A Systems Approach to Digitalizing a Traditional Manufacturing SME: Implementing Low-Cost Industry 4.0 Solution for a Hand Press Machine

The traditional manufacturing industry, often reliant on paper-based manual processes, must digitalize to remain competitive in an era where artificial intelligence drives everyday processes. Accurate and realistic production planning and control are crucial for the success of small and medium-sized manufacturing enterprises (SMEs), and data is key to these processes. However, legacy machines in traditional manufacturing make data collection challenging. This work presents a data acquisition approach for collecting real-time production data, such as daily production volumes and downtime from a hand press machine. In this study, as a demonstration, a hand press machine in a manufacturing industry¹ was digitalized by installing electronic hardware, including an inductive proximity sensor and an ESP8266 microcontroller with an optocoupler relay. The Arduino IDE environment was used for coding, and the Tdslite open-source library facilitated the transmission of sensor data from the microcontroller to the Microsoft Structured Query Language (MSSQL) Server Management Studio, which also served as a database to store live data. The processed data was then visualized using a Power BI dashboard, enabling the monitoring of hourly production rates and downtime. To measure the effectiveness of digitalization in business growth, KPI benchmarking was established for all relevant departments in the SME. This demonstration highlighted the potential of digitalization and showed how data acquisition and visualization help monitor and implement data-driven decision-making processes in production planning.