A new methodology for providing insight into manufacturing using KPIs based on SMKL (Smart Manufacturing Kaizen Level), utilizing industry standards (OPC UA, FDT, PLCopen and AutomationML)

This paper presents a new methodology for providing insight into manufacturing using key performance indicators (KPIs: ISO 22400), and to achieve a Smart Manufacturing (SM) using a simple indicator. This indicator is very easy to understand for factory owner, production manager and installation staff, and so on. It is Smart Manufacturing Kaizen Level (SMKL). Industry 4.0 (I4.0) was announced by Germany in 2011. Also, Industrial IoT (IIoT) and SM has become a global trend now. Various consortiums related to this trend have been established in each country, such as PI 4.0 in Germany, IIC in the United States, RRI and IVI in Japan. There are very significant discussions in every consortium. As a result, various types of test beds have been evaluated and various international standards have been discussed. However, there are some issues to achieve a SM using the IIoT. First, it is difficult for factory owners to understand the Return on Investment (ROI) of the SM, and to make the decisions of continuous investment. Second, especially for Small and Medium Enterprises (SMEs), the company has few or no IIoT experts and only limited consultants who can support the IIoT. By using SMKL, factory owner will be possible to understand the ROI of the SM, and to make the decisions of continuous investment. As a result, the growth of the IIoT market, including SMEs, may be expected to accelerate in the future. In addition, SM system can be easily constructed by using the international standardization technology for industry (OPC UA (IEC 62541), FDT® (IEC 624 53), PLCopen® (IEC 61131-3), AutomationML (IEC 62714)). In this paper, we propose a use case for SMKL and created a demonstration using miniature machines and 3D simulator in IIFES2019 and proved the effectiveness of our proposal. Also, it is concluded that SMKL use case takes important role to assist the MBSE approach for SM implementation.