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Data Science within Supply Chain Man...

Foster, Stephen.

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Data Science within Supply Chain Management: An Analysis of Skillset Relevance.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data Science within Supply Chain Management: An Analysis of Skillset Relevance./
作者:	Foster, Stephen.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	152 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-07, Section: B.
Contained By:	Dissertations Abstracts International81-07B.
標題:	Management. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27671787
ISBN:	9781392872376

Data Science within Supply Chain Management: An Analysis of Skillset Relevance.
Foster, Stephen.

Data Science within Supply Chain Management: An Analysis of Skillset Relevance. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 152 p.

Source: Dissertations Abstracts International, Volume: 81-07, Section: B.

Thesis (D.B.A.)--Capella University, 2020.

This item must not be sold to any third party vendors.

The general problem is that organizational hiring agents for data scientists and analysts find it challenging to match the skills required for internal operations and Big Data projects with the data scientist candidate. The purpose of this research was to identify the data science skillsets held by supply chain professionals and how their data science skills relate to a data scientist type. The specific business problem is the lack of awareness and understanding the types of skills needed to analyze data, which leads organizations to waste effort in hiring qualified individuals. The research question that guided the research study was based on current research and attempted to fill the gaps in information concerning data science skills, training, and Big Data Analytics (BDA) usage. What are data science skill sets currently used by data scientists/analysts to analyze data within the supply chain management (SCM) domain? This research used the theoretical framework of the Data Science Taxonomy, which outlines six technical areas to train data scientists, focused on the tool evaluation and the data analyst. The population of this study consisted of individuals who analyze data, with a focus on the segment defined as supply chain management professionals who are actively analyzing data to drive their day-to-day operational and strategic supply chain decisions within their SCM organizations. The researcher recruited participants from eight different SCM LinkedIn groups who self-identify as SCM professionals working in the field using a river sampling method, which yielded 33 samples for analysis. The participants engaged in an online survey using the Decision Skills Survey Instrument to capture demographics, data science skills, and how participants self-identified to several data scientist types. This research found that 22 types of data science skills are in use in SCM organizations and that data science skills exist in different proportions depending on the individual's self-ID type and the area the individuals work. Business skills, data visualization, and statistics were the highest ranked and most common skills among SCM professionals. Among the self-ID types, the Data Businessperson type was the most common in all areas with the Data Researcher ID type second. This research contributed to resolving the business problem by studying the individuals who analyze data within SCM organizations to build data scientist archetypes for organizations to match the individual and their skills with the areas SCM organizations need data scientists and analysts to work.

ISBN: 9781392872376Subjects--Topical Terms:

516664
Management.
Subjects--Index Terms:

Data science

Data Science within Supply Chain Management: An Analysis of Skillset Relevance.
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