Novel poka-yoke approaching toward Industry-4.0

Widjajanto, Sugiri; Purba, Humiras Hardi; Jaqin, Choesnul

doi:10.31181/oresta20303065w

Cited by 23 publications

(12 citation statements)

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“…In addition, productive systems of vertical integration (FT24), social valorization through artisanal production (FT20) that strengthens local communities, organic production systems (FT22, FT23), and precision agriculture (FT12) are also organizational contexts that have adhered to Industry 4.0 technologies in a solid way [1,16]. Industry 4.0 and operational research [46] is another example.…”

Section: Discussion Sectionmentioning

confidence: 99%

Industry 4.0 and the Circular Economy: Integration Opportunities Generated by Startups

Silva

Sehnem

2022

Logistics

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Background: The purpose of this study is to analyze the adoption and implementation of technologies related to Industry 4.0 and the integration with circular economy (CE) to minimize the effects of resource scarcity in emergency situations. Method: The study was developed in two stages: The first consists of a systematic review of the literature for analytical categorization of the technologies of Industry 4.0 that can be useful for implementation in the circular economy. In the second step, empirical validation was carried out with 45 foodtech startups from the agribusiness value chain, farm to table, reuse, and waste management and disposal segments. Results: The contributions of the study show that: (i) The business models of startups in the food segment are closely aligned with the assumptions of circular economy. (ii) The technologies of industry 4.0 are supporting the implementation of circular economy in foodtech companies. (iii) Digitization, traceability, software as a service, traceability, digital solutions, and shared platforms occupy the leadership in the interface between CE and industry 4.0. (iv) Partnerships are strategic elements in the creation of innovative ecosystems for the success of foodtech startups. Conclusions: We conclude this paper with a diagnosis about companies involved in the foodtech segment in addition to the proposition of a business and sectorial agenda to consolidate the Industry 4.0 technologies in order to promote circular economy.

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Section: Discussion Sectionmentioning

confidence: 99%

Industry 4.0 and the Circular Economy: Integration Opportunities Generated by Startups

Silva

Sehnem

2022

Logistics

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“…Alternatively, proactive approaches can be employed that try to reduce the risk of mistakes by changing the assembly process or product design before the product is placed in production. Examples of proactive approaches are design methodologies such as Design for Assembly (Boothroyd et al, 2011;Eskilander, 2001), Poka-Yoke (Lazarevic et al, 2019;Widjajanto et al, 2020) and Design for Assembly Meaning (Parmentier et al, 2020). While not all methodologies focus directly on operator mistakes, they do contain principles that reduce the risk of mistakes.…”

Section: Related Workmentioning

confidence: 99%

Automatic evaluation of the misplacement risk during manual assembly based on a CAD design

De Cock,

Khanyile,

Decraemer

2024

Proc. Des. Soc.

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In HMLV manufacturing, assembly mistakes by operators are common due to the ever increasing product variability and complexity. If mistakes can be detected early-on in the design process, product designers can reduce the possibility for mistakes. We present an algorithm to automatically detect and evaluate potential misplacements of parts that need to be fastened. Evaluation starts from a product CAD and returns the risk of misplacement as well as visual feedback on possible misplacements. An implementation with FreeCAD of our algorithm is illustrated on different use cases.

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“…Given the above definition of manufacturing systems, combined with the ontology presented in Figure 5, production management, quality management, machine and equipment production rate, and human resource (HR) management will serve as factors to be optimized to improve performance and timely delivery. e research purpose of Widjajanto et al [39] is to put forward the concept of poka-yoke (mistake proofing), which traces the causes of quality errors from manufacturing processes and reduces variability by analyzing the root causes related to human errors. e equipment design and the working environment affect reliability, and understanding the causes greatly reduces various errors.…”

Section: Manufacturing Execution System (Mes)mentioning

confidence: 99%

Intelligent Supply Chain Management Modules Enabling Advanced Manufacturing for the Electric‐Mechanical Equipment Industry

et al. 2022

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Electric-mechanical equipment manufacturing industries focus on the implementation of intelligent manufacturing systems in order to enhance customer services for highly customized machines with high-profit margins such as electric power transformers. Intelligent manufacturing consists in using supply chain data that are integrated for smart decision making during the production life cycle. This research, in cooperation with a large electric power transformer manufacturer, provides an overview of critical intelligent manufacturing (IM) technologies. An ontology schema forms the terminology relationships needed to build two intelligent supply chain management (SCM) modules for the IM system demonstration. The two core modules proposed in this research are the intelligent supplier selection and component ordering module and the product quality prediction module. The intelligent supplier selection and component ordering module dispatches orders that match the best options of suppliers based on combined analytic hierarchy process (AHP) analysis and multiobjective integer optimization. In the case study, the intelligent supplier selection and component ordering module demonstrates several acceptable Pareto solutions based on strict constraints, which is a very challenging task for decision makers without assistance. The second module is the product quality prediction module which uses multivariate regression and ARIMA to predict the quality of the finished products. Results show that the R square values are very close to 1. The module shortens the time for the company to accurately judge whether the two semifinished iron cores for the product meet the quality requirements. The component supplier selection module and the finished product quality prediction module developed in this research can be extended to other IM systems for general high-end equipment manufacturers using mass customization.

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Novel poka-yoke approaching toward Industry-4.0

Cited by 23 publications

References 0 publications

Industry 4.0 and the Circular Economy: Integration Opportunities Generated by Startups

Industry 4.0 and the Circular Economy: Integration Opportunities Generated by Startups

Automatic evaluation of the misplacement risk during manual assembly based on a CAD design

Intelligent Supply Chain Management Modules Enabling Advanced Manufacturing for the Electric‐Mechanical Equipment Industry

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