Leveraging dynamic capabilities for machine learning integration in retail demand planning
Devlin, Bryce (2023) Leveraging dynamic capabilities for machine learning integration in retail demand planning. Master of Philosophy thesis, Queensland University of Technology.
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Description
The COVID-19 pandemic and recent natural disasters have demonstrated that traditional demand planning solutions cannot effectively forecast during major disruptions, causing product shortages and subsequent effects on consumer behaviour. Artificial intelligence can provide advanced insights which better inform a firm’s demand planning decisions and adapt to domestic and global disruptions. In a dynamic market, firms that can meet consumer demands will succeed over their competitors. This thesis incorporates interviews with supply chain professionals and finds that planning teams with strategically aligned hybrid skillsets coupled with machine learning-based planning solutions positions firms well to minimise demand risks in extreme market conditions.
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| ID Code: | 242464 |
|---|---|
| Item Type: | QUT Thesis (Master of Philosophy) |
| Supervisor: | Mathews, Shane & Isaksson, Lars |
| Keywords: | Demand planning, Demand forecasting, Machine learning, Dynamic capabilities, Supply chain risk management, COVID-19, Supply chain transparency, Supply network coordination |
| DOI: | 10.5204/thesis.eprints.242464 |
| Pure ID: | 143370932 |
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Business & Law Current > Schools > School of Advertising, Marketing & Public Relations |
| Institution: | Queensland University of Technology |
| Deposited On: | 31 Aug 2023 11:25 |
| Last Modified: | 16 Jan 2025 01:00 |
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