Version 1
: Received: 8 April 2023 / Approved: 10 April 2023 / Online: 10 April 2023 (04:27:16 CEST)
How to cite:
Al-Haidari, R.; Al-Rawashdeh, S.; Zeidan, A.; Omambala, J.; Nagarur, N. Impact of Covid-19 Pandemic on Demand and Demand Forecasting in a Furniture Wholesale Company. Preprints2023, 2023040144. https://doi.org/10.20944/preprints202304.0144.v1
Al-Haidari, R.; Al-Rawashdeh, S.; Zeidan, A.; Omambala, J.; Nagarur, N. Impact of Covid-19 Pandemic on Demand and Demand Forecasting in a Furniture Wholesale Company. Preprints 2023, 2023040144. https://doi.org/10.20944/preprints202304.0144.v1
Al-Haidari, R.; Al-Rawashdeh, S.; Zeidan, A.; Omambala, J.; Nagarur, N. Impact of Covid-19 Pandemic on Demand and Demand Forecasting in a Furniture Wholesale Company. Preprints2023, 2023040144. https://doi.org/10.20944/preprints202304.0144.v1
APA Style
Al-Haidari, R., Al-Rawashdeh, S., Zeidan, A., Omambala, J., & Nagarur, N. (2023). Impact of Covid-19 Pandemic on Demand and Demand Forecasting in a Furniture Wholesale Company. Preprints. https://doi.org/10.20944/preprints202304.0144.v1
Chicago/Turabian Style
Al-Haidari, R., Joshua Omambala and Nagendra Nagarur. 2023 "Impact of Covid-19 Pandemic on Demand and Demand Forecasting in a Furniture Wholesale Company" Preprints. https://doi.org/10.20944/preprints202304.0144.v1
Abstract
Accurate demand forecasting plays a critical role in most furniture businesses’ operational, tactical, and strategic decisions, as the demand in the furniture business is considered seasonal and becomes more complex in crises. In this work, a neural network model using the Long Short-Term Memory (LSTM) method was developed to forecast the demand for specific product groups. LSTM is a leading deep learning model for time series prediction, particularly seasonal, multi-item, and non-linear situations. The developed model was used to predict the demand based on old data before the Covid-19 pandemic and recent data of the first months of the pandemic as a fast response to the crisis. In addition, a comparison study was conducted between the developed model and the traditional planning inventory used by furniture businesses that provided us with the data. The results showed that the Covid-19 pandemic significantly impacted demand forecasting. Also, the fast response to Covid-19 pandemic has slightly increased the model performance. Finally, the comparison study demonstrated that our model is robust and better than the traditional demand forecasting method. Therefore, the developed model may help the business improve inventory and production planning to create a more flexible supply chain.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
