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Demand Planning - Modeling special cases of Demand Product Life Cycle & Long-term Planning

Product Lifecycle and trend • Launch Forecasting • Volume effect on line extension

Event Modeling Baseline vs. Incremental • Illustration of Event Models

Planning for Intermittent Demand What is Intermittent Demand & what causes it? Strategies to handle intermittent demand & Statistical Models for Intermittent Demand

Higher-order Models

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Demand Planning -Advanced smoothing models

First Order Exponential Smoothing

Holt Models to accommodate the trend

Holt-Winters Model

Exponential Trend and Dampening

Interaction between components

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Demand Planning - Stat Modeling and AI-driven Planning

Demand Modeling

Key components of demand

Additive Vs. Multiplicative Seasonality in Models

Modeling by decomposition

Introduction to Forecast Modeling

Qualities of a good statistical forecast

Balancing between Model Fit Vs. Model Robustness

Uni-Variate Time Series vs. Multi-Variate methods

Moving Average

AI-driven Planning and Expert Models

Introduction to Planvida

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Data Integration and Cleansing

It is all about the data

The Forecast Problem and Data collection

Define True Demand

Data challenges – Shipment Vs. Orders – Gross Demand Vs. Net Demand

Historical shifts in demand

Data filtering

Outliers– Identification and Correction

The process to Identify Outliers

Tolerance band

Methodology for outlier correction

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Demand Planning Overview

Planning objectives

The Service – Cost – Balance Model

Define your Plan

Budgeting vs. Forecasting vs. Planning

Beyond Statistical Forecasting

Key Components of a Demand Plan

Terminology in Planning – Forecast Horizon, Buckets & Periodicity

Forecast Pass

Demand Management

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Demand Planning for S&OP

A best-practice Demand Planning process helps you reduce inventory costs and increase customer service levels by providing better demand visibility and demand clarity to the Supply Chain.  When properly implemented, the demand plan helps create a lean and customer-centric supply chain.

We covers statistical modeling to create accurate forecasts, in-depth discussion on model diagnostics to improve the quality of the forecast models with specific references to popular applications such as SAP IBP, Kinaxis, ForecastPro, etc.  We will also make references to Python libraries which will expand the horizons of your planners to develop more sophisticated models.

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