This paper presents a mean-value analysis method for closed multichain queuing networks with product-form solutions. The method allows computation of mean queue sizes, waiting times, and throughputs without calculating normalization constants or product terms, improving computational efficiency and avoiding numerical issues. The approach is based on a recursive relation between the mean waiting time and queue size of a system with one fewer customer. It uses Little's law to derive equations that can be solved numerically. The method avoids the need for normalization constants, leading to simpler implementations and faster performance, especially for multiserver systems. The algorithm is shown to be asymptotically valid for large chain populations, enabling the approximate solution of networks with many closed chains. A heuristic extension replaces recursion over chain populations with iteration for fixed populations, allowing efficient computation of large-scale networks. This approach is particularly useful for communication networks where exact methods are intractable. The method is based on three key principles: (i) an arriving customer "sees" the system with himself removed in equilibrium; (ii) Little's law applied to chains; and (iii) Little's law applied to service centers. These principles provide a simple and stable way to compute performance metrics for product-form queuing networks. The algorithm is implemented in a recursive manner, with efficient operations and storage requirements. The paper also discusses extensions to handle multiple server FCFS service centers and general service demand distributions. The method is validated through theoretical analysis and numerical examples, showing its effectiveness for large-scale networks.This paper presents a mean-value analysis method for closed multichain queuing networks with product-form solutions. The method allows computation of mean queue sizes, waiting times, and throughputs without calculating normalization constants or product terms, improving computational efficiency and avoiding numerical issues. The approach is based on a recursive relation between the mean waiting time and queue size of a system with one fewer customer. It uses Little's law to derive equations that can be solved numerically. The method avoids the need for normalization constants, leading to simpler implementations and faster performance, especially for multiserver systems. The algorithm is shown to be asymptotically valid for large chain populations, enabling the approximate solution of networks with many closed chains. A heuristic extension replaces recursion over chain populations with iteration for fixed populations, allowing efficient computation of large-scale networks. This approach is particularly useful for communication networks where exact methods are intractable. The method is based on three key principles: (i) an arriving customer "sees" the system with himself removed in equilibrium; (ii) Little's law applied to chains; and (iii) Little's law applied to service centers. These principles provide a simple and stable way to compute performance metrics for product-form queuing networks. The algorithm is implemented in a recursive manner, with efficient operations and storage requirements. The paper also discusses extensions to handle multiple server FCFS service centers and general service demand distributions. The method is validated through theoretical analysis and numerical examples, showing its effectiveness for large-scale networks.