Overview of Logistics & Supply Chain Systems2（PDF 23）.pdf

文档介绍：Demand Forecasting
Lectures 2 & 3
Fall 2003
Caplice
1
Agenda
The Problem and Background
Four Fundamental Approaches
Time Series
„ General Concepts
„ Evaluating Forecasts – How ‘good’ is it?
„ Forecasting Methods (Stationary)
Š Cumulative Mean
Š Naïve Forecast
Š Moving Average
Š Exponential Smoothing
„ Forecasting Methods (Trends & Seasonality)
Š OLS Regression
Š Holt’s Method
Š Exponential Method for Seasonal Data
Š Winter’s Model
Other Models
MIT Center for Transportation & Logistics – 2 © Chris Caplice, MIT
2
Demand Forecasting
The problem:
„ Generate the large number of short-term, SKU
level, locally dis-aggregated demand forecasts
required for production, logistics, and sales to
operate essfully.
„ Focus on:
Š Forecasting product demand
Š Mature products (not new product releases)
Š Short time horiDon (weeks, months, quarters, year)
Š Use of models to assist in the forecast
Š Cases where demand of items is independent
MIT Center for Transportation & Logistics – 3 © Chris Caplice, MIT
3
Demand Forecasting – Punchline(s)
Forecasting is difficult – especially for the future
Forecasts are always wrong
The less aggregated, the lower the accuracy
The longer the time hori zon, the lower the accuracy
The past is usually a pretty good place to start
Everything exhibi ts seasona l ity of some sort
A good forecast is not just a number – i t should
include a range, description of distribution, etc.
Any analytical method should be supplemented by
external information
A forecast for one function in pany might not be
useful to another function (Sales to Mkt to Mfg to Trans)
MIT Center for Transportation & Logistics – 4 © Chris Caplice, MIT
4
Cost of Forecasting vs uracy
Å Overly Naïve Models Æ Å Good Region Æ Å Excessive Causal Models Æ
Total Cost
Cost
Cost of Errors Cost of Forecasting
In F