INFORMATION IN THE ENTERPRISE PRODUCTION MANAGEMENT SYSTEM

Abstract

The role of information support for improving the efficiency of the management decision-making process at all levels of production systems is investigated. The methods of statistical observation are allocated, namely: sample observation, observation of the main array, monographic observation, questionnaire observation, monitoring. The stages of the life cycle of commercial information in the system of collection-processing-protection are proposed. It is found that the collected data, in order to become information, must meet all the requirements for commercial information. From the point of view of commodity-money relations, information can be considered depending on whether additional processing is necessary for the consumer who buys it on the market. Depending on this, information can be considered useful data or just data. A selective approach to the stages of commercial data processing, depending on their type and quality, is used to solve problematic decision-making issues. That is, the number and content of the stages of data conversion into information may vary. The author describes the tasks that developers face when creating an information system, namely: technical, software, linguistic, organizational, and information. The following modules of information support are also identified: production management and its technical preparation, business process management, document management, logistics processes, project development, management of production assets, business analysis, budgeting and controlling, and accounting. A system of planned indicators for forecasting production has been created, which includes: a plan for sales volumes, a plan for production, a plan for labor costs, a plan for material costs, a plan for production overhead, a plan for production costs, a plan for administrative costs, a plan for sales costs, and a plan for financial indicators. The functions that information performs for artificial intelligence are formed, the main ones being: model training, data processing, decision-making, information recognition, analysis and forecasting.