The Dean Door Corporation (DDC) manufactures steel and aluminum exterior doors for commercial and residential applications. DDC landed a major contract as a supplier to Walker Homes, a builder of residential communities in several major cities through the Midwest. Because of the large voluma of demand, DDC had to expand its manufacturing operations to three shifts and hire additional workers. No long after DDC began shipping doors to Walker Homes, it began receiving some complaints about excessive gaps between the door and frame. This problem was somewhat alarming to DDC, because its reputation as high-quality manufacturer was the principal reason that it was selected as a supplier to Walker Homes. DDC placed a great deal of confidence in its manufacturing capability because of its well-trained and dedicated employees, and it never felt the need to cosider formal process contol approaches. In view of recent complaints, Jim Dean, the company president, supected that the expansion to three-shift operation and meet-just-in-time delivery requests was causing a breakdown in their quality. On the recommendation of the plant manager, DDC hired a quality consultant to train the shift supervisors and selected line workers in statistical process control methods. As a trial project, the plant manager wants to evaluate the capability of a critical cutting operation that he suspects might be the source of the gap problem. The nominal specification for this cutting operation is 30.000 inch with a tolerance of 0.125 inch; therefore, the upper and lower specifications are LSL = 29.875 inch and USL = 30.125 inch. The consultant sugested inspecting five consecutive door panels in the middle of each shift over 10-day period and recording the dimension of the cut. Table 1 shows 10 days’ data collected for each shift. QUESTIONS 1. Interpret the data in table 1, establish a state statistical control, and evaluate the capability of the process to meet specifications. Consider the following questions: What do the initial control charts tell you? Do any out-of-control conditions exist? If the process is not in control, what might be likely causes, based on the information that is avaliable? What is the process capability? What do the process capability index tell the company? Is DDC facing a serious problem that it needs to address? How might the company ensure that the problems that Walker Homes found be eliminated? 2. The plant manager implemented the recommendations the resulted from the initial study. Because of the success un using contol charts, DDC made a dicision to continue using them in the cutting operation. After establishing control, additional samples were taken over the next 20 shifts, shown in table 2. Evaluate whether the process remains in control, and suggest any actions that shouldbe taken. Consider the following issues: Does any evidence suggest that the process has changed relative to the stablished control limits? If an out-of-control paterns are suspected, what might be the cause? What should the company investigate?

Table 1 Observation Shift Operator Sample 1 2 3 4 5 1 Tom 1 30.046 29.978 30.026 29.986 29.961 2 Paul 2 29.972 29.966 29.964 29.942 30.025 3 Dora 3 30.046 30.004 30.028 29.986 30.027 1 Tom 4 29.997 29.997 29.980 30.000 30.034 2 Paul 5 30.018 29.922 29.992 30.008 30.053 3 Dora 6 29.973 29.990 29.985 29.991 30.004 1 Tom 7 29.989 29.952 29.941 30.012 29.984 2 Paul 8 29.969 30.000 29.968 29.976 29.973 3 Carmen 9 29.852 29.978 29.964 29.896 29.876 1 Tom 10 30.042 29.976 30.021 29.996 30.042 2 Paul 11 30.028 29.999 30.022 29.942 29.998 3 Dora 12 29.955 29.984 29.977 30.008 30.033 1 Tom 13 30.040 29.965 30.001 29.975 29.970 2 Paul 14 30.007 30.024 29.987 29.951 29.994 3 Dora 15 29.979 30.007 30.000 30.042 30.000 1 Tom 16 30.073 29.998 30.027 29.986 30.011 2 Paul 17 29.995 29.966 29.996 30.039 29.976 3 Dora 18 29.994 29.982 29.998 30.040 30.017 1 Tom 19 29.977 30.013 30.042 30.001 29.962 2 Paul 20 30.021 30.048 30.037 29.985 30.005 3 Carmen 21 29.879 29.882 29.990 29.971 29.953 1 Tom 22 30.043 30.021 29.963 29.993 30.006 2 Paul 23 30.065 30.012 30.021 30.024 30.037 3 Carmen 24 29.899 29.875 29.980 29.878 29.877 1 Tom 25 30.029 30.011 30.017 30.000 30.000 2 Paul 26 30.046 30.006 30.039 29.991 29.970 3 Dora 27 29.993 29.991 29.984 30.022 30.010 1 Tom 28 30.057 30.032 29.979 30.027 30.033 2 Paul 29 30.004 30.049 29.980 30.000 29.986 3 Dora 30 29.995 30.000 29.922 29.984 29.968 Table 2 Observation Shift Operator Sample 1 2 3 4 5 1 Tom 31 29.97 30.017 29.898 29.937 29.992 2 Paul 32 29.947 30.013 29.993 29.997 30.079 3 Dora 33 30.05 30.031 29.999 29.963 30.045 1 Teom 34 30.064 30.061 30.016 30.041 30.006 2 Paul 35 29.948 30.009 29.962 29.99 29.979 3 Dora 36 30.016 29.989 29.939 29.981 30.017 1 Tom 37 29.946 30.057 29.992 29.973 29.955 2 Paul 38 29.981 30.023 29.992 29.992 29.941 3 Dora 39 30.043 29.985 30.014 29.986 30 1 Tom 40 30.013 30.046 30.096 29.975 30.019 2 Paul 41 30.043 30.003 30.062 30.025 30.023 3 Dora 42 29.994 30.056 30.033 30.011 29.948 1 Tom 43 29.995 30.014 30.018 29.966 30 2 Paul 44 30.018 29.982 30.028 30.029 30.044 3 Dora 45 30.018 29.994 29.995 30.029 30.034 1 Tom 46 30.025 29.951 30.038 30.009 30.003 2 Paul 47 30.048 30.046 29.995 30.053 30.043 3 Dora 48 30.03 30.054 29.997 29.993 30.01 1 Tom 49 29.991 30.001 30.041 30.036 29.992 2 Paul 50 30.022 30.021 30.022 30.008 30.019