A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry

Siddiqui, Raheel; Azmat, Muhammad; Ahmed, Shehzad; Kummer, Sebastian

doi:10.1080/16258312.2021.1967081

Cited by 32 publications

(21 citation statements)

References 53 publications

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“…In a similar aspect, our findings ranked strong information technology (IT) system and information sharing in the first position (0.298764), followed by real-time data (0.278505). Information sharing among supply chain players enhance forecast accuracy (Siddiqui et al , 2022). Products, assets and people visibility is ranked third (0.216686), followed by knowledge acquisition and evaluation (0.117411); lowest ranking is business intelligence gathering (0.088634).…”

Section: Resultsmentioning

confidence: 99%

Application of AHP and G-TOPSIS for prioritizing capabilities and related practices for a mature and resilient supply chain during disruption

Echefaj

Charkaoui

Cherrafi

et al. 2022

JGOSS

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Purpose The purpose of this study is to identify and prioritize capabilities and practices to ensure a resilient supply chain during an unexpected disruption. In addition, this study ranks maturity factors that influence the main capabilities identified. Design/methodology/approach This paper is conducted in three stages. First, capabilities and practices are extracted through a literature review. Second, capabilities and practices are ranked using the analytical hierarchical process method. Third, a gray technique for order preference by similarity to ideal solution method is used to rank maturity factors influencing capabilities. Findings The findings indicate that responsiveness, readiness, flexibility and adaptability are the most important capabilities for supply chain resilience. Also, commitment and communication are the highest maturity factors influencing resilience capabilities. Research limitations/implications The findings provide a hierarchical vision of capabilities and practices for industries to increase resilience. Limitations of the paper are related to capabilities, practices and number of experts consulted. Practical implications This paper highlights the importance of high-maturity practices in resilience capability adoption. The findings of this study will encourage decisions-makers to increase maturity practices to build resilience against disruption. Originality/value The paper reveals that developing powerful capabilities, good practices and a high level of maturity improve supply chain resilience.

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Section: Resultsmentioning

confidence: 99%

Application of AHP and G-TOPSIS for prioritizing capabilities and related practices for a mature and resilient supply chain during disruption

Echefaj

Charkaoui

Cherrafi

et al. 2022

JGOSS

View full text Add to dashboard Cite

show abstract

“…As in our study, the classical exponential smoothing model was not the most appropriate. Similar models involved here were also used by Siddiqui et al (2022). ARIMA, etc, Theta models were also applied here.…”

Section: Discussionmentioning

confidence: 99%

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

Kolková

Rozehnal²

2022

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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.

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“…Autoregressive Integrated Moving Average Model (ARIMA) is a widely used forecasting model due to its simplicity and ability to generalize to non-stationary series. A detailed description of this model can be found in [ 14 , 23 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model

Rathipriya

Rahman

Dhamodharavadhani

et al. 2022

Neural Comput & Applic

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Demand forecasting is a scientific and methodical assessment of future demand for a critical product.The effective Demand Forecast Model (DFM) enables pharmaceutical companies to be successful in the global market. The purpose of this research paper is to validate various shallow and deep neural network methods for demand forecasting, with the aim of recommending sales and marketing strategies based on the trend/seasonal effects of eight different groups of pharmaceutical products with different characteristics. The root mean squared error (RMSE) is used as the predictive accuracy of DFMs. This study also found that the mean RMSE value of the shallow neural network-based DFMs was 6.27 for all drug categories, which was lower than deep neural network models. According to the findings, DFMs based on shallow neural networks can effectively estimate future demand for pharmaceutical products.

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A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry

Cited by 32 publications

References 53 publications

Application of AHP and G-TOPSIS for prioritizing capabilities and related practices for a mature and resilient supply chain during disruption

Application of AHP and G-TOPSIS for prioritizing capabilities and related practices for a mature and resilient supply chain during disruption

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

Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model

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