Application Case of Safety Stock Policy based on Demand Forecast Data Analysis

2020 ◽  
Vol 43 (3) ◽  
pp. 61-67
Author(s):  
Hung-Su Park ◽  
◽  
Woo-Yong Choi
Keyword(s):  
Data Analysis ◽  
Demand Forecast ◽  
Safety Stock ◽  
On Demand ◽  
Forecast Data ◽  
Stock Policy

Are You Hooked on Paid Music Streaming?

2018 ◽  
Vol 14 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Charlie C. Chen ◽  
Steven Leon ◽  
Makoto Nakayama
Keyword(s):  
Data Analysis ◽  
Music Industry ◽  
Clear Understanding ◽  
On Demand ◽  
Behavioral Dynamics ◽  
Revenue Sources ◽  
Streaming Services ◽  
Facilitating Conditions ◽  
Communication Control ◽  
Music Streaming

The proliferation of free on-demand music streaming services (e.g., Spotify) is offsetting the traditional revenue sources (e.g., purchases of downloads or CDs) of the music industry. In order to increase revenue and sustain business, the music industry is directing its efforts toward increasing paid subscriptions by converting free listeners into paying subscribers. However, most companies are struggling with these attempts because they lack a clear understanding of the psychological and social purchase motivations of consumers. This study compares and contrasts the two different phases of Millennial generation consumer behaviors: the alluring phase and the hooking phase. A survey was conducted with 73 paying users and 163 non-paying users of on-demand music streaming services. The authors' data analysis shows two separate behavioral dynamics seen between these groups of users. While social influence and attitude are primary drivers for the non-paying users in the alluring phase, facilitating conditions and communication control capacity play critical roles for the paying users in the hooking phase. These results imply that the music industry should apply different approaches to prospective and current customers of music streaming services.

scholarly journals Validating Virtual Safety Stock Effectiveness through Simulation

10.5772/56859 ◽  
2013 ◽  
Vol 5 ◽  
pp. 41 ◽  
Author(s):  
Maria Elena Nenni ◽  
Massimiliano M. Schiraldi
Keyword(s):  
Service Level ◽  
Inventory System ◽  
Time Interval ◽  
Common Condition ◽  
Simple Method ◽  
Safety Stock ◽  
Safety Stocks ◽  
Base Stock Policy ◽  
Stock Policy ◽  
Business To Business

As a means of avoiding stock-outs, safety stocks play an important role in achieving customer satisfaction and retention. However, traditional safety stock theory is based on the assumption of the immediate delivery of the ordered products, which is not a common condition in business-to-business contexts. Virtual safety stock theory was conceived to raise the service level by exploiting the potential time interval in the order-to-delivery process. Nevertheless, its mathematical complexity prevented this technique from being widely adopted in the industrial world. In this paper, we present a simple method to test virtual safety stock effectiveness through simulation in an inventory system using a base stock policy with periodic reviews and backorders. This approach can be useful for researchers as well as practitioners who want to model the behaviour of an inventory system under uncertain conditions and verify the opportunity for setting up a virtual safety stock on top of, or instead of, the traditional physical safety stock.

scholarly journals Use of reorderable matrices and heatmaps to support data analysis of students transcripts

2019 ◽  
Author(s):  
Rafael Tavares Carvalho Barros ◽  
Thiago Gonçalves Mendes ◽  
Celmar Guimarães Da Silva
Keyword(s):  
Data Analysis ◽  
Visual Representations ◽  
Numerical Approach ◽  
Specific Subject ◽  
On Demand ◽  
Color Scale ◽  
Student Grades ◽  
Order Algorithm

For a course coordinator, the analysis of several students’ transcripts to identify the situation of subjects or students is often an old-fashioned process executed through a textual and numerical approach. This work is part of a larger project aimed at choosing appropriate visual representations to help course coordinators to analyze sets of students transcripts. In this work, we developed a system that allows the visualization of student transcripts through a heatmap of student grades per subject. The heatmap represent grades based on a user-defined color scale. To assist in the analysis, it is possible to reorder subjects and students using the optimal leaf order algorithm, or even to reorder according to the grades of a specific subject or student. In addition, some features have been developed to meet visual guidelines, such as overview, zoom, filter and details-on-demand.

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