a)Natural variation exceeds design specifications; ____________________ Design Specifications Process (b) Design specifications and natural variation the same; ____________________ Design Specifications Process

27 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 27 Process Capability adapted from Russell and Taylor (2003) (c) Design specifications greater than natural variation; ____________________ Design Specifications Process (d) Specifications greater than natural variation, but process off center; ____________________ Design Specifications Process

28 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 28 Process Capability Measures  Process Capability Ratio (C p )  Process Capability Index (C pk )

29 IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT 29 Ex 6: Process Capability A part has a length specification of 5 inches with tolerances of +.004 inches. The current process has an average length of 5.001 inches with a standard deviation of.001 inches. Calculate the C p and C pk for this process. Indicate the capability of the current process.

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