본 연구의 목적은 중소 제조기업의 제조 빅데이터 활용욕구가 무엇인지를 파악하고, 그러한 욕구들이 실제적으로 제조 빅데이터 기술의 도입의도에 미치는 영향이 어느 정도인지를 규명하는 것이다. 아울러서 본 연구는 어떠한 특성을 갖는 중소기업들이 제조 빅데이터를 활용하고자 하는 욕구가 큰 지를 파악하는 것이다. 본 연구는 중소 제조기업들의 제조 빅데이터에 대한 잠재적 활용욕구를 파악하기 위해 기존 문헌을 조사하고 3개국내 제조기업들을 인터뷰조사를 실시하였다. 기존 연구와 현장인터뷰에서 본 연구는 제조기업에서 제조 빅데이터 활용욕구로 3가지 차원 - (1) 전통적 생산성과 개선 욕구(원가, 품질, 납기, 유연성), (2) 사회적 책임욕구 (환경보호, 안전), (3) 고객 서비스 지향의 새로운 비즈니스 모델 개발(customer service-oriented new business development) 욕구 -을 발견할 수 있었다. 또한 본 연구는 제조 빅데이터 활용욕구에 긍정적 영향을 끼치는 동인(driving factors)으로 2가지 변수를- (1) 경쟁도, (2) 정보기술 능력 - 기존 문헌에서 찾을 수 있었다. 본 연구는 국내 200개의 중소 제조기업들을 대상으로 설문조사를 실시하였다. 실증분석 결과는 다음과 같다. 첫째, 국내 중소 제조기업들이 제조 빅데이터에 대해 갖고 있는 활용욕구는 원가, 품질, 유연성, 납기와 같은 전통적 생산영역에서 생산성과를 개선하려는 욕구와 환경보호와 안전이라는 사회적 책임영역에서 생산성과를 개선하려는 욕구인 것으로 나타났다. 이들 활용욕구들은 중소 제조기업들이 제조 빅데이터 기술의 도입의도와 깊은 관련이 있는 것으로 나타났다. 그러나 제조 빅데이터 기술을 활용하여 고객 서비스 지향의 새로운 비즈니스모델을 개발하려는 고차원의 요구는 없는 것으로 나타났다. 둘째, 중소 제조기업에서 경쟁도가 높을수록 제조 빅데이터를 이용하여 전통적 생산성과와 사회적 책임 생산성과를 향상시키려는 욕구가 증가하는 것으로 나타났다. 셋째, 경쟁도의 경우와 마찬가지로, 중소 제조기업에서 IT능력이 클수록 제조 빅데이터 기술을 이용하여 전통적 생산성과와 사회적 책임 생산성과를 향상시키려는 욕구가 증가하는 것으로 나타났다.
Considerable attention has been paid to smart manufacturing which utilizes manufacturing big data technology. Previous studies on the utilization of manufacturing big data have mainly focused on large corporations or conglomerates. For example, they can quickly develop vaccine through big data or successfully use big data in predicting the situation in which the parts of their product may experience breakdown. However, the small and medium-sized manufacturing companies(SMMCs) are facing difficulties in realizing and predicting their need for utilizing manufacturing big data technology. It is difficult to implement technology of which the usage needs are unclear. For this reason, only 25% of firms are reported to utilize the big data technology. The purpose of this paper is to identify the underlying needs for manufacturing big data in the context of SMMCs and to assess the extent to which those needs are related to the intention to adopt the manufacturing big data technology. Additionally, this study is designed to identify which companies have higher level of usage needs of the manufacturing big data technology. This study comprehensively reviewed previous literature and conducted in-depth interviews with three domestic manufacturing firms in order to understand potential usage needs of SMMCs for the manufacturing big data technology. This study developed the following three big dimensions of manufacturing big data usage need based on the comprehensive review of literature and interview - (1) traditional production needs(cost, quality, flexibility, delivery), (2) social responsibility needs(environmental protection and safety), and (3) customer service-oriented new business development needs. Also, this study suggested the following two driving factors positively influencing the needs - (1) competition and (2) information technology capabilities. This study developed a research model that presents two driving factors → three dimensions of usage needs of manufacturing big data → adoption intention of manufacturing big data technology. Further, this study conducted a survey with 200 SMMCs in order to empirically test the validity of the research model. The empirical test results were summarized as follows. First, the significant usage needs of small and medium-sized manufacturing companies for the manufacturing big data technology include (i) the need to improve production performances in terms of cost, quality, flexibility, and delivery, and (ii) the need to improve social responsibility performances in the areas of environmental protection and safety. These needs were found to be significantly correlated with the intention of SMMCs to adopt the manufacturing big data technology. However, SMMCs do not want to use manufacturing big data to develop a customer service-oriented new business model. Second, competition was correlated with the needs to utilize manufacturing big data to improve both production and social responsibility performances in a positive manner. Third, as in the case of competition, SMMCs with higher level of IT capabilities are likely to have stronger need to use manufacturing big data to increase their production and social responsibility performances. This study has following practical implications. First, the results of this study indicate that the intention of the SMMCs to adopt manufacturing big data technology are related to their needs to improve the performances in traditional production areas and social responsibility areas. These are the perceived purposes of the small and medium-sized companies to introduce the manufacturing big data technology. Therefore, Korean government research institutes or technical consulting firms have to develop appropriate manufacturing big data technologies to meet these purposes. For example, as small and medium-sized manufacturing companies do not introduce manufacturing big data technologies to develop a new business model that is customer service-oriented, the manufacturing big data technology platform must not be overdesigned. Second, this study suggests competition and IT capabilities as the driving factors influencing the needs for utilizing manufacturing big data. Recently, Korean government or Korean government agencies have been trying to promote the widespread adoption of the manufacturing big data technology. Further, they currently carry out policies for selecting companies that are in need of technology support of the manufacturing big data technology. When choosing the companies for such a support, they might prefer to selecting the small and medium-sized manufacturing companies which have the high level of IT capabilities and are faced with fierce competition.