• Journal of Food Hygiene and Safety
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• v.28 no.1
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• pp.69-74
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• 2013

This study has been performed for 150 days from February 1 - June 31, 2012 aiming at analyzing biologically hazardous factors in order to develop HACCP system for the vinegared pickle radishes. A process chart was prepared as shown on Fig. 1 by referring to manufacturing process of manufacturer of general vinegared pickle radishes regarding process of raw agricultural products of vinegared pickle radishes, used water, warehousing of additives and packing material, storage, careful selection, washing, peeling off, cutting, sorting out, stuffing (filling), internal packing, metal detection, external packing, storage and consignment (delivery). As a result of measuring Coliform group, Staphylococcus aureus, Salmonella spp., Bacillus cereus, Listeria Monocytogenes, E. coli O157:H7, Clostridium perfringens, Yeast and Mold before and after washing raw radishes, Bacillus cereus was $5.00{\times}10$ CFU/g before washing but it was not detected after washing and Yeast and Mold was $3.80{\times}10^2$ CFU/g before washing but it was reduced to 10 CFU/g after washing and other pathogenic bacteria was not detected. As a result of testing microorganism variation depending on pH (2-5) of seasoning fluid (condiment), pH 3-4 was determined as pH of seasoning fluid as all the bacteria was not detected in pH3-4. As a result of testing air-borne bacteria (number of general bacteria, colon bacillus, fungus) depending on each workplace, number of microorganism of internal packing room, seasoning fluid processing room, washing room and storage room was detected to be 10 CFU/Plate, 2 CFU/Plate, 60 CFU/Plate and 20 CFU/Plate, respectively. As a result of testing palm condition of workers, as number of general bacteria and colon bacillus was represented to be high as 346 $CFU/Cm^2$ and 23 $CFU/Cm^2$, respectively, an education and training for individual sanitation control was considered to be required. As a result of inspecting surface pollution level of manufacturing facility and devices, colon bacillus was not detected in all the specimen but general bacteria was most dominantly detected in PP Packing machine and Siuping machine (PE Bulk) as $4.2{\times}10^3CFU/Cm^2$, $2.6{\times}10^3CFU/Cm^2$, respectively. As a result of analyzing above hazardous factors, processing process of seasoning fluid where pathogenic bacteria may be prevented, reduced or removed is required to be controlled by CCP-B (Biological) and threshold level (critical control point) was set at pH 3-4. Therefore, it is considered that thorough HACCP control plan including control criteria (point) of seasoning fluid processing process, countermeasures in case of its deviation, its verification method, education/training and record control would be required.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.2 no.4
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• pp.71-102
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• 2007

There were many comprehensive analyses conducted within the existing research activities wherein factors affecting technology progress including investment in R&D vis-${\Box}$-vis their influences act as the determinants of TFP. Note, however, that there were few comprehensive analysis in the industrial research performed regarding the impact of the economy of scale as it affects TFP; most of these research studies dealt with the analysis of the non -parametric Malmquist productivity index or used the stochastic frontier production function models. No comprehensive analysis on the impacts of individual independent variables affecting TFP was performed. Therefore, this study obtained the TFP increase rate of each industry by analyzing the factors of the existing growth accounting equation and comprehensively analyzed the TFP determinants by constructing a comprehensive analysis model considering the investment in R&D and economy of scale (smallness by industry) as the influencers of TFP by industry. First, for the TFP increase rate of the 15 industries as a whole, the annual average increase rate for 1993${\sim}$ 1997 was approximately 3.8% only; during 1999${\sim}$ 2000 following the foreign exchange crisis, however, the annual increase rate rose to approximately 7.8%. By industry, the annual average increase rate of TFP between 1993 and 2000 stood at 11.6%, the highest in the electrical and electronic equipment manufacturing business and IT manufacturing sector. In contrast, a -0.4% increase rate was recorded in the furniture and other product manufacturing sectors. In the case of the service industry, the TFP increase rate was 7.3% in the transportation, warehousing, and communication sectors. This is much higher than the 2.9% posted in the electricity, water, and gas sectors and -3.7% recorded in the wholesale, food, and hotel businesses. The results of the comprehensive analysis conducted on the determinants of TFP showed that the correlations between R&D and TFP in general were positive (+) correlations whose significance has yet to be validated; in the model where the self-employed and unpaid family workers were used as proxy variables indicating the smallness of industry out of the total number of workers, however, significant negative (-) correlations were noted. On the other hand, the estimation factors of variables surrogating the smallness of scale in each industry showed that a consistently high "smallness of scale" in an industry means a decrease in the increase rate of TFP in the same industry.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.129-141
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• 2010

An Internet shopping mall for clothing operates a warehouse for packing and shipping products to fulfill its orders. All the products in the warehouse are put into the boxes of same brands and the boxes are stored in a row on shelves equiped in the warehouse. To make picking and managing easy, boxes of the same brands are located side by side on the shelves. When new products arrive to the warehouse for storage, the products of a brand are put into boxes and those boxes are located adjacent to the boxes of the same brand. If there is not enough space for the new coming boxes, however, some boxes of other brands should be moved away and then the new coming boxes are located adjacent in the resultant vacant spaces. We want to minimize the movement of the existing boxes of other brands to another places on the shelves during the warehousing of new coming boxes, while all the boxes of the same brand are kept side by side on the shelves. Firstly, we define the adjacency of boxes by looking the shelves as an one dimensional series of spaces to store boxes, i.e. cells, tagging the series of cells by a series of numbers starting from one, and considering any two boxes stored in the cells to be adjacent to each other if their cell numbers are continuous from one number to the other number. After that, we tried to formulate the problem into an integer programming model to obtain an optimal solution. An integer programming formulation and Branch-and-Bound technique for this problem may not be tractable because it would take too long time to solve the problem considering the number of the cells or boxes in the warehouse and the computing power of the Internet shopping mall. As an alternative approach, we designed a fast heuristic method for this reallocation problem by focusing on just the unused spaces-empty cells-on the shelves, which results in an assignment problem model. In this approach, the new coming boxes are assigned to each empty cells and then those boxes are reorganized so that the boxes of a brand are adjacent to each other. The objective of this new approach is to minimize the movement of the boxes during the reorganization process while keeping the boxes of a brand adjacent to each other. The approach, however, does not ensure the optimality of the solution in terms of the original problem, that is, the problem to minimize the movement of existing boxes while keeping boxes of the same brands adjacent to each other. Even though this heuristic method may produce a suboptimal solution, we could obtain a satisfactory solution within a satisfactory time, which are acceptable by real world experts. In order to justify the quality of the solution by the heuristic approach, we generate 100 problems randomly, in which the number of cells spans from 2,000 to 4,000, solve the problems by both of our heuristic approach and the original integer programming approach using a commercial optimization software package, and then compare the heuristic solutions with their corresponding optimal solutions in terms of solution time and the number of movement of boxes. We also implement our heuristic approach into a storage location assignment system for the Internet shopping mall.