Инновационная модель агропромышленного воспроизводства в условиях индустрии 4.0: особенности и перспективы
The term “Industry 4.0” has become an increasingly popular in the last few years due to recent developments in cyber‐physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change in our day‐to‐day life providing new opportunities. At the same time, organizations face the difficulties doing the business when well‐known practices and tools are not suitable anymore or do not ensure expecting results. Thus, the necessity to new business models accounting for current phase of economic development occurs. Business model is one of important things in business activities and in new industrial and market requirements. The purpose is to study the concept of “Industry 4.0” and analyze approaches to business model design in the digitalization era to understand the gap between theory and practice and determine the future ways of research. The literature review was carried out using the main scientific literature databases, journal articles, conference papers, books and other documentation, as the source of the secondary data. Due to the fact that business models are specific for each company, it’s difficult to assemble all approaches. We may find just aggregate directions. But this paper may be useful to scholars and managers who are interesting in topic of business model design.
We address the external effects on public sector efficiency measures acquired using Data Envelopment Analysis. We use the health care system in Russian regions in 2011 to evaluate modern approaches to accounting for external effects. We propose a promising method of correcting DEA efficiency measures. Despite the multiple advantages DEA offers, the usage of this approach carries with it a number of methodological difficulties. Accounting for multiple factors of efficiency calls for more complex methods, among which the most promising are DMU clustering and calculating local production possibility frontiers. Using regression models for estimate correction requires further study due to possible systematic errors during estimation. A mixture of data correction and DMU clustering together with multi-stage DEA seems most promising at the moment. Analyzing several stages of transforming society’s resources into social welfare will allow for picking out the weak points in a state agency’s work.