Research Design and Methodology for Business Decision - Free Essay Example

Sample details Pages: 12 Words: 3594 Downloads: 2 Date added: 2017/09/24 Category Advertising Essay Type Narrative essay Did you like this example? 2. 1 INTRODUCTION OF RESEARCH REPORT: What is Research Design? Research design can be thought of as the structure of research – it is the glue that holds all of the elements in a research project together. There are many definitions of design, but no single definition imparts the full range of important aspects. 2. 2 BACKGROUND OF THE RESEARCH REPORT Company wants to know that quality of product is under control limits or not and they want to detect assignable causes of variation and to remove those because the following reason. . They face the difficulty in the consistency in the quality. Company keeps the eye on the ISO-9001 certification implementation, so they want to check the quality assurance. They are facing the high rework cost due to performance, features, reliability, conformance durability, serviceability of product, when they not met their standard specification. 17 SIEM/FM 2. 3 LITERATURE REVIEW Quality control According to American society for quality control, â€Å"quality is the totality of features and satisfies stated or implied needs. †The process of monitoring specific activities to determine the compliance of their quality with relevant quality standards and identifying ways to eliminate causes of nsatisfactory performance is called quality control. Organizations lay a lot of emphasis on the concept of quality control. Most organizations design their own quality control programs to check any defects in the products or services. Statistical quality control Statistical quality control is a number of different techniques designed to evaluate quality from a conformance view. SIGNIFICANCE In our company maintain the many criteria which we can say the many significance of the statistical quality control like: 1. SQC as a competitive tool 2. SQC as a statistical control process 3. SQC as a improving quality by control charts 18 SIEM/FM OVERVIEW OF CONTROL CHARTS If analysis of the control chart indicates that the proce ss is currently under control (i. e. is stable, with variation only coming from sources common to the process) then data from the process can be used to predict the future performance of the process. If the chart indicates that the process being monitored is not in control, analysis of the chart can help determine the sources of variation, which can then be eliminated to bring the process back into control. A control chart is a specific kind of run chart that allows significant change to be differentiated from the natural variability of the process. The control chart can be seen as part of an objective and disciplined approach that enables correct decisions regarding control of the process, including whether or not to change process control parameters. Process parameters should never be adjusted for a process that is in control, as this will result in degraded process performance. The control chart is one of the seven basic tools of quality control. CHART USAGE: If the process is in control, all points will plot within the control limits. Any observations outside the limits, or systematic patterns within, suggest the introduction of a new (and likely unanticipated) source of variation, known as a special-cause variation. Since increased variation means increased quality costs, a control chart signaling the presence of a specialcause requires immediate investigation. This makes the control limits very important decision aids. The control limits tell you about process behavior and have no intrinsic relationship to any specification targets or engineering tolerance. In practice, the process mean (and hence the center line) may not coincide with the specified value (or target) of the quality characteristic because the process design simply cant deliver the process characteristic at the desired level. 19 SIEM/FM Control charts limit specification limits or targets because of the tendency of those involved with the process (e. g. , machine operators) to foc us on performing to specification when in fact the least-cost course of action is to keep process variation as low as possible. Attempting to make a process whose natural center is not the same as the target perform to target specification increases process variability and increases costs significantly and is the cause of much inefficiency in operations. Process capability studies do examine the relationship between the natural process limits (the control limits) and specifications, however. The purpose of control charts is to allow simple detection of events that are indicative of actual process change. This simple decision can be difficult where the process characteristic is continuously varying; the control chart provides statistically objective criteria of change. When change is detected and considered good its cause should be identified and possibly become the new way of working, where the change is bad then its cause should be identified and eliminated. The purpose in ad ding warning limits or subdividing the control chart into zones is to provide early notification if something is amiss. Instead of immediately launching a process improvement effort to determine whether special causes are present, the Quality Engineer may temporarily increase the rate at which samples are taken from the process output until its clear that the process is truly in control. Note that with three sigma limits, one expects to be signaled approximately once out of every 370 points on average, just due to common-causes. 20 SIEM/FM 2. 4 RESEARCH OBJECTIVE Core objective: To know the quality of the product are under control limit or not. Sub objective ? As per the ISO-9001 implementation the company wants to maintain the quality of the product and also wants some improvement in it. ? To measure the quality characteristics of the products with standard. ? To measure the attribute and the variables of the product that the match the std. or not. To measure daily variation in the samples in fraction that how much variance of them with actual. ? To decide whether specification limits are consistence with the process limit or not 21 SIEM/FM 2. 5 METHODOLOGY RESEARCH DESIGN: For the research purpose I have done the research on how the quality of the product is maintained and observe it with the variables and the attribute design. I just analysis with the various dimension like p chart, c chart, chart, R chart As per observation approach I have selected process or activity analysis of non behavior and nonverbal behavior method of behavior approach are best suitable of our problem. SAMPLE DESIGN: The target population is total No. of days and Units (industrial blades) SAMPLING METHOD: For observation study, we select Random sampling (probability method). ? Construction of the observation studies: Types of study: There are two types of studies Simple Systematic As in systematic observation, I required scientific instrument and training for observation but t o these limitations, I selected simple observation study. 22 SIEM/FM 2. 6 DATA COLLECTION The data collection plan prescribes detail of the task. In essence it answers the questions who, what, when. Who? I solely observe and collected data at Ravi sunrise Industries What? I have observed and collected data about work: Variables Data Of sample unit Length Width Thickness Attributes of sample units Data of sample through which the is reject/accept or go/no-go When? Time of study is important because many situational factors affect validity and reliability of study. I have collected data at time of regular production, which free from situational problem facing like stock out, any kind accidents, strikes etc. I have collected data from 21th to 30 June during idle time. 23 SIEM/FM 2. 7 ASSUMPTION OF THE PROJECT:- My study is based on some assumption are as under†¦Ã¢â‚¬ ¦. The samples are the perfect and reliable sample of the population. There are no external factors (environm ent) affecting to the production. There are limited sample size and time for research. Project is carried with time and money constraints. The std. deviation is 3 (99. 7%). ( recommendation by company) 24 SIEM/FM 2. 8 DATA ANALYSIS 2. 8. 1 TYPES OF CONTROL CHARTS Quality control techniques (charts) FOR ATTRIBUTE FOR VARIABLES P CHART C CHART CHART R CHART 2. 8. 2 CONTROL FOR ATTRIBUTE CHARTS In statistical quality control, the p-chart is a type of control chart used to monitor the proportion of nonconforming units in a sample, where the sample proportion nonconforming is defined as the ratio of the number of nonconforming units to the sample size. The p-chart only accommodates pass/fail-type inspection as determined by one or more go-no go gauges or tests, effectively applying the specifications to the data before theyre plotted on the chart. Other types of control charts display the magnitude of the quality characteristic under study, making troubleshooting possible directly from those charts. In many situations, quality measurements are expressed as attributes (good or bad etc. ). In such situation, the percent defective chart (p chart) or the number of defectives per sample area (c chart) are considered to be more suitable control charts to control the quality. 25 SIEM/FM Both charts convey a similar type of information, but p charts is based on normal distribution, summarized below. P CHART The other name for p – chart is percent defective charts. The purposes of the charts are summarized blow. a) To discover the average proportion of non-conforming articles or parts submitted for inspection over a period. b) To bring to the management attention, if there is any change in average quality level. The formulas for the control limit are as follows UCL = +3 OR UCL = + 3 Sp LCL = -3 OR LCL = 3 Sp = Total no of defects from all samples No. of samples X sample size = 23 40 = UCL LCL 0. 0575 = 0. 092419 = 0. 022581 26 SIEM/FM ANNEXURE- I SAMPLE (IN DAYS) 1 2 3 4 5 6 7 8 9 10 NO OF INSPECTED 40 40 40 40 40 40 40 40 40 40 400 DEFECTS 2 3 1 2 3 3 2 2 1 4 23 FRECTION (%)DEFECTIVE 0. 05 0. 075 0. 025 0. 05 0. 075 0. 075 0. 05 0. 05 0. 025 0. 1 P CHART 0. 12 0. 1 0. 08 0. 06 0. 04 0. 02 0 1 2 3 4 5 6 7 8 9 10 10th observation is out of control limit so situation is out of control FRECTION DEFECTIVE UCL LCL 27 SIEM/FM INTERPRITATION (p chart): One sample point increased variation means increased quality costs, a control chart signaling the presence of a special-cause requires immediate investigation. That means the assignable auses of variation must be found out and removed. The sample no 10 is outside of control limit. So we can say that on that day the quality is out of control. And the sample no. 3and 9 is also very near to cross the LCL so we can say that on those days the quality is very good at all level. We can also observe that day is affected the all days of the observation. We also found that continuity of the quality i s not maintained by the company. On 9 th day the quality is very good near the LCL (Lower Control Limit) and 10 th day the quality is out of control. 28 SIEM/FM C CHART: This chart applies to the number nonconformities in the samples of constant size. C is a variable representing the number of nonconformities (defects) in each sample. Usually, the sample size is considered to be one. The control limit of this chart is based on the poison distribution. When the no. of defects per unit can be counted, c chart can be draw. In statistical quality control, the c-chart is used to monitor count-type data, typically total number of nonconformities per unit. It is also occasionally used to monitor the total number of events occurring in a given unit of time. The c-chart differs from the p-chart in that it accounts for the possibility of more then one nonconformity per inspection unit. The p-chart models pass/fail-type inspection only. Nonconformities may also be tracked by type or loca tion which can prove helpful in tracking down assignable causes. Some application of C- Chart is listed below. To control the number of non conforming rivets in an aircraft wing. To control the number of imperfections observed in a galvanized sheet. To control the number of surface imperfections on a large casting Equation UCL = LCL = +3 -3 = 1. 45 UCL = 5. 6 LCL = -2. 16248 NOTE: If the value of LCL (lower control limit) is in negative (-) so it is taken as a zero 29 SIEM/FM ANNEXURE- II NO OF SAMPLE 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 NO OF DEFECT 2 1 2 1 3 2 1 3 0 2 1 0 2 1 0 2 1 2 1 2 6 5 4 3 2 1 0 C Chart The quality is under control limit and some points are very good at quality i. e. 10th, 13th and 16th No of Defect UCL LCL 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 30 SIEM/FM INTERPRETATION (C chart): As all the points fall within the control limits, the process may be regarded in statistical control. Only chance variations are present in process . Again the two points are very good on quality but the continuity is not available. The 3 point (observation days) 10 th, 13th, 16th are very good in the quality. On that day the quality is very good So from the chart we can interpret that the units are the no of defects of the units are in the control limit but in future to maintain it use the corrective measure. 31 SIEM/FM 2. 8. 3 VARIABLE CONTROL CHART Chart and R chart is calculated for the different dimension (variables) of the products. Variable data are continuous in nature and are measurable on a sliding scale. These data can have rage of values and provide more information than the attribute data. Examples of variable data are: dimension, voltage, weight, length etc. The chart actually consists of a pair of charts: One to monitor the process standard deviation (as approximated by the sample moving range) and another to monitor the process mean, as is done with the and s and individuals control charts. The and R chart pl ots the mean value for the quality characteristic across all units in the plus the standard deviation of the quality characteristic across all units in the sample, sample as follows: R = xmax xmin. The normal distribution is the basis for the charts and requires the following assumptions: The quality characteristic to be monitored is adequately modeled by a normallydistributed random variable The parameters ? and ? for the random variable are the same for each unit and each unit is independent of its predecessors or successors The inspection procedure is same for each sample and is carried out consistently from sample to sample As per the name indicates, these charts will use variable data of a process. Chart gives an idea of the central tendency of the observations. These charts will reveal the variation between sample observations. R chart gives an idea about the spread (dispersion) of the observation. This chart shows the variation within the sample. These charts use when we want the deep analysis of the variations. There are three variable (dimensions) for the quality objective which we can measure. 1. Length 2. Width 3. Thickness We had done an every particular control limit analysis as below. 32 SIEM/FM FOR LENGTH CHART UCL = LCL = +A2 A2 = A2 = UCL = LCL = 228. 69 0. 58 229. 762 227. 6184 ANNEXTURE- III FOR LENGTH UNIT IN SAMPLE SAMPLE DAYS/UNITS 1 2 3 4 5 6 7 8 9 10 1 229. 77 229 227. 5 228. 5 228. 4 227. 86 228. 43 228. 4 228. 85 229. 2 228. 56 229. 73 227. 43 229. 52 227. 8 229. 35 227. 89 228. 29 229. 64 227. 74 3 229. 81 228. 5 228 228 230 228 228. 5 229 228. 75 229. 14 4 228. 5 229. 5 229 228. 15 229. 5 227 228 228 230 227. 5 5 229 228 230 229. 5 228 228. 5 229. 5 227. 5 229. 5 230 = 228. 69 Average 229. 128 228. 946 228. 386 228. 734 228. 74 228. 142 228. 464 228. 238 229. 348 228. 776 RANGE 1. 25 1. 73 2. 57 1. 52 2. 2 2. 35 1. 61 1. 5 1. 25 2. 5 =1. 848 NOTE: A2 value table value which is std. for the 5 samples. 33 SIEM/FM CHART (LENGTH ) 230 229. 5 229 AVERAGE UCL LCL 228. 5 228 227. 5 227 226. 5 1 2 3 4 5 6 7 8 9 10 INTERPRETATION These sample quality is called better in those samples only those which sample are near the average. Those all samples are insignificant which are near the UCL LCL. And From the variation analysis we found in length at chart all the points are under the control limit. The observation no. 4th, 5th and 10th are very good from the quality point of view. But it is stated from the above chart 2 points are near the CL so the causes for that should be found it stated that company have to control it. For the ISO 9001 certification it is necessary to maintain the control limit of variable continuously. 34 SIEM/FM R CHART UCL =D4 LCL =D3 = D3 = D4 = UCL = LCL = 1. 848 0. 00 2. 11 3. 89928 0 4. 5 4 3. 5 3 2. 5 2 1. 5 1 0. 5 0 R CHART(LENGTH) LENGTH 1 3. 89 0 1. 84 2 1. 73 3. 89 0 1. 84 3 2. 57 3. 89 0 1. 84 4 1. 52 3. 89 0 1. 84 5 2. 2 3. 89 0 1. 84 6 2. 35 3. 89 0 1. 84 7 1. 61 3. 89 0 1. 84 8 1. 5 3. 89 0 1. 84 9 1. 25 3. 89 0 1. 84 10 2. 5 3. 89 0 1. 84 RANGE 1. 25 UCL LCL INTERPRETATION The same in the r chart if the sample points are near the UCL LCL then the product will be insignificant for the company quality in future. The Length R chart is not out of the limit, but the variation of the samples is again continued. We can say from the observation that company should have to find the causes behind the variation which are continuing. NOTE: D4 D3 value are table value which is std. for the 5 samples. 35 SIEM/FM FOR WIDTH CHART UCL = LCL = +A2 A2 = A2 = UCL = LCL = 103. 346 0. 58 104. 4277 102. 2643 ANNEXTURE- IV FOR WIDTH UNIT IN SAMPLE SAMPLE DAYS/UNITS 1 2 3 4 5 6 7 8 9 10 1 103 103. 5 102. 25 103. 75 102. 95 104. 86 103. 95 103. 67 103. 45 104. 01 2 103. 19 104. 05 103. 15 103 102. 64 102. 97 102. 95 102. 89 102. 69 103. 87 3 102. 99 102. 81 101 105. 1 103. 25 103. 95 102. 61 103. 61 104. 2 106 4 103. 87 103. 25 103. 37 103. 44 102. 65 104. 1 104 102. 5 103. 6 103. 89 5 104 102. 25 103 103. 87 102. 95 102. 25 102. 89 104 102. 95 102. 25 AVERA GE 103. 41 103. 172 102. 554 103. 832 102. 888 103. 608 103. 28 103. 334 103. 378 104. 004 RANGE 1. 01 1. 8 2. 37 2. 1 0. 61 2. 61 1. 39 1. 5 1. 51 3. 75 = 103. 346 =1. 865 NOTE: A2 value table value which is std. for the 5 samples. 36 SIEM/FM CHART (WIDTH) 105 104. 5 104 103. 5 103 102. 5 102 101. 5 101 Average UCL LCL 1 2 3 4 5 6 7 8 9 10 INTERPRETATION For the width control limit all the points in chart are under the control limit. And the observation no. 1st, 7th, 8th and 9th are very good from the quality point of view. We found that on the 3rd and 10th day of observation the quality is bad which is near the UCL. It is apprehensive for the company. 37 SIEM/FM R CHART UCL LCL = = D4 D3 1. 865 D3 = D4 = UCL = LCL = 0. 00 2. 11 3. 93515 0 4. 5 4 3. 5 3 2. 5 2 1. 5 1 0. 5 0 RANGE UCL LCL R CHART(WIDTH) WIDTH 1 1. 01 0 1. 865 2 1. 8 0 1. 865 3 2. 37 0 1. 865 4 2. 1 0 1. 865 5 0. 61 0 1. 865 6 2. 61 0 1. 865 7 1. 39 0 1. 865 8 1. 5 0 1. 865 9 1. 51 0 1. 865 10 3. 75 0 1. 865 3. 9351 3. 9351 3. 9351 3. 9351 3. 9351 3. 9351 3. 9351 3. 9351 3. 9351 3. 9351 NOTE: D4 D3 value are table value which is std. for the 5 samples. 38 SIEM/FM INTERPRETATION From the above observation of the sample of 10 days we found that at 10 th day the quality is very near to upper control limit. The upper control limit is range is 3. 93 and the quality observation on that day is 3. 75 range measures which is insignificance for the company. We can say it is huge difference of width (dimension) from the any observation day. The company should have to find out the variation causes for that to maintain the quality in future. 39 SIEM/FM FOR THIKNESS CHART UCL = LCL = +A2 A2 22. 4296 A2 = UCL = LCL = 0. 58 23. 46432 21. 39488 ANNEXURE V FOR THIKNESS UNIT IN SAMPLE SAMPLE DAYS/UNITS 1 2 3 4 5 6 7 8 9 10 1 22. 5 22. 35 21. 57 22. 53 22. 64 24. 65 22. 35 22. 35 24. 02 22. 35 2 21. 95 22. 13 21. 68 21. 89 22. 13 21. 88 22. 6 22. 13 21. 68 22. 45 3 24. 02 21. 86 21. 87 22. 13 22. 35 22. 43 24. 02 22. 79 22. 83 21. 68 4 21. 68 21. 29 22. 35 22. 47 22. 41 21. 89 22. 13 22. 35 21. 9 22. 13 5 22. 35 22. 6 24. 02 23. 6 23. 38 22. 46 21. 87 21. 89 22. 35 22. 55 Average 22. 5 22. 046 22. 298 22. 524 22. 582 22. 662 22. 594 22. 302 22. 556 22. 232 =22. 4296 RANGE 2. 34 1. 06 2. 45 1. 71 1. 25 2. 77 2. 15 0. 9 2. 34 0. 87 =1. 784 NOTE: A2 value table value which is std. for the 5 samples. 0 SIEM/FM CHART(THIKNESS) 24 23. 5 23 22. 5 22 21. 5 21 20. 5 20 1 2 3 4 5 6 7 8 9 10 AVERAGE UCL LCL INTERPRETATION From the above observation of the sample of 10 days we found all the points in chart are within the control. All the points in chart are very good representative of best quality. 41 SIEM/FM R CHART UCL LCL = = D4 D3 = D3 = D4 = UCL = LCL = 1. 784 0. 00 2. 11 3. 76424 0 R CHART (THIKNESS) 4 3. 5 3 THIKNESS 2. 5 2 1. 5 1 0. 5 0 RANGE UCL LCL 1 2 3 4 5 6 7 8 9 10 2. 34 0 1. 784 1. 06 0 1. 784 2. 45 0 1. 784 1. 71 0 1. 784 1. 25 0 1. 784 2. 77 0 1. 784 2. 15 0 1. 784 0. 9 0 1. 784 2. 34 0 1. 784 0. 87 0 1. 784 . 76424 3. 76424 3. 76424 3. 76424 3. 76424 3. 76424 3. 76424 3. 76424 3. 76424 3. 76424 INTERPRETATION All the points on r chart are between the controls limits. The process is under control with respect to range. Thus variation between samples is significant, while variation within samples is insignificant. Assignable causes may be present. They should be found out and removed. NOTE: D4 D3 value are table value which is std. for the 5 samples. 42 SIEM/FM FINDINGS Control charts are not maintained earlier by company, so all the benefits of that is not known by the company I found that the p-chart is out of control. So we can say that process or nonconformity of the units is 5 % in 400 samples which is insignificance for the ISO implementation at company. I find from the observation study on quality control that company is doing well in the quality. Appro ximately at all level of Control Limit Company quality is under control. The continuity of quality of sample is far at all level in all control charts. In any chart the continuity is not available. There is much variation we found in all the dimension at all level We found that company is facing a problem of reworking quality cost for the defective sample which consuming both time and cost. 3 SIEM/FM RECOMMENDATION: The company have to find out the reason for the some points which are out of control which is very useful and necessary for the ISO implementation The company should need one quality department which does not presently exist in the company the all quality is check by the supervisor, the department/ the general manager. I must say that it gives the benefits to the company. Another problem I found that the control charts should be made by the company it is very useful and helpful to achieve the quality objective. It also gives the idea about how much limit the company can bear the variation in the units. From the research report I found that the quality is under control, which favours the ISO certification. The process control chart should be improved with immediately. With the help of remove the 10th day observation company can find the actual control limit and also cause behind insignificant quality of products. Company should transfer from Traditional Measuring Approach to Modern measuring approach by investing tools Like Digital Verner, digital meter gauge etc. 44 SIEM/FM CONCLUSION As the trainee I found that company is establish there image in the market very tremendously from their quality only. The quality is the main weapon for the Ravi sunrise Industries to be stable in the competitive world. For the more global export the company is taking the ISO certification. During the research repot on the company I found that the company is quite much better in their quality. So they can take the ISO certification we can ensure that company wil l not face any problem to implement it. We found that company is growing every year 40%. The company is providing all types of infrastructure facility for all the workers. 45 SIEM/FM BIBLIOGRAPHY Books Used Author- Donald R Cooper Book- Business Research Methods Publisher– TATA MCGRAW HILL Edition– 9th Author- R. Panneerselvam Book- production operation management Publisher– PHI Edition– 2nd Author- Chase and Jacobs Book- production operation management Publisher– VIKAS PUBLISHING Edition– 9th Websites www. ravisunrideblades. com www. Tradeindia. Com www. wikipedia. com 46 SIEM/FM ANNEXURE ANNEXURE- I (The no. of defectives in fraction from the inspected 40 units. ) SAMPLE 1 2 3 4 5 6 7 8 9 10 NO OF INSPECTED DEFECTS 47 SIEM/FM ANNEXURE- II (The data for c chart for the No. f defectives in each unit) NO OF SAMPLE 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 NO OF DEFECT 48 SIEM/FM ANNEXTURE- III (No. of 5 samples length measuremen t for the 10 days) FOR LENGTH UNIT IN SAMPLE (5 nos) SAMPLE DAYS /NO. 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 49 SIEM/FM ANNEXTURE- IV (No. of 5 sample measurement for the 10 days in dimension WIDTH) FOR WIDTH UNIT IN SAMPLE (5 nos) SAMPLE DAYS /NO. 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 50 SIEM/FM ANNEXURE V (No. of 5 sample measurement for the 10 days in dimension THICKNESS) FOR THICKNESS UNIT IN SAMPLE (5 nos) SAMPLE DAYS /NO. 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 51 SIEM/FM Don’t waste time! Our writers will create an original "Research Design and Methodology for Business Decision" essay for you Create order