An intelligent algorithm for final product demand forecasting in pharmaceutical units

Amalnick, Mohsen Sadegh; Habibifar, Naser; Hamid, Mahdi; Bastan, Mahdi

doi:10.1007/s13198-019-00879-6

Cited by 19 publications

(15 citation statements)

References 32 publications

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“…Among the many networks, the multi-layer perceptron (MLP) is the one that is most frequently utilized in engineering research. MLP networks are composed of an input layer and an output layer that are linked by one or more layers of hidden nodes [ 3 , 13 , 36 ]. Moreover, a radial basis function (RBF) is employed, in which the first layer represents the input of the ANN model, the second is the hidden layer consisting some non-linear activation units of RBF, and the third layer represents the outputs of the model [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

An intelligent algorithm to evaluate and improve the performance of a home healthcare center considering trust indicators

Torabzadeh¹,

Tavakkoli–Moghaddam²,

Samieinasab³

et al. 2022

Computers in Biology and Medicine

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Section: Methodsmentioning

confidence: 99%

An intelligent algorithm to evaluate and improve the performance of a home healthcare center considering trust indicators

Torabzadeh¹,

Tavakkoli–Moghaddam²,

Samieinasab³

et al. 2022

Computers in Biology and Medicine

Self Cite

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“…The ANN is a renowned machine learning tool, which can learn and detect patterns in data. 28,29 This system includes input, hidden, and output layers, and processing entities are called neurons. 30 ANNs provide non-linear equations between input and output variables without the need to know the precise information and formulation about their relationship.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Then, in the test process, some output variables are predicted, and the performance of the network is assessed. 28,35…”

Section: Proposed Methodsmentioning

confidence: 99%

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A data-driven multi-criteria decision-making approach for assessing new product conceptual designs

Arbabi

Vahedi-Nouri

Iranmanesh

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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The surge in competition among companies to acquire a more significant portion of the market as well as respecting customer preferences in high quality and diverse products result in a reduction of product life cycles. Accordingly, companies are under enormous pressure to introduce new high quality and diverse products on time. Assessing new product designs at the primary phases of new product development (NPD) is a necessary and complex activity that can considerably reduce the time and cost of introducing new products to the market. The current methods of evaluating new product conceptual designs, including employing decision-making methods based on subjective opinions of experts, utilizing simulation packages, and following trial-and-error approaches in prototyping, may be inefficient, very time-consuming, and costly. To overcome this issue, this paper develops a quantitative data-driven Multi-Criteria Decision-Making (MCDM) approach founded on the combination of an Artificial Neural Network (ANN) method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to assess the new conceptual designs. So that the ANN method is utilized to predict the performance characteristics of new designs based on the related existed data of similar products, and TOPSIS is employed to score and rank different proposed alternatives designs. Finally, a case study of evaluating new product conceptual designs in an automotive research and development company is considered to demonstrate the performance and applicability of the proposed approach.

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“…Demand forecasting is the basis of all managerial decisions in the pharmaceutical industry and others to plan activities and execution processes (Merkuryeva et al , 2019). While forecasting is still a big challenge in the pharmaceutical industry (Durbha, 2016), research is limited in the pharmaceutical industry for demand forecasting (Amalnick et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Demand forecasting accuracy in the pharmaceutical supply chain: a machine learning approach

Yani

Aamer²

2022

IJPHM

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Purpose Demand foresting significantly impacts supply chain (SC) design and recovery planning. The more accurate the demand forecast, the better the recovery plan and the more resilient the SC. Given the paucity of research about machine learning (ML) applications and the pharmaceutical industry’s need for disruptive techniques, this study aims to investigate the applicability and effect of ML algorithms on demand forecasting. More specifically, the study identifies machine learning algorithms applicable to demand forecasting and assess the forecasting accuracy of using ML in the pharmaceutical SC. Design/methodology/approach This research used a single-case explanatory methodology. The exploratory approach examined the study’s objective and the acquisition of information technology impact. In this research, three experimental designs were carried out to test training data partitioning, apply ML algorithms and test different ranges of exclusion factors. The Konstanz Information Miner platform was used in this research. Findings Based on the analysis, this study could show that the most accurate training data partition was 80%, with random forest and simple tree outperforming other algorithms regarding demand forecasting accuracy. The improvement in demand forecasting accuracy ranged from 10% to 41%. Research limitations/implications This study provides practical and theoretical insights into the importance of applying disruptive techniques such as ML to improve the resilience of the pharmaceutical supply design in such a disruptive time. Originality/value The finding of this research contributes to the limited knowledge about ML applications in demand forecasting. This is manifested in the knowledge advancement about the different ML algorithms applicable in demand forecasting and their effectiveness. Besides, the study at hand offers guidance for future research in expanding and analyzing the applicability and effectiveness of ML algorithms in the different sectors of the SC.

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An intelligent algorithm for final product demand forecasting in pharmaceutical units

Cited by 19 publications

References 32 publications

An intelligent algorithm to evaluate and improve the performance of a home healthcare center considering trust indicators

An intelligent algorithm to evaluate and improve the performance of a home healthcare center considering trust indicators

A data-driven multi-criteria decision-making approach for assessing new product conceptual designs

Demand forecasting accuracy in the pharmaceutical supply chain: a machine learning approach

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