Forecasting

What’s Forecasted in the Supply Chain?

• Demand, sales, or requirements,

• Purchase prices.

• Replenishment and delivery times.

Nature of Forecasting

• Spatial vs. Temporal Demand.

• Lumpy vs. Regular Demand.

• Dependent (Derived) vs. Independent Demand.

Forecasting Horizons

• Long-term (greater than 3 years).

• Midrange (one to three years).

• Short-term (in terms of months).

Long-Term

• Greater than 3 years.

• Used for Strategic Planning - Production capacity, Inventory levels, Product Range, etc.

Midrange

• One to three years.

• Used for budgeting and sales planning.

Short-Term

• In terms of months.

• Used for Tactical planning - Production Schedules, Logistics Plans, Material Planning etc.

Forecasting Steps

1. Identify what to forecast

   • Level of detail, units, of analysis & time horizon required.

2. Identify what data is available

   • Identify needed data & whether it’s available.

3. Select and test a forecasting model

   • Cost, ease of use & accuracy.

4. Generate the forecast

5. Monitor forecast accuracy over time

Identifying Demand Patterns

Involves recognizing typical time series patterns like random, random with trend, random with trend and seasonal, and lumpy.

Managing Highly Uncertain Demand

Strategies include delaying forecasting, seeking information from customers, prioritizing supply based on uncertainty, applying postponement, and creating flexible supply.

Delay forecasting for

As long as possible.

Collaborative Forecasting

• Demand is lumpy or highly uncertain.

• Involves multiple participants each with a unique perspective—“two heads are better than one.”

• Goal is to reduce forecast error.

• The forecasting process is inherently unstable.

Qualitative Methods

[Used when situation is vague & little data exist]

• Market Research

• Panel Consensus

• Delphi Method

Market Research

• Uses customer surveys and interviews to determine customer preferences.

Panel Consensus

• Executives meet and develop a forecast together.

Delphi Method

• Develop a consensus forecast among a group of experts.

Quantitative Methods

• Regression Analysis

• Time-Series Analysis

Time-Series Analysis

• A time series is a sequence of observations of a process over points in time, e.g., monthly sales, annual enrollment, weekly production, etc.

• It is used when historical data contains patterns that can be exploited.

• Average demand for the period, trend, seasonal elements, cyclical elements, random variation.

Time Series Forecasting Methods

Include simple moving average, exponential smoothing, exponential smoothing with trend, and exponential smoothing with trend and seasonality.

Moving Average (MA)

A method used for smoothing data, providing an overall impression of data over time.

Weighted Moving Average (WMA)

Similar to simple moving average but assigns different weights to actual demand based on intuition or experience.

Exponential Smoothing

A form of weighted moving average requiring only three pieces of information for forecasts.

Forecasting Error Metrics

Include range of the forecast and selecting a forecast model based on cost and accuracy.

Forecasting Exercise

Involves making uncertain and unavoidable forecasts for various purposes using different methods.

Forecasting Summary

• Forecasts are uncertain, imprecise, and unavoidable.

• Long-term forecasts are less accurate than short-term forecasts (forecast horizon is important).

• Aggregate forecasts are more accurate than disaggregate forecasts.

• Forecasts are made for many purposes, using many methods.

• Selecting what is forecast, what model to use must be done carefully.