Introduction to Forecasting | SCMT 3623

SCMT 3623: Advanced Inventory Management examines two important aspects of logistics: inventory control and forecasting. In this course, coverage of the inventory specifically focuses on inventory control methods for stochastic demand and lead times. This class covers a variety of inventory control and forecasting techniques that students will learn how to implement in Excel.
Lesson 1: Introduction to Forecasting
Lesson 2: Introduction to Time Series Forecasting
Lesson 3: Forecast Accuracy and Time Series Regression
Lesson 4: Exponential Smoothing Methods
Lesson 5: Seasonal Indexes
Lesson 6: Introduction to Inventory Management
Lesson 7: Inventory Management for Dependent Demand & Independent Demand Items
Lesson 8: Introduction to Inventory Control for Stochastic Demand and Lead Times; Periodic and Continuous Review Systems
Lesson 9: Reorder Point Model (Optimal R,Q Model)
Lesson 10: Reorder Point Model (Managerial R,Q Model)
Lesson 11: Risk Pooling
Lesson 12: Newsvendor Model
Find more information about the Walton College Supply Chain Management Department at walton.uark.edu/departments/su...
Find more information about the online Walton College Supply Chain Management degree at online.uark.edu/programs/bache...

[@rachellochmann7871]

So glad these are posted on YT. I took this course at U of A and now work in a full-time position where these videos are helping guide my inventory management decisions. Great videos, very informative and useful.

[@DoanThiTrucLinh]

Pp

[@Utmostneck_123]

Thank you very much for the slow speak and clear voice. it's very understandable.

[@ciai7317]

Excelent introduction, thank you.

[@alihammadshah]

Are slides available for download?

[@mikealcantara5011]

❤️ ❤️ ❤️ ❤️ ❤️ ❤️

[@akemcolins9935]

Very accurate

[@bontu4018]

Thank so much I have one question how to a form product dd for the least period is given below period dd period 1,2,3,4,5,6,7,8,9 dd 44,52,50,54,55,55,60,56,62 develop a linear trend equation ,predict the dd value for the next period and find the regression equation solution