Distributed Approaches to Supply Chain Simulation

Mustafee, Navonil; Katsaliaki, Korina; Taylor, Simon J. E.

doi:10.1145/3466170

Cited by 12 publications

(5 citation statements)

References 124 publications

(160 reference statements)

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“…For three of the four categories of the PPMO framework (Mustafee et al, 2021), namely profiling research (P) and problem definition and context (P) (considered together in this review) and study outcome (O) we included several variables that fit the context of our study. Regarding the model implementation category (M), we reported whether the DES models had standalone implementations or were combined with other techniques.…”

Section: Resultsmentioning

confidence: 99%

“…The PPMO framework for literature synthesis (Mustafee et al, 2021) describes the variables of interest for literature reviews in M&S. The variables focus on profiling research (P), problem definition and context (P), model development & implementation (M), and study outcome (O). Given the relatively small sample of articles and the distinct nature of the current review, the PPMO framework provides an overall structure for reporting the findings from the literature.…”

Section: Methodology and Framework For Literature Analysismentioning

confidence: 99%

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Proceedings of SW21 The OR Society Simulation Workshop

2023

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Simulation, and now digital twins, excel in estimating parameters associated with complex and timedependent stochastic processes, e.g., large airport, highway, and warehouse operations. Within such contexts, we consider statistical inference, whereby one seeks to quantify the error in the obtained simulation estimate. Historically, statistical inference in simulation has been considered challenging because parameters needing estimation are often complicated, and simulation output often autocorrelated and non-normal. However, we argue that the remarkably simple idea of batching can be used as an "omnibus" inference device in simulation settings. Batching for inference works in three simple steps: (i) divide simulation output data into overlapping batches; (ii) construct parameter estimates from each batch; and (iii) use the batch estimates, after accounting for dependence, to perform statistical inference. As we describe in this paper, the resulting procedures are usually trivial to implement in software, and are provably correct and efficient. Batching ideas originated in the 1950s, and have enjoyed steady development in the simulation community since the 1970s mostly within the problem of variance estimation. Our thesis is that batching ideas have much wider utility, and that batching should be considered alongside other resampling ideas such as the bootstrap or the jackknife in modern statistics.

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Section: Resultsmentioning

confidence: 99%

Section: Methodology and Framework For Literature Analysismentioning

confidence: 99%

Proceedings of SW21 The OR Society Simulation Workshop

2023

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“…In this regard, demand forecasting is a crucial strategy for tackling supply chain uncertainty [24]. Due to the diversity of suppliers, goods, and consumers, supply chain data is produced at many points in the chain for a variety of reasons in high volumes and at a high velocity, which is reflected in the numerous transactions that are continually processed throughout supply chain networks [25]. In light of these complexities, there has been a shift away from traditional (statistical) demand forecasting methods that rely on finding statistically significant trends (characterized by mean and variance attributes across historical data) in favor of intelligent forecasts that can intelligently evolve by learning from the past and adjusting to anticipate the constantly shifting demand in supply chains [26].…”

Section: Issn: 2550-794xmentioning

confidence: 99%

Enhancing Sales Demand Prediction for Supply Chain Management: A Dimensionality Reduction Approach at Mukwano Company Limited, Uganda

Mercy,

Paul,

Eze

2024

IDOSR-JSR

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In the dynamic landscape of today's business environment, the accurate prediction of sales demand plays a pivotal role for companies striving to maintain competitiveness. This study focuses on refining demand sales prediction for supply chain management at Mukwano Industries Limited through the application of dimensionality reduction techniques. The research involved a systematic iterative process encompassing problem identification, data preparation, modeling, evaluation, validation, optimization, and documentation. To ensure confidentiality, secondary data from the Mukwano IT department underwent meticulous merging and anonymization. Four dimensionality reduction algorithms, namely PCA, SVD, MDS, and t-SNE, were employed and evaluated using RMSE metrics. The results reveal that MDS and t-SNE exhibited exceptional performance, achieving accuracies of 89% and 88.8%, respectively. PCA and SVD also demonstrated commendable performance with an accuracy of 82.4%. The study underscores the crucial role of dimensionality reduction in enhancing predictive accuracy and optimizing inventory management. Recommendations include the incorporation of feature selection and regularization techniques to address the challenges associated with the curse of dimensionality. In conclusion, this research contributes valuable insights into the effectiveness of diverse dimensionality reduction techniques for demand prediction and inventory management. Additionally, the study highlights the need to address the curse of dimensionality and suggests exploring further research avenues in other aspects of supply chain management. These recommendations are essential for guiding future research efforts in this evolving field. Keywords: Demand sales prediction, Supply chain management, Feature selection, Inventory management, Predictive accuracy

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“…Simulations can be useful in testing and validating tools and systems [110]. This technology allows the measurement of process efficiency before its implementation through the simulation of activities [92].…”

Section: Internet Of Thingsmentioning

confidence: 99%

“…AM [1,11,12,92-100] Autonomous vehicles [92-94,98] Automation/robotics [92-94,97,98] Big data [11,92,96,101-104] Blockchain [57,92,96,105-108] Cloud computing [11,92] IoT [79,92,96,98,109] Simulation[12,92,94,110] …”

mentioning

confidence: 99%

Technologies Associated with Industry 4.0 in Green Supply Chains: A Systematic Literature Review

Morella¹,

Lambán

Royo

et al. 2023

Sustainability

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This study aims to review the literature published in the last 15 years (2007–2022) that relates to Industry 4.0 and the green supply chain (GSC). This review identifies the enabling technologies for Industry 4.0 that are being applied to improve GSC practices and establishes the points of its future research agenda. A systematic literature review (SLR) of this topic was conducted using Web of Science and Scopus as databases. Our study combined descriptive and conceptual analysis, guided by three review questions. These questions were proposed to identify how Industry 4.0 technologies can enhance the development of GSCs. After refinement, 75 papers were analyzed. This research underlines what technologies of Industry 4.0 are being implemented to improve GSC aspects. In addition, it shows in which GSC practices the application of these technologies is being considered and what challenges of implementing Industry 4.0 in GSCs have been identified in the literature. Finally, all these findings enhance establishing a future research agenda. Current studies have focused on analyzing the benefits of implementing Industry 4.0 in GSCs. However, this paper delves into enabling technologies for Industry 4.0 and GSC practices and does not only discuss them in general terms.

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Distributed Approaches to Supply Chain Simulation

Cited by 12 publications

References 124 publications

Proceedings of SW21 The OR Society Simulation Workshop

Proceedings of SW21 The OR Society Simulation Workshop

Enhancing Sales Demand Prediction for Supply Chain Management: A Dimensionality Reduction Approach at Mukwano Company Limited, Uganda

Technologies Associated with Industry 4.0 in Green Supply Chains: A Systematic Literature Review

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