Beyond Technology: Management Challenges in the Big Data Era

Francisco, Eduardo de Rezende; Kugler, José Luiz; Kang, Soong Moon; Silva, Ricardo; Whigham, Peter A.

doi:10.1590/s0034-759020190603

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…e current study rarely considers the demand forecasting of goods, and, at the same time, the assumptions of the constructed space allocation model do not meet the actual operational requirements of the enterprise. In the new management context of the big data era, enterprises' management decisions and optimization ideas must be future-oriented [27,28]. Mezzogori and Zammori [29] discussed the application of deep learning two-layer architecture in fashion product demand forecasting, which after ten years of sales analysis has been proved; the forecasting method has advantages over the existing marketing strategies of fashion companies.…”

Section: Literature Reviewmentioning

confidence: 99%

Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA-III

Chen

2022

Complexity

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Establishing a rapid-response mechanism to manage customer orders is very important in managing demand surges. In this study, combined with predicting order requests, we established a multiobjective optimization model to solve the warehouse space allocation problem. First, we developed a model based on the NAR neural network to predict order requests. Subsequently, we used the improved NSGA-III based on good point set theory to construct a multiobjective optimization model to minimize resource loss, maximize efficiency in goods selection, and maximize goods accumulation. The following three modes were tested to allocate warehouse storage space: random, ABC, and prediction-oriented. Finally, using actual order data, we conducted a comparative analysis of the three modes regarding their efficiency in goods selection. The method proposed by this study improved goods selection efficiency by a sizable margin (23.8%).

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Section: Literature Reviewmentioning

confidence: 99%