Internal cooperation: Modern approaches to order management in mechanical engineering

Aim . The work aimed to develop a methodology and tools for optimizing order management at mechanical engineering enterprises in the context of single-item and small batch production, aimed at increasing internal cooperation and reducing time expenditures. Objectives . The work seeks to conduct an analysis of the problems of coordination of departments and production processes; study the approaches to order management, including digitalization and the use of ERP/MRP systems; develop a model for integrating digital technologies and graph-matrix methods to optimize the interaction of departments; assess the impact of key departments on deviations in timing of order fulfillment; propose solutions for the implementation of digital platforms and the adaptation of Agile approaches. Methods. The study is based on graph-matrix analysis, regression and correlation statistics, as well as the integration of digital solutions, namely ERP/MRP systems, BI analytics, CRM and Agile methods. Interviews, surveys, and analysis of real data from a machine-building enterprise with a single type of production were used. Results . Key departments influencing order timing were identified. The effectiveness of graphmatrix analysis for identifying bottlenecks in the production process was confirmed. A model for integrating BI analytics with an ERP system was proposed, allowing for prompt change management and reducing timing of orders. The Agile methods were implemented and proved the applicability in the machine-building environment. Conclusions . An integrated approach that includes digitalization, graph-matrix analysis and adaptive resource management increases significantly the efficiency of order management. The model ensures the process transparency, reduced production delays, and enhanced internal cooperation. The practical application of the developed solutions can be applied at machinebuilding enterprises to strengthen their competitiveness under digital transformation and market uncertainty.