Intelligent cost estimation by machine learning in supply management: A structured literature review

Bodendorf, Frank; Merkl, Philipp; Franke, Jörg

doi:10.1016/j.cie.2021.107601

Cited by 30 publications

(6 citation statements)

References 53 publications

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“…Remembering that the higher the CH, the better the quality of the clusters, or else, the more cohesive and outlined are the groups formed. This metric is calculated by Equation (18), representing a relationship between SSB, SSW, amount of data ( ), and the number of centroids ( ). In this way, equating the correlation at once to maximizes the SSB and minimizes the SSW.…”

Section: Computational Resultsmentioning

confidence: 99%

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Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study

Guerreiro

Barchi

et al. 2021

Applied Sciences

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In automotive industries, pricing anomalies may occur for components of different products, despite their similar physical characteristics, which raises the total production cost of the company. However, detecting such discrepancies is often neglected since it is necessary to find the problems considering the observation of thousands of pieces, which often present inconsistencies when specified by the product engineering team. In this investigation, we propose a solution for a real case study. We use as strategy a set of clustering algorithms to group components by similarity: K-Means, K-Medoids, Fuzzy C-Means (FCM), Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Self-Organizing Maps (SOM), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). We observed that the methods could automatically perform the grouping of parts considering physical characteristics present in the material master data, allowing anomaly detection and identification, which can consequently lead to cost reduction. The computational results indicate that the Hierarchical approach presented the best performance on 1 of 6 evaluation metrics and was the second place on four others indexes, considering the Borda count method. The K-Medoids win for most metrics, but it was the second best positioned due to its bad performance regarding SI-index. By the end, this proposal allowed identify mistakes in the specification and pricing of some items in the company.

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Section: Computational Resultsmentioning

confidence: 99%

“…Bodendorf, Merkl and Franke [18] make a literature review on intelligent cost estimation methods for parts to be procured in the manufacturing industry is carried out by text mining. Consequently, in this paper, approaches derived from Multitask Learning and Explainable Machine Learning can be found.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study

Guerreiro

Barchi

et al. 2021

Applied Sciences

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“…Companies and individuals purchase a product, if the benefits are equal or higher than the price of the product (Golder and Tellis, 2010). Due to the pooling of various resources, JV and SA can ensure affordability for the consumer due to cost reduction in manufacturing which allows companies the leeway to make price adjustments (Bodendorf et al , 2021; Bodendorf et al , 2022). Therefore, firms can extend the maturity phase of products on the market, as new customers can be acquired through price reductions.…”

Section: Discussionmentioning

confidence: 99%

How do joint ventures and non-equity strategic alliances impact the complexity of the supply chain and thus supply chain performance?

Bodendorf,

Feilner,

Franke

2024

SCM

Self Cite

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Purpose This paper aims to explore the significance of resource sharing in business to capture new market opportunities and securing competitive advantages. Firms enter strategic alliances (SAs), especially for designing new products and to overcome challenges in today’s fast changing environment. Research projects have dealt with the creation of SAs, however without concrete referencing the impact on selected supply chain resources. Furthermore, academia rather focused on elaborating the advantages and disadvantages of SAs and how this affects structural changes in the organization than examining the effects on supply chain complexity and performance. Design/methodology/approach The authors collected and triangulated a multi-industry data set containing primary data coming from more than 200 experts in the field of supply chain management along and secondary data coming from Refinitiv’s joint ventures (JVs) and SA database and IR solutions’ database for annual reports. The data is evaluated in three empirical settings using binomial testing and structural equation modeling. Findings The results show that nonequity SAs and JVs have varying degrees of impact on supply chain resources due to differences in the scope of the partnership. This has a negative impact on the complexity of the supply chain, with the creation of a JV leading to greater complexity than the creation of a nonequity SA. Furthermore, the findings prove that complexity negatively impacts overall supply chain performance. In addition, this study elaborates that increased management capabilities are needed to exploit the potentials of SAs and sheds light on hurdles that must be overcome within the supply network when forming a partnership. Finally, the authors give practical implications on how organizations can cope with increasing complexity to lower the risk of poor supply chain performance. Originality/value This study investigates occurring challenges when establishing nonequity SAs or JVs and how this affects their supply chain by examining supply networks in terms of complexity and performance.

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“…These advantages extend beyond the applications listed in Table 5, offering competitive edge in the global market. energy consumption changes in product specifications [130] costs manufacturing producing battery cells [131] automotive cost control within supply chain [132] production efficiency energy waterflooding for oil production [133] manufacturing wire arc additive manufacturing [134] product quality tube hydroforming estimate product parameters [135] food freshness inspection [136]…”

Section: Machine Learningmentioning

confidence: 99%

A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

Lee

2023

Processes

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In the evolving landscape of manufacturing, the integration of intelligent control theory stands as a pivotal advancement, driving both process optimization and the paradigm of smart manufacturing. This review delves into the multifaceted applications of intelligent control theory, emphasizing its role in equipment, operations, and controls optimization. With a focus on three primary methodologies—fuzzy logic, neural networks, and genetic algorithms—the paper elucidates their biological parallels and their significance in simulation, modeling, and optimization. The transformative potential of smart manufacturing, synonymous with Industry 4.0, is also explored, highlighting its foundation in data, automation, and artificial intelligence. Drawing from a comprehensive analysis of recent literature, the review underscores the growing interest in this domain, as evidenced by the surge in publications and citations over the past decade. The overarching aim is to provide contemporary discourse on the applications and implications of intelligent control theory in the realms of process optimization and smart manufacturing.

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Intelligent cost estimation by machine learning in supply management: A structured literature review

Cited by 30 publications

References 53 publications

Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study

Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study

How do joint ventures and non-equity strategic alliances impact the complexity of the supply chain and thus supply chain performance?

A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

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