Search Results - subject:"data driven"

An analytical approach to risk assessment in agri-food supply chains using fuzzy inference systems

Jayalath, Madushan Madhava; Ratnayake, R. M. Chandima; … - In: Supply chain analytics 13 (2026), pp. 1-22

This study presents a structured, quantitative risk assessment framework for agri-food supply chains (AFSCs), aligned with the guidelines of ISO 31000:2018. The approach integrates Fuzzy Inference Systems (FIS) to quantify and mitigate risks, offering an effective tool to reduce subjectivity,...

Persistent link: https://www.econbiz.de/10015640881

An analytical review of vendor-managed inventory models in sustainable supply chains

Salas-Navarro, Katherinne; Rojano-Flores, Melissa; … - In: Supply chain analytics 13 (2026), pp. 1-27

A vendor-managed inventory system is a collaborative strategy in network logistics. It helps establish a sourcing and inventory control policy that optimizes logistics costs and enhances the efficient use of resources. This research presents a meta-analysis and systematic literature review of...

Persistent link: https://www.econbiz.de/10015640955

Global spatiotemporal multi-criteria analysis of coastal risk : current and future hot spots and clusters

Bidoia, Marco; Giupponi, Carlo - 2026

Coastal zones are among the environments most threatened by climate change. Although various efforts for global mapping and classification of coastal social and ecological systems have been undertaken, the ability to analyse and describe the spatial heterogeneity and multidimensionality of these...

Persistent link: https://www.econbiz.de/10015588204

An analytics-driven hybrid method for multi-item demand forecasting in supply chains

Lingkon, Md. Limonur Rahman; Hossain, Md. Sanowar; … - In: Supply chain analytics 13 (2026), pp. 1-25

This study proposes a new deep learning (DL)-based approach for multi-item demand forecasting in multi-wave distribution networks. In modern merchandising supply systems, traditional forecasting techniques such as moving averages and autoregressive integrated moving average (ARIMA) models are...

Persistent link: https://www.econbiz.de/10015640893

Temporal focal points and economic outcomes : evidence from U.S. mortgage lending

Giacoletti, Marco; Heimer, Rawley Z.; Yu, Edison - 2026

Persistent link: https://www.econbiz.de/10015650542

Digitalizing business models in hospitality ecosystems : toward data-driven innovation

Troisi, Orlando; Visvizi, Anna; Grimaldi, Mara - In: European journal of innovation management 26 (2023) 7, pp. 242-277

Persistent link: https://www.econbiz.de/10014500866

Data or business first? : manufacturers' transformation toward data-driven business models

Stahl, Bastian; Häckel, Björn; Leuthe, Daniel; … - In: Schmalenbach journal of business research : SBUR 75 (2023) 3, pp. 303-343

Driven by digital technologies, manufacturers aim to tap into data-driven business models, in which value is generated … data-driven business models require substantially different business and technical capabilities. While there are manifold … capabilities for distinct data-driven business model archetypes is needed. This perspective promises to enhance research …

Persistent link: https://www.econbiz.de/10014491672

Data or business first? Manufacturers' transformation toward data-driven business models

Stahl, Bastian; Häckel, Björn; Leuthe, Daniel; … - In: Schmalenbach Journal of Business Research (SBUR) 75 (2023) 3, pp. 303-343

Persistent link: https://www.econbiz.de/10014512395

Which opinion is more trustworthy : an analysts' earnings forecast quality assessment framework based on machine learning

Song, Yingying; Chen, Xinxin - In: The North American journal of economics and finance : a … 75 (2025) 2, pp. 1-22

Persistent link: https://www.econbiz.de/10015359902

Data-driven digital transformation for uncertainty reduction : application of satellite imagery analytics in institutional crop credit management

Narayanamurthy, Gopalakrishnan; Jayanth, Reddy Sai Shiva; … - In: International journal of production economics 280 (2025), pp. 1-17

Persistent link: https://www.econbiz.de/10015372578