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

Unlocking the potential of blockchain technology in enhancing the fisheries supply chain: an exploration of critical adoption barriers in China

The application of blockchain technology holds significant potential for improving efficiency, resilience, and transparency within the Fisheries Supply Chain (FSC). This study addresses the critical barriers hindering the adoption of blockchain technology (BT) in the Chinese FSC, recognizing the unique challenges posed by its intricacies. Through a comprehensive literature review, fourteen Critical Barrier Factors (CBFs) were identified, and a grey Delphi method was employed to distill this set. Five pivotal CBFs emerged, including "Regulatory Compliance," "Cost of Implementation," and "Complex Supply Chain Network". A subsequent grey Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis revealed the causal relationships among these factors, categorizing them into effect and cause groups. "Regulatory Compliance," "Cost of Implementation," and "Complex Supply Chain Network" were identified as primary influencing factors demanding attention for effective BT integration in the FSC. The findings serve as a valuable resource for FSC stakeholders, assisting in prioritizing efforts to address these barriers. The discerned causal relationships provide guidance for managers in optimizing resource allocation. Ultimately, this research advocates for the adoption of blockchain technology in the fisheries supply chain to enhance overall performance and operational efficiency.

A new rolling forecasting framework using Microsoft Power BI for data visualization: A case study in a pharmaceutical industry

CONTEXT AND OBJECTIVES

Demand forecasting is a vital step for production planning and consequently, for supply chain efficiency, especially for the pharmaceutical (pharma) supply chain due to its unique characteristics. Numerous models and techniques that are proposed in the literature but little in concrete and generic framework to forecasting process, mainly for pharmaceutical supply chain. Unlike studies in the literature, this study not only perfectly predict the sales of a pharma manufacturer, but also visualize the results via a developed dashboard using modern information technology and business intelligence.

MATERIAL AND METHODS

In this research, a rolling forecasting framework comprising of different steps and specialized tools is proposed that can assist supply chain managers to perform an accurate sales forecasting and consequently a better performance and specifically patient satisfaction. The proposed generic framework combines the use of Visual studio C++ software to extract optimal forecasting and the Power BI software to monitor the accuracy of the obtained sales forecasts. Three exponential smoothing methods are integrated in the proposed framework, which is open to adding more new forecasting methods.

RESULTS

The proposed framework is tested for many data sets from a pharmaceutical manufacturer company, and the results obtained show superior performance, especially a clear decline in both forecast errors, which can reach 75% and a drop of stock level to 50%. Therefore, the company is currently using it and a future integration with their ERP is being carried out.

CONCLUSION

The proposed rolling forecasting framework contributes to insightful decision-making through the visualization of accurate future sales and turnover, and consequently, an efficient stock management and effective production planning.

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

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.

Analysis of efficiency and performance of global retail supply chains using integrated fuzzy SWARA and fuzzy EATWOS methods

The current paper aims to fill the two severe and significant gaps in the literature related to global retail chains. First, it presents the criteria set identified by performing comprehensive fieldwork together with experts highly experienced and have extensive knowledge of the retailing industry and a detailed literature review. Secondly, it proposes a robust, applicable, and powerful novel integrated MCDM framework dealing with many complicated uncertainties. As one of the significant practical and managerial implications, the current paper highlights the significance of sustainable retailing operations to better global retail chains. After the proposed model was implemented, a comprehensive sensitivity analysis was performed to test the validation of the model and its obtained results. According to the validation test results, A12 Walmart&ASDA has remained the best option for all scenarios. It has been observed that there are slight changes that did not change the overall results in the ranking performance of some decision alternatives. As a result, the analysis results prove that the proposed integrated fuzzy approach can be applied to solve highly complex decision-making problems encountered in various fields and the retailing industry.