Forecasting a customer's Next Time Under Safety Stock

Ducharme, Corey; Agard, Bruno; Trépanier, Martin

doi:10.1016/j.ijpe.2021.108044

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…All three errors are scaled using the mean value of all in‐sample observations to allow for averaging the errors of multiple series. These error measurements are standard metrics for time series (Ducharme et al, 2021; Petropoulos et al, 2016).…”

Section: Multivariate Clustering Demand Forecastingmentioning

confidence: 99%

Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering

Ducharme,

Agard,

Trépanier

2024

Journal of Forecasting

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In a collaborative supply chain arrangement like vendor‐managed inventory, information on product demand at the point of sale is expected to be shared among members of the supply chain. However, in practice, obtaining such information can be costly, and some members may be unwilling or unable to provide the necessary access to the data. As such, large collaborative supply chains with multiple members may operate under a mixed‐information scenario where point‐of‐sale demand information is not known for all customers. Other sources of demand information exist and are becoming more available along supply chains using Industry 4.0 technologies and can serve as a substitute, but the data may be noisy, distorted, and partially missing. Under mixed information, leveraging existing customers' point‐of‐sale demand to improve the intermittent demand forecast of customers with missing information has yet to be explored. We propose a supervised demand forecasting method that uses multivariate time series clustering to map multiple sources of demand data. Members with missing downstream demand data have their resulting demand forecast improved by averaging over customers with similar delivery patterns for their final demand forecast. Our results show up to a 10% accuracy improvement over traditional intermittent demand forecasting methods with missing information.

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Section: Multivariate Clustering Demand Forecastingmentioning

confidence: 99%

Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering

Ducharme,

Agard,

Trépanier

2024

Journal of Forecasting

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based a modified objective function, the proposed FIDE and DIVIDE approaches offer flexibility between forecast accuracy and the reduction of shortages.However, we recognise the lack of explicit inventory performance metrics in the current study Petropoulos, Wang, and Disney (2019). combined financial, operational, and service metrics to form a holistic measure for inventory control objectives Ducharme, Agard, and Trépanier (2021). focused on stock-out events and proposed a novel metric called Next Time Under Safety Stock.…”

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confidence: 99%

Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications

Li¹,

Kang²,

Petropoulos³

et al. 2022

Preprint

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Intermittent demand forecasting is a ubiquitous and challenging problem in operations and supply chain management. There has been a growing focus on developing forecasting approaches for intermittent demand from academic and practical perspectives in recent years. However, limited attention has been given to forecast combination methods, which have been proved to achieve competitive performance in forecasting fast-moving time series. The current study aims to examine the empirical outcomes of some existing forecast combination methods, and propose a generalized feature-based framework for intermittent demand forecasting. We conduct a simulation study to perform a large-scale comparison of a series of combination methods based on an intermittent demand classification scheme. Further, a real data set is used to investigate the forecasting performance and offer insights with regards the inventory performance of the proposed framework by considering some complementary error measures. The proposed framework leads to a significant improvement in forecast accuracy and offers the potential of flexibility and interpretability in inventory control.

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Feature-based intermittent demand forecast combinations: accuracy and inventory implications

Kang

Petropoulos

et al. 2022

International Journal of Production Research

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Forecasting a customer's Next Time Under Safety Stock

Cited by 4 publications

References 39 publications

Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering

Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering

Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications

Feature-based intermittent demand forecast combinations: accuracy and inventory implications

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