• Journal of Legislation Research
• /
• no.53
• /
• pp.525-551
• /
• 2017

This Article discusses the current trend of European competition damage actions focused on the recent Damage Directive and its transposition by the United Kingdom and Germany. The relevant Directive was signed into law in November 2014, and it requires the EU Member States to adopt certain measures to support competition damage actions. The required measures and principles by the Directive include right to full compensation, rebuttable presumption of harm, extensive disclosure of evidence, use of pass-on for defense and indirect purchaser suits. Although many Member States did not meet the deadline to transpose the Directive, the end of 2016, it is reported that 23 Member States have now, as of September 2017, made enactments according to the Directive. When we look at the transposition done by the United Kingdom and Germany, the revisions on their competition laws closely follow the contents of the Directive. However, it will take quite a long time before the amended provisions apply to actual cases since most of the new provisions apply to the infringement that take place after the date of the amendment. A similar situation regarding application time may happen in some other Member States. Furthermore, even if the terms of the competition laws of the Member States become similar following the Directive, the interpretations of the laws may differ by the courts of different countries. EU also does not have a tool to coordinate the litigations that are brought in different Member States under the same facts. It is true that the EU made a big step to enhance competition damage actions by enacting Damage Directive. However, it needs to take more time and resources to have settled system of competition private litigation throughout the Member States. Korea has also experienced increase in competition damage actions during the last fifteen years, and there have been some revisions of the relevant fair trade law as well as development of relevant legal principles by court decisions. Although there are some suggestions that Korea should have more enactments similar to the EU Directive, its seems wiser for Korea to take time to observe how EU countries actually operate competition damage actions after they transposed the Directive. Then, it will be able to gain some wisdom to adopt competition action measures that are suitable for Korean legal system and culture.

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