Selecting manufacturing partners in push and pull-type smart collaborative networks

Mladineo, Marko; Celar, Stipe; Celent, Luka; Crnjac, Marina

doi:10.1016/j.aei.2018.08.001

Cited by 25 publications

(12 citation statements)

References 39 publications

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“…e necessity of this type of integration has already been recognized, but, at the time, could not be properly developed. It was hard to find a suitable partnership to develop a nonhierarchical network, but Industry 4.0 tools seem to support the CMM structure [19]. A network needs to be organized that integrates the various customer-supplier and design-support aspects most effectively in order to increase the efficiency of the business and production [20].…”

Section: Requirements Of Vertical Integrationmentioning

confidence: 99%

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

et al. 2021

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The Fourth Industrial Revolution means the digital transformation of production systems. Cyber-physical systems allow for the horizontal and vertical integration of these production systems as well as the exploitation of the benefits via optimization tools. This article reviews the impact of Industry 4.0 solutions concerning optimization tasks and optimization algorithms, in addition to the identification of the new R&D directions driven by new application options. The basic organizing principle of this overview of the literature is to explore the requirements of optimization tasks, which are needed to perform horizontal and vertical integration. This systematic review presents content from 900 articles on Industry 4.0 and optimization as well as 388 articles on Industry 4.0 and scheduling. It is our hope that this work can serve as a starting point for researchers and developers in the field.

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Section: Requirements Of Vertical Integrationmentioning

confidence: 99%

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

et al. 2021

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“…Some researchers noticed the importance of collaborative manufacturing network. One example was given by [25], presenting an intelligent collaborative platform to manage the production network. With the current trend of increased connectivity to external networks, CPPSs are increasingly targeted by cyber-attacks.…”

Section: ) Cyber Levelmentioning

confidence: 99%

Cyber Physical Production Systems: A Review of Design and Implementation Approaches

Goepp

Siadat

2019

2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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Cyber Physical System (CPS) is a very crucial and promising technology in Industry 4.0 context. The application of CPS in the production and manufacturing environment gave rise to the term Cyber Physical Production Systems (CPPSs). CPPSs hold great potential to make production systems become intelligent, resilient and self-adaptive by utilizing the cyber world to realize the distributed collaboration in the physical world. There is growing interest in CPPSs, yet there is a scarcity of review to document the current status of CPPSs. This review aims to classify the current research activities within CPPSs field with a special focus on design and implementation approaches in view of industrial engineering and to analyze research gaps based on the literature review. Findings of this review can be used as the basis for future research in CPPSs and related topics.

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“…There are some practical IoT technologies used in manufacturing industries, such as being able to track different parts of the same product, different parts of production in its supply chain. The usefulness and flexibility of IoT in DSS has been well-discussed by practitioners and researchers in different types of production systems (Xu et al , 2018; Ghobakhloo, 2018; Yu et al , 2018; Mladineo et al , 2018). In the following sections, we present an overview of decision making and planned obsolescence in DSS and discuss production planning scenarios with different varying time-period components used.…”

Section: Introductionmentioning

confidence: 99%

“…In our rapidly dynamic world, all physical products are designed and developed by many geographically dispersed networked firms to tailor all consumers’ needs in the society (Mladineo et al , 2018). There are few different ways to describe the virtual environment for firms to support collaborative activities including network of enterprises, extended enterprises or virtual enterprises.…”

Section: Introductionmentioning

confidence: 99%

A model-driven decision approach to collaborative planning and obsolescence for manufacturing operations

Kuik

Diong

2019

IMDS

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Purpose The purpose of this paper is to present the model-driven decision support system (DSS) for small and medium manufacturing enterprises (SMMEs) that actively participates in collaborative activities and manages the planned obsolescence in production. In dealing with the complexity of such demand and supply scenario, the optimisation models are also developed to evaluate the performance of operations practices. Design/methodology/approach The model-driven DSS for SMMEs, which uses the optimisation models for managing and coordinating planned obsolescence, is developed to determine the optimal manufacturing plan and minimise operating costs. A case application with the planned obsolescence and production scenario is also provided to demonstrate the approach and practical insights of DSS. Findings Assessing planned obsolescence in production is a challenge for manufacturing managers. A DSS for SMMEs can enable the computerised support in decision making and understand the planned obsolescence scenarios. The causal relationship of different time-varying component obsolescence and availability in production are also examined, which may have an impact on the overall operating costs for producing manufactured products. Research limitations/implications DSS can resolve and handle the complexity of production and planned obsolescence scenarios in manufacturing industry. The optimisation models used in the DSS excludes the variability in component wear-out life and technology cycle. In the future study, the optimisation models in DSS will be extended by taking into the uncertainty of different component wear-out life and technology cycle considerations. Originality/value This paper demonstrates the flexibility of DSS that facilitates the optimisation models for collaborative manufacturing in planned obsolescence and achieves cost effectiveness.

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Selecting manufacturing partners in push and pull-type smart collaborative networks

Cited by 25 publications

References 39 publications

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

Cyber Physical Production Systems: A Review of Design and Implementation Approaches

A model-driven decision approach to collaborative planning and obsolescence for manufacturing operations

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