Pharmaceutical Sales Forecasting with Machine Learning: A Strategic Management Tool for Decision-Making
Keywords:
pharmaceutical sales forecasting, machine learning, comparative analysis, predictive performance, LSTMAbstract
This investigation explores the adequacy of machine learning strategies for pharmaceutical deal estimating, displaying a comparative investigation of four calculations: Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM), and AutoRegressive Integrated Moving Average (ARIMA). Real-world pharmaceutical deals information was utilized to assess the prescient execution of these calculations utilizing measurements such as Cruel Absolute Error (MAE), Mean Squared Error (MSE), and Root Cruel Squared Error (RMSE). The results demonstrate that LSTM beats the other calculations, accomplishing the most reduced MAE of 900, MSE of 13000, and RMSE of 113.96. Moreover, the research gives a comprehensive survey of later progressions in prescient analytics and machine learning over different divisions, counting healthcare, supply chain administration, back, and natural supportability. The discoveries emphasize the transformative potential of progressed analytics in driving key decision-making, optimizing asset assignment, and relieving dangers in pharmaceutical deals. Moving forward, the integration of machine learning-driven determining models into organizational procedures will proceed to revolutionize the pharmaceutical industry and clear the way for maintainable development and advancement.
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ALAGUMALAI, A., DEVARAJAN, B., SONG, H., WONGWISES, S., LEDESMA-AMARO, R., MAHIAN, O., SHEREMET, M. and LICHTFOUSE, E., 2023. Machine learning in biohydrogen production: a review. Biofuel Research Journal, 10(2), pp. 1844-1858.
AUTHORSHIP and SCIMAGO, I.R., 2023. Analysis of Lean Six Sigma Use in Pharmaceutical Production. Brazilian Journal of Pharmaceutical Sciences, 59.
BEŞTAŞ, M., 2023. Keşifçi Veri Analizi ile Eczane Satış Analizi ve Satış Tahmini. Third Sector Social Economic Review, 58(1), pp. 765-782.
CHAUDHURI, K.D. and ALKAN, B., 2022. A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications. Applied Intelligence, 52(10), pp. 11489-11505.
CHEN, Y., XIE, X., PEI, Z., YI, W., WANG, C., ZHANG, W. and JI, Z., 2024. Development of a Time Series E-Commerce Sales Prediction Method for Short-Shelf-Life Products Using GRU-LightGBM. Applied Sciences, 14(2), pp. 866.
ĐAKOVIĆ, D., KLJAJIĆ, M., MILIVOJEVIĆ, N., DODER, Đ. and ANĐELKOVIĆ, A.,S., 2024. Review of Energy-Related Machine Learning Applications in Drying Processes. Energies, 17(1), pp. 224.
GANGWANI, D., ZHU, X. and FURHT, B., 2023. Exploring investor-business-market interplay for business success prediction. Journal of Big Data, 10(1), pp. 48.
HASSAN, J., SAFIYA, M.S., DEKA, L., MD, J.U. and DAS, D.B., 2024. Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges. Pharmaceutics, 16(2), pp. 260.
HELMINI, S., JIHAN, N., JAYASINGHE, M. and PERERA, S., 2019. Sales forecasting using multivariate long short term memory network models. PeerJ PrePrints, .
LANGEN, H. and HUBER, M., 2023. How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign. PLoS One, 18(1),.
LIAPIS, C.M., KARANIKOLA, A. and KOTSIANTIS, S., 2023. Investigating Deep Stock Market Forecasting with Sentiment Analysis. Entropy, 25(2), pp. 219.
LIU, Y., YANG, X., ZHU, C. and MENG, J., 2021. Drugs Sale Forecasting Based on SVR Integrated Promotion Factors. Journal of Physics: Conference Series, 1910(1),.
LUH PUTU, E.Y. and AAMER, A., 2023. Demand forecasting accuracy in the pharmaceutical supply chain: a machine learning approach. International Journal of Pharmaceutical and Healthcare Marketing, 17(1), pp. 1-23.
MENG, J., YANG, X., YANG, C. and LIU, Y., 2021. Comparative Analysis of Prophet and LSTM Model in Drug Sales Forecasting. Journal of Physics: Conference Series, 1910(1),.
RIZINSKI, M., JANKOV, A., SANKARADAS, V., PINSKY, E., MISHKOVSKI, I. and TRAJANOV, D., 2024. Comparative Analysis of NLP-Based Models for Company Classification. Information, 15(2), pp. 77.
SALTIK, Ö., REHMAN, W.U., SÖYÜ, R., DEĞIRMEN, S. and ŞENGÖNÜL, A., 2023. Predicting loss aversion behavior with machine-learning methods. Humanities & Social Sciences Communications, 10(1), pp. 183.
SARKAR, C., DAS, B., VIKRAM, S.R., WAHLANG, J.B., NONGPIUR, A., TIEWSOH, I., LYNGDOH, N.M., DAS, D., BIDAROLLI, M. and HANNAH, T.S., 2023. Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development. International Journal of Molecular Sciences, 24(3), pp. 2026.
TERNERO, R., SEPÚLVEDA-ROJAS, J., ALFARO, M., FUERTES, G. and VARGAS, M., 2023. INVENTORY MANAGEMENT WITH STOCHASTIC DEMAND: CASE STUDY OF A MEDICAL EQUIPMENT COMPANY. South African Journal of Industrial Engineering, 34(1), pp. 131-142.
WONG, S., YEUNG, J.K., YUI-YIP LAU and KAWASAKI, T., 2023. A Case Study of How Maersk Adopts Cloud-Based Blockchain Integrated with Machine Learning for Sustainable Practices. Sustainability, 15(9), pp. 7305.
YOO, M., 2024. Development of a Simulator for Household Refrigerator Using Equation-Based Optimization Control with Bayesian Calibration. Machines, 12(1), pp. 12.
ABDULLAHI, T., NITSCHKE, G. and SWEIJD, N., 2022. Predicting diarrhoea outbreaks with climate change. PLoS One, 17(4),.
ALJOHANI, A., 2023. Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility. Sustainability, 15(20), pp. 15088.
BORUCKA, A., 2023. Seasonal Methods of Demand Forecasting in the Supply Chain as Support for the Company’s Sustainable Growth. Sustainability, 15(9), pp. 7399.
FARIDI, S., ZAJ, M.M., DANESHVAR, A., SHAHVERDIANI, S. and ROODPOSHTI, F.R., 2023. Portfolio rebalancing based on a combined method of ensemble machine learning and genetic algorithm. Journal of Financial Reporting and Accounting, 21(1), pp. 105-125.
FATIMA, G., KHAN, S., AADIL, F., KIM, D.H., ATTEIA, G. and ALABDULHAFITH, M., 2024. An autonomous mixed data oversampling method for AIOT-based churn recognition and personalized recommendations using behavioral segmentation. PeerJ Computer Science, .
GIANNAKOPOULOS, N.T., TERZI, M.C., SAKAS, D.P., KANELLOS, N., TOUDAS, K.S. and MIGKOS, S.P., 2024. Agroeconomic Indexes and Big Data: Digital Marketing Analytics Implications for Enhanced Decision Making with Artificial Intelligence-Based Modeling. Information, 15(2), pp. 67.
HAMZEHI, M. and HOSSEINI, S., 2022. Business intelligence using machine learning algorithms. Multimedia Tools and Applications, 81(23), pp. 33233-33251.
KAYAKUŞ, M., TERZIOǦLU, M. and YETIZ, F., 2022. Forecasting housing prices in Turkey by machine learning methods. Aestimum, (80), pp. 33-44.
KESKIN, F.D. and SOYUER, H., 2023. A Lot Sizing and Scheduling Approach on Non-Identical Parallel Machines for Cement Grinding Process Considering Process-Specific Characteristics. International Journal of Supply and Operations Management, 10(3), pp. 396-416.
KHALID, H., 2021. Forecasting neural networks, such as forecasting sale the plastic injection machine market. Journal of Physics: Conference Series, 1963(1),.
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