# Developing a Recommendation Model for the Smart Factory System
## 1 Introduction  
The Smart Factory has become quite important in the manufacturing field in recent years, as big data, cloud computing, and the Internet of Things (IoTs) have changed the way of manufacture, leading to Industry 4.0 [1, 2, 3, 4, 5]. The market value of Smart Factory was at 120.98 billion USD in 2016 and is expected to grow approximately 9.3% to 188.72 billion USD between 2017 and 2022. (Markets and Markets, 2017). Grand View Research estimated that the global smart manufacturing market size will reach 395.2 billion USD by 2025 (Grand View Research, 2017). From the above, it is evident that the Smart Factory is quite important. The Smart Factory System (SFS) is a solution that combines big data, cloud computing, and IoTs to drive manufacture automation, leading to better production efficiency [6, 7, 8, 9]. In practical application, SFS is clouded, synchronizing data in real-time. Combined withIoTs, it allows for transmitting information interactively by sensors, enabling real-time monitoring, better quality control, and data synchronization, which improves efficiency and reduces costs [10,11]. Big data analysis can predict future demand to find out potential customers, and interconnected procedure interactions assist in the decision-making of smart analysis [12]. There are more investments in SFS of enterprises and many suppliers have provided solutions for the Smart Factory. However, the Smart Factory field contains lots of complicated technologies and skills, such as cloud computing, IoTs, Smart Systems, and so on. Besides, most purchasers do not know much about these skills or abilities to select the appropriate solutions. As SFS is quite expensive, selecting a wrong SFS will lead to tremendous financial losses in the long-term; consequently, knowing how to select an appropriate SFS is imperative. This study reviewed the traditional factory Information System research and realized that Information System purchasers mostly focused on quality, functionality, cost, and risk. In addition, the system only proposed SFS selection in terms of quality, functionality, cost, risk, and information security dimensions [13]. Most of the past research studies lack complete derivation processes in selection procedures, as dimensions are heterogeneous, and some focus only on certain production process. The core concept of the Smart Factory is to use information technology and services of the Internet of Things to integrate processes, so that production can run more professionally and operate efficiently [14]. At present, most of the research works focus on intelligent technology and equipment, and few discuss the construction goals of Smart Factory systems. Smart Factory system will have a huge demand in the market. However, the cost of constructing a factory system will be high, and the complexity of the processes and the introduction time must be considered. If the wrong Smart Factory model is selected, the company will waste a lot of money and time. Thus, solving this problem is the motivation of this research. Traditional factories have some problems, such as the lack of systematic integration of plant sites, workshops, equipment, and personnel [15]. In addition, there is the issue of how to collect, store, and generate real-time statistics on the huge amount of data of the equipment, which may affect the decision-making efficiency [16]. Moreover, traditional manufacturing may cause environmental damage and consume a large amount of non-renewable energy, and the labor force is constantly shrinking. Compared with Smart Factories, IoT can be used to integrate business processes, and the production can be run in a more streamlined and efficient manner to bring about the benefits of high quality and low cost [17]. However, a traditional factory usually has already caused time and money losses when machine failure is found. This problem is easy to avoid for Smart Factories.