Coping with demand volatility in retail pharmacies with the aid of big data exploration

DOI10.1016/J.COR.2017.08.009zbMath1391.90022OpenAlexW2750922909MaRDI QIDQ1651681

Christos I. Papanagnou, Omeiza Matthews-Amune

Publication date: 12 July 2018

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: http://usir.salford.ac.uk/id/eprint/43887/1/BDSCM_REVISED_Paper.pdf

zbMATH Keywords

time seriesforecastingdata miningdemand uncertaintybig dataretail pharmacy

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Inventory, storage, reservoirs (90B05) Case-oriented studies in operations research (90B90)

Uses Software

• MapReduce

Cites Work

• Unnamed Item
• Calculating target inventory levels for constrained production: a fast simulation-based approximation
• A non-parametric approach to demand forecasting in revenue management
• Robust newsvendor problem with autoregressive demand
• Using online pickup site inclusion policies to manage demand in retail/e-tail organizations
• Testing for a unit root in time series regression
• Likelihood-Based Inference in Cointegrated Vector Autoregressive Models
• Information Distortion in a Supply Chain: The Bullwhip Effect
• Supply-chain modelling and control under proportional inventory-replenishment policies
• Elements of multivariate time series analysis

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