Application of Enterprise Architecture and Artificial Neural Networks to Optimize the Production Process

Production optimization is a complex process because it must take into account various resources of the company and its environment. In this process, it is necessary to consider the enterprise as a whole, taking into account the interaction between its key elements, both in the technological and business layer. For this reason, the article proposes the use of enterprise architecture, which facilitates the interaction of these layers in the production optimization process. As a result, a proprietary meta-model of enterprise architecture was presented, which, based on good practices and the assumptions of enterprise architecture, facilitates the construction of detailed optimization models in the area of planning, scheduling, resource allocation, and routing. The production optimization model formulated as a mathematical programming problem is also presented. The model was built taking into account the meta-model. Due to the computational complexity of the optimization model, a method using an artificial neural network (ANN) was proposed to estimate the potential result based on the structure of the model and a given data instance before the start of optimization. The practical application of the presented approach has been shown based on the example of optimization of the production of an exemplary production cell where the cost of storage and the number of unfulfilled orders and maintenance are optimized.

Automated summary

Production optimization is a complex process that considers various resources of the company and its environment. This article proposes the use of enterprise architecture to facilitate the interaction between different layers in the optimization process. A proprietary meta-model of enterprise architecture is presented, which helps in constructing detailed optimization models for planning, scheduling, resource allocation, and routing. The article also introduces a mathematical programming problem for production optimization and suggests using an artificial neural network to estimate potential results before optimization.