Forecasting of lead-time demand variance: Implications for safety stock calculations

Babaï, Mohamed Zied; Dai, Yong; Li, Qinyun; Syntetos, Aris A.; Wang, Xun

doi:10.1016/j.ejor.2021.04.017

Cited by 17 publications

(9 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dynamic changes in the business environment, caused first by the Covid-19 pandemic and then by the war conflict in Ukraine, show that the popular concept of just in time is no longer suitable. Babai et al (2022) confirm that the classic purchasing strategy often leads to higher inventory costs and emphasize the importance of forecasting lead-time demand variance. This concept may be an interesting addition to this study.…”

Section: Discussionmentioning

confidence: 52%

Hybrid demand forecasting models: pre-pandemic and pandemic use studies

Kolková

Rozehnal²

2022

View full text Add to dashboard Cite

Research background: In business practice and academic sphere, the question of which of the prognostic models is the most accurate is constantly present. The accuracy of models based on artificial intelligence and statistical models has long been discussed. By combining the advantages of both groups, hybrid models have emerged. These models show high accuracy. Moreover, the question remains whether data in a dynamically changing economy (for example, in a pandemic period) have changed the possibilities of using these models. The changing economy will continue to be an important element in demand forecasting in the years to come. In business, where the concept of just in time already proves to be insufficient, it is necessary to open new research questions in the field of demand forecasting. Purpose of the article: The aim of the article is to apply hybrid models to bicycle sales e-shop data with a comparison of accuracy models in the pre-pandemic period and in the pandemic period. The paper examines the hypothesis that the pandemic period has changed the accuracy of hybrid models in comparison with statistical models and models based on artificial neural networks. Models: In this study, hybrid models will be used, namely the Theta model and the new forecastHybrid, compared to the statistical models ETS, ARIMA, and models based on artificial neural networks. They will be applied to the data of the e-shop with the cycle assortment in the period from 1.1. 2019 to 5.10 2021. Whereas the period will be divided into two parts, pre-pandemic, i.e. until 1 March 2020 and pandemic after that date. The accuracy evaluation will be based on the RMSE, MAE, and ACF1 indicators. Findings & value added: In this study, we have concluded that the prediction of the Hybrid model was the most accurate in both periods. The study can thus provide a scientific basis for any other dynamic changes that may occur in demand forecasting in the future. In other periods when there will be volatile demand, it is essential to choose models in which accuracy will decrease the least. Therefore, this study provides guidance for the use of methods in future periods as well. The stated results are likely to be valid even in an international comparison.

show abstract

Section: Discussionmentioning

confidence: 52%

Hybrid demand forecasting models: pre-pandemic and pandemic use studies

Kolková

Rozehnal²

2022

View full text Add to dashboard Cite

show abstract

“…The findings of this work provide some insights to managers into the impact of the lead times' uncertainty on inventory systems under lost sales. Since there is an empirical evidence that replenishment lead times are uncertain in real inventory systems due to supply and transportation issues (Boute et al, 2007;Jakšič et al, 2011;Babai et al, 2022), managers can use our models and performance evaluation solutions with any lead time distribution. Moreover, most of the findings of this paper show that, regardless of the relationship between the waiting time threshold and the lead time, the inventory levels are not very sensitive to the lead time's uncertainty and therefore, managers can rely on the deterministic assumption to set the base stock level.…”

Section: Discussionmentioning

confidence: 99%

Performance evaluation of a two-echelon inventory system with network lost sales

Kouki,

Arts,

Babai

2024

European Journal of Operational Research

Self Cite

View full text Add to dashboard Cite

“…Safety stocks are a function of service level and demand and replenishment lead‐time uncertainty. Hence, we focus on safety stock estimation as is the norm in the literature (e.g., Altendorfer, 2019; Babai et al., 2022; Chopra et al., 2004). Furthermore, widely used enterprise resource planning (ERP) systems, such as SAP (Akhtar, 2019), require the entry of calculated safety stocks for production planning purposes.…”

Section: Introductionmentioning

confidence: 99%

A nonparametric approach for setting safety stock levels

Saldanha

Price

Thomas

2023

Production and Operations Management

View full text Add to dashboard Cite

In practice, lead time demand (LTD) can be skewed, multi-modal or highly variable, and these factors compromise the validity of typical approaches used for setting safety stock levels. Motivated by encountering this problem at our industry partner, we develop an approach for setting safety stock levels using the bootstrap, a widely used statistical procedure. Existing bootstrap approaches for inventory management either operate directly on observed LTD or assume deterministic lead times, permitting direct application of the bootstrap approach for univariate quantile estimation. As LTD is a convolution of multiple random demands over a random lead time, a multivariate bootstrap approach is required. As we demonstrate, when lead times are stochastic, our multivariate approach provides improved safety stock estimates. We develop a multivariate central limit theorem for the bootstrap mean and bootstrap quantile-components of the safety stock calculation-highlighting why the generalization of these bootstrap methods is critical for inventory management. These results provide a theoretical underpinning for the bootstrap estimator of safety stock and permit the construction of confidence intervals for safety stock estimates, allowing decision makers to understand the reliability with which the desired service level will be achieved. Building on our theoretical results, and supported by numerical experiments, we provide insights on the behavior of the bootstrap for various LTD distributions, which our results demonstrate are critical when employing the bootstrap method. Implementation of our approach with our industry partner resulted in an inventory investment reduction of $1.17 million combined with an overall increase in service level. Our approach is general and can be implemented without modification in other settings.

show abstract

Forecasting of lead-time demand variance: Implications for safety stock calculations

Cited by 17 publications

References 45 publications

Hybrid demand forecasting models: pre-pandemic and pandemic use studies

Hybrid demand forecasting models: pre-pandemic and pandemic use studies

Performance evaluation of a two-echelon inventory system with network lost sales

A nonparametric approach for setting safety stock levels

Contact Info

Product

Resources

About