# Fog Computing Architecture for Load Balancing in Parallel Production with a Distributed MES
## 2 Literary Review and State of the Art  
This paper presents a systematic literature review (SLR) of practical implementation studies related to those proposed in this article, focusing on the following aspects: the needs of SMEs when their market participation is continuous, parallel production lines in support of sustained expansion, and the implementation of FC in parallel production line operations. The review was conducted using a structured and replicable approach in academic databases, including IEEE, Crossref, ProQuest, ResearchGate, and SCOPUS. Recent research was prioritized to ensure the relevance and timeliness of the results. _Technology Integration: MES, Cloud, and Fog Computing to Boost Production_  
When a company reaches a growth turning point in production, its operational logistics suggest that various lean manufacturing tools, such as process optimization, resource management, capacity expansion, and technology integration, can be applied to achieve a demanding production rate. In this regard, ref. [19], in its systematic review, and ref. [20], in its implementation of manufacturing production optimization and resource management strategies, mention that this path is viable for improving the balance in task allocation within an assembly line. Similarly, ref. [21] indicates that mass production lines are rigid in allocating tasks across different stations, making it difficult to adapt to more flexible schemes. Consequently, it proposes adjusting the installed capacity to operate in parallel with a production line that has process optimization characteristics. However, these studies focused on companies that were already established in the commercial market, leaving aside those that were in the process of development. For this reason, ref. [22] describes the reality of SMEs in Ibero-America, saying that they are fragmented in a certain way because they are not supported or illuminated by the economic focus of the governments of these countries and, as a result, a total disconnection of innovation and development (R&D) in their production processes occurs, displacing them towards the metaphor of the revolving door. Furthermore, ref. [23] indicates that if we add to these aspects a lack of strategic planning for marketing and expansion, they would be out of the commercial game. These characteristics enable this study to focus on a structural solution that facilitates the integration of flexible technology with affordable hardware for process digitization. Maintaining this context [24,25], they highlight the benefit of distributed investment (gradual implementation, reducing initial investment, and facilitating continuous improvement) that companies can anchor by having an MES; that is, the expansion of a company is based on key functional dimensions for its maturity such as computerization, connectivity, visibility, transparency, predictability, and adaptability. Similarly, ref. [26] implements algorithms for parallel processes in multi-process and multi-order production lines managed from an MES, thus scaling up to a production line and adjusting to the growing instability of the current market, marked by a greater variety of products and a reduction in the quantity of production per variant. Taking a step further and valuing the intrinsic adaptability provided by these management systems, ref. [27] integrates a technological module (edge node) into this system for visual inspection tasks with Machine Learning (ML) algorithms. Their decisions are sent to an MES to execute various tasks, such as predictive maintenance, quality control processes, energy consumption optimization, or a combination of these or other tasks, depending on the plant's or cloud's needs. A similar approach was implemented in [28] in a laboratory that simulates a manufacturing line for quality control. The authors incorporated a legacy broker device that links the plant via OPC-AU or MQTT to the edge MES, which in turn links to an ERP. The edge management system is responsible for executing user orders via state machines.