As the next wave of productivity, Industry 4.0 aims to enhance the competitiveness and efficiency of manufacturers by bridging the gap between industrial manufacturing and information technology. Through digitalization, it provides the advantage of enabling the real-time/near-real-time monitoring of manufacturing. This digital information allows monitoring tools such as Value stream mapping (VSM) to help the decision makers efficiently capture the non-value-adding processes on the factory floor. However, the application of VSM into small and medium sized enterprises (SMEs), including diverse manufacturing environments, is not an easy task. It is even more challenging especially when the product processing is more complicated and requires improvements to labour management and facility utilization. Conventional VSM is not competent to handle the contemporary rapid dynamic manufacturing environment, complex material flow or efficiency of machine and labour performance. These three are the most important resources on the shop floor to bring transparency to the decision maker. We present a multi-agent system composed of several cost effective embedded Arduino systems as agents and a Raspberry-Pi ® as a core agent. Equipped with Cyber-Physical System (CPS) technology, these agents, placed on or near the station, could reflect the non-linear material value flow without modelling the process or using RFID tags. Moreover, through the sensor node installed in each machine and by knowing the staff ID, the agents could send the relevant information in the form of dynamic value stream mapping (DVSM) in near-real-time for storage, analysis and visualization. We present a suitable visualization tool based in Node-RED ® to carry out DVSM.

With the emergence of Industry 4.0, digitalization and intelligent manufacturing are vital to ensure competitivity, especially for manufacturers reliant on legacy machines. Upgrading legacy machines with cyber physical technology under Industry 4.0 frameworks can enable connection of these machines to existing IoT networks to allow the sharing and exchange of production information. In this paper, a legacy machine used in sheet metal folding operations is upgraded by integrating switch sensors which provide detailed data on the machine status to stakeholders, enabling in-depth analysis of the production activity before and after the implementation of lean manufacturing methods. Furthermore, it is shown that the data collected can be applied to conduct dynamic value stream mapping (DVSM) in near real time to provide deeper level insight into manufacturing processes. More detailed mapping enables identification of wastes involved with labour and design. Therefore, an innovative graphical technique is proposed to improve the flattened pattern to reduce manual handling and ease bottlenecks identified by VSM. From the collected VSM data, a leanness measure was established to provide objective and quantitative evaluation of the process performance.

No abstract

No abstract