## 1 Introduction  
Lean manufacturing is a collection of synchronized methods and principles for organizing and controlling production sites in a technology-independent way to reach shortest lead time with minimum costs and the highest quality [1]. Due to the evolution of technology and its introduction in the factories, industry and manufacturing processes have changed and evolved throughout the industrial revolutions, from the introduction of mechanical production facilities powered by water and steam to the current cyber-physical production systems (CPPSs) and intelligent automation, keys of the Fourth Industrial Revolution, also known as \"Industry 4.0\" [2]. The integration of automation technologies and lean manufacturing is called \"lean automation\" [3]. These CPPSs monitor the physical processes, make decentralized decisions, and trigger actions, communicating and cooperating with each other and with humans in real time. Networked machines perform more efficiently, collaboratively, and resiliently [4]. The intelligent automation also requires the use of autonomous machines or robots, which are controlled by the CPPS and the humans. The main objective is to increase productivity and safety, which were traditionally limited by manual processes. The safety conditions have limited the use of robots in industrial environments, as they were isolated from people and some tasks were not affordable. The introduction of process automation and intelligent collaborative robots results in a rapid increase in productivity, major material and energy savings, and safer working conditions (repetitive and dangerous tasks can be done by robots). Robots provide versatility and flexibility; thus they are perfect substitutes for a skilled workforce for some repeatable, general, and strategically-important tasks [5]. Moreover, if robots' capacities are combined with humans' qualities, cost-effective productivity can be guaranteed [6]. Up until fairly recently, the information about any physical object or process was relatively inseparable from the physical object or process itself [7]. Digital data and artificial intelligence allow the dematerialization and the coexistence of the real factory with digital twins [8], where manufacturing processes are virtually simulated, monitored, and controlled. While at first, this digital twin was merely descriptive and static, in recent years it has become actionable and experimentable [9]. The digital information related to a physical system can be created as an entity itself. This means that there is a mirroring or twinning of systems between what exists in real space to what exists in virtual space and vice versa [7]. For this purpose, access to very realistic models of the current state of the process is necessary [10]. The use of sensors and 3D visualization technologies, such as virtual and augmented reality, makes this connection possible and facilitates the interaction with humans [11; 12]. The digital twin is fed with the data from the sensors, PLCs, controllers, etc., and the 3D environment is visualized using the 3D glasses. A digital twin not only allows a static perspective at design stage, but also a real-time synchronization and optimization of the virtual object [13]. At this point, robotized processes can be mirrored using digital twin models during the design, implementation, and operation steps. Each robot manufacturer has its own simulation environment for cell design and program testing, but the challenge arises when the same cell contains robots from different manufacturers working collaboratively with humans. Multiple robots can perform tasks in parallel, speed up the execution time, and improve system performance [14]. Thus, this work presents a novel methodology for process automation design, enhanced implementation, and real-time monitoring in operation.