A statistical analysis study on Del Monte Philippines, Incorporated - Boyonas, Mark Ismael Vital; Francisco, Reynald Sy.; Hofilena, John Ryan Guillon (Diliman, Quezon City College of Engineering, 2001)
Del Monte Philippines, Incorporated is one of the largest producers of processed pineapple in the country. It currently produces an average of 1459 metric tons of canned pineapple per day. Sliced pineapple represents at least 50 percent of this production size. The statistical analysis study focuses on canned sliced pineapple which, aside from being the bulk of production of processed fruit solids, is also known to have a manufacturing process that is very much similar to that of other pineapple product styles like chunks and tidbits which are just residual products of sliced pineapple. The production process starts from the harvesting of the fruit in the plantation to its processing and packing. At almost every step, quality inspection is done to assure consumers of the quality they had known Del Monte products to have. In some cases like the slicing of the pineapple, 100 percent visual inspection is being done. Three core problems in the production of sliced pineapple were identified : slipping of slicer creating irregular cuts, non-uniform ripeness of the fruit and overfilling of syrup. These problems affected the net weight of the canned product. Three factors derived from these problems were used in the design of experiments : fruit weight, brix-acid ratio and syrup weight. Through the use of a general factorial design and a 2 factorial design, these factors were known to have a significant effect on the net weight. A normal probability plot strengthened the findings. Through a regression model and residual analysis, the team recommends a combination of factors with errors of least variability. It is also suggested that design of experiments would be beneficial to Del Monte's new product development.
Feasibility study
Submitted in partial fulfillment of the course requirements in IE 28 : Statistical Analysis for Industrial Engineering
Keywords: Statistics