返回到 Inventory Analytics
学生对 Rutgers the State University of New Jersey 提供的 Inventory Analytics 的评价和反馈
264 个评分
课程概述
Inventory analytics is the corner stone of supply chain analytics. A company in trade industries may have 30-50% of their assets tied up in inventory. An effective inventory management can improve revenue by increasing product variety and availability, and reduce cost and speed up cash cycle by reducing excessive inventory and waste.
Through real-life examples (e.g., Macy’s vs. Ross Stores), you will learn hands-on tools and skills to discover and solve inventory problems by data analytics. Upon completion, you can answer the following questions:
1. For which industries is inventory important and relevant?
2. How may inventory drive a company’s financial performance?
3. How do I know that I have an inventory problem?
4. How to classify inventory and manage it accordingly?
Note: This course is for beginners and practitioners, and so it provides practical data-driven analytics for business inventory management, including inventory metrics, inventory problem diagnosis, the financial impact of inventory, and practical techniques of inventory management, rather than the mathematical models of inventory control that you can find in many other courses.
热门审阅
S
Jul 2, 2020
This is an excellent course for those who would like to get the exact knowledge of how inventory is classified and its importance in industry
YH
Mar 29, 2021
Absolutely loved this course, I a sure it will always be guiding to those involved in supply chain activities.
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51 - Inventory Analytics 的 55 个评论(共 55 个)
创建者 Fabiola R W
•Nov 13, 2020
Just fine, not exceptional. Has some pretty basic concepts, maybe I would have liked to dig somewhat deeper in them.
创建者 Adrian S
•Mar 4, 2021
no me fue posible continuar por el idioma, ni tenia la facilidad de subtítulos o alguna herramienta que me ayudara a traducir.
创建者 Joel R
•May 28, 2022
did not understand one thing.
创建者 Divine T
•Jul 26, 2023
terrible course structure
创建者 Məhəmməd C
•Jul 11, 2023
it is not like.