突尼斯制药行业的需求预测:一项比较研究
Mariem Mrad1, Younes Boujelbene1
1Department of Economics, Faculty of Economics and Management of Sfax, University of Sfax, Sfax, Tunisia.
Recent patents on biotechnology
|October 15, 2025
概括
在突尼斯,准确的制药需求预测至关重要. 多层感知器 (MLP) 神经网络在预测药物销售方面明显优于霍尔特-温特 (HW),提供了卓越的适应性和准确性.
科学领域:
- 制药行业 制药行业 制药行业
- 数据科学是数据科学.
- 机器学习是机器学习.
背景情况:
- 准确的需求预测对于高效的制药业务,库存管理和分销至关重要.
- 突尼斯制药行业在实现准确的需求预测方面面临着挑战.
- 动态的市场需要先进的预测策略.
研究的目的:
- 为了比较霍尔特-温特斯 (HW) 和多层感知器 (MLP) 神经网络的预测准确度.
- 评估抗病毒,抗生素和缓解疼痛药物类别的预测性能.
- 为提升突尼斯制药需求预测策略提供建议.
主要方法:
- 对24个月的历史销售数据集 (2020年10月 - 2022年9月) 的分析.
- 霍尔特-温特斯模型用于季节性调整的应用.
- 使用多层感知器神经网络来捕捉非线性销售模式.
主要成果:
- 与霍尔特-温特斯 (HW) 方法相比,多层感知器 (MLP) 神经网络的预测准确度明显更高.
- 在所有分析的药物类别中,MLP在所有分析的药物类别中实现了明显较低的平均平方误差 (MSE) 值 (例如,抗病毒药物为0.0206,HW为30.06).
- 对于需求变化和复杂的销售模式,MLP表现出更大的适应能力.
结论:
- 多层感知器 (MLP) 神经网络在制药需求预测方面优于霍尔特-温特 (HW) 网络,因为它们的准确性和适应性与非线性数据相匹配.
- 结果支持突尼斯制药公司采用先进的机器学习来改进规划.
- 未来的研究应该研究混合模型,并纳入外部市场动态,以提高预测.
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