Smart production systems: automating decision-making in manufacturing environment

Alavian, Pooya; Eun, Yongsoon; Meerkov, Semyon M.; Zhang, Liang

doi:10.1080/00207543.2019.1600765

Cited by 53 publications

(19 citation statements)

References 13 publications

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“…Advanced, flexible and adaptable human–machine interfaces will enhance operations and increase productivity without jeopardizing the safety of workers (Ardanza et al , 2019). Smart manufacturing systems can autonomously detect the health of the system and also aid in continuous improvement of projects (Osterrieder et al , 2019) which will enhance productivity (Alavian et al , 2020). Smart manufacturing aims to enhance manufacturing system performance as well as enhance the decision-making power of humans and machines (Mittal et al , 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework

Bag

Pretorius

2020

IJOA

314

179

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Purpose The digital revolution has brought many challenges and opportunities for the manufacturing firms. The impact of Industry 4.0 technology adoption on sustainable manufacturing and circular economy has been under-researched. This paper aims to review the latest articles in the area of Industry 4.0, sustainable manufacturing and circular economy and further developed a research framework showing key paths. Design/methodology/approach Qualitative research is performed in two stages. In the first stage, a review of the extant literature is performed to identify the barriers, drivers, challenges and opportunities. In the second stage, a research framework is proposed to integrate Industry 4.0 technology (big data analytics powered artificial intelligence) adoption, sustainable manufacturing and circular economy capabilities. Findings This research extends the knowledge base by providing a detailed review of Industry 4.0, sustainable manufacturing, and circular economy and proposes a research framework by integrating these three contemporary concepts in the context of supply chain management. Through an exploration of this integrative research framework, the authors propose a future research agenda and seven research propositions. Research limitations/implications It is important to understand the interplay between institutional pressures, tangible resources and human skills for Industry 4.0 technology (big data analytics powered artificial intelligence) adoption. Industry 4.0 technology (big data analytics powered artificial intelligence) adoption can positively influence sustainable manufacturing and circular economy capabilities. Managers must also put more attention to sustainable manufacturing to develop circular economic capabilities. Social implications Factory workers and the local communities generally suffer from various adverse effects resulting from the traditional manufacturing process. The quality of the environment is deteriorating to such an extent that people even staying miles away from the factory are also affected due to environmental pollution that is generated from factory operations. Hence, sustainable manufacturing is the only choice left to manufacturers that can help in the transition to a circular economy. The research framework can help firms to enhance circular economy capabilities. Originality/value This review paper contains the most updated work on Industry 4.0, sustainable manufacturing and circular economy. It also proposes a research framework to integrate these three concepts.

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Section: Literature Reviewmentioning

confidence: 99%

Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework

Bag

Pretorius

2020

IJOA

314

179

View full text Add to dashboard Cite

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“…Furthermore, an increasement in energy efficiency in a smart production system by controlling peripheral equipment is done by Bermeo-Ayerbe et al [4]. Alavian et al [2] developed a programmable production advisor that is able to find the current status of the system and improvements of a smart production system. Also, a smart production system under a SCM is recently developed by Dey et al [9] in which the lead time is controllable and safety stock, as well as planned backorder, have been considered.…”

Section: Smart Production Systemmentioning

confidence: 99%

“…Production system Reliability Carbon Geometric Setup cost emission programming Leung [21] EPQ Production process NA Degree = 1 Variable Liu [22] NA NA NA Degree = 0 NA Liu [23] NA NA NA Signomial NA Sadjadi et al [25] NA Production process NA Considered Variable Ahmed and Sarkar [1] Sustainable NA Considered NA NA Chavarrían-Barrientos [7] Smart and sustainable NA NA NA NA Jafarian et al [17] NA NA NA Considered NA Kusiak [20] Smart NA NA NA NA Tiwari et al [31] Sustainable NA Considered NA NA Wang et al [32] Constant NA Considered NA NA Asim et al [3] Hybrid Cost NA NA Reduced Cao and Wang [5] NA NA NA Fuzzy NA Chassein and Goerigk [6] NA NA NA Robust NA Dressler et al [11] NA NA NA Considered NA El-Wakeel et al [12] NA NA NA Considered NA Ghavami et al [13] NA NA NA Fuzzy NA Guchhait et al [16] Constant Unreliable SCM NA NA NA Sarkar [26] Multi-stage, multi-cycle NA NA NA Reduced Alavian et al [2] Smart NA NA NA NA Ghobakhloo [14] Smart NA NA NA NA Nahas [24] Production line Unreliable NA NA NA Sarkar et al [27] Single-stage, clean NA NA NA Constant Sarkar and Sarkar [30] Sustainable, smart, NA Considered NA Reduced multi-stage Bermeo-Ayerbe et al [4] Smart NA NA NA NA Chen et al [8] Multi…”

Section: Table 1 Authors Contribution Tablementioning

confidence: 99%

Geometric programming solution of second degree difficulty for carbon ejection controlled reliable smart production system

2022

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Smart manufacturing systems should always aim to be fully sustainable while simultaneously being as reliable as possible which is difficult to reach. Furthermore, climate change especially by carbon emission ejection in the industry is a significant topic and carbon emission ejection should be controlled and reduced to save the environment. Contributing towards a greener environment in a positive manner is done by reducing the amount of insufficient items that are produced in a smart production system which also can be reached with a higher reliability in the system. Therefore, this study models a smart reliable production system with controlled carbon ejection. To solve the proposed smart production system in this study, a geometric programming approach with a degree of difficulty level two is used which results into optimum results that are quasi-closed. Furthermore, numerical experiments are conducted in order to validate the proposed model and prove that by using a higher degree geometric programming approach, an optimal solution is found. The numerical results do not only show optimal solutions but also that the smart production system with controlled carbon ejection is reliable.

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“…Smart decision-making relies on the integration of advanced manufacturing capabilities with ICT, HPC, MSA, and MEC technologies, including a real-time collection of data, image and data processing, data-driven strategies, modeling and optimization tools, cyber-physical interconnection, wireless integration, cloud computing, system monitoring, robotic automation, among others [10], [33]- [35]. This requires integrated capabilities to continuously and efficiently manage the entire process network.…”

Section: Related Work In Smart Decision-making For Cyber-physical Sys...mentioning

confidence: 99%

Cyber-Physical System Demonstration of an Automated Shuttle-Conveyor-Belt Operation for Inventory Control of Multiple Stockpiles: A Proof of Concept

et al. 2022

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Smart manufacturing in the so-called Industry 4.0 age pushes the research and development of laboratory-scale proof of concepts before its deployment in pilots and real-size equipment. As such, we present a cyber-physical system (CPS) demonstration in the mining industry field engineered to autonomously manage the handling of solids flowing in a conveyor-belt that drops materials in containers, forming multiple stockpiles per belt. The CPS operates to control multiple stockpiles' inventories using mixed-integer optimization that minimizes the square deviation of the measured inventory to their targets (heights). Within the sensing-optimizing-actuating (SOA) cycle, the CPS demonstration is performed as follows. First, the sensing (data measurement, data processing, and system evaluation) uses a deep neural network in real-time to assess the level of materials stored in transparent containers. Second, the optimizing (mathematical programming, optimization techniques, and decision-making capabilities) is performed using a flowsheet network formulation called unit-operation-port-state superstructure (UOPSS) that permits a fast solution for the position-idle-time-varying discrete manipulated variables as operational schedules. Third, the actuating (cyber-physical integration) implements a physical actuation solution through an integrated CPS environment. According to the findings of our experimentation, stockpiling process control in a smart manufacturing context has enormous potentials to control multiple stockpiles' inventory autonomously.

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Smart production systems: automating decision-making in manufacturing environment

Cited by 53 publications

References 13 publications

Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework

Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework

Geometric programming solution of second degree difficulty for carbon ejection controlled reliable smart production system

Cyber-Physical System Demonstration of an Automated Shuttle-Conveyor-Belt Operation for Inventory Control of Multiple Stockpiles: A Proof of Concept

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