Improvement of Delivery Reliability by an Intelligent Control Loop between Supply Network and Manufacturing

Bauer, Dennis; Bauernhansl, Thomas; Sauer, Alexander

doi:10.3390/app11052205

Cited by 6 publications

(4 citation statements)

References 67 publications

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“…Second, CoJoSim can also be combined and applied with dispatching methods that integrate not only manufacturing but also the supply network. Therefore, it could serve as an environment to train reinforcement-learning-based agents optimizing production planning and control of semiconductor manufacturers when reacting to events in their supply network [58].…”

Section: Discussionmentioning

confidence: 99%

Complex Job Shop Simulation “CoJoSim”—A Reference Model for Simulating Semiconductor Manufacturing

et al. 2023

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The manufacturing industry is facing increasing volatility, uncertainty, complexity, and ambiguity, while still requiring high delivery reliability to meet customer demands. This is especially challenging for complex job shops in the semiconductor industry, where the manufacturing process is highly intricate, making it difficult to predict the consequences of changes. Although simulation has proven to be an effective tool for optimizing manufacturing processes, reference data sets and models often produce disparate and incomparable results. CoJoSim is introduced in this article as a reference model for semiconductor manufacturing, along with an associated reference implementation that accelerates the implementation and application of the reference model. CoJoSim can serve as a testbed and gold standard for other implementations. Using CoJoSim, different dispatching rules are evaluated to demonstrate an improvement of almost 15 percentage points in adherence to delivery dates compared to the reference. Findings emphasize the importance of optimizing setup time, particularly in products with high variance, as it significantly impacts adherence to delivery dates and throughput. Moving forward, future applications of CoJoSim will evaluate additional dispatching rules and use cases. Combining CoJoSim with dispatching methods that integrate manufacturing and supply networks to optimize production planning and control through reinforcement-learning-based agents is also planned. In conclusion, CoJoSim provides a reliable and effective tool for optimizing semiconductor manufacturing and can serve as a benchmark for future implementations.

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Section: Discussionmentioning

confidence: 99%

Complex Job Shop Simulation “CoJoSim”—A Reference Model for Simulating Semiconductor Manufacturing

et al. 2023

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“…We contend that DDC are likewise necessary to achieve more advanced SM-enabled operational capacities. As an example, consider autonomous production systems that build on SM technologies such as Big Data and Analytics (Bauer et al ., 2021). Firms that have developed digital sensing capabilities can be facilitated in understanding the potential of these technologies, which require expertise beyond their current knowledge base (Arcidiacono et al ., 2022).…”

Section: Model Developmentmentioning

confidence: 99%

“…To exemplify, Chang et al (2022) conceptualize a production system that optimizes energy consumption by integrating Cloud, IoT, Big Data and Analytics. Similarly, Bauer et al (2021) propose an architecture enabled by IoT, Analytics and virtualization technologies, which automatically adjusts production planning and control.…”

Section: Construct Definition and Foundationsmentioning

confidence: 99%

Linking competitive priorities, smart manufacturing advancement and organizational microfoundations

Arcidiacono

Ancarani

Mauro

et al. 2023

IJOPM

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PurposeSmart Manufacturing (SM) lies at the core of Industry 4.0. Operations management research has investigated the determinants of SM advancement but there is still limited understanding of the linkages between SM and organizational factors and about whether both the technological and organizational subsystems for SM are guided by firms’ competitive priorities. To close these gaps, building on operations strategy theory, this paper aims to empirically test a model positing that competitive priorities drive SM advancement. The relation between competitive priorities and SM advancement is assumed to be mediated by organizational microfoundations.Design/methodology/approachUsing data from a single respondent survey with 234 firms in the automotive component industry, structural equation modeling is adopted to test the model hypotheses. Relevant constructs are measured with reference to the lead plant for SM.FindingsFindings highlight that SM advancement is driven by the need to simultaneously compete in terms of cost, quality and delivery, thus suggesting that manufacturers view SM as a mean to develop multiple manufacturing capabilities. Organizational microfoundations fully mediate the relation between competitive priorities and SM advancement.Originality/valueResults have implications for SM research, as they provide an understanding of the strategic priorities of firms engaging in SM. Findings also bear relevance for manufacturing executives engaged in the SM transformation, as they provide quantitative evidence that shaping an adequate organizational environment is a prerequisite for SM advancement.

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“…However, individuals are highly random and cannot be predicted directly. Safety, especially the active safety, is the main factor that promotes the growth of automatic driving vehicle demand, and self-driving vehicles can be very good to avoid traffic accidents caused by drivers because it is based on a highly integrated computer technology among many other emerging science and technology developments in the integration of smart car which is AI technology and an inevitable result of the rapid development of computer intelligent control technology [ 13 , 14 ]. It has a broad application prospect in the field of national defense and national economy.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Ethical Design of Autonomous Driving Crash Algorithms

Xiao

2022

Computational Intelligence and Neuroscience

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The age of algorithms is here, and it is really changing people’s lives. More and more ethical problems related to algorithms have attracted people’s attention, but the related ethical research is still far behind the research of algorithms. As more intelligent algorithms emerge in an endless stream, there will also be a lot of algorithmic ethical issues. On the other hand, with the continuous improvement of the development level of the automobile industry, people have a stronger demand for the safety and stability of modern transportation, and more and more autonomous driving technology has been promoted and applied in the market. At present, most of the studies on the longitudinal collision avoidance system of vehicles use collision warning or emergency braking to avoid collision. However, when the vehicle is in a special situation such as high speed and slippery road, emergency steering is more effective. In order to further improve the vehicle safety and ethical algorithm design points, this article revolves around vehicle lateral active collision avoidance control method research, the collision avoidance decision-making, and path planning and collision avoidance transverse vehicle longitudinal motion control is analyzed, and based on automated driving simulation experiment, the tests carried out to verify the designed control strategy. The experimental results show that the proposed method not only has a good effect of preventing automatic driving collision but also can meet the requirements of algorithm ethics. This research can effectively guide the research of algorithmic ethics in the field of autonomous driving and effectively reduce the occurrence of traffic accidents.

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Improvement of Delivery Reliability by an Intelligent Control Loop between Supply Network and Manufacturing

Cited by 6 publications

References 67 publications

Complex Job Shop Simulation “CoJoSim”—A Reference Model for Simulating Semiconductor Manufacturing

Complex Job Shop Simulation “CoJoSim”—A Reference Model for Simulating Semiconductor Manufacturing

Linking competitive priorities, smart manufacturing advancement and organizational microfoundations

Application of Machine Learning in Ethical Design of Autonomous Driving Crash Algorithms

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