scholarly journals Public Workshop Summary Report on Fiscal Year 2021 Generic Drug Regulatory Science Initiatives: Data Analysis and Model‐Based Bioequivalence

2020 ◽  
Author(s):  
Jieon Lee ◽  
Yuqing Gong ◽  
Sid Bhoopathy ◽  
Charles E. DiLiberti ◽  
Andrew C. Hooker ◽  
...  
Keyword(s):  
Data Analysis ◽  
Generic Drug ◽  
Regulatory Science ◽  
Fiscal Year ◽  
Summary Report ◽  
Model Based ◽  
Workshop Summary

Marketing customer response scoring model based on machine learning data analysis

2020 ◽  
pp. 1-11
Author(s):  
Tang Yan ◽  
Li Pengfei
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Extraction ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Customer Data ◽  
Modeling And Analysis ◽  
Scoring Model ◽  
Model Based ◽  
Learning Data

In marketing, problems such as the increase in customer data, the increase in the difficulty of data extraction and access, the lack of reliability and accuracy of data analysis, the slow efficiency of data processing, and the inability to effectively transform massive amounts of data into valuable information have become increasingly prominent. In order to study the effect of customer response, based on machine learning algorithms, this paper constructs a marketing customer response scoring model based on machine learning data analysis. In the context of supplier customer relationship management, this article analyzes the supplier’s precision marketing status and existing problems and uses its own development and management characteristics to improve marketing strategies. Moreover, this article uses a combination of database and statistical modeling and analysis to try to establish a customer response scoring model suitable for supplier precision marketing. In addition, this article conducts research and analysis with examples. From the research results, it can be seen that the performance of the model constructed in this article is good.

scholarly journals Cost Budget Variance

2021 ◽  
Vol 9 (1) ◽  
pp. 60-64
Author(s):  
Seto Sulaksono Adi Wibowo ◽  
Dwi Amelia Putri ◽  
Yosi Handayani
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Quantitative Methods ◽  
Fiscal Year ◽  
Budget Management ◽  
Time Service ◽  
Knowing How ◽  
Engineering Department ◽  
The Difference ◽  
Collection Methods

The research was conducted at the Scanner Engineering Department of PT Epson Batam. The topic is written about budget management for repair and maintenance (die & mold) costs with the aim of knowing how to minimize the variance between budgeted costs and their realization. Data collection methods used in this study were interviews, observation, and documentation, and descriptive and quantitative methods were used for data analysis. The existing budgeting system in the Scanner Engineering Department has been carried out correctly and effectively met the objectives, but overall during the 2018 fiscal year there were budget inefficiencies. Based on observations, it is known that from the budget made there are still differences (favorable variance and unfavorable variance). Where the difference arises due to unexpected costs for damaged equipment, the difference between the scheduled repair time (service) and the reality, as well as delays in sending invoices or debit notes.

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