Business Planning and Forecasting

Course Curriculum

Chapter 1: Introduction to Business Planning and Forecasting

Lecture 1: Introduction

Lecture 2: Need for planning and forecasting

Lecture 3: How does an Organization bridge the gap between Plan & forecast

Lecture 4: Steps in Business forecasting

Lecture 5: 4 Ws of forecasting

Lecture 6: What should be forecasted?

Lecture 7: A typical business setup

Lecture 8: Who is it forecasted for & why?

Lecture 9: When is the forecasted needed

Lecture 10: Time lag in forecasting

Lecture 11: How the forecasting process works

Lecture 12: Forecasting Method

Lecture 13: B2B Forecast

Chapter 2: Data Management

Lecture 1: Introduction to Data Cleansing

Lecture 2: Trend Component

Lecture 3: Seasonality

Lecture 4: Cyclical Variation & Leading Indicator

Lecture 5: Trading Days

Lecture 6: Event, Outliers and Irregular Variations

Lecture 7: Importance of data length

Lecture 8: Importance of hierarchy

Lecture 9: Importance of hierarchy – Example

Chapter 3: Simple Forecasting Models

Lecture 1: Simple Methods and Their Utility

Lecture 2: Naive Method

Lecture 3: Simple Moving Average

Lecture 4: The Same As Last Year

Chapter 4: Exponential Smoothing

Lecture 1: Introduction to Exponential Smoothing

Lecture 2: History of Exponential Smoothing

Lecture 3: EXSM definition

Lecture 4: Pros & Cons

Lecture 5: EXSM Characteristics

Lecture 6: Exponential Smoothing – Family Of Models

Lecture 7: Choosing the Right Exponential Smoothing Model

Lecture 8: Exponential Smoothing Constants

Lecture 9: Product Life Cycle & Smoothing constant

Lecture 10: EXSM Example

Lecture 11: Extension of Exponential Smoothing

Lecture 12: Croston’s Intermittent Data Model Example

Lecture 13: Discrete Data Model

Lecture 14: Custom Component Model

Lecture 15: Event Model

Chapter 5: ARIMA

Lecture 1: Introduction to ARIMA

Lecture 2: What is ARIMA?

Lecture 3: Components of ARIMA

Lecture 4: Reading the ARIMA model

Lecture 5: ARIMA – Pros and Cons

Lecture 6: Exponential Smoothing Vs. ARIMA

Lecture 7: Regression Vs. ARIMA

Lecture 8: ARIMA – Examples and Computations

Lecture 9: ARIMA Examples Using Forecasting Solution

Chapter 6: Error Measurement

Lecture 1: Introduction to Error and Improvement Measurement

Lecture 2: Measure of Model Fitment R – Squared

Lecture 3: Behavioral Measure Bias

Lecture 4: Mean Absolute Deviation

Lecture 5: Mean Absolute Percentage Error

Lecture 6: Comparison in MAD & MAPE

Lecture 7: Process Measure Forecast Value Add

Lecture 8: Analyzing the Error

Chapter 7: New Product Forecasting Using Machine Learning

Lecture 1: Introduction

Lecture 2: Need & Challenges Of New Product

Lecture 3: Types of New Product Classification

Lecture 4: New to the World Product

Lecture 5: New to the Firm Product

Lecture 6: Repositioning's

Lecture 7: Addition to Existing Product Lines

Lecture 8: Improvement to Existing Product

Lecture 9: Cost Reduction

Lecture 10: Types of New Product Forecasting

Lecture 11: Forecasting By Analogy – Introduction

Lecture 12: Forecasting By Analogy – Steps

Lecture 13: Step 1 : – Pick Similar Product

Lecture 14: Step 2 : – Select Period Range

Lecture 15: Step 3 : – Align Seasonality

Lecture 16: Step 4 : – Specify Launch Total

Lecture 17: Exercise

Lecture 18: Similar Product Selection by Machine Learning

Lecture 19: How to Pick Similar Product

Lecture 20: K means Clustering Steps

Lecture 21: K means Clustering Introduction

Lecture 22: Step 1 : – Identifying the attributes

Lecture 23: Step 2 : – Scaling the Attributes

Lecture 24: Step 3 : – Assign Weight

Lecture 25: Step 4 : – Identifying the Number of Clusters

Lecture 26: Step 5 : – Initialize the Centroids

Lecture 27: Step 6 : – Identify the Distance Basis Parameters

Lecture 28: Step 7 : – Identify the Cluster for Each Item