Artificial neural networks for intelligent cost estimation – a contribution to strategic cost management in the manufacturing supply chain

Bodendorf, Frank; Merkl, Philipp; Franke, Jörg

doi:10.1080/00207543.2021.1998697

Cited by 19 publications

(2 citation statements)

References 72 publications

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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

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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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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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“…However, despite the complexity and the novelty that brings the neural networks to the field, it has been demonstrated that these algorithms underperform more traditional ML algorithms in many scenarios (Bodendorf et al., 2021).…”

Section: Related Workmentioning

confidence: 99%

An automated machine learning approach for classifying infrastructure cost data

Dopazo,

Mahdjoubi,

Gething

et al. 2023

Computer aided Civil Eng

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Data on infrastructure project costs are often unstructured and lack consistency. To enable costs to be compared within and between organizations, large amounts of data must be classified to a common standard, typically a manual process. This is time‐consuming, error‐prone, inconsistent, and subjective, as it is based on human judgment. This paper describes a novel approach for automating the process by harnessing natural language processing identifying the relevant keywords in the text descriptions and implementing machine learning classifiers to emulate the expert's knowledge. The task was to identify “extra over” cost items, conversion factors, and to recognize the correct work breakdown structure (WBS) category. The results show that 94% of the “extra over” cases were correctly classified, and 90% of cases that needed conversion, correctly predicting an associated conversion factor with 87% accuracy. Finally, the WBS categories were identified with 72% accuracy. The approach has the potential to provide a step change in the speed and accuracy of structuring and classifying infrastructure cost data for benchmarking.

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The interplay of intelligent manufacturing, innovation equilibrium and cost stickiness in the artificial intelligence era

Wang,

Li,

Chen

2024

Syst Res Behav Sci

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This study investigates the impact of intelligent manufacturing methods driven by artificial intelligence (AI) on cost stickiness in Chinese manufacturing enterprises. Leveraging the ABJ model， a regression analysis explores how different AI‐enabled intelligent manufacturing approaches influence cost stickiness through the lens of innovation equilibrium. The sample comprises manufacturing companies listed on China's A‐share market from 2013 to 2021. The findings reveal a negative correlation between intelligent manufacturing adoption and cost stickiness among these firms. Specifically, production‐based intelligent manufacturing exhibits a more significant effect on reducing cost stickiness compared with collaborative intelligent manufacturing methods. Moreover, intelligent manufacturing positively impacts both joint equilibrium innovation and matching equilibrium innovation. While joint equilibrium innovation is negatively associated with cost stickiness, matching equilibrium innovation shows no significant relationship with cost stickiness. The results indicate that innovation equilibrium plays a mediating role in the relationship between AI‐driven intelligent manufacturing and cost stickiness. Overall, this research sheds light on how AI capabilities enabling intelligent manufacturing processes and innovation equilibrium dynamics can help alleviate cost stickiness issues faced by manufacturing enterprises. It highlights the strategic value of adopting AI systems to enhance operational efficiency and cost management flexibility within manufacturing contexts.

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Artificial neural networks for intelligent cost estimation – a contribution to strategic cost management in the manufacturing supply chain

Cited by 19 publications

References 72 publications

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

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

An automated machine learning approach for classifying infrastructure cost data

The interplay of intelligent manufacturing, innovation equilibrium and cost stickiness in the artificial intelligence era

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