# Forecasting – Important Points – Summary – Krajewski – 12th Edition

**Managing Demand**

- Explain how managers can change demand patterns.

**Key Decisions on Making Forecasts**

- Describe the two key decisions on making forecasts.

**Forecast Error**

- Calculate the five basic measures of forecast errors.

**Judgment Methods**

- Compare and contrast the four approaches to judgmental forecasting.

**Causal Methods: Linear Regression**

- Use regression to make forecasts with one or more independent variables.

**Time-Series Methods**

- Make forecasts using the five most common statistical approaches for time-series analysis.

**Forecasting as a Process**

- Describe the six steps in a typical forecasting process.

8 FORECASTING

Kimberly-Clark Mini Case

Accurate forecasting is crucial to maintaining the proper amount of material flow in the supply chain and supply the quantity demanded by customers in any period.

Balancing supply and demand begins with making accurate forecasts of finished goods demand, and then planning supplies of materials and components to assemble them across the supply chain.

**Managing Demand**

Demand Patterns

Demand Management Options

It is important to understand that the timing and sizing of customer demand can often be modified by the managerial and marketing actions of the firm.

Forecasting demand requires uncovering the underlying patterns from available information. There are five basic patterns of most demand time series:

1. Horizontal. The fluctuation of data around a constant mean.

2. Trend. The systematic increase (positive) or decrease (negative) in the mean of the series over time.

3. Seasonal. A repeatable pattern of increases or decreases in demand over each year, depending on the time of day, week, month, or season.

4. Cyclical. The less predictable gradual increase in demand for number of years followed by a decrease in demand for number of years over longer periods of time .

5. Random. The unforecastable variation in demand.

Various options are available in managing demand, including complementary products (product portfolio), promotional pricing (to increase demand during slack periods), prescheduled appointments, reservations, revenue management (like airlines offering steep discount for early booking), backlogs, backorders, and stockouts. The manager may select one or more of them

**Key Decisions on Making Forecasts**

Deciding What to Forecast

Choosing the Type of Forecasting Technique

(1) What to forecast (Level of Aggregation & Units of Measurement) , and

(2) What type of forecasting technique to select for different items.

Available forecasting methods:

- Judgment methods based on the opinions of managers, expert opinions, consumer surveys, and salesforce estimates.
- Quantitative methods include causal methods, time-series analysis, and trend projection with regression. Causal methods use historical data on independent variables, such as promotional campaigns, economic conditions, and competitors’ actions, to predict demand.
- Time-series analysis is a statistical approach that relies heavily on historical demand data to project the future size of demand and recognizes trends and seasonal patterns.
- Trend projection with regression is a hybrid between a time-series technique and the causal method.

**Forecast Error**

Cumulative Sum of Forecast Errors

Dispersion of Forecast Errors

Mean Absolute Percent Error

Computer Support

Forecast error for a given period t is simply the difference between the forecast and

actual demand.

Et = Dt – Ft

where

Et = forecast error for period t

Dt = actual demand for period t

Ft = forecast for period t

For a given number of periods, cumulative sum can be calculated and also its variance and standard deviation.

The mean absolute percent error (MAPE) relates the forecast error to the level of demand and is useful for putting forecast performance in the proper perspective:

Computer support, such as from OM Explorer or POM for Windows, makes error calculations easy when evaluating how well forecasting models fit with past data. Errors are measured across past data, often called the history file in practice.

**Judgment Methods**Opinions of managers, expert opinions, consumer surveys (market research), and salesforce estimates

**Causal Methods: Linear Regression**

In linear regression, one variable, called a dependent variable, is related to one or more independent variables by a linear equation. Even nonlinear relations also can be estimated by machine learning algorithms now.

**Time-Series Methods**

Naïve Forecast

Horizontal Patterns: Estimating the Average

Trend Patterns: Using Regression

Seasonal Patterns: Using Seasonal Factors

Criteria for Selecting Time-Series Methods

Insights into Effective Demand Forecasting

Big Data

Managerial Practice 8.1 Big Data and Health Care

Rather than using independent variables for the forecast as regression models do, time-series methods use historical information regarding only the dependent variable and find the following and through them develop forecast for future periods.

1. Horizontal. The fluctuation of data around a constant mean.

2. Trend. The systematic increase (positive) or decrease (negative) in the mean of the series over time.

3. Seasonal. A repeatable pattern of increases or decreases in demand over each year, depending on the time of day, week, month, or season.

4. Cyclical. The less predictable gradual increase in demand for number of years followed by a decrease in demand for number of years over longer periods of time .

**Forecasting as a Process **

A Typical Forecasting Process

Using Multiple Forecasting Methods

Adding Collaboration to the Process

Forecasting as a Nested Process

Many inputs to the forecasting process are informational, beginning with the history file on past demand. The history file is kept up-to-date with the actual demands. Clarifying notes and adjustments are made to the database to explain unusual demand behavior, such as the impact of special promotions.

Outputs of the process are forecasts for multiple time periods into the future. Typically, they are on

a monthly basis and are projected out from six months to two years.

The forecast process itself, typically done on a monthly basis, consists of structured steps. These

steps often are undertaken or facilitated by someone who might be called a demand manager, forecast analyst, or demand/supply planner. However, many other people are typically involved before the plan for the month is authorized.

Step 1. The cycle begins at the start of a new month as the actual demand of the previous month is available. The information is updated in the history file and forecast accuracy is reviewed. Forecast error is determined for the month.

Step 2. Prepare initial forecasts using some forecasting software package or manual method. Adjust the parameters of the software to find models that fit the past demand well and yet reflect the demand

manager’s judgment on irregular events and information about future sales gathered from judgment forecasts.

Step 3. Hold consensus meetings with the stakeholders, such as marketing, sales, supply chain planners, and finance. Invite business unit and field sales personnel to give further inputs. Use the Internet based communication channels to get collaborative information from key customers and suppliers. The goal is to arrive at consensus forecasts that are acceptable to all of the important players.

Step 4. Revise the forecasts using judgment, considering the inputs from the consensus meetings and collaborative sources.

Step 5. Present the forecasts to the operating committee for review and to reach a final set of forecasts. It is important to have a set of forecasts that everybody agrees upon and will work to support.

Step 6. Finalize the forecasts based on the decisions of the operating committee and communicate them. Supply chain planners are usually the biggest users.

Forecasting is also is process and should be continually reviewed for improvements. A better process will foster better relationships between departments such as marketing, sales, and operations. It will also produce better forecasts.

Learning Goals in Review

Video Case Forecasting and Supply Chain Management at Deckers Outdoor Corporation

Case Yankee Fork and Hoe Company

Experiential Learning 8.1 Forecasting a Vital Energy Statistic

### Index to Summaries of all Chapters of Krajewski’s Book

Operations Management – Krajewski – 12th Edition – Chapter Summaries – Important Points