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Data analytics and global logistics performance: an exploratory study of informatization in the logistics sector

Anwar Muhammad Fahad, Wong Wai Peng, Saad Nor Hasliza, Mushtaq Naveed

LogForum

2022

Vol. 18, no. 2

137--160

Background: Informatization has enabled global logistics and supply chains (LSC) to capitalize on data-driven analytics to improve logistics performance. At the country level, logistics performance is gauged through the logistics performance index (LPI), where globally 61.25% or 98 countries perform below the mean LPI score. Previous studies focused on logistics informatization in high and moderate LPI rank economies. The paper aims to conduct an exploratory case study in a low LPI performing country to assess the informatization practices of logistics entities and develop a logistics informatization continuum to unlock data analytics for other countries. Methods: The study implements qualitative methods to develop strategic recommendations to reduce global logistics imbalance. We employ a two-layer methodology consisting of thematic analysis and a novel strategic choice approach (SCA) to involve stakeholders for recommendations on obstruction. For thematic analysis, 16 semi-structured interviews were conducted from logistics companies, also onboard 10 trade associations and government representatives for the SCA analysis. Results: We observed many obstructions in informatization; low willingness on informatization, fear of information leakage by humans, low-reciprocity for collaboration, the myth of information and communication technologies (ICT) as an expensive tool, self-interest, and opportunistic behavior. Conclusion: Information-centric and integrated LSC enables data-driven technologies for real-time decision making, vigilance, and data analytics to distinguished the success of a country’s logistics performance. Originality: This study explores the informatization conformity in the logistics sector to connect data analytics. We introduced a novel strategic choice approach in the technology domain for problem structuring. The paper further contributes by suggesting a logistics informatization continuum for low LPI countries to straighten digitalization in the logistics sector.

Introducing Advanced Data Analytics in Perspective of Industry 4.0 in a Die Casting Foundry

Perzyk M., Dybowski B., Kozłowski J.

Archives of Foundry Engineering

2019

iss. 1

53--57

The paper presents some aspects of a development project related to Industry 4.0 that was executed at Nemak, a leading manufacturer of the aluminium castings for the automotive industry, in its high pressure die casting foundry in Poland. The developed data analytics system aims at predicting the casting quality basing on the production data. The objective is to use these data for optimizing process parameters to raise the products’ quality as well as to improve the productivity. Characterization of the production data including the recorded process parameters and the role of mechanical properties of the castings as the process outputs is presented. The system incorporates advanced data analytics and computation tools based on the analysis of variance (ANOVA) and applying an MS Excel platform. It enables the foundry engineers and operators finding the most efficient process variables to ensure high mechanical properties of the aluminium engine block castings. The main features of the system are explained and illustrated by appropriate graphs. Chances and threats connected with applications of the data-driven modelling in die casting are discussed.