Details
| Original language | English |
|---|---|
| Journal | International Transactions in Operational Research |
| Early online date | 5 Aug 2025 |
| Publication status | E-pub ahead of print - 5 Aug 2025 |
Abstract
Despite increasing demands for resilient and sustainable supply chains, inventory management often relies on outdated single-criterion analyses. While multi-criteria ABC (MCABC) analyses provide a theoretically mature assessment of resilience-sustainability-benefit trade-offs in inventory, their adoption remains limited due to fragmented methodologies, lack of standardization, and unclear design guidance. This study addresses this gap by developing a taxonomy of eight dimensions and 25 characteristics to structure the MCABC analysis design space. Using a dataset of 108 configurations, cluster analysis identifies six recurring archetypes that serve as implementation-ready templates for inventory analysis. Archetype 2: artificial intelligence (AI)+-driven cluster minimalist enables rapid, cost-focused inventory structuring with minimal data and no expert input. In contrast, Archetype 1: AI+-driven complex ranker uses expert-weighted multi-criteria analysis to support holistic and sustainability oriented inventory strategies. The taxonomy and archetypes provide a unified framework for researchers to theorize inventory design trade-offs and for practitioners to apply scalable blueprints for mature inventory analyses.
Keywords
- inventory management, multi-criteria analysis, resilient supply chain, taxonomy, archetypes
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Business and International Management
- Computer Science(all)
- Computer Science Applications
- Business, Management and Accounting(all)
- Strategy and Management
- Decision Sciences(all)
- Management Science and Operations Research
- Business, Management and Accounting(all)
- Management of Technology and Innovation
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In: International Transactions in Operational Research, 05.08.2025.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Bridging the implementation gap in MCABC inventory management
T2 - from a taxonomy to practical archetypes
AU - Grützner, Lukas
AU - Breitner, Michael H.
N1 - Publisher Copyright: © 2025 The Author(s). International Transactions in Operational Research published by John Wiley & Sons Ltd on behalf of International Federation of Operational Research Societies.
PY - 2025/8/5
Y1 - 2025/8/5
N2 - Despite increasing demands for resilient and sustainable supply chains, inventory management often relies on outdated single-criterion analyses. While multi-criteria ABC (MCABC) analyses provide a theoretically mature assessment of resilience-sustainability-benefit trade-offs in inventory, their adoption remains limited due to fragmented methodologies, lack of standardization, and unclear design guidance. This study addresses this gap by developing a taxonomy of eight dimensions and 25 characteristics to structure the MCABC analysis design space. Using a dataset of 108 configurations, cluster analysis identifies six recurring archetypes that serve as implementation-ready templates for inventory analysis. Archetype 2: artificial intelligence (AI)+-driven cluster minimalist enables rapid, cost-focused inventory structuring with minimal data and no expert input. In contrast, Archetype 1: AI+-driven complex ranker uses expert-weighted multi-criteria analysis to support holistic and sustainability oriented inventory strategies. The taxonomy and archetypes provide a unified framework for researchers to theorize inventory design trade-offs and for practitioners to apply scalable blueprints for mature inventory analyses.
AB - Despite increasing demands for resilient and sustainable supply chains, inventory management often relies on outdated single-criterion analyses. While multi-criteria ABC (MCABC) analyses provide a theoretically mature assessment of resilience-sustainability-benefit trade-offs in inventory, their adoption remains limited due to fragmented methodologies, lack of standardization, and unclear design guidance. This study addresses this gap by developing a taxonomy of eight dimensions and 25 characteristics to structure the MCABC analysis design space. Using a dataset of 108 configurations, cluster analysis identifies six recurring archetypes that serve as implementation-ready templates for inventory analysis. Archetype 2: artificial intelligence (AI)+-driven cluster minimalist enables rapid, cost-focused inventory structuring with minimal data and no expert input. In contrast, Archetype 1: AI+-driven complex ranker uses expert-weighted multi-criteria analysis to support holistic and sustainability oriented inventory strategies. The taxonomy and archetypes provide a unified framework for researchers to theorize inventory design trade-offs and for practitioners to apply scalable blueprints for mature inventory analyses.
KW - inventory management
KW - multi-criteria analysis
KW - resilient supply chain
KW - taxonomy, archetypes
UR - http://www.scopus.com/inward/record.url?scp=105012494357&partnerID=8YFLogxK
U2 - 10.1111/itor.70083
DO - 10.1111/itor.70083
M3 - Article
AN - SCOPUS:105012494357
JO - International Transactions in Operational Research
JF - International Transactions in Operational Research
SN - 0969-6016
ER -