• Korean small business review
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• v.32 no.2
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• pp.209-223
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• 2010

Korea's business is composed of a few large-sized enterprises (which can be abbreviated as LSE) and a majority of small-sized enterprises (SSE). Although there has been a growing recognition of the need for the development of medium-sized enterprises (MSE) which can serve as a link between SSE and LSE, as yet there has not yet been a consensus on the definition, characteristics and the function of the MSE in Korea. Nowadays, the world is being globalized, and Japan and China are in competition to ne a great economic power. While East Asia is experiencing rapid changes, promoting MSE which can secure flexibility and efficiency through covering up the limitation of LSE and SSE is needed in order to respond the global market which is being specialized. The features of MSE in Japan can be listed as follows. First, the MSE in Japan is developing the company through getting into niche markets which are hard for major companies to enter rather than developing markets in order to compete against major companies directly. While MSEs are endeavoring to build the business firmly in the domestic market, they can possess special and competitive technical skills through trials and errors; so that they can get a chance develop their business through independent business system rather than putting their effort to compete against major companies. Second, from the MSEs with competitive edge in the market, there are many contributions to the national exportation. Those MSEs produce in domestic and maintain the quality of high price products which need cutting-edge technology, while they relocate the low and middle priced goods to the country where manufacturing costs are low, so that they can maintain the price competitiveness. Third, the industrial structure in Japan is formed from dual structure between major companies and small sized companies. In other words, in Japan's industrial structure which are composed of subcontract structure, this dual structure has taken a major role of small sized companies' growth and manufacturing businesses' international competitive power. Forth, MSE in Japan adopt a strategy of putting their value on qualitative scale growth rather than quantitative scale growth. In this paper, the case of Japanese MSE is analyzed. Along with its long history of Industrialization, Japan has a corporate environment where the SSEs can develop as a MSE and later a LSE through a full-support system. Among its SSEs, there are a number of world class corporations equipped with a large domestic market, win-win cooperation with the LSEs and an independent technology development. It can also be observed that these SSEs develop into MSEs with sustainable growth potentials. This study will focus on the condition under which the MSEs of Japan have been developed, and how they have survived the competition between SSEs and LSEs. Through this study, this paper attempts to offer solutions to Korea's polarization between the SSE and LSE, while providing the basis for SSEs revitalization. In general, if both extremities phenomenon deepen between LSE and SSE, there are possible fears of occurring disutility in national economy by the monopolization of LSE. For that reason, enterprise group, which can make SSE or MSE compete LSE in some area and ease the monopoly and oligopoly problem, is needed. This awareness has been shared for ages long. Nevertheless, there is no legal definition for MSE in Japan, and there is no definition about the enterprise size or unified view of MSE between scholars, but it is defined differently by each of academical person or research institution and study meeting. For that reason, this paper will organize the definition of MSE in Japan, and then will propose the characteristics of the background which has made MSE secure competitiveness and sustainable growth in global market. This study focus on that because through this process, the positive change to the awareness of MSE can be proposed in Korea and to seek the policy direction for building institutional framework which can make SSE become MES. Through this way, the fundamentals for SSE to become MSE can be managed and some appropriate suggestions which will be able to make MSE enter the global market in the future can also be proposed. Due to these facts, this study is very important and well timed task. In a sense of this way, this study will examine the definition and role of MSE in Japan. after this examination, this study will deal with the status, special feature, and promotion policy for MSE. Through this analysis of MSE in Japan, the foundation which be able to set the desirable role model for MSE in Korea can be proposed. Also, the political implication which is needed to push ahead to contribute to creating employment and economic growth through sustainable growth of MSEs in economic system of Korea can be offered through this study. It has been found that Japan's MSE functions as an indispensable link among various industrial structures by holding a significant position in employment rate, production and value added. Although the MSEs took up less than 1% of the entire number of businesses with 2700 manufacturing firms and 7000 non-manufacturing firms, its employment ratios are about 15%, while taking about 25% of the manufacturing industry's exports. In industries such as machinery and electronics which is considered Japan's major industry, the MSEs showed a higher than average ratio of manufacturing exports and employment rate. It can be analyzed that behind Japan's advantageous industries, close and deeply knit MSEs exist. Although there are no clearly stated policies geared towards the MSEs by the Japanese government, various political measures exist such as the R&D Project and the inducement of cooperation between enterprises which gives room for MSEs to participate in the SSE policies. In relation to these findings, the following practical measures can be considered in order to revitalize Korea's MSEs: First, there is a need for a legal definition of MSE and the incentives to provide legal support for its growth. Second, if a law to support the MSEs is established, it could provide a powerful inducement for the SSE to grow as a MSE, rather than stay as a SSE. Third, there is a need for a strategy of MSEs to establish a stable base in the domestic market and then advance to the global market with the accumulated trial and error and competitiveness. Fourth, the SSE themselves need the spirit of entrepreneurship in order to make the leap to a MSE. Because if nothing is to be changed about the system on the firms that grew, and the parts of the past custom was left to be managed alone, confusion and absence of management can take place. No matter how much tax favors the government will give and no matter how much incentive there could be through the policies, there are limits for industries to higher the ability to propagate. And because of that it is a period where industries need their own innovative skills to reform their firms.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.101-113
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• 1995

The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.