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A method and a model framework for planning R&D changes in manufacturing enterprises

This paper presents a method and a model framework for R&D changes planning in manufacturing enterprises, implementing digital transformation projects. The relevance of development of such method is evident because of growing number of factors, influencing the decision-making process and simultaneously the complexity of such influence estimation increases. Classical changes planning methods in such cases do not ensure required level of estimation objectivity and credibility. The objects of research are industrial enterprises, actively engaged in research, design and engineering. The subject of research are methods and models for R&D process changes planning in context of digital transformation endeavors, being implemented in the companies. The research objective is to develop a R&D process planning method, enabling to account for corporate changes, related to digital transformation processes. The proposed method is based on the analysis of the discrepancies between the actual enterprise architecture and the target one and search for possible solution to rectify these discrepancies. For quantitative estimation of the changes, an integral indicator "stakeholder satisfaction level" is proposed. This indicator is calculated using a set of models (a model framework), those preparation and application sequence is defined by the considered method. The paper describes the concept of the method, the problems, being solved within each stage, tools used and final outcomes. The example of planning R&D changes in manufacturing enterprise illustrates the method in work and provides for better understanding of the concepts, presented in the paper. Read more...

The multi-model decision support method for R&D management

Research and development (R&D) ensure stable functioning and forms the innovative potential of most companies in the production sector. Ineffective R&D management leads to the fact that many initiated projects go beyond planned deadlines and budgets, and much of the intermediate R&D results are not completed. The complexity of R&D management is associated with high information uncertainty regarding the performance of R&D and the productivity of employees. The paper considers a multi-model method of decision support for R&D management in companies. To reduce information uncertainty in solving various management problems it is proposed to use an ontological model of intellectual capital of the company, simulation models of R&D processes and individual stages, fuzzy logic models to obtain integral assessments of management decisions. The method provides a basis for making decisions on the possibility and expediency of using previously obtained R&D results (scientific and technological reserve); on the feasibility of the proposed project based on the assessment of its feasibility; on the project organization (volume-calendar planning); on the allocation of resources to tasks; on the incentives for performers; on the planning of activities for additional training and organization of information support. The paper provides a general description of the method, as well as an example of its use to support decision-making on the feasibility of an R&D project based on its assessment. Two structures for organizing the R&D process in a manufacturing company are considered as alternatives. After selecting the best structure, the impact of staffing quality on the integral feasibility assessment is evaluated. Read more...