- Title
- Demand forecasting in the presence of systematic events: cases in capturing sales promotions
- Creator
- Abolghasemi, Mahdi; Hurley, Jason; Eshragh, Ali; Fahimnia, Behnam
- Relation
- ARC.IC140100032 http://purl.org/au-research/grants/arc/IC140100032
- Relation
- International Journal of Production Economics Vol. 230, Issue December 2020, no. 107892
- Publisher Link
- http://dx.doi.org/10.1016/j.ijpe.2020.107892
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2020
- Description
- Reliable demand forecasts are critical for effective supply chain management. Several endogenous and exogenous variables can influence the dynamics of demand, and hence a single statistical model that only consists of historical sales data is often insufficient to produce accurate forecasts. In practice, the forecasts generated by baseline statistical models are often judgmentally adjusted by forecasters to incorporate factors and information that are not incorporated in the baseline models. There are however systematic events whose effect can be quantified and modeled to help minimize human intervention in adjusting the baseline forecasts. In this paper, we develop and test a novel regime-switching approach to quantify systematic information/events and objectively incorporate them into the baseline statistical model. Our simple yet practical and effective model can help limit forecast adjustments to only focus on the impact of less systematic events such as sudden climate change or dynamic market activities. The model is validated empirically using sales and promotional data from two Australian companies. The model is also benchmarked against commonly employed statistical and machine learning forecasting models. Discussions focus on thorough analysis of promotions impact and benchmarking results. We show that the proposed model can successfully improve forecast accuracy and avoid poor forecasts when compared to the current industry practice which heavily relies on human judgment to factor in all types of information/events. The proposed model also outperforms sophisticated machine learning methods by mitigating the generation of extremely poor forecasts that drastically differ from actual sales due to changes in demand states.
- Subject
- demand forecasting; systematic events; time series regression models; sales promotions; judgmental forecasting; supply chain; SDG 13; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1432817
- Identifier
- uon:39115
- Identifier
- ISSN:0925-5273
- Rights
- © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
- Language
- eng
- Full Text
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Goal 13: Climate Change
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