“Not a box of nuts and bolts”: Distribution channels for specialty drugs

Xu, Liang; Mani, Vidya; Zhao, Hui

doi:10.1111/poms.13973

Cited by 2 publications

(2 citation statements)

References 43 publications

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“…Trade partners can be categorized as “traditional wholesalers” and “specialty distributors.” The former typically have large networks, carry a large variety of drugs, and more often distribute to hospitals, retail pharmacies, and homecare providers, while the latter typically have more controlled networks, specialize in specialty drugs and more often distribute to physician offices, clinics, and independent specialty pharmacies (Xu et al. 2018). Regardless of its type, each TP has its own network of DCs, through which POCs receive the drugs.…”

Section: Literature Reviewmentioning

confidence: 99%

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Demand Forecasting with Supply‐Chain Information and Machine Learning: Evidence in the Pharmaceutical Industry

Zhu

Ninh

Zhao

et al. 2021

Production and Operations Management

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A ccurate demand forecasting is critical for supply chain efficiency, especially for the pharmaceutical (pharma) supply chain due to its unique characteristics. However, limited data have prevented forecasters from pursuing advanced models. Such problems exist even when long history of demand data is available because historical data in the distant past may bring little value as market situation changes. In the meantime, demands are also affected by many hidden factors that again require a large amount of data and more sophisticated models to capture. We propose to overcome these challenges by a novel demand forecasting framework which "borrows" time series data from many other products (crossseries training) and trains the data with advanced machine learning models (known for detecting patterns). We further improve performance of the cross-series models through various "grouping" schemes, and learning from non-demand features such as downstream inventory data across different products, information of supply chain structure, and relevant domain knowledge. We test our proposed framework with many modeling possibilities on two large datasets from major pharma manufacturers and our results show superior performance. Our work also provides empirical evidence of the value of downstream inventory information in the context of demand forecasting. We conduct prior and post-hoc field work to ensure the applicability of the proposed forecasting approach.

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Section: Literature Reviewmentioning

confidence: 99%

“…For instance, EDI 852 contains inventory, product stocking, and product movement records from the trade partners' distribution centers to the manufacturer (Xu et al. 2018). However, there has been a “lack of new models for increasing forecasting intelligence" (Merkuryeva et al.…”

Section: Introductionmentioning

confidence: 99%