재무제표를 활용한 사이클 타임 측정방안에 관한 연구: 국내 제조업 패널자료 분석 (A study on the Measurement of Cycle Time using Financial Data: Panel Data Analysis of Domestic Manufacturing Industry)

오늘날 많은 제조기업들이 시간기준 경쟁을 펼치게 되면서 사이클 타임의 효율적 측정과 관리가 기업의 핵심 경쟁력을 좌우하는 중요한 요인으로 자리 잡게 되었다. 그러나 사이클 타임의 효율적 측정과 분석방법의 부재로 말미암아 성과평가의지표로 널리 활용되지 못하고 있다. 또한 사이클 타임의 변동요인에 대한 체계적인 연구가 미흡하여, 이를 고려하지 못한상태에서 사이클 타임 자체를 비교하고 향상 여부를 평가하는 것은 무의미하다. 따라서 본 연구는 기업성과분석, 경쟁기업의 벤치마킹, 관리적 의사결정 등을 지원할 수 있는 사이클 타임 측정모형을 개발하고 재무제표를 이용해 측정한 사이클 타임을 변동시키는 운영외적 요인들을 분석하여 사이클 타임을 효율적으로 관리하고 개선시킬 수 있는 방안을 마련함을 목적으로 하였다. 1981~2002 기간의 국내 제조기업 재무자료를 활용한 패널자료 분석을 실시하여 다음과 같은 연구결과를 산출하였다. 첫째, 제조기업 성과평가의 지표로서 사이클 타임이 효율적으로 활용될 수 있도록 재무자료를 이용한측정방법을 제시하였다. 둘째, 측정된 사이클 타임의 불분명한 변동을 유발하는 운영외적 원인으로써 매출총이익률, 자본집약도, 투입산출변동률을 규명하여, 재무자료를 활용한 사이클 타임 개념이 그 자체로서는 시간성과를 측정 및 분석하는데 한계가 있음을 보여주었다. 셋째, 사이클 타임 자체가 기업 혹은 연도별 성과분석에 이용되어 질 수 있도록 사이클 타임을 변동시키는 원인에 따른 차이를 통제한 수정 사이클 타임 측정모형을 제시하였다.

As manufacturing firms in today’s environment have involved more and more in timebased competition, an ability to manage and allocate cycle time efficiently becomes one of core competencies for the firms. However the absence of appropriate measurement and evaluation tools for cycle time prevents firms from actively utilizing the notion of cycle time as a performance measure. Furthermore, since no extant research provides sound explanation of factors that systematically influence the measured cycle time, it is important to identify and understand the factors to make sound managerial judgments on inter- and intra-firm comparisons and consequent improvement of cycle time.
The purposes of present study are to develop a model of cycle time measurement using publicly available financial data and to identify non-operational factors that systematically influence the measure cycle time, which enables managers performance evaluation, competitive benchmarking, and effective managerial decision making processes with respect to cycle time performance. In the current study, we have utilized a panel of financial data for domestic manufacturing firms through the periods of 1981-2002, and generated the following results. First, a model of cycle time measurement using financial data is proposed. Second, we identify non-operational factors such as gross margin ratio, capital intensity, and input-output ratio that systematically influence the measured cycle time, and show that the proposed model per se has some inherent limits to be used as a sound operational measure of cycle time. Third, we also propose an adjusted measure of cycle time which takes into account of the effect of the factors on cycle time measurement, and show through case analyses how the adjusted measure can be used to make within-firm analyses and between-firms comparisons of cycle time. Our results show that the overall cycle time performance is deteriorating over the past 20 years, which is consistent with the results from other researches: Kekre and Srinivasan (199), Rajagopalan and Malhotra (2001), Gaur et al. (2004), and Ginter and La Londe (2004). Reasons include increased product variety, shorter product life cycle, increasing degree of global outsourcing that leads to longer manufacturing cycle time, and the increased degree of complexity in supply chain network.
Some of the limits encountered during the course of the current study include the methodological limits that are known to exist in panel data analyses, reliability and consistency of accounting policies across firms, the use of fixed effect model that prevents us from understanding variance that could possibly be explained by the time and firm specific variables, and lack of more detail analyses to understand the differences across industry sectors.
Based the limits addressed, the current study shed some lights on the directions for the future research. First, to make our model more useful in practice, it is necessary to verify the directional consistency between the results from the model and the observations from the industry in terms of actual cycle time. The theoretically verified consistency will provide more concrete support for the use of the model. Second, using the model, one can study how the operational improvements and changes initiated by managers can be linked to actual improvement of cycle time. Thus, one can systematically track the effectiveness of cycle time improvement programs using financial data. Third, while the current study employed time and firm specific effects as fixed effects in the model, one can study the factors that influence the changes in firm and time specific effects. Furthermore, one can identify other significant non-operational factors to be included in the model that influence the cycle time performance. Last, similar studies can be done to understand the differences among industry sectors, and can also be extended to non-manufacturing sectors such as distribution and retail industries.