ch. 18 (forecasting)

Forecasting

a vital function and affects every significant management decision.

• Finance and accounting use this as the basis for budgeting and cost control.

• Marketing relies on this to make key decisions such as new product planning and personnel compensation.

• Production uses this to select suppliers; determine capacity requirements; and drive decisions about purchasing, staffing, and inventory.

strategic

Decisions about overall directions require ________ forecasts

Tactical

________ forecasts are used to guide day-to-day decisions

Decoupling point (slightly refined)

Point at which some form of inventory is stored that allows supply chain partners/entities to operate independently

level of inventory

• The choice of the decoupling point in a supply chain is strategic.

• Forecasting helps determine the _____ __ _______ needed at the decoupling points.

• The decision will be impacted by the error produced in the forecast and the type of product (easily inventoried vs. perishable).

forecasts

four basic types of ________:

Qualitative (e.g., Delphi, panel, analogy)

Time series analysis

Causal relationships

Simulation

future behaving

Time series analysis relies on the _____ _______, at least to some extent, similarly to the past

Components of Demand

1. Average demand for a period of time

2. Trend

3. Seasonal element

4. Cyclical elements

5. Random variation

6. Autocorrelation

tactical

Short term – forecasting less than 3 months is used to make ______ decisions

strategy

Medium term – forecasting 3 months to 2 years is used to develop a ______ that will be implemented over the next 6 to 18 months (e.g., meeting demand)

turning points

Long term – forecasting greater than 2 years Useful for detecting general trends and identifying major _____ ______

model selection

Choosing an appropriate forecasting model depends upon:

1. Time horizon to be forecast

2. Data availability

3. Accuracy required

4. Size of forecasting budget

5. Availability of qualified personnel

Simple Moving Average

Forecast is the average of a fixed number of past periods (implies equal weighting for all periods)

• Useful when demand is not growing or declining rapidly and no seasonality is present.

• Removes some of the random fluctuation from the data.

• Selecting the period length is important.

• Longer periods provide more smoothing.

• Shorter periods react to trends more quickly.

Weighted Moving Average

allows unequal weighting of prior time periods.

• Experience and/or trial-and-error are the simplest approaches.

• The recent past is often the best indicator of the future, so weights are generally higher for more recent data.

• If the data are seasonal or cyclical, weights should reflect this appropriately

Exponential Smoothing

A weighted average method that includes all past data in the forecasting calculation

More recent results weighted more heavily

The most used of all forecasting techniques

An integral part of computerized forecasting

why exponential smoothing is accepted

Exponential Smoothing is well accepted for six reasons

1. Exponential models are surprisingly accurate.

2. Formulating an exponential model is relatively easy.

3. The user can understand how the model works.

4. Little computation is required to use the model.

5. Computer storage requirements are small.

6. Tests for accuracy are easy to compute.

alpha/delta

A trend in data in Exponential Smoothing causes the exponential forecast to always lag the actual data

Can be corrected somewhat by adding in a trend adjustment

To correct the trend, we need two smoothing constants

• Smoothing constant ____ ()

• Trend smoothing constant _____ (δ)

Regression

used to identify the functional relationship between two correlated variables, usually from observed data.

– One variable (the dependent variable) is predicted for given values of the other variable (the independent variable

Linear regression

a special case that assumes the relationship between the variables can be explained with a straight line.

Least Squares Method

determines the parameters a and b such that the sum of the squared errors is minimized – “least squares”

Time Series

Chronologically ordered data are referred to as a ____ _____.

may contain one or many elements:

-Trend, seasonal, cyclical, autocorrelation, and random --Identifying these elements and separating the time series data into these components is known as decomposition.

Decomposition Using Least Squares Regression

Decompose the time series into its components.

1. Find the seasonal component.

2. Deseasonalize the demand.

3. Find the least squares regression trend component.

Forecast future values of each component.

4. Project the trend component into the future.

5. Multiply the trend component by the seasonal component.

Forecast Error

the difference between the forecast value and what actually occurred.

true

All forecasts contain some level of error. (T/F)

bias/random

Sources of error:

• ____ – when a consistent mistake is made

• ________ – errors that are not explained by the model being used

Measures of error

• Mean absolute deviation (MAD)

• Mean absolute percent error (MAPE)

• Tracking signal

these are alll…

larger

Ideally, Mean absolute deviation (MAD) will be zero (no forecasting error).

_______ values of MAD indicate a less accurate model.

magnitude

Mean absolute percent error (MAPE) scales the forecast error to the _______ of demand

accumulating

Tracking signal indicates whether forecast errors are ________ over time (either positive or negative errors).

casual relationship forecasting

uses independent variables other than time to predict future demand.

This independent variable must be a leading indicator.

Many apparently causal relationships are merely correlated events – care must be taken when selecting causal variables.

Collaborative Planning, Forecasting, and Replenishment

A web-based process used to coordinate the efforts of a supply chain.

-Demand forecasting

-Production and purchasing

-Inventory replenishment

Integrates all members of a supply chain – manufacturers, distributors, and retailers.

Depends upon the exchange of internal information to provide a more reliable view of demand.

CPFR Steps

1. Creation of a front-end partnership agreement

2. Joint business planning

3. Development of demand forecasts

4. Sharing forecasts

5. Inventory replenishment