RFID-Enabled Dynamic Value Stream Mapping for Smart Real-Time Lean-Based Manufacturing System

Lean Manufacturing has become the most popular and dominant management strategy in the pursuit of perfection and in strengthening the competitive edges of manufacturers to face the challenges in the global markets. However, today’s global markets drive manufacturers to create highly customer-oriented job-shop manufacturing systems characterized by high dynamic behavior, uncertainty and high variability, in contradiction to lean being originally designed for high repetitive-production systems with a high-volume low-mix work environment with stable demand and a low degree of customization. Moreover, since the product is the changing agent, another challenging aspect that faces the effectiveness of lean is that the product life cycle is rapidly decreasing; and thus some of the lean initiatives often die after the product life cycle ends. In this regard, in order to constantly cope with the resulting rapid changes and adapt new process designs while reviving lean initiatives and keeping them alive; an effective real-time lean-based IT system should be developed, since lean without a real-time IT system has become impracticable and unthinkable in today’s high-customized manufacturing environments. In this context, due to the special characteristics and superior capabilities of Radio Frequency Identification technology (RFID), it could be the major enabler to support such a real-time IT system with real-time production data. However, RFID remains questionable and doubtable and manufacturers are still quite hesitant to adopt it in their manufacturing systems. This thesis introduces a solid basis for a standard framework of a digitalized smart real-time lean-based system. This framework describes the best practice of RFID technology through the integration of real-time production data captured via RFID with lean manufacturing initiatives in manufacturing systems, in order to overcome today’s lean manufacturing challenges. The introduced framework represents a new kind of smart real-time monitoring and controlling lean-based IT mechanism for the next-generation of manufacturing systems with dynamic and intelligent aspects concerning lean targets. The idea of this mechanism has been derived from the main concepts of traditional value stream mapping (VSM), where the time-based flow is greatly emphasized and considered as the most critical success factor of lean. The proposed mechanism is known as Dynamic Value Stream Mapping (DVSM), a computerized event-driven lean-based IT system that runs in real-time according to lean principles that cover all manufacturing aspects through a diversity of powerful practices and tools that are mutually supportive and synergize well together to effectively reduce wastes and maximize value. Therefore, DVSM represents an intelligent, comprehensive, integrated, and holistic real-time lean- based manufacturing system. The DVSM is proposed to contain different types of engines of which the most important engine is the “Lean Practices and Tools Engine” (LPTE) due to its involvement with several lean modules that guarantees the comprehensiveness of the real-time lean system. Each of these modules is specified to control a specific lean tool that is equipped with suitable real-time monitoring and controlling rules called “Real-Time Lean Control Rules” (RT-LCRs), which are expressed using “Complex Event Processing” (CEP) method. The RT-LCRs enable DVSM to smartly detect any production interruptions or incidents and accordingly trigger real-time re/actions to reduce wastes and achieve a smart real-time lean environment. Practically, the basis of this introduced framework in this dissertation is derived based on a highly customized job-shop manufacturing environment of an international switchgear manufacturer in Germany. The contributions of this dissertation are represented as follows: building the main framework of the DVSM starting with a systematic RFID deployment scheme on the production shop floor; introducing the main components of the DVSM (i.e. Event Extractor-engine, AVSM-engine, VVSM-engine, Real-time Rules-engine, and LPTE); demonstrating the feasibility of the DVSM concerning lean targets through developing a number of Lean Practices and Tools Modules that are supplied with RT-LCRs (e.g. Real-time Manufacturing Lead-time Analysis, Smart Real-time Waste Analysis, Real-time Dispatching Priority Generator (RT-DPG), Real-time Smart Production Control (RT-SPC), Smart-5S, Smart Standardized Work, Smart Poka-Yoke, Real-time Manufacturing Cost Tracking (RT-MCT), etc.); verifying the effectiveness of RT-LCRs in RT-DPG and RT-MCT modules through building simulation models using ProModel simulation software and finally proposing a framework of the tools “Smart-5S, Smart Standardized Work, Smart Poka-Yoke” to be implemented in the switchgear manufacturing environment

Industry 4.0-Based Real-Time Scheduling and Dispatching in Lean Manufacturing Systems

Lean manufacturing is one of the most popular improvement agents in the pursuit of perfection. However, in today’s complex and dynamic manufacturing environments, lean tools are facing an inevitable death. Industry 4.0 can be integrated with lean tools to avoid their end. Therefore, the primary purpose of this paper is to introduce an Industry 4.0-based lean framework called dynamic value stream mapping (DVSM) to digitalize lean manufacturing through the integration of lean tools and Industry 4.0 technologies. DVSM with its powerful features is proposed to be the smart IT platform that can sustain lean tools and keep them alive and effective. This paper specifically tackles the scheduling and dispatching in today’s lean manufacturing environments, where the aim of this research is developing a smart lean-based production scheduling and dispatching model to achieve the lean target through optimizing the flow along the VSM and minimizing the manufacturing lead time. The developed model, called the real-time scheduling and dispatching module (RT-SDM), runs on DVSM. The RT-SDM is represented through a mathematical model using mixed integer programming. Part of the testing and verification process, a simplified IT-based software, has been developed and applied on a smart factory lab

Lab Scale Implementation of Industry 4.0 for an Automatic Yogurt Filling Production System—Experimentation, Modeling and Process Optimization

Currently, Industry 4.0 is word of mouth, and its implementation has gained increased attention from industrial and academic researchers, entrepreneurs, and service providers all over the world. With Industry 4.0, the integration of facilities and products enables real-time data exchange, and the overall production system becomes self-reliant and intelligent to predict and maintain its operational performance. In this research, the lab-scale implementation of Industry 4.0 is implemented for an automatic yogurt filling production system. A mathematical model for the process optimization of Industry 4.0 was also developed. A real-life problem was solved optimally using linear programming techniques with the objective of maximizing the speed of the conveyor belt. Moreover, the sequencing of processing orders using single-dimensional rules was performed. The effects of changes in the feed rate of the yogurt valve and length of the conveyor belt on the feed rate of the flavor valve, speed of conveyor belt, waiting time, processing times, and the different performance measures were investigated at the end