A methodology for the design and operation of pull‐based supply chains

González‐R, Pedro L.; Framiñán, José M.; Ruiz‐Usano, Rafael

doi:10.1108/17410381311318855

Cited by 11 publications

(4 citation statements)

References 47 publications

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“…Nag et al (2014) present a simulation framework for mapping inbound and outbound supply chain inventory strategies. González et al (2013) propose a methodology for the design and operation of a just-in-time cooperation mechanism to control the material flow in the supply chain using discrete-event simulation and the Cross-entropy (CE) algorithm. Singhal et al (2011) explore the collection of supply chain risk management (SCRM) literature relying on collaboration, strategic thinking and sector-wise SCRM implementation challenges using multi-layered top town taxonomy.…”

Section: Analysis and Synthesismentioning

confidence: 99%

Lean supply chain management and Industry 4.0 interrelationships: the status quo and future perspectives

Jaouhari

Arif

Fellaki

et al. 2022

IJLSS

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Purpose This study aims to address Industry 4.0 (I4.0) technologies that can improve the research and implementation of lean supply chain management (LSCM) and the enhanced LSCM subfields in I4.0 technologies. Design/methodology/approach The authors conducted a systematic literature review to detect, categorize and assess recent data, highlighting patterns and providing suggestions for potential research in this field, to investigate I4.0 literature and its effect on LSCM. The authors examined 79 published types of research from the Scopus database that were published between 2010 and 2021 and classified them into four LSCM fields: logistics, production, supply chain and marketing. Findings The authors can emphasize the fact that the literature on this topic is in progress, from early German academic research to the current creation of new effects around the world. The majority of the potential effects investigated were discovered to improve specific areas that ultimately enhance the practices of the four LSCM domains as well as performance outcomes. The authors were also able to assess the extent to which present and upcoming I4.0 technologies can improve LSCM research and implementation. Originality/value To the best of the authors’ knowledge, this is the first study of its kind. Although some research looked into various areas of I4.0 and LSCM topics, there has been no research specifically looking into the impact of I4.0 on LSCM. The originality of this study lies in the treatment of the main fields and sub-fields of LSCM, which can benefit from the technologies of I4.0. Academic scholars interested in the research topics may benefit from the findings of this study. Organizations in various industrial sectors, particularly manufacturing, where lean thinking is used, business professionals specialized in lean operations and supply chain management, along with anyone else who wants to learn more about the interrelationships between I4.0 and LSCM.

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Section: Analysis and Synthesismentioning

confidence: 99%

Lean supply chain management and Industry 4.0 interrelationships: the status quo and future perspectives

Jaouhari

Arif

Fellaki

et al. 2022

IJLSS

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“…Just-in-time delivery is enhanced by pull mechanisms linking customers and suppliers. However, despite pull production is relatively common at the firm level, pull-based supply chains are still a challenging achievement (González-R et al , 2013).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A graph-theoretic approach for assessing the leanness level of supply chains

Santos

Reul

Gohr

2021

SCM

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Purpose Developing a lean supply chain is a continuous improvement effort, so it requires tracking the implementation progress by assessing the achieved level of leanness. However, lean supply chain practices depend on firm-to-firm relationships and an assessment method should consider these network interactions. Given the absence of such a method, this study aims to propose an alternative approach for assessing the leanness level of supply chains. Design/methodology/approach The authors extracted 18 typical lean supply chain practices from the literature, constituting the leanness assessment parameters. Then, the authors developed an assessment method based on the graph-theoretic approach due to its properties of capturing the interdependencies between elements of a system. The authors tested the proposed method in an automotive supply chain, selecting the focal firm and three of its first-tier suppliers. Findings The authors positioned partial and overall results in a visual classification scale and the supply chain presented a moderate leanness level. After empirical testing, the proposed method demonstrated its practical feasibility. Research limitations/implications This paper contributes to the literature with a novel approach for supply chain leanness assessment. However, to increase its generalizability, it would be beneficial to test the method in non-automotive supply chains, extending the assessment scope to downstream firms and second-tier customers and suppliers. Practical implications For practitioners, this paper provides a self-assessment tool to monitor the supply chain leanness and set priorities for improvement. Originality/value Besides tackling the interrelationships between firms, the proposed method considers the interdependencies between lean practices, providing partial and overall feedback to enable a systemic assessment.

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“…Other feasible modeling strategy is to use statistical methodology and neural network techniques, which have been used by a number of researchers to anticipate the best operating parameters in chemical systems in order to get the largest production of the target product. [ 9–11 ] Khan et al [ 12 ] used the analysis of variance (ANOVA) approach and response surface methodology, this first‐of‐its‐kind statistical modeling with experimental validation for the process parameters of PP manufacture was carried out. In the investigation, three significant process parameters were evaluated as important input parameters for PP production: system pressure, reactor temperature, and hydrogen percentage.…”

Section: Introductionmentioning

confidence: 99%

Prediction of polypropylene business strategy for a petrochemical plant using a technique for order preference by similarity to an ideal solution‐based artificial neural network

Singh

Majumder

Bera

2022

Polymer Engineering & Sci

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The quality of polypropylene is one of the major components in the plastic industry. The quality of the polypropylene depends upon the melt flow index and the xylene solubility under the given condition such as hydrogen flow, donor flow, pressure, temperature, etc. This study investigates the use of artificial neural network (ANN) modeling for the prediction of the quality of polypropylene for petrochemical plants. This study proposes an ANN model to predict the “melt flow index (MFI) and xylene solubility” as the quality of polypropylene depends upon these two factors. Hydrogen (H2) flow, pressure, temperature, and donor flow are the controlling parameters for the MFI and xylene solubility. The study proposes a new approach for the selection of the best topology using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) multicriterion decision‐making (MCDM) process for the ANN technique. Experimental data are trained with three training algorithms each with four combinations of training functions and each with three combinations of numbers of neurons. In this study, the best model for ANN is found by the Levenberg–Marquardt backpropagation training algorithm with logarithmic sigmoid and hyperbolic tangent sigmoid transfer functions. The best topology for the ANN model for prediction is selected by using the TOPSIS method. Some sensitivity analyses are provided graphically to show the physical nature of the problem.

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A methodology for the design and operation of pull‐based supply chains

Cited by 11 publications

References 47 publications

Lean supply chain management and Industry 4.0 interrelationships: the status quo and future perspectives

Lean supply chain management and Industry 4.0 interrelationships: the status quo and future perspectives

A graph-theoretic approach for assessing the leanness level of supply chains

Prediction of polypropylene business strategy for a petrochemical plant using a technique for order preference by similarity to an ideal solution‐based artificial neural network

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