Industrial design and simulation of a JIT material handling system

Ezema, Chukwuedozie N.; Okafor, Eric C.; Okezie, Christiana C.

doi:10.1080/23311916.2017.1292864

Cited by 7 publications

(4 citation statements)

References 1 publication

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“…Simulation is also frequently used with JIT and Kanban (Mendes and Ribeiro, 2014;Azouz and Pierreval, 2019;Munyai et al, 2019). For example, a drug process plant utilised simulation to design and optimise a JIT material handling system for production (Ezema, Okafor and Okezie, 2017). The optimisation is essential for Kanban systems where the variables, such as the number of feeders and lot sizes, can be optimised through the simulation of possible scenarios (Che Ani, Kamaruddin and Azid, 2018).…”

Section: Integration Of Lean Principles and Industry 40 Toolsmentioning

confidence: 99%

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Lean and industry 4.0: Mapping determinants and barriers from a social, environmental, and operational perspective

Yilmaz

Dora

Hezarkhani

et al. 2022

Technological Forecasting and Social Change

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Section: Integration Of Lean Principles and Industry 40 Toolsmentioning

confidence: 99%

“…Lean principles based on social aspects related to employee empowerment and organisational learning are essential to a successful journey (Liker, 2020). In the context of organisational culture, successful implementation is also dependent on the ability and motivation to tackle problems creatively (Ezema, Okafor and Okezie, 2017).…”

Section: Social Benefitsmentioning

confidence: 99%

Lean and industry 4.0: Mapping determinants and barriers from a social, environmental, and operational perspective

Yilmaz

Dora

Hezarkhani

et al. 2022

Technological Forecasting and Social Change

View full text Add to dashboard Cite

“…Thus, the data identification is not only made on papers that address the MFA only but also on papers that discuss MHS issues in general. As a result, twenty-two articles were selected and only twelve are retained due to the redundancy in the mentioned data [2,6,21,22,9,10,14,[16][17][18][19][20]. The analysis led to the identification of 197 parameters grouped into 42 categories (such as machine data, product data, flow data, etc.).…”

Section: Data Definitionmentioning

confidence: 99%

A Reference Data Model for Material Flow Analysis in the Context of Material Handling System Design and Reconfiguration

Soufi

David

Yahouni

2022

2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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The design and reconfiguration of Material Handling Systems (MHS) at the factory scale is known to be complex. Various data and analyses are required to define the internal logistics needs the MHS must fulfill. In the literature, the identification of the MHS' needs is performed through Material Flow Analysis (MFA). The MFA is expressed through charts and diagrams which are manually developed. The manual development of charts and diagrams leads to gathering data in a disseminated way. Additionally, MFA is differently addressed in the literature; each work analyzes a different and restrained set of data. In this paper, we aim to generalize MFA by proposing a Reference Data Model (RDM) using UML (Unified Modeling Language) class diagrams. It allows listing and structuring all the data required for the MFA. The RDM can be used to conduct a data-driven MFA which enables data integrity and the reduction of the development time of charts and diagrams. A proof of concept is also given to show the ability to simultaneously generate charts and diagrams, while ensuring data integrity.

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“…In the evaluated studies on JIT production systems, the constant demand optimization model is inadequate when the supply chain system confronts time-varying demand (Ezema et al 2017a). DOI: https://doi.org/10.…”

Section: Introductionmentioning

confidence: 99%

Manufacturing system optimization using computer-based control kanban loops

Ezema

Nwobodo-Nzeribe

Eneh

et al. 2022

Bangladesh J. Sci. Ind. Res.

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The study assessed manufacturing system optimization using computer-based control kanban loops. The study assessed JIT's effect on average throughput-time, demand fill rate, net operating income for a certain product mix complexity and manufacturing overhead level. ARENA/SIMAN and WITNESS simulation tools were used to study implementation. The Drug Process Plant's JIT system reduced end buffer inventory, shortened customer lead time, and improved visual control. The mean response of the simulated new physical system in terms of NOI was 85.54 but gave a mean response of 85.30 when implemented on the shop floor. The mean response of the simulated new physical system in terms of cycle time was 617 but gave a mean response of 634 when implemented on the shop floor. Simulated new physical system in terms of DFR gave a mean response of 93.11 when implemented on the shop floor. Overwhelmingly, the new JIT system outperformed the old physical system. Bangladesh J. Sci. Ind. Res. 57(4), 221-228, 2022

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Industrial design and simulation of a JIT material handling system

Cited by 7 publications

References 1 publication

Lean and industry 4.0: Mapping determinants and barriers from a social, environmental, and operational perspective

Lean and industry 4.0: Mapping determinants and barriers from a social, environmental, and operational perspective

A Reference Data Model for Material Flow Analysis in the Context of Material Handling System Design and Reconfiguration

Manufacturing system optimization using computer-based control kanban loops

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