# VR Simulation and Implementation of Robotics: A Tool for Streamlining and Optimization
## 2 Literature Review  
In today's dynamic product and service markets, characterized by numerous participants, companies must continually assess and monitor their performance to remain competitive. Depending on industries and systems, there are various performance metrics, with efficiency being one of the fundamental and commonly used indicators. However, there's no universally accepted definition, as it varies among authors and differs between manufacturing and service companies. Efficiency measurement in logistics is particularly significant in recent years, with various aspects to consider. Depending on the timing and importance of decision-making, efficiency measurement occurs at three hierarchical levels: strategic, tactical, and operational. Within logistics, efficiency can be measured across different aspects such as activities, processes, subsystems, systems, and supply chains. Efficiency measurement and monitoring in logistics encompass both forward and reverse logistics flows, which is depicted in Figure 1. It is essential to analyse logistics activities and processes even in systems not primarily focused on logistics services [4]. Material handling plays a pivotal role in logistics and manufacturing industries, constituting a significant portion of operations. In manufacturing, the time allocated to product handling and transportation often rivals that of value-added processes. According to [PERSON] and others [5], material handling accounts for approximately 85% of total manufacturing time. Therefore, the ability to analyse and assess material handling systems is crucial for decision-making to enhance material flow management and optimize manufacturing and distribution operations. However, these systems, characterized by complex interactions among components and uncertainties in the real world, pose challenges for analytical methods. Hence, computer-based simulation modelling emerges as a more effective and efficient solution. Unlike mathematical models, simulation can handle various complex factors encountered in real-world scenarios. Moreover, the performance measures derived from simulation models generally exhibit higher accuracy compared to analytical methods, which often rely on unrealistic assumptions. Across various domains such as engineering, finance, and business, many real-life problems can be framed as optimization problems. These entail finding the best possible solutions(s) to meet specific criteria. With advancements in simulations tools, decreasing costs of computational power, and the development of specialized simulation languages and tools, simulation modelling has become a widely accepted approach for system analysis and solving optimization problems [6]. In business management, waste is predominantly associated with quality management techniques, representing a decline in efficiency or economy. Waste encompasses anything that does not add value or contributes to losses. In the production sector, various types of waste are identified:  
* Overproduction (resulting in losses, poor quality, lack of transparency, and problem concealment);
* Waiting (caused by machine breakdowns, material delays, testing delays, or awaiting subsequent steps);
* Excessive transportation and handling (attributed to poor layout or material disposition);
* Unnecessary movements (arising from poorly organized workplaces or processes);
* Unnecessary processes and operations (e.g., idle machines);
* Error rates (resulting in additional transport, inspection, or production of defective parts);
* Untapped human potential (stemming from worker dissatisfaction or loss of motivation);
* Poor communication (due to lack of information, concealment, or unclear strategy). To mitigate waste within companies, numerous methods and logistics systems have been devised to streamline operations and eliminate inefficiencies [7]. Addressing customer needs and meeting expectations while establishing objectives is paramount.