Data analytics in pharmaceutical supply chains: state of the art, opportunities, and challenges

Nguyen, Angie; Lamouri, Samir; Pellerin, Robert; Tamayo, Simòn; Lekens, Béranger

doi:10.1080/00207543.2021.1950937

Cited by 54 publications

(33 citation statements)

References 94 publications

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“…Total energy describes the energy consumed by the mill motor over the entire batch time of the mill (Equation 5) and useful energy is defined as the energy required by the mill impeller to break down the granules to sizes smaller than the mill screen Equation (9). The total energy of continuous milling can also be calculated using Equation (10).…”

Section: Comil Energy Modelmentioning

confidence: 99%

“…To build a torque model for a high shear granulator, historical data was used from four previously experimental studies where, torque during the granulation process was studied. [7][8][9][10] Most of the reported data was presented as a figure and data was not reported in tables for easy access. Thus, torque profiles as a function of time had to be digitized to create data for modeling.…”

Section: High Shear Granulationmentioning

confidence: 99%

“…[ 4 ] BDA increases the potential for the company to perform more key‐performance indicator (KPI) based optimization, prediction, maintenance and also provides feedback to process and product design. [ 5 ] This has been demonstrated in several chemical industries, [ 6 ] pharmaceutical manufacturing, [ 7 ] drug discovery, [ 8 ] supply chain, [ 9 ] and so forth. Enterprises with a SM infrastructure and accurate process models typically respond in a more coordinated and performance‐oriented manner, minimizing energy and material usage while maximizing environmental sustainability, health and safety and economic competitiveness.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enabling energy‐efficient manufacturing of pharmaceutical solid oral dosage forms via integrated techno‐economic analysis and advanced process modeling

Sampat

Kotamarthy

Bhalode

et al. 2022

J Adv Manuf & Process

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The global pharmaceutical industry is a trillion-dollar market. However, the pharmaceutical sector often lags in manufacturing innovation and automation which limits its potential to maximize energy efficiency. The integration of technoeconomic analysis (TEA) with advanced process models as part of an overarching smart manufacturing platform, can help industries create business models, which can be adapted for manufacturing to reduce energy consumption and operating costs while ensuring product quality which can further enable a more sustainable process operation. In this study, a rational design of experiment on three unitoperations (wet granulation, drying, and milling) was performed on a batch (case 1) and continuous (case 2) pharmaceutical process to obtain experimental data. Process models for predicting product quality and energy efficiency of each of the three-unit operations were developed. The experimental data were used to validate the models and good agreement was observed. The energy consumption of each unit operation was calculated using statistical models relating the power consumption and the process parameters. The developed process models and energy models were further integrated into a TEA framework, which quantified the energy and monetary cost of manufacturing for both batch and continuous manufacturing cases. With this integrated framework, energy costs savings of ~33% was obtained in the continuous manufacturing process (case 2) over the batch process (case 1). K E Y W O R D Sadvanced process models, energy efficiency, Industry 4.0, smart manufacturing, technoeconomic analysis Chaitanya Sampata and Lalith Kotamarthy contributed equally to this study.

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Section: Comil Energy Modelmentioning

confidence: 99%

Section: High Shear Granulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enabling energy‐efficient manufacturing of pharmaceutical solid oral dosage forms via integrated techno‐economic analysis and advanced process modeling

Sampat

Kotamarthy

Bhalode

et al. 2022

J Adv Manuf & Process

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“…Their contributions include (1) proposal of a framework focused on challenges and data resources to assess the current state of data analytics in pharmaceutical supply chains (PSC); (2) examples of techniques exemplified that will serve as inspiring references; (3) mapping of existing literature to identify gaps and research perspectives. The authors conclude that in spite of promising results obtained from ML algorithms to address drug shortages and inventories optimization, the various data resources have not yet been fully harnessed (Nguyen et al. , 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Using AI and ML to predict shipment times of therapeutics, diagnostics and vaccines in e-pharmacy supply chains during COVID-19 pandemic

Mariappan

Devi

Yegnanarayanan

et al. 2022

IJLM

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PurposeThis paper aims to address the pressing problem of prediction concerning shipment times of therapeutics, diagnostics and vaccines during the ongoing COVID-19 pandemic using a novel artificial intelligence (AI) and machine learning (ML) approach.Design/methodology/approachThe present study used organic real-world therapeutic supplies data of over 3 million shipments collected during the COVID-19 pandemic through a large real-world e-pharmacy. The researchers built various ML multiclass classification models, namely, random forest (RF), extra trees (XRT), decision tree (DT), multilayer perceptron (MLP), XGBoost (XGB), CatBoost (CB), linear stochastic gradient descent (SGD) and the linear Naïve Bayes (NB) and trained them on striped datasets of (source, destination, shipper) triplets. The study stacked the base models and built stacked meta-models. Subsequently, the researchers built a model zoo with a combination of the base models and stacked meta-models trained on these striped datasets. The study used 10-fold cross-validation (CV) for performance evaluation.FindingsThe findings reveal that the turn-around-time provided by therapeutic supply logistics providers is only 62.91% accurate when compared to reality. In contrast, the solution provided in this study is up to 93.5% accurate compared to reality, resulting in up to 48.62% improvement, with a clear trend of more historic data and better performance growing each week.Research limitations/implicationsThe implication of the study has shown the efficacy of ML model zoo with a combination of base models and stacked meta-models trained on striped datasets of (source, destination and shipper) triplets for predicting the shipment times of therapeutics, diagnostics and vaccines in the e-pharmacy supply chain.Originality/valueThe novelty of the study is on the real-world e-pharmacy supply chain under post-COVID-19 lockdown conditions and has come up with a novel ML ensemble stacking based model zoo to make predictions on the shipment times of therapeutics. Through this work, it is assumed that there will be greater adoption of AI and ML techniques in shipment time prediction of therapeutics in the logistics industry in the pandemic situations.

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“…Big Data Analytics has been playing a key role in streamlining the supply chain activities that take place in the health care domain. The application of data analytics has been identified as the state-of-the-art technology that has revolutionised supply chain networking operations in the medical domain [10]. The collection of a large volume of data at diverse stages of value chain gives an insight into the real-world usage of drugs.…”

Section: B Improving Supply Chain Of Drugsmentioning

confidence: 99%

Big Data Analytics in Healthcare using Google Cloud

Jampala

2022

IJRASET

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The health care domain has undergone transformational change in the 21st century. The integration of new kinds of digital technologies has been acting as a catalyst. Big Data Analytics serves as a promising technology that has considerably changed how diverse functions are carried out in the health care discipline. It encompasses the integration as well as analysis of a humongous volume of data such as electronic health records, biomedical data and omics data [1]. The emphasis of the research is on Google Cloud and how it has been contributing to the health domain by evolving the health care paradigm and progressing research at scale and empowering diverse stakeholders to transform and innovate

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Data analytics in pharmaceutical supply chains: state of the art, opportunities, and challenges

Cited by 54 publications

References 94 publications

Enabling energy‐efficient manufacturing of pharmaceutical solid oral dosage forms via integrated techno‐economic analysis and advanced process modeling

Enabling energy‐efficient manufacturing of pharmaceutical solid oral dosage forms via integrated techno‐economic analysis and advanced process modeling

Using AI and ML to predict shipment times of therapeutics, diagnostics and vaccines in e-pharmacy supply chains during COVID-19 pandemic

Big Data Analytics in Healthcare using Google Cloud

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