'Polaroid Case Study\r'

'BACKDROP Polaroid is manu eventurer of photographic equipment, accessories and related items used in instant photography. The physical composition was divided into two main divisions †The Consumer Photography socio-economic class and the Technical and Industrial Division with each of these divisions lend around 40% of Polaroid’s revenues of $ 1. 3 gazillion in 1984. The keep company produced two main types of films: 1. The hide apart film which required the user to physicall(a)y pull the film lay down up of the camera and, 2. The intrinsical film, which came out of the camera automatically.\r\nThe integral films were fabricate in the R2 building at the Waltham Massachusetts site. The operations at R2 included exertion of sheet metal springs, pods, flexible cartridges and plastic end caps and then assemb conduct these into film cartridges. R2 ran one-third faults, five days a week, employing approximately 900 workers out of which 700 were part time. QUALITY A ND PROCESS CONTROL PROCEDURES AT R2 All films were vetted by the Quality Control section before being released into the food market. The QC procedure included try of 15 finished cartridges (each containing 10 frames) out of all(prenominal) quite a little of 5000 cartridges.\r\nIf the exampled cartridges contained disgraces in excess of allowable limits, the lot was held and further interrogatory was done. Additional testing ordinarily led to reworking, or rejection of a portion or all of the lot. Subsequent lots were the subjected to even more(prenominal)(prenominal) rigorous testing by increasing the sample size tested. Quality checks were not the sole indebtedness of the QC department. The operators usually sampled around 32 samples out of all lot. If the measurements went against the knowledge of the operator, the sample was jilted.\r\nAfter wait on maneuver was initiated in R2 in the late 1970’s, affect engineering technicians were made responsible for g athering data and making rough analyses. PROBLEMS WITH EXISTING QUALITY CONTROL Since the testing of cartridges was destructive, it resulted in sampled scrap. This, along with the product that failed acceptance taste resulted in $3. 28 million in 1984. some other issue was that ingest did nothing to improve graphic symbol, it scarcely improved the AOQ. In fact, due to the large production and low defect rates, if the production and quality lead take in were halved, the outgoing regretfuls would be 0. 3% of production. On the other hand, increasing the AOQ further would lead to prohibitively high cost due to increased sampling. The sampling answer employed was also inaccurate. Time was slump on trying to reduce beta or consumer risk. Cartridges which were inspected and passed were sent back to production to be repackaged. merely the handling of these cartridges itself increased the chances of their developing defects which resulted in a vicious cycle of tests and retests and did not contribute to alter quality significantly tour increasing costs considerably.\r\nTo avoid losing production, operators often ‘salted’ boxes. Operators did not disposition frequently collected data and if they were in doubt, they would pass the ingredient on to the QC Department believing that they would be able to detect the defect and reject the component if the defect was serious enough. â€Å"Tweaking” machines was an accepted practice in the plant. The objective of the exercise was to enable machines running and divergent speeds and variations to produce at their maximum capacity. The QC department did not focus on defects that were normally detected by consumers.\r\nFor example, the most stringent auditors tested for excess reagent by flipping the film over right after exposing it, a defect that would not be noticed by a consumer. These stringent auditors averaged about 10% regretfuls. The conditions downstairs(a) which the tests were si mulated were also out of sync with authentic market realities. External customers often used cameras which did not function precisely to stipulation, whereas the QC Department used ‘ consummate(a)’ cameras to test the film. This precluded the possibility of decision defects which would occur with imprecisely functioning cameras. GREENLIGHT\r\nThe project objective was quality observe costs reduction while at the said(prenominal) time improving the quality of the product. The improvements in quality lead belongmentes were focused along with reducing the egress of samples. The plan consisted of three distinct elements: 1. Statistical cultivate reserve would be adapted as processes in experience and capable of producing at bottom specifications would produce more consistent quality. 2. Production operators would be given the process ascertain tools that the process engineering technicians had been using and in conjunction with sampling would be expected to ex tend to disposition decisions themselves. 3.\r\nQuality control auditors would c one timentrate on training operators and operationalizing specifications on their new products. The statistical process control system involving twain acceptance sampling and automated process control was to be implemented. SPC elusive testing for productions within a pre-specified range. If the production went beyond the range, the production process had to be shut defeat charge was to be called to perform maintenance and renormalization. As a part of the process, the operators were to take six stochastic measurements of a process feature article during the course of their shift and then plot the imply measured value.\r\nThis led to a drastic reduction in the deed of samples tested and consequently the scrapping costs. The central line of work in this project was the estimation of the central level and the control limits. Initially, the Quality Control auditors helped the operators in plotting the ranges and the operators’ protocol was to in a flash shut down the machines and call for help whenever, the characteristic crossed the specified range. Moreover, eight consecutive mingy values lying into the upper or set down zones near the control limits, or consistently up(a) trends were to be investigated by maintenance as well.\r\nThe sentiment behind the project was to cut down the defect and testing losses. However, the idea backfired when the average defects detection by auditors shot up to 10% from 1% while at the operator level, it halved to about 0. 5%. other problem was the lack of trust between the auditors and the operators. regularise maintenance procedures also met with a lot of protection as they were seen as making the whole maintenance process impersonalized and bureaucratic. The operators believed that they could obtain remedy results by ‘tweaking’ the machines.\r\nAt the same time, operators refused to come out of the â€Å"maxi mize deem” mindset and kept adjusting the machines for increased output. Also, the operators were sampling and testing more units than they were recording and adjusted the machines on the rear end of the unrecorded defects. The nature of defects also changed. The variability in the kinds of defects detected increased, as the defects recorded by the auditors were markedly different from the defects recorded by the operators. ANALYSIS The routine of inspection is to determine the level to which the product manufactured conforms to the specifications.\r\nControl graphs and run tests argon used for process control with the objective being to identify the causes of assignable variation, and to leave the system alone if the variation is random and the process is under control. The data given in exhibit 5 was used to calculate the pith and ranges of the variables (pod weight and finger height) and the control limits for them were calculated. These have been plan on control grap hs. seedcase Weight · two the X bar and the R chart show that the process is in control, and that the process is capable. The variation make up is random variation. Although the X bar chart shows that the process is in control, the last four informations whitethorn paint a picture a trend if further values move towards the lower control limit. Also, between the 16th and twenty-eighth readings, there are making of trends. · The R chart shows that though the values of R lie within the control limits the range variation is high. Also, the behaviour of the readings is peculiar which is a reason for investigation. Finger Length · The X bar graph shows that the process is out of control very often, signifying that an assignable cause of variation may be present. The values in the R chart are within the control limits. Thus, although the process mean is out of control, the process variability is in control. other(a) Analysis · The random sample of defects from Exhibit 4 is tabulated at a lower place. Operator Defects Auditor Defects Excess Reagent 4 11 Excess Flash on box 2 2 Negative sheet defect 3 2 Positive sheet defect 3 3 Double feed 3 3 Frame feed failure 2 9 Damaged spring 3 3 deformed box 1 3 Insufficient reagent 1 4 Misalignment 1 3 Marginal lamination 1 2 Dirt from assembly 0 5 After Greenlight was initiated, the number of defects reported by operators has halved from 1% to 0. % while those reported by auditors has increased from 1% to 10%. This may be due to the fact that the operators are not recording all the defective samples which they are using to adjust their machines. Also, since the auditors feel that intercommunicate the operators to be incharge of the quality is like handing over the cage to the foxes, most of them may have shifted to stringent checking of the cartridges which would let off the jump from 1% rejects to 10% rejects, which was the level of rejects which lone(prenominal) the stringent auditors had earlier. Th ere is some evidence for both the above points.\r\nThe tweaking of the machines by the operators may explain wherefore so many readings are out of the control limits, though the machine should have undergone maintenance and calibration as soon as the first reading was outside the control limits, which explains why the auditors are finding many more rejects due to the feed than the operators. Also, the auditors are finding more rejects due to the reagent, although the process is under control. This may be due to stringent checking. another(prenominal) indication of stringent checking is that cartridges are being rejected due to their having dirt which has been attributed to assembly. RECOMMENDATIONS Control measures fill to be incorporated at the injection molding machines in order to minimize defect rates, and defects need to be prioritized, to help in setting control limits and the ratings on the quality of products. · The operators need to realize that the process downstream is the customer, and they need to shutdown the machine for maintenance as soon as the process goes out of control rather than waiting for the machine to start producing defective pieces. · Polaroid can carry out a market research exercise on consumers, to determine which attributes need compliance from the customer’s point of view.\r\nIt leave also need to establish the technical specification limits for various components. These will need to build into a 6-sigma process to increase quality by improving the processes and reduce variation in outputs. · The citizenry, especially the realise management, need to be convinced about the posture of process control, which doesn’t have any problem with the quality apart from above observations. · Proper sustenance of all the procedures and processes should be assured, in order to keep on people focused on quality once defect rates drop significantly below 1%.\r\nThis documentation should be accessible to all conc erned people and they should be instructed unambiguously to tie to the norms. · Automated methods for data collection need to be adopted, like the ones mentioned in the case, since the operators have proved to be unreliable. The investment is not large enough to make a serious dent in the company’s bottom line, and should be considered. · A better and more comprehensive training model ask to be introduced to train the workers and supervisors in basic statistics and the applications programme to process control The high-volume driven mindset of the people needs to be changed, and an atmosphere needs to be built which engenders mutual trust between operators and auditors. attachment take Statistical Process Control Measurements Pod Weight (grams) Sample Number Day vex 1 2 3 4 5 6 Mean Range 3-Aug A 2. 800 2. 799 2. 760 2. 802 2. 805 2. 803 2. 795 0. 045 B 2. 750 2. 820 2. 850 2. 740 2. 850 2. 790 2. 800 0. cx C 2. 768 2. 807 2. 807 2. 804 2. 804 2. 803 2. 799 0. 039 4-Aug A 2. 841 2. 802 2. 802 2. 806 2. 807 2. 807 2. 811 0. 039 B 2. 801 2. 770 2. 833 2. 770 2. 840 2. 741 2. 93 0. 099 C 2. 778 2. 807 2. 804 2. 804 2. 803 2. 804 2. 800 0. 029 5-Aug A 2. 760 2. 804 2. 804 2. 806 2. 805 2. 806 2. 798 0. 046 B 2. 829 2. 804 2. 805 2. 806 2. 807 2. 807 2. 810 0. 025 C 2. 741 2. 850 2. 744 2. 766 2. 767 2. 808 2. 779 0. 109 6-Aug A 2. 814 2. 804 2. 803 2. 805 2. 807 2. 804 2. 806 0. 011 B 2. 787 2. 802 2. 805 2. 804 2. 805 2. 804 2. 801 0. 018 C 2. 766 2. 805 2. 804 2. 802 2. 804 2. 806 2. 798 0. 040 7-Aug A 2. 774 2. 801 2. 805 2. 805 2. 805 2. 804 2. 799 0. 031 B 2. 770 2. 801 2. 833 2. 770 2. 840 2. 741 2. 793 0. 099 C 2. 832 2. 836 2. 794 2. 843 2. 13 2. 743 2. 810 0. degree centigrade 10-Aug A 2. 829 2. 846 2. 760 2. 854 2. 817 2. 805 2. 819 0. 094 B 2. 850 2. 804 2. 805 2. 806 2. 807 2. 807 2. 813 0. 046 C 2. 803 2. 803 2. 773 2. 837 2. 808 2. 808 2. 805 0. 064 11-Aug A 2. 815 2. 804 2. 803 2. 804 2. 803 2. 802 2. 805 0. 013 B 2. 782 2. 806 2 . 806 2. 804 2. 803 2. 802 2. 801 0. 024 C 2. 779 2. 807 2. 808 2. 803 2. 803 2. 803 2. 801 0. 029 12-Aug A 2. 815 2. 815 2. 803 2. 864 2. 834 2. 803 2. 822 0. 061 B 2. 846 2. 854 2. 760 2. 829 2. 817 2. 805 2. 819 0. 094 C 2. 767 2. 804 2. 834 2. 803 2. 803 2. 803 2. 802 0. 067 13-Aug A 2. 850 2. 04 2. 804 2. 804 2. 804 2. 804 2. 812 0. 046 B 2. 810 2. 820 2. 814 2. 794 2. 798 2. 787 2. 804 0. 033 C 2. 850 2. 820 2. 750 2. 740 2. 850 2. 790 2. 800 0. 110 14-Aug A 2. 750 2. 765 2. 850 2. 760 2. 790 2. 840 2. 793 0. 100 B 2. 830 2. 770 2. 848 2. 760 2. 750 2. 830 2. 798 0. 098 C 2. 740 2. 770 2. 833 2. 770 2. 840 2. 800 2. 792 0. 100 17-Aug A 2. 753 2. 807 2. 805 2. 804 2. 802 2. 804 2. 796 0. 054 B 2. 851 2. 751 2. 752 2. 773 2. 849 2. 806 2. 797 0. 100 C 2. 845 2. 804 2. 803 2. 806 2. 805 2. 806 2. 812 0. 042 18-Aug A 2. 844 2. 777 2. 754 2. 791 2. 833 2. 811 2. 802 0. 90 B 2. 806 2. 839 2. 805 2. 804 2. 850 2. 740 2. 807 0. 110 C 2. 849 2. 801 2. 804 2. 762 2. 814 2. 791 2. 804 0. 087 19-Aug A 2. 820 2. 793 2. 812 2. 833 2. 853 2. 812 2. 821 0. 060 B 2. 790 2. 780 2. 764 2. 843 2. 843 2. 818 2. 806 0. 079 C 2. 850 2. 806 2. 805 2. 814 2. 807 2. 807 2. 815 0. 045 20-Aug A 2. 767 2. 831 2. 808 2. 793 2. 836 2. 811 2. 808 0. 069 B 2. 833 2. 825 2. 793 2. 813 2. 823 2. 766 2. 809 0. 067 C 2. 824 2. 799 2. 790 2. 764 2. 817 2. 805 2. 800 0. 060 21-Aug A 2. 778 2. 775 2. 799 2. 805 2. 833 2. 772 2. 794 0. 061 B 2. 801 2. 832 2. 758 2. 759 2. 773 2. 14 2. 790 0. 074 C 2. 770 2. 787 2. 744 2. 766 2. 807 2. 803 2. 780 0. 063 modal(a) 2. 8025 0. 0640 UCL for mean = 2. 8332 UCL for Range = 0. 1280 LCL for mean = 2. 7718 LCL for Range = 0. 0000 Sample Statistical Process Control Measurements Finger extremum (mm) Sample Number Day Shift 1 2 3 4 5 6 Mean Range 3-Aug A 1. 90 1. 95 1. 94 2. 00 2. 05 2. 16 2. 00 0. 26 B 2. 15 2. 17 2. 11 2. 13 2. 02 2. 03 2. 10 0. 15 C 1. 73 1. 90 2. 07 1. 89 1. 76 1. 88 1. 87 0. 34 4-Aug A 2. 30 2. 41 2. 54 2. 37 2. 32 2. 16 2. 35 0. 38 B 2. 28 2. 16 2. 19 2. 08 2. 25 2. 24 2. 20 0. 20 C 1. 92 2. 24 2. 1 1. 89 1. 88 2. 17 2. 04 0. 36 5-Aug A 2. 39 2. 28 2. 10 2. 36 2. 54 2. 25 2. 32 0. 44 B 2. 11 2. 21 2. 24 2. 21 2. 17 2. 24 2. 20 0. 13 C 1. 89 1. 90 1. 73 2. 07 1. 89 1. 76 1. 87 0. 34 6-Aug A 2. 51 2. 25 2. 08 2. 35 2. 29 2. 32 2. 30 0. 43 B 2. 22 2. 19 2. 22 2. 24 2. 01 2. 23 2. 19 0. 23 C 1. 89 1. 90 1. 78 2. 07 1. 89 1. 76 1. 88 0. 31 7-Aug A 1. 95 2. 07 2. 25 1. 95 2. 11 2. 16 2. 08 0. 30 B 2. 08 2. 03 2. 27 2. 23 2. 24 2. 13 2. 16 0. 24 C 2. 31 1. 90 1. 86 1. 91 1. 89 1. 87 1. 96 0. 45 10-Aug A 2. 23 2. 25 2. 21 1. 89 2. 15 2. 11 2. 14 0. 36 B 2. 23 2. 21 2. 05 2. 19 2. 7 2. 16 2. 15 0. 18 C 1. 73 2. 00 1. 79 1. 75 1. 84 1. 74 1. 81 0. 27 11-Aug A 2. 21 2. 11 2. 21 2. 44 2. 17 2. 30 2. 24 0. 33 B 2. 17 2. 19 2. 15 2. 04 2. 07 2. 22 2. 14 0. 18 C 2. 01 1. 90 1. 90 1. 81 2. 06 1. 89 1. 93 0. 25 12-Aug A 2. 08 2. 19 2. 28 2. 29 2. 21 2. 45 2. 25 0. 37 B 1. 93 2. 09 1. 90 1. 95 2. 04 2. 09 2. 00 0. 19 C 1. 84 2. 12 1. 90 1. 89 2. 01 1. 75 1. 92 0. 37 13-Aug A 2. 23 2. 01 2. 25 2. 11 2. 39 2. 15 2. 19 0. 38 B 2. 19 2. 22 2. 18 2. 15 2. 23 2. 04 2. 17 0. 19 C 1. 96 2. 05 2. 16 1. 87 2. 13 1. 90 2. 01 0. 29 14-Aug A 2. 27 2. 00 2. 06 1. 97 2. 13 2. 05\r\n'