Smart manufacturing and energy systems

Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective

In Industry 4.0, smart manufacturing is facing its next stage, cybermanufacturing, founded upon advanced communication, computation, and control infrastructure. Cybermanufacturing will unleash the potential of multi-modal manufacturing data, and provide a new perspective called computation service, as a part of service-oriented architecture (SOA), where on-demand computation requests throughout manufacturing operations are seamlessly satisfied by data analytics and machine learning. However, the complexity of information technology infrastructure leads to fundamental challenges in modeling and analysis under cybermanufacturing, ranging from information-poor datasets to a lack of reproducibility of analytical studies. Nevertheless, existing reviews have focused on the overall architecture of cybermanufacturing/SOA or its technical components (e.g., communication protocol), rather than the potential bottleneck of computation service with respect to modeling and analysis. In this paper, we review the fundamental challenges with respect to modeling and analysis in cybermanufacturing. Then, we introduce the existing efforts in computation pipeline recommendation, which aims at identifying an optimal sequence of method options for data analytics/machine learning without time-consuming trial-and-error. We envision computation pipeline recommendation as a promising research field to address the fundamental challenges in cybermanufacturing. We also expect that computation pipeline recommendation can be a driving force to flexible and resilient manufacturing operations in the post-COVID-19 industry.

Methodological Proposals for the Development of Services in a Smart City: A Literature Review

This literature review analyzes and classifies methodological contributions that answer the different challenges faced by smart cities. This study identifies city services that require the use of artificial intelligence (AI); which they refer to as AI application areas. These areas are classified and evaluated, taking into account the five proposed domains (government, environment, urban settlements, social assistance, and economy). In this review, 168 relevant studies were identified that make methodological contributions to the development of smart cities and 66 AI application areas, along with the main challenges associated with their implementation. The review methodology was content analysis of scientific literature published between 2013 and 2020. The basic terminology of this study corresponds to AI, the internet of things, and smart cities. In total, 196 references were used. Finally, the methodologies that propose optimization frameworks and analytical frameworks, the type of conceptual research, the literature published in 2018, the urban settlement macro-categories, and the group city monitoring–smart electric grid, make the greater contributions.

Industry 4.0 Implementation in B2B Companies: Cross-Country Empirical Evidence on Digital Transformation in the CEE Region

Previous research has pointed out that Industry 4.0 will lead to more sustainable production; however, it may have profound effects on European competitiveness. Today, firms in the CEE countries face a limited scope for continued economic expansion, and hence there is a growing pressure on them to move upward in the global value chain by investing heavily in advanced technology. Consequently, the authors argue that B2B firms from the CEE countries can have a massive impact on Europe’s long-term competitiveness by reversing the effect of digital transformation due to their strong interdependencies on other European economies. Therefore, in a cross-country survey, research data were collected (n = 302) about the driving forces, barriers, and role of stakeholders in the implementation of digital transformations. The authors found that some technologies are implemented much more frequently compared to others, with some indication for country specialization. The leading driving force is customer satisfaction followed by productivity and financial motives. Shortage of skilled human resources and high implementation costs are the main barriers. The firms tend to rely on universities and research centers, while the government is perceived only as a financial provider. The results are also informative for practitioners and policymakers—providing them with benchmarks.

Optimization of Safety Stock under Controllable Production Rate and Energy Consumption in an Automated Smart Production Management

A smart production system is essential to produce complex products under the consumption of efficient energy. The main ramification of controllable production rate, amount of production size, and safety stocks is simultaneously optimized under proper utilization of energy within a smart production system with a random breakdown of spare parts. Due to the random breakdown, a greater amount of energy may be used. For this purpose, this study is concerned about the optimum safety stock level under the exact amount of energy utilization. For random breakdown, there are three cases as production inventory meets the demand without utilization of the safety stock, with using of the safety stock, and consumed the total safety stock amount and facing shortages. After the random breakdown time, the smart production system may move to an out-of-control state and may produce defective items, where the production rate of defective items is a random variable, which follows an exponential distribution. The total cost is highly nonlinear and cannot be solved by any classical optimization technique. A mathematical optimization tool is utilized to test the model. Numerical study proves that the effect of energy plays an important role for the smart manufacturing system even though random breakdowns are there. it is found that the controllable production rate under the effect of the optimum energy consumption really effects significantly in the minimization cost. It saves cost regarding the corrective and preventive maintenance cost. The amount of safety stock can have more support under the effect of optimum energy utilization. The energy can be replaced by the solar energy.