A forecasting analytics model for assessing forecast error in e-fulfilment performance

Ho, G.T.S.; Choy, Siu-Kai; Tong, P.H.; Tang, Valerie

doi:10.1108/imds-01-2022-0056

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…Today, India continues to rely on outdated methods of forecasting that are based on deterministic demand parameters; hence, the aforementioned issues may impact the forecast and increase the cost to the government. According to Ho et al (2022), an inaccurate demand forecast, whether it results in an overestimation or underestimation, has the potential to negatively impact the performance and efficiency of a supply chain. Therefore, taking into account the possibility of fluctuating demand while drafting supply contracts can substantially reduce VSC costs.…”

Section: Vaccine Supply Chain Under Demand Uncertaintymentioning

confidence: 99%

An incentive mechanism contract model to coordinate a vaccine supply chain under demand uncertainty

Chandra,

Jain,

Chan

2023

IMDS

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PurposeThe increasing prevalence of a wide range of infectious diseases, as well as the underwhelming results of vaccination rates that may be traced back to problems with vaccine procurement and distribution, have brought to the fore the importance of vaccine supply chain (VSC) management in recent years. VSC is the cornerstone of effective vaccination; hence, it is crucial to enhance its performance, particularly in low- and middle-income countries where immunization rates are not satisfactory.Design/methodology/approachIn this paper, the authors focus on VSC performance improvement of India by proposing supply contracts under demand uncertainty. The authors propose three contracts – wholesale price (WSP), cost sharing (CS) and incentive mechanism (IM) for the government-operated immunization program of India.FindingsThe authors' findings indicate that IM is capable of coordinating the supply chain, whereas the other two contracts are inefficient for the government. To validate the model, it is applied to a real-world scenario of coronavirus disease 2019 (COVID-19) in India, and the findings show that an IM contract improves the overall efficiency of the system by 23.72%.Originality/valuePrevious studies focused mainly on the influenza VSC industry within developed nations. Nonetheless, there exists a dearth of literature pertaining to the examination of supply contracts and their feasibility for immunization programs that are administered by the government and aimed at optimizing societal benefits. The authors' findings can be beneficial to the immunization program of India to optimize their VSC cost.

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Section: Vaccine Supply Chain Under Demand Uncertaintymentioning

confidence: 99%

An incentive mechanism contract model to coordinate a vaccine supply chain under demand uncertainty

Chandra,

Jain,

Chan

2023

IMDS

View full text Add to dashboard Cite

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Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS

Hamdan

Wulamu

Zhang

et al. 2023

Int J Syst Assur Eng Manag

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Data-driven optimization for production planning with multiple demand features

Su,

Zeng,

Shao

et al. 2023

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PurposeThe production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.Design/methodology/approachIn this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.FindingsNumerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.Originality/valueCompared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

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A forecasting analytics model for assessing forecast error in e-fulfilment performance

Cited by 3 publications

References 47 publications

An incentive mechanism contract model to coordinate a vaccine supply chain under demand uncertainty

An incentive mechanism contract model to coordinate a vaccine supply chain under demand uncertainty

Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS

Data-driven optimization for production planning with multiple demand features

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