scholarly journals Textual Data Science for Logistics and Supply Chain Management

Logistics ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 56
Author(s):  
Horst Treiblmaier ◽  
Patrick Mair
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Multidimensional Scaling ◽  
Sentiment Analysis ◽  
Correspondence Analysis ◽  
Data Science ◽  
Topic Models ◽  
Chain Management ◽  
Word Clouds ◽  
Textual Data

Background: Researchers in logistics and supply chain management apply a multitude of methods. So far, however, the potential of textual data science has not been fully exploited to automatically analyze large chunks of textual data and to extract relevant insights. Methods: In this paper, we use data from 19 qualitative interviews with supply chain experts and illustrate how the following methods can be applied: (1) word clouds, (2) sentiment analysis, (3) topic models, (4) correspondence analysis, and (5) multidimensional scaling. Results: Word clouds show the most frequent words in a body of text. Sentiment analysis can be used to calculate polarity scores based on the sentiments that the respondents had in their interviews. Topic models cluster the texts based on dominating topics. Correspondence analysis shows the associations between the words being used and the respective managers. Multidimensional scaling allows researchers to visualize the similarities between the interviews and yields clusters of managers, which can also be used to highlight differences between companies. Conclusions: Textual data science can be applied to mine qualitative data and to extract novel knowledge. This can yield interesting insights that can supplement existing research approaches in logistics and supply chain research.

Big Data Analytics in Supply Chain Management

2021 ◽  
pp. 2443-2457
Author(s):  
Nenad Stefanovic
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Current Approach ◽  
Data Systems ◽  
Chain Management ◽  
Big Data Systems

The current approach to supply chain intelligence has some fundamental challenges when confronted with the scale and characteristics of big data. In this chapter, applications, challenges and new trends in supply chain big data analytics are discussed and background research of big data initiatives related to supply chain management is provided. The methodology and the unified model for supply chain big data analytics which comprises the whole business intelligence (data science) lifecycle is described. It enables creation of the next-generation cloud-based big data systems that can create strategic value and improve performance of supply chains. Finally, example of supply chain big data solution that illustrates applicability and effectiveness of the model is presented.

Real-time data processing in supply chain management: revealing the uncertainty dilemma

2019 ◽  
Vol 49 (10) ◽  
pp. 1003-1019 ◽  
Author(s):  
Sabrina Lechler ◽  
Angelo Canzaniello ◽  
Bernhard Roßmann ◽  
Heiko A. von der Gracht ◽  
Evi Hartmann
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Data Processing ◽  
Real Time ◽  
Data Science ◽  
Research Question ◽  
Time Data ◽  
Chain Management ◽  
Content Type ◽  
Real Time Data Processing

Purpose Particularly in volatile, uncertain, complex and ambiguous (VUCA) business conditions, staff in supply chain management (SCM) look to real-time (RT) data processing to reduce uncertainties. However, based on the premise that data processing can be perfectly mastered, such expectations do not reflect reality. The purpose of this paper is to investigate whether RT data processing reduces SCM uncertainties under real-world conditions. Design/methodology/approach Aiming to facilitate communication on the research question, a Delphi expert survey was conducted to identify challenges of RT data processing in SCM operations and to assess whether it does influence the reduction of SCM uncertainty. In total, 14 prospective statements concerning RT data processing in SCM operations were developed and evaluated by 68 SCM and data-science experts. Findings RT data processing was found to have an ambivalent influence on the reduction of SCM complexity and associated uncertainty. Analysis of the data collected from the study participants revealed a new type of uncertainty related to SCM data itself. Originality/value This paper discusses the challenges of gathering relevant, timely and accurate data sets in VUCA environments and creates awareness of the relationship between data-related uncertainty and SCM uncertainty. Thus, it provides valuable insights for practitioners and the basis for further research on this subject.

Application of Data Science in Supply Chain Management: Real-World Case Study in Logistics

2021 ◽  
pp. 205-237
Author(s):  
Emir Žunić ◽  
Kerim Hodžić ◽  
Sead Delalić ◽  
Haris Hasić ◽  
Robert B. Handfield
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Real World ◽  
Data Science ◽  
Chain Management

Modeling big data enablers for operations and supply chain management

2018 ◽  
Vol 29 (2) ◽  
pp. 629-658 ◽  
Author(s):  
Kuldeep Lamba ◽  
Surya Prakash Singh
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Science ◽  
Structural Modeling ◽  
Security And Privacy ◽  
Interpretive Structural Modeling ◽  
Chain Management ◽  
Content Type

Purpose The purpose of this paper is to identify and analyse the interactions among various enablers which are critical to the success of big data initiatives in operations and supply chain management (OSCM). Design/methodology/approach Fourteen enablers of big data in OSCM have been selected from literature and consequent deliberations with experts from industry. Three different multi criteria decision-making (MCDM) techniques, namely, interpretive structural modeling (ISM), fuzzy total interpretive structural modeling (fuzzy-TISM) and decision-making trial and evaluation laboratory (DEMATEL) have been used to identify driving enablers. Further, common enablers from each technique, their hierarchies and inter-relationships have been established. Findings The enabler modelings using ISM, Fuzzy-TISM and DEMATEL shows that the top management commitment, financial support for big data initiatives, big data/data science skills, organizational structure and change management program are the most influential/driving enablers. Across all three different techniques, these five different enablers has been identified as the most promising ones to implement big data in OSCM. On the other hand, interpretability of analysis, big data quality management, data capture and storage and data security and privacy have been commonly identified across all three different modeling techniques as the most dependent big data enablers for OSCM. Research limitations/implications The MCDM models of big data enablers have been formulated based on the inputs from few domain experts and may not reflect the opinion of whole practitioners community. Practical implications The findings enable the decision makers to appropriately choose the desired and drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM. The most common driving big data enablers can be given high priority over others and can significantly enhance the performance of OSCM. Originality/value MCDM-based hierarchical models and causal diagram for big data enablers depicting contextual inter-relationships has been proposed which is a new effort for implementation of big data in OSCM.

Big Data Analytics in Supply Chain Management

2022 ◽  
pp. 1801-1816
Author(s):  
Nenad Stefanovic
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Current Approach ◽  
Data Systems ◽  
Chain Management ◽  
Big Data Systems

The current approach to supply chain intelligence has some fundamental challenges when confronted with the scale and characteristics of big data. In this chapter, applications, challenges and new trends in supply chain big data analytics are discussed and background research of big data initiatives related to supply chain management is provided. The methodology and the unified model for supply chain big data analytics which comprises the whole business intelligence (data science) lifecycle is described. It enables creation of the next-generation cloud-based big data systems that can create strategic value and improve performance of supply chains. Finally, example of supply chain big data solution that illustrates applicability and effectiveness of the model is presented.

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