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Xavier Lehnhoff - Operations Management



1.      Using the sample data given in Exhibit 10, make a recommendation for how many units of each style Wally Obermeyer should order during the initial phase of production. Assume that all ten styles in the sample problem are made 
in Hong Kong, and that Obermeyer’s initial production commitment must be at least 10,000 units. (Ignore price differences among styles in your initial analysis).
                                                                                                                                                                                                  China - FIRST ORDER                               HK - FIST ORDER
                                                                                       Average            Standard           Standard                           Variance                          Z value in Expected Expected                    Z value in Expected Expected
            Style             Price              Profit (sold)        Loss (not sold) Forecast            deviation          deviation *2       Units produced coefficient K            Z         table      loss        gain      Z              table      loss       gain
            Gail                           110                   26.4             -8.8            1017                194                   388             605       0.191      1.06        0.47       0.68       -5.98      8.45          -1.07       0.14      -1.23      22.70
            Isis                            99              23.76               -7.92             1042                323                   646            357      0.310                    0.24         0.60         -4.73        9.58    -0.68      0.25      -1.96      17.89
            Entice                          80               19.2                -6.4             1358                248                   496            832      0.183                   -0.32         0.38         -2.40       12.00    -1.53      0.06      -0.40      17.99
            Assault                         90               21.6                -7.2             2525                340                   680           1804      0.135                   -1.95         0.03         -0.18       21.05    -2.83      0.00      -0.02      21.55
            Teri                           123              29.52               -9.84             1100                381                   762            292      0.346                    0.13         0.55         -5.43       13.22    -0.66      0.26      -2.52      21.97
            Electra                        163              39.12             -13.04              2150                404                808              1293      0.188                   -1.18         0.12         -1.56       34.43    -1.92      0.03      -0.36      38.04
            Stephanie                      133              31.92             -10.64              1113                524               1048                 1      0.471                    0.08         0.53         -5.67       14.90    -0.49      0.31      -3.32      21.95
            Seduced                         73              17.52              -5.84              4017                 556              1112             2837       0.138                   -2.53         0.01         -0.03       17.42    -3.07      0.00      -0.01      17.50
            Anita                           93              22.32              -7.44              3296                1047              2094             1075       0.318                   -1.00         0.16         -1.18       18.78    -1.29      0.10      -0.74      20.11
            Daphne                         148              35.52             -11.84              2383                 697              1394              904       0.292                   -0.85         0.20         -2.34       28.49    -1.28      0.10      -1.19      31.95
                                                                                                 20001                                                  10000

                                                                                                     Standard
                                                 Average             Standard                        deviation
                              Style              Forecast            deviation *2    Units produced multiplier                                                                                                                       629
                              Gail                           1017                388          605.42                  1.06
                              Isis                           1042                646          356.74
                              Entice                         1358                496          831.85
                              Assault                        2525                680         1803.67
                              Teri                           1100                762          291.69
                              Electra                        2150                808         1292.89
                              Stephanie                      1113               1048            1.31
                              Seduced                        4017               1112         2837.42
                              Anita                          3296               2094         1074.73
                              Daphne                         2383               1394             904.28
                                                                                             10000




2.     What operational change would you recommend to Wally to improve performance?
         Use similar raw materials for products (i.e. fabrics, colors)
         Have cheap raw materials in inventory
         Start producing most predictable products in advance and leave capacity in peak months for unpredictable products
         Seek providers that are closer to the factories




3.     How should Obermeyer management think (both short-term and long-term) about sourcing in Hong Kong versus China?

            Hong Kong                                                                                                                                                       China
            More demand certainty (leftover expensive)                                                                                                                      Cheaper to produce
            Smaller volumes                                                                                                                                                 Larger volumes required
            Staff is more crosstrained                                                                                                                                      Staff is less crosstrained
            Staff is more efficient (10 people in each parka line)                                                                                                          Staff is more inefficient (40 people in each parka line)
            Each employee produces twice as much output                                                                                                                     Quality concerns
                                                                                                                                                                            Quotas

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Obermeyer Case Xavier Lehnhoff

  • 1. Xavier Lehnhoff - Operations Management 1.      Using the sample data given in Exhibit 10, make a recommendation for how many units of each style Wally Obermeyer should order during the initial phase of production. Assume that all ten styles in the sample problem are made  in Hong Kong, and that Obermeyer’s initial production commitment must be at least 10,000 units. (Ignore price differences among styles in your initial analysis). China - FIRST ORDER HK - FIST ORDER Average Standard Standard Variance Z value in Expected Expected Z value in Expected Expected Style Price Profit (sold) Loss (not sold) Forecast deviation deviation *2 Units produced coefficient K Z table loss gain Z table loss gain Gail 110 26.4 -8.8 1017 194 388 605 0.191 1.06 0.47 0.68 -5.98 8.45 -1.07 0.14 -1.23 22.70 Isis 99 23.76 -7.92 1042 323 646 357 0.310 0.24 0.60 -4.73 9.58 -0.68 0.25 -1.96 17.89 Entice 80 19.2 -6.4 1358 248 496 832 0.183 -0.32 0.38 -2.40 12.00 -1.53 0.06 -0.40 17.99 Assault 90 21.6 -7.2 2525 340 680 1804 0.135 -1.95 0.03 -0.18 21.05 -2.83 0.00 -0.02 21.55 Teri 123 29.52 -9.84 1100 381 762 292 0.346 0.13 0.55 -5.43 13.22 -0.66 0.26 -2.52 21.97 Electra 163 39.12 -13.04 2150 404 808 1293 0.188 -1.18 0.12 -1.56 34.43 -1.92 0.03 -0.36 38.04 Stephanie 133 31.92 -10.64 1113 524 1048 1 0.471 0.08 0.53 -5.67 14.90 -0.49 0.31 -3.32 21.95 Seduced 73 17.52 -5.84 4017 556 1112 2837 0.138 -2.53 0.01 -0.03 17.42 -3.07 0.00 -0.01 17.50 Anita 93 22.32 -7.44 3296 1047 2094 1075 0.318 -1.00 0.16 -1.18 18.78 -1.29 0.10 -0.74 20.11 Daphne 148 35.52 -11.84 2383 697 1394 904 0.292 -0.85 0.20 -2.34 28.49 -1.28 0.10 -1.19 31.95 20001 10000 Standard Average Standard deviation Style Forecast deviation *2 Units produced multiplier 629 Gail 1017 388 605.42 1.06 Isis 1042 646 356.74 Entice 1358 496 831.85 Assault 2525 680 1803.67 Teri 1100 762 291.69 Electra 2150 808 1292.89 Stephanie 1113 1048 1.31 Seduced 4017 1112 2837.42 Anita 3296 2094 1074.73 Daphne 2383 1394 904.28 10000 2. What operational change would you recommend to Wally to improve performance? Use similar raw materials for products (i.e. fabrics, colors) Have cheap raw materials in inventory Start producing most predictable products in advance and leave capacity in peak months for unpredictable products Seek providers that are closer to the factories 3. How should Obermeyer management think (both short-term and long-term) about sourcing in Hong Kong versus China? Hong Kong China More demand certainty (leftover expensive) Cheaper to produce Smaller volumes Larger volumes required Staff is more crosstrained Staff is less crosstrained Staff is more efficient (10 people in each parka line) Staff is more inefficient (40 people in each parka line) Each employee produces twice as much output Quality concerns Quotas