• Asian Journal of Business Environment
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• v.2 no.1
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• pp.25-34
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• 2012

It is possible to use pallet for forwarding as chinese cabbages and radishes are general large-scale trading items at the agricultural wholesale market though, however, most of these are forwarded as it have packed in net bags or in P·E bags. Thus, it is still hard for palletizing. The type of packing the product in the net bag makes it difficult for palletizing. It is not a stable shape enough and easily collapsed for pallet loading. Because of this collapsibility, the corrugated cardboard box is being used to enhance forwarding efficiency, but the existing corrugated cardboard box could be crushed easily by moist what is from the agricultural product's property and it also could be squashed by the mass of the loaded box layers on itself. In contrary, the functional waterproof corrugated cardboard box is not collapsed through palletizing and it is efficient for product management with it's ventilation function in respond to pre-cooling effect. Furthermore, because it has various functional shapes as the open type, the partition type and so on, it is effective for maintaining freshness of the product and standardizing the distribution of agricultural product. It is well-known that it is possible to introduce this box to cargo-works of agricultural product. Consequently, the recognition of main distributors about the pallet distribution of the chinese cabbage and the radish was apprehended in this study for activating mechanization of loading and unloading. The survey was conducted to the main distributors such as the forwarder, the auction dealer and the commission merchant with Garak-dong wholesale market as the center. The appropriate packing materials and problems of the existing method for loading and unloading were derived through the survey. Especially, it was focused on analyzing the difference of recognition between the subject groups for the way of using waterproof cardboard corrugated box to deal with the difficult product for packing in normal corrugated box because of the box's absorption of moist from the agricultural product like a chinese cabbage and a radish. Total In the cases of the forwarders and the commission merchants, the net was highly responded as 45%, 74% from each groups for the best packing material for mechanization of distribution and the waterproof corrugated cardboard box was responded as 20%, 22% from each groups as much preferable than multi-stage wooden box. However, for the radish, the waterproof corrugated cardboard box was the best material as 56%, and the auction trader group supported it for 80%. So, the using the waterproof corrugated cardboard box for mechanization of distribution was negative for the chinese cabbage, but it was positive for the radish. The average was 2.42, the standard deviation was 1.24. The negative response(about 55%) was prevailing more than positive response(about 23%). It could be analyzed that even there was the positive recognition for using the waterproof corrugated cardboard box for the radish though the preference for low price of net bag in the chinese cabbage forwarding procedure. Still now, it seems that is a burden for using the waterproof corrugated cardboard box with high price. In the analysis on the recognition differences about using the waterproof corrugated cardboard box for the chinese cabbages and the radish between the forwarders and the commission merchants, generally the negative recognition was prevailing, but the forwarders(2.696) were more positive for using the waterproof corrugated cardboard box than the commission merchants(2.145).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.679-687
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• 2021

The purpose of the present study was to document the standardization and management process of interface terminology regarding the chief complaints, diagnoses, and procedures, including surgery in a four-hospital consortium. The process was proposed, discussed, modified, and finalized in 2016 by the Terminology Standardization Committee (TSC), consisting of personnel from four hospitals. A request regarding interface terminology was classified into one of four categories: 1) registration of a new term, 2) revision, 3) deleting an old term and registering a new term, and 4) deletion. A request was processed in the following order: 1) collecting testimonies from related departments and 2) voting by the TSC. At least five out of the seven possible members of the voting pool need to approve of it. Mapping to the reference terminology was performed by three independent medical information managers. All processes were performed online, and the voting and mapping results were collected automatically. This process made the decision-making process clear and fast. In addition, this made users receptive to the decision of the TSC. In the 16 months after the process was adopted, there were 126 new terms registered, 131 revisions, 40 deletions of an old term and the registration of a new term, and 1235 deletions.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.