Supply Chain Management - Crossref Bibliographic Metadata

This dataset provides detailed bibliographic metadata records for scholarly publications related to 'Supply Chain Management' (SCM), as retrieved from Crossref.org. This metadata corpus facilitates in-depth exploration of the academic discourse surrounding SCM. Contextual Overview of Supply Chain Management: 1. Definition and Context: Supply Chain Management encompasses the planning and management of all activities involved in sourcing, procurement, conversion, and logistics management. Crucially, it also includes coordination and collaboration with channel partners, which can be suppliers, intermediaries, third-party service providers, and customers. Gaining prominence from the 1980s onwards, SCM evolved from logistics and transportation to address the need for integrated, efficient, and responsive global value chains across diverse industries. 2. Strengths and Weaknesses: Effective SCM offers significant benefits, including cost reduction, improved efficiency, enhanced customer satisfaction through better availability and responsiveness, and increased competitive advantage. However, implementation challenges include the complexity of coordinating multiple entities, information sharing risks, vulnerability to disruptions (geopolitical, natural disasters), and the high investment in technology and process re-engineering. Success is contingent on trust, collaboration, and sophisticated information systems across partners. 3. Relevance and Research Potential: SCM remains highly relevant due to globalization, e-commerce, sustainability concerns, and recent disruptions highlighting the need for resilience and agility. Theoretically, it draws from systems theory, transaction cost economics, and network theory. SCM is a cornerstone of operations and strategic management. Research opportunities include supply chain resilience, digital SCM (AI, blockchain, IoT), sustainable supply chains, circular economy models, and managing supply chain risk in volatile environments. Dataset Structure and Content: The dataset consists of one or more archives. Each archive contains a series of approximately 850 monthly folders (e.g., spanning from January 1950 to January 2025), reflecting a granular month-by-month process of metadata retrieval and curation for SCM. Within each monthly folder, users will find several JSON files documenting the search and filtering process for that specific month: term_results/: A subfolder containing JSON files for results of initial broad keyword searches related to SCM. merged_results.json: Aggregated results from these individual term searches before advanced filtering. filtered_results.json: Results after applying a more specific, complex Boolean query (e.g., ("supply chain management" OR ...) AND ("logistics" OR ...)) and exact phrase matching to refine relevance for SCM. The exact query used for filtering is detailed within this file. final_results.json: This is the primary file of interest for most users. It contains the curated, deduplicated (by DOI) list of unique publication metadata records deemed most relevant to 'Supply Chain Management' for that specific month. Includes fields like Title, Authors, DOI, Publication Date, Source Title, Abstract (if available from Crossref). statistics_results.json: Summary statistics of the search and filtering process for the month. This granular monthly structure allows researchers to trace the evolution of academic discourse on SCM and identify relevant publications with high temporal precision. For an overview of the general retrieval methodology, refer to the parent Dataverse description (Management Tool Bibliographic Metadata (Crossref)). Users interested in aggregated publication counts or trend analysis for SCM should consult the corresponding datasets in the Raw Extracts Dataverse and the Comparative Indices Dataverse.