The study of data analytics in inventory management

Khurana, M; Kumar, Deepak

doi:10.1109/ictus.2017.8285993

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…The text mining algorithms support transformation of the textual data gathered into categories (Walha, Ghozzi and Gargouri 2019). To understand and predict the condition of a system, several techniques and tools perform 'data modelling and analytics', such as simulation, machine learning and deep learning algorithms, hierarchical and k-means clustering, long-short term memory neural network, linear discriminant analysis, image recognition and topic analytics carried out by IBM Alexandria (Heath et al 2015;Kameswari and Babu 2016;Krumeich, Werth and Loos 2016;Khurana and Kumar 2017;Mehdiyev et al 2017;Zhuang, Liu and Xiong 2018;Chen et al 2019;Liu, Miao and Lin 2019;Moreno et al 2019;Barrad, Gagnon and Valverde 2020;Gaikwad et al 2020). Management activities are also supported by 'data visualization and representation' techniques and tools, such as charts, graphs, tables, maps, data tip, data brushing, dynamic query, and dashboard interfaces (Heath et al 2015;Husain et al 2016;Woo et al 2019).…”

Section: Domentioning

confidence: 99%

Linking data science to lean production: a model to support lean practices

Pozzi

Cannas

Ciano

2021

International Journal of Production Research

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The literature discusses data science (DS) as a very promising set of techniques and tools to support lean production (LP) practices. DS could aid manufacturing companies in transforming massive real-time data into meaningful knowledge, increasing process transparency and product quality information and supporting improvement activities through data-driven decision-making. However, no attempt has been made in the literature to formalise the links between DS and LP practices. Thus, this study aims to overcome this gap by clarifying the DS techniques and tools that can support LP practices and how to apply them. This study employs a quantitative bibliometric method-specifically, a keyword co-occurrence network analysis-on a set of papers extracted from Scopus. The results obtained allowed the researchers to identify a set of DS techniques and tools that can support LP practices and to develop a model to guide their implementation based on the typical improvement implementation stages of the plan-do-check-act cycle. The model shows how to use DS techniques and tools in LP for: identifying areas for improvement and subsequent implementation (plan); enabling a better knowledge and process management (do); identifying/predicting potential problems and employing statistical process control (check); providing remedial actions and effectively applying process improvement (act).

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