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Additional information is available at the end of the chapter http://dx.doi.org/10.5772/intechopen.73085. The implementation of the concepts of Industry 4.0 and the Industrial Internet of Things [which deals with the collection of information about each production unit and ensures the management of production processes in PS] [1] opens up new possibilities for the development of industrial engineering methods [2]. Such operation can be determined with the help of a model of a production system that implements innovation projects wj ¼fU;Sg, where U is a vector of management parameters, S is the set of project needs for resources, is the project number.

The result of solving such tasks will also be the function of time (with numerical solutions in the form of a table). The set N∗∗ is the required number of points from the set Λ∗∗ (N∗∗ is the parameter of the algorithm).

The generation of the area of feasible solutions by solving the tasks for optimal control of projects and production systems

In the result of the solution we can determine the ranges and values ​​of the values ​​that can be conveniently presented to the decision maker (for example, in a form of Gantt chart that is so widespread in management) [20]. Generating the area of ​​feasible solutions by solving tasks for the optimal control of projects and production systems. Therefore, the constraints can be represented by functionsF mð 1ð Þ;t m2ð Þ;t tÞthat can be represented in a form of additional criteria and used by performing the criterion compression operation.

In practice, the constraints can be considered not so strict and we can determine the feasible deviation of values. Since constraints can be destroyed in this case, the obtained functions must therefore be ranked using weighting coefficientsK.

The problems of obtaining solutions as functions of time

Thus, the task solution will be a set of development paths that can technically be shown as a tree for each of the required parameter values ​​(see Figure 4) that can be considered as Bayesian network. If the probability pð Þ10 indicates that we are placed in the state si and the state fully complies with the expected state (determined on the basis of previous stages), pij shows the probability of the transfer from the state si. Therefore, it is possible to define the probabilities for obtaining decisions that will be taken into account for further selection of the most probable ones based on the method of dynamic programming (Bellman method) (see Figure 5).

At the same time, the time step can be a changing dimension (∆t ¼f tð Þ), but it must be placed in the range τ≤∆t≤Tðiþ1Þ Tð Þi, where τ is the minimum time required to change the production capacity, reset the technological cycle, etc. system characteristics), Tðiþ1Þ Tð Þi is the time for the next decision point. On the other hand, work related to changing production capacity, scheduling of production and procurement, etc., brings additional costs to the company (in general, we encounter situations when production capacity must first be increased and then reduced, which in some cases can be offset, especially with stocks).


Therefore, together with the use of forecasting models, it not only helps to improve the automation and reduce a certain part of the personnel production expenses, but also considers factors such as increasing power intensity and resource consumption of productions, inertia of integration and management processes in production systems, and the situations related to repairs, the mortality of the equipment, the quality of the demand and the prices, etc. AFM; more than that, we can observe a change in production volume that creates additional increased capacity for production system (related to the change in production schedule). The author thanks the government of Perm Krai for supporting the project for "Development of software and economic and mathematical models for the support of innovation project management processes in production systems", which is implemented in accordance with decree No.

The risk assessment method in prognostic models for production system management taking into account the time factor. Special conditions in solving the problems of support for managerial decision-making in production and financial systems using the statistical data.

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Influence of Strategic Technology Management on Smart Manufacturing: The Concept of ‘Smart

Manufacturing Management’

Influence of Strategic Technology Management on Smart Manufacturing: The Concept of ‘Smart

  • Introduction
  • Technology management
    • Missing links in technology management
    • Overemphasis on technologies in smart manufacturing
    • The strategic content in technology management
  • Smart manufacturing and strategic technology management
  • Strategic technology management in advanced manufacturing

The use of advanced technologies must be aligned with the company's strategy, so technology strategy and technology management must be considered as the main drivers of smart production. The concept of strategic technology management presented in this chapter—a combination of technology strategy (TS) and technology management (TM)—attempts to address this question in terms of measuring the performance of firms in terms of various technology strategies and management dimensions. Acquisition of smart manufacturing capabilities is a moderator of firm performance, and strategic technology management is a driver.

It would not be wrong to say that smart manufacturing is actually a technology management trend. This integration could be achieved through the use of technology management strategies at a strategic level within the organization to ensure alignment with business strategy and provide a competitive advantage.

  • Influence of R&D on technology strategies
  • Methodology
  • Data analysis
  • Implications
  • Appendix A: factor analysis 1. Technology strategy
    • Technology management
  • Appendix B: regression analysis

In addition to investigating the impact of technological strategy factors on firm success, this study also investigated whether R&D investment in terms of the number of people employed in the R&D department is related to firm performance. To collect data on the level of technological awareness of the respondents and on their understanding of the role of technology and the competitive environment. The pilot study involved 10 companies and sought to assess the clarity and usability of the questionnaire items.

Each item is measured with inductively developed items to develop a richer description of the item and to triangulate item value. An in-depth analysis of the environment in which the research took place showed that 32 items can be used to measure these dimensions. In order to determine the adequacy of the factor analysis framework, we used a number of methods.

Extraction using PCA for the 'technology strategy' variables revealed that the three components accounted for 71.3% of the total variance. Extraction using PCA for the 'technology management' variables revealed that the four components accounted for 83.2% of the total variance. The strategic management of technology in this research was understood in terms of the technology strategies developed by companies and the processes for implementing or managing these strategies.

This measure also reflects maximizing the incorporation of technology into a firm's plant and processes to gain an advantage over competitors. These two factors represent the technology management and technology strategy dimensions of strategic technology management; Thus, it can be said that the application of strategic technology management factors has contributed to the positive performance of advanced manufacturing firms during the 10-year period under review. The results can be extremely useful in providing an insight to national technology planners about the impact of STM on smart manufacturing and firm performance.

Author details

Executive Levers for Strategically Managing Technology: Leaders must have the tools to move their enterprises toward using technology as a strategic resource. Reinventing Asian Management for Global Challenges: Proceedings of the Asian Academy of Management Conference; Kuala Terengganu, Malaysia; July 16-17, 1999. Selection of our books indexed in the Book Citation Index in Web of Science™ Core Collection (BKCI).

Renewing a University to Support Smart Manufacturing Within a Region

  • Industry 4.0 as a framework for regional development
  • Adaption of new technologies supporting Industry 4.0
  • Competitiveness of regions
    • Legislation
    • Land use
    • Regional strengths
    • Enterprise ecosystem
    • History
  • Renewal of university structure and curricula to support adaptation of Industry 4.0 in the region
    • Transdisciplinary approach
    • System design management
    • Smart specialization
    • Field labs
    • Innovations
    • Organizational culture
  • Applying Industry 4.0 as a framework for increasing competitiveness in the region
  • Discussion and conclusions

How structure and curricula of universities can be renewed to support adaptation of Industry 4.0 in the region. Renewal of university structure and curricula to support adaptation of Industry 4.0 in the region adaptation of Industry 4.0 in the region. In universities, engineering students should, among other things, be prepared to meet the requirements of Industry 4.0 in order to be able to function in future service domains [9].

Industry 4.0 should not be related to the competence requirements of only mechanical engineering students and thus future engineers. It is likely that Industry 4.0 mainly affects the whole society, so all students should somehow be involved in different perspectives of Industry 4.0. We recommend that Industry 4.0 be used as a transdisciplinary framework that supports the development of a local service ecosystem.

Education: innovate educational content so that it responds to the new ICT-based technologies needed in Industry 4.0 and a transdisciplinary approach. The core idea of ​​the figure is that the Industry 4.0 framework should be understood in the existing innovation environment so that smart clusters can be established. In order to adapt the Industry 4.0 framework to education, so that education could be called 4.0, the following things should be considered: The system design management course should be introduced as part of the master's studies.

The main idea behind this article has been to combine the principles of Industry 4.0 to evaluate network thinking and digitization. Research and teaching environments in universities should be used to pilot new technologies related to Industry 4.0. To be successful in the new development challenges of Industry 4.0, enterprise-university partnerships must be intensive and the main objective must be a shared learning.

Digital Smart Jewelry: Next Revolution of Jewelry Industry?

  • Research methodology
  • Preliminary theoretical understanding through literature review
    • Uncertainty of innovation
    • User-centered innovation
    • User-study methods
  • Action research
    • Background of smart jewelry innovation
    • Prototypes and user tests of light jewelry
    • Prototypes and user tests of functional jewelry 1. Lifesaving jewelry
  • Conclusions

The smart jewelry was divided into two product groups: the LED technology-based light jewelry and the functional jewelry that can contain different kinds of technologies. In addition, it was investigated what kind of smart jewelry is already on the market. One of the researchers was involved in the workshop, and through this innovation of smart jewelery started.

Action researchers have been the foremost innovators of smart jewelry and potential users of jewelry itself. Presented below are six different smart jewelery prototypes and how users have experienced them. A quarter of the participating test users were not 'jewellery people'. Eighty-five percent of them were also not interested in the smart jewelry.

Check out what smart jewelry is on the market and how much it sells for. Jewelry with an effect would then bring aesthetic and perhaps functional value (cf. light sabers in Star Wars). Smart Bola jewelry received the least hype of all the smart jewelry in this study.

The smart jewelry is positioned in the so-called suicide quadrant of innovation when new products and new markets are created. According to this study, the smart jewelry seems to be a so-called high concept, which arouses people's interest. On the other hand, the smart jewelry sellers are already in the market but have not yet broken through to the big scale.

Therefore, the smart jewelry users will mainly come from subgroup of traditional jewelry users. This seems to be the only cause to entice non-jewellery people into smart jewelry users.

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