An integrated Decision Support System for planning production, storage and bulk port operations in a fertilizer supply chain

Bouzekri, Hamza; Bara, Najat; Alpan, Gülgün; Giard, Vincent

doi:10.1016/j.ijpe.2022.108561

Cited by 9 publications

(4 citation statements)

References 39 publications

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“…The majority of the work considering AI in lean manufacturing describes various ways in which AI and ML can be used for smart production planning and control. This includes decision support for production scheduling (Bouzekri et al , 2022), optimization of resource utilization (Tripathi et al , 2022b) and multi-skilled worker assignment (Xin et al , 2015). Furthermore, Mjimer et al (2023) present ML as a means of improving losses due to changeover time, by classification and optimization of machine set-up actions.…”

Section: Resultsmentioning

confidence: 99%

“…The majority of the work considering AI in lean manufacturing describes various ways in which AI and ML can be used for smart production planning and control. This includes decision support for production scheduling (Bouzekri et al, 2022), optimization of resource utilization (Tripathi et al, 2022b) and multi-skilled worker assignment (Xin et al, 2015). (Abusaq et al, 2023;Arana-Landín et al, 2023;Marinelli, 2022;Shahin et al, 2023aShahin et al, , 2023bShahin et al, , 2023cShahin et al, , 2023dShahin et al, 2023a;Thiede et al, 2017;Trabucco and De Giovanni, 2021;Tripathi et al, 2022a;Tseng et al, 2021) Source: Author's own work adapt to changing environments and respond quickly to market changes.…”

Section: Smart Production Planning and Controlmentioning

confidence: 99%

“…Results Ahmed et al, 2023;Antosz et al, 2020;Herwan et al, 2023;Hosseinzadeh et al, 2023;Küfner et al, 2021a;Mjimer et al, 2023;Shahin et al, 2023c;Shakir and Iqbal, 2018) Smart production planning and control(Bouzekri et al, 2022;Castej on-Limas et al, 2022;Duhem et al, 2023;Fanti et al, 2022;Herwan et al, 2023;ITO et al, 2020; Jan et al, 2023; Javaid et al, 2022; Khadiri et al, 2022; Küfner et al, 2021b; Kutschenreiter-Praszkiewicz, 2018; Paraschos et al, 2023; Puche et al, 2019; Rossit et al, 2019; Sordan et al, 2022; Tripathi et al, 2022b, 2022c; Ulhe et al, 2023; Vickranth et al, 2019; Villalba-Díez et al, 2020; Xia et al, 2022; Xin et al, 2015) Quality control (Bhatia et al, 2023; Duc and Bilik, 2022; Kumar et al, 2021; Park et al, 2020; Perera et al, 2021; Pongboonchai-Empl et al, 2023; Shahin et al, 2023b; Yadav et al, 2020) Towards Industry 5.0: Sustainability, Resilience, Human-centricity…”

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confidence: 99%

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Artificial intelligence in lean manufacturing: digitalization with a human touch?

Powell

2024

IJLSS

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Section: Resultsmentioning

confidence: 99%

“…The majority of the work considering AI in lean manufacturing describes various ways in which AI and ML can be used for smart production planning and control. This includes decision support for production scheduling (Bouzekri et al, 2022), optimization of resource utilization (Tripathi et al, 2022b) and multi-skilled worker assignment (Xin et al, 2015). (Abusaq et al, 2023;Arana-Landín et al, 2023;Marinelli, 2022;Shahin et al, 2023aShahin et al, , 2023bShahin et al, , 2023cShahin et al, , 2023dShahin et al, 2023a;Thiede et al, 2017;Trabucco and De Giovanni, 2021;Tripathi et al, 2022a;Tseng et al, 2021) Source: Author's own work adapt to changing environments and respond quickly to market changes.…”

Section: Smart Production Planning and Controlmentioning

confidence: 99%

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confidence: 99%

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Artificial intelligence in lean manufacturing: digitalization with a human touch?

Powell

2024

IJLSS

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“…Bouzekri et al [60] introduced a decision support system (DSS) for planning actions to achieve maximum economic benefits from the supply chain. They designed a new storage model to obtain optimal solutions for production and port models.…”

Section: Minimum Backordersmentioning

confidence: 99%

Cost-Effective Imperfect Production-Inventory System under Variable Production Rate and Remanufacturing

Ganguly,

Dey,

Pareek

et al. 2023

Mathematics

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Several industries are facing many challenges in their production systems due to increasing customer demand. Customer demand is growing for products with innovative features that are flexible, good quality, and appealing. This paper presents a flexible production-inventory system that produces multiple parts of a product. Defective products may be produced during the production process. Those defective products are remanufactured immediately after inspection. Limited budget and space constraints are considered, along with product assembly. Based on different distribution functions, non-linear equations are calculated using the Kuhn–Tucker optimization technique. Numerical examples, a graphical representation, and sensitivity analysis are presented in this paper. The solution procedure evaluates the minimization of the total investment based on the χ2 distribution. This study examines electronic products those are more likely to be defective rather than perfect during production.

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Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain

El Harraki,

Abedin,

Belhadi

et al. 2024

Bus Strat Env

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This paper addresses the challenges of low‐carbon sourcing in intertwined supply chains by proposing a data‐driven control framework and a prey–predator model for sourcing decisions. The objective is to optimize low‐carbon objectives and reduce environmental impact. Existing static models fail to capture the dynamic nature of supply chain systems and overlook the ripple effects when sourcing decisions propagate throughout the interconnected network. To bridge this gap, our study develops a dynamic model that explicitly captures the bullwhip effect and leverages real‐time and historical data. This model conceptualizes suppliers as prey and manufacturers and consumers as predators, employing an ecological analogy to decipher the intricate interactions and dependencies within the supply chain. Through this approach, we identify strategies to promote sustainable practices and motivate suppliers to adopt low‐carbon measures. We assess two data‐driven algorithms, the nonlinear auto‐regressive exogenous (NARX) network and sparse identification of nonlinear dynamic systems with input variables (SINDYc). The results reveal that SINDYc outperforms prediction accuracy and control, offering significant advantages for rapid decision‐making. The study highlights how shifts in market demands and regulatory pressures critically influence the strategies of chemical firms and fertilizer markets. Moreover, it discusses the economic challenges in transitioning from high carbon footprint suppliers (HCFSs) to low carbon footprint suppliers (LCFSs), exacerbated by a notable cost disparity where HCFSs are approximately 30% cheaper. By advancing beyond conventional static models, this research provides a deeper understanding of the environmental impacts and operational dynamics within supply chains, emphasizing the significant “ripple effect” where decisions at one node profoundly affect others within the chain.

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An integrated Decision Support System for planning production, storage and bulk port operations in a fertilizer supply chain

Cited by 9 publications

References 39 publications

Artificial intelligence in lean manufacturing: digitalization with a human touch?

Artificial intelligence in lean manufacturing: digitalization with a human touch?

Cost-Effective Imperfect Production-Inventory System under Variable Production Rate and Remanufacturing

Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain

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