ࡱ> IKHn07 [5bjbjUU "~7|7|h-ldddd444H!!!! "HH9#L`#`#`#`#-N--8888888$9 <894 .3+\- . .8u1dd`#`#9u1u1u1 .pdx`#4`#8u1 .8u1u13X43`## NH!y.3390H93</x<3u1HHddddThe Ten Step Process for Doing Laundry Step 1: The Mission The mission for our team is to decrease the amount of time it takes to do one load of laundry. Our goal is to reduce the load time by ten minutes, or thirty minutes for three loads of laundry. We intend to improve our entire laundry process time within three weeks after the final observation, which is on Nov. 6. It would be impossible to reduce the actual machine time, so our objective is to focus on decrease the amount of time spent during the process of sorting, folding, and putting the clothes away. Step 2: Analyze Symptoms During this step of the ten-step process, three team members: Erica, Kristina, and Theresa, collected data twice a week. On Monday and Thursday we each did three loads of laundry disregarding an attempt to control our experiment. Each observation accounted for the amount of time it took to complete one load of laundry. The visual representation of the data can be found in the figure below.  EMBED Excel.Chart.8 \s  Figure 1 Data Collect By Erica Jackson, Kristina Zeek, and Theresa Sebree Oct.6-Nov.6 We have estimated the average machine time for doing a load of laundry is 80 minutes (30 minutes for washing and 50 minutes for the dry cycle). The current average for the three operators participating is 94.54 minutes. We have included the upper and lower control limits on the line graph to display the natural variation of the process within +/- 3 sigma. As illustrated by the graph, our process is out of control. Step 3: Theorize about the causes In order to theorize about the causes, we need to understand the process of doing laundry. The variation of the process could come from the inputs, outputs, or the actual process itself. See the figure below: Inputs Process Outputs Laundry Detergent Sorting Freshly washed Fabric Softener Gathering Clothing Water Soaking Washing Machine Washing Dryer Drying Manpower Ironing Hamper Folding Putting clothes away In this step, we brainstormed the possible causes for what we consider to be an excessive amount of time spent performing the process of doing laundry. We chose to use two statistical tools for theorizing about the causes, a fishbone diagram and a Pareto chart. The Check sheet below is a table of the results from interviews we conducted on family members. We asked the question, Which activity would you consider adds the most time to your laundry process? Based on this data, we constructed a Pareto chart also found below. Laundry Process Check Sheet Reasons for extra time spent: Reason# of Occurrences1. Folding Socks reason # 3////2. Putting Clothes Away reason #2//// ///3. Sorting / Gathering reason # 1//// ////4. Soaking / Prewashing reason # 4///5. Bed Linens reason #5//Laundry Process Pareto Chart  EMBED Word.Picture.8  We were able to come up with additional causes that were possible during the laundry process in a fishbone diagram. The team was able to obtain these ideas by analyzing the process inputs, process, and outputs. These causes are not as easily measured as the vital few displayed in the Pareto chart; therefore, we will focus our attention on those in order to reduce the total amount of time spent performing the entire laundry process.      We analyzed the possible causes and determined from the Pareto chart that the vital few consisted of extra time for sorting and gathering clothes as well as the time spent folding and putting the clothes away. Step 4: Test Theories of the Cause Upon deciding on what causes to test, we determined we would need additional materials to help decrease the amount of time spent sorting and gathering. All three operators purchased a quick sorter, which is a basket divided into three sections in order to pre-sort the clothing prior to the actual laundry process. We decided to test whether or not the extra time spent sorting caused an increase in the total average as a whole for the complete laundry process. We ran a preliminary experiment to decipher whether or not this possible has an effect on our process average. The experiment was conducted during the week of Nov. 10 - Nov. 13 (for six loads of laundry) and the results are as follows. Initial Data:  EMBED Excel.Chart.8 \s  Data after experiment:  EMBED Excel.Chart.8 \s  Figure 5 Data collected by Erica, Kristina, and Theresa Using 95% confidence we used a t-test to decipher whether there was a significant difference in the average time spent performing the total laundry process from beginning to end. The average before the experiment was 94.544 minutes and the average precluding the experiment was 90.84 minutes. The test results are as follows: t-Test: Two-Sample Assuming Equal VariancesVariable 1Variable 2Mean94.5444490.83333Variance101.846319.67647Observations9018Pooled Variance88.66813Hypothesized Mean Difference0Df106t Stat1.526391P(T<=t) one-tail0.064945t Critical one-tail1.659355P(T<=t) two-tail0.129891t Critical two-tail1.982598 According to the data, there is very little difference in the means. We tested using 95% confidence; therefore, on a two-tailed test we would want our p-value to be less than .05. Currently the p-value is .1298 leading us to the conclusion that our problem with sorting the clothes may not be the root of the cause for spending additional time doing laundry. Step 5: Agree on the Causes During step four, we determined that the possible causes for excess time spent on laundry were extra time sorting and gathering and extra time putting laundry away. We agreed the majority of excess time was most probably caused from the sorting and gathering process. Therefore we have constructed a table, found below, to identify the root cause of this. Table Two: The  Five Whys Process ?When we do laundry, we spend too much time.%Why??We need to sort clothes based on colors and type and we need to get the laundry into the laundry room.%Why??So that we can put the loads through quicker.%Why??So we can get done with laundry faster.%Why??So we can move to the next house chore. %Why??Because we do not focus on one chore at a time. Table Two illustrates some of the problems encountered during the laundry process. The first problem as identified above is the time spent sorting. The second is the time spend gathering. The root cause for the extra time spent on doing laundry appears to be correlated with the fact that we do not have enough allotted time to concentrate on one chore at a time. We often do laundry while cleaning the rest of the house in order to save time. Step 6: Propose Remedies After brainstorming all of the possible causes to our problem, we composed a list of potential remedies to resolve them. These actions, if implemented, might allow us to become more efficient while doing laundry and ultimately save us time. CauseProposed Remedy1. We spend too much time trying to sort and gather 1. Purchase a quick-sorter to distribute the clothing in the appropriate bin for washing (ex. Reds, jeans, darks, towels, delicates, whites, etc.).2. We haphazardly place the clothing in random drawers instead of having a designated place for each type of clothing because we are in a hurry.2. Set aside drawers that correspond to each type of clothing (ex. One for jeans, one for reds, one for whites, etc.)3. We have busy lives so we try to do our chores all in one day.3. Allot a short period of time each day for different chores. This will allow us to focus on one chore at a time. Step 7: Implement the Remedies After implementing the remedies only the three operators will benefit from the improvement in the process. We will reduce the amount of time spent doing laundry, and therefore, rewarding ourselves with more free time for other chores and personal activities. After purchasing the sorter and rearranging the drawers, we will begin to realize the benefits. We will also conduct another t-test to evaluate if the remedies made a significant difference. If the t-test reveals that the remedies made a significant difference, we will be able to not only realize our initial goal but also be able to understand the possibilities for further improvement in the process. Step 8: Analyzing the Effectiveness of the Remedies This set of data represents our process after the purposed remedies were implemented on the week of November 17 through November 22nd. The figure below is a chart that represents our new process averages.  Figure 6 Data Collected by Erica, Kristina, and Theresa The remedies which we implemented caused the process to be in control. The new average is 85.06 minutes just about 9 minutes lower than the original average. The goal of a reduction of 10 minutes was not achieved; however, there is ample room for improvement. The goal may eventually become realized after we make this new process a routine. The figure below is our second t-test. We conducted this test as we mentioned in step seven in order to realize the significant difference the remedies made or did not make. Furthermore, we hope to use this data to further recognize opportunities for improvement in the laundry process. t-Test: Two-Sample Assuming Equal VariancesVariable 1Variable 2Mean94.5444444485.05556Variance101.84631715.825397Observations9036Pooled Variance74.74363799Hypothesized Mean Difference0df124t Stat5.56564469P(T<=t) one-tail7.6871E-08t Critical one-tail1.657235771P(T<=t) two-tail1.53742E-07t Critical two-tail1.97927875Figure 7 According to the test, we can conclude that the data is significant because the two-tailed p-value is less than .05. Therefore, we accept our hypothesis that the means are in fact different with 95% confidence. Step 9: Controls to Hold the Gains In figure 6 above, the process is shown to be in control. The new upper control limit is set at 89.41 minutes and the lower control limit is set at 80.71 minutes. All data points lie within this range. Again, the mission has not been completed entirely, however over time, this process can become routine, in turn lowering the average time by the full ten minutes.  Figure 8 We feel that by saving 9 minutes per load of laundry we can save on average 27 minutes per day if we do three loads of laundry. This time savings will give us more time to allot for other chores. By simply purchasing the sorting basket, we will continue to experience a time savings of at least 9 minutes. We also believe we will have additional consistent time savings by setting up a carefully organized arrangement of drawers. We will continue to use a control chart to monitor and control our process on a regular basis. Every week after collecting the data we will analyze the process and check for special causes. We will troubleshoot if any arise again and use the ten-step process to eliminate them. Periodically, we will recalculate the control limits to satisfy our new averages and improve our process even more. Over time, we will deliberately expand our quality control program for doing laundry to include other process variables that could be improved. Fishbone Diagram Machines Water Temperature, Capacity, Maintenance, Dryer Filter clean vs. clogged. Settings: One rinse Cycle vs. Two. Timed Dry vs. Automatic settings. Environment Distractions: T.V., Telephone, other cleaning. Causes Of Variation Of Time Spent On Laundry Man Power Laziness, exhaustion, illness. Methods Sort while doing Laundry. Vs. 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From Sheet 1, max observation = min observation = So the range in the data is :If I divided this up into, say, 10 bins, each one would bein width or about .005So, if I start my bins at '(just below the min) and added them in increments, my bins would be:rSo there are my bins. Notice that they are nice and evenly spaced, and I'll capture all 150 observations in them.Note: I don't check the box in the Histogram command for their histogram. I like to make my own (the control freak in me I guess).`So I just make a simple bar graph using the graph wizard and the bin and frequency output above:And there it is& for n = 3 A2 = 1.023X double bar =EricaKristinaTheresa USL = 112.374 LSL = 76.714DateSo, now we're ready to rip with the histogram command. I've copied the bins below. 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MF Sheet6ESheet4ER Sheet1 (2)Sheet1úSheet2Sheet3&T2*p   3  @@  M#La Ventana Window Company Case Dataa) Initial Data Day/ShiftSample1-1Juanita1-2Tex2-12-23-13-24-14-25-1Shane5-26-16-27-17-28-18-29-19-210-110-211-111-212-113-113-214-114-215-115-2x-barMinMaxR for n = 5 A2 = .577 USL = 25.530 LSL = 25.470 UCL = 25.507 LCL = 25.491BinMore Frequencyx-doublebarMF's Histogram ApproachdI like my histograms to have 8 -12 "bins" that are evenly divided at round numbers for the data set.UFirst, I need to get a feel for the range in individual observations. From Sheet 1, max observation = min observation = So the range in the data is :If I divided this up into, say, 10 bins, each one would bein width or about .005So, if I start my bins at '(just below the min) and added them in increments, my bins would be:rSo there are my bins. Notice that they are nice and evenly spaced, and I'll capture all 150 observations in them.Note: I don't check the box in the Histogram command for their histogram. I like to make my own (the control freak in me I guess).`So I just make a simple bar graph using the graph wizard and the bin and frequency output above:And there it is& for n = 3 A2 = 1.023X double bar =EricaKristinaTheresa USL = 112.374 LSL = 76.714DateSo, now we're ready to rip with the histogram command. I've copied the bins below. I've also copied the 150 observations from Sheet 1 here (for whatever reason, the histogram command wants the data range on the same page as the bin range--I have no clueR u NB"k_  Mv\\printserv3\BP312_1_LEXT622 XX⤗Lexmark T622BP312_1_LEXT622*Lexmark T622 Rx(  r'A>⤗X,X ⤗"dXX??3` f!h` f!1` N  ` N !l(  ldB l s *D@..]`rjB l 0D   ]`hrjB l 0D ]` P3d23 M NM4 3QQ ;#QQ3_43_4E4D$% M<3O&!Q4$% M<3O& Q4FAV 3OBB7 3*>@#M43*R@\@$@@N#M4% 9 >qM3O?&Q Sample'4% 7MZ3Ov&Q  Time (minutes)'43d" 44% 3L:M03O&Q ,Laundry Process Time'44eejX@U@UUUUUX@jX@X@UUUUUY@UUUUUV@W@V@ jW@ UUUUUW@ W@ UUUUUX@ V@U@W@jZ@UUUUUW@X@UUUUUV@UUUUUUW@W@W@UUUUUU@UUUUUW@V@\@UUUUUUV@W@W@e>    dMbP?_*+%MHP LaserJet 4P4C odXXLetterDINU"4z="dXX??UColumn IXN                         (3 4 5 6%$9@ ;# B 7%Kw9@ ;# B 8 ! ~jt? DD  9  1U0*s? D   : ;  < ~ @ = ~ ? >&ףp= w9@ D  !&Qx9@( DD !&y9@( DD !&zGz9@( DD !&[(|9@( DD !&< ףp}9@( DD !&Q~9@( DD !&9@( DD !&zG9@( DD !&(\9@( DD !&p= ׃9@( DD !&Q9@( DD  ? L2***YYWmd;%%%%%%%%%%% ! 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