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# Welco Lumber Case Analysis

679 words - 3 pages

Welco Lumber Company Case Analysis
Group 2 – Case 1
MBA 610-T301_2127_1
Bellevue University

Welco Lumber Company Case Analysis
Gene Denning, an employee of Welco Lumber Company, decided to run a study by videotaping a sample size of 365 logs being processed. However, actual data provided proved that it was a true sample size of only 362 logs, as data for logs # 30, 123, and 127 are missing from his report. He videotaped 3 operators, April, Sid, and Jim, marking the logs, how each log was broken down and the degree to which the cants were properly centered. Gene then did a comparison of what the cost was of the log in its current condition (actual value), to what would have been the correct value of the log if the cut had been accurate (potential value). This allowed him to see the potential revenue that Welco Lumber Company is losing out on their business (profit gain).
Question 1: How much profit is lost on imperfect board cuts?
...view middle of the document...

51 to \$4.50 ranges.

Question 2: How does performance vary among operators?
Below is a table that shows the analysis of each operator by log size, log volume, and with the monetary values of actual versus potential values. We can see that April produces the most volume of logs; however has the highest monetary lost in revenue. Next Sid has the second highest volume of logs and the least amount of revenue lost. Finally, Jim has the lowest volume of logs with the second highest revenue lost. By evaluating the data below, we can rank the operators by overall production and by revenue loss. We have gathered that Sid is the most proficient in his overall cuts, followed by Jim, and then by April. Further, Sid has the lowest average loss of revenue per cut by a considerable margin over Jim and April. The most common error among operators is off-center cant. See Appendixes A-B for further charts pertaining to individual operator production. Also, see Appendix D for a chart with categorized error types by operator.

Operator | Large/Small Log | Count of Large/Small Log | Sum of Actual Value | Sum of Potential Value | Sum of Potential Gain |
April | Large | 35 | \$1,901.76 | \$2,060.76 | \$159.00 |
| Small | 110 | \$3,032.94 | \$3,240.72 | \$207.78 |
Jim | Large | 13 | \$784.52 | \$865.31 | \$80.79 |
| Small | 76 | \$1,781.05 | \$1,908.14 | \$127.09 |
Sid | Large | 14 | \$877.11 | \$964.69 | \$87.58 |
| Small | 114 | \$3,120.83 | \$3,193.73 | \$72.90 |
Grand Total |   | 362 | \$11,498.21 | \$12,233.35 | \$735.14 |

Operators | Perfect Cuts | Average Loss |
April | 39.31% | \$2.53 |
Jim | 35.96% | \$2.34 |
Sid | 71.88% | \$1.25 |

Question 3: Does operator performance vary based on log size?
The sample of logs processed included many more small logs than large logs (see Appendix E). Keeping this limited sample size in mind, the company executes more perfect cuts on smaller logs than it does on larger logs. The average loss of unrealized profit is also much less on smaller logs. This is evidenced by the table and graphs, below.

Size of Log | Perfect Cuts | Average Loss |
Small | 53.67% | \$1.36 |
Large | 32.26% | \$5.28 |

Appendix
Appendix A

Appendix B

Appendix C

Appendix D

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