OPMA Chapter 3: Forecasting

strategic forecasts

medium and long term forecasts that are used for decisions related to strategy and aggregate demand

Strategic forecasts are typically longer term and usually involve forecasting demand for a group of products.

Tactical Forecasts

Short-term forecasts used as input for making day-to-day decisions related to meeting demand.

Tactical forecasts would cover only a short period of time, at most a few weeks in the future, and would typically be for individual items.

Forecasting can be classified into 4 basic types

qualitative (Qualitative techniques are subjective or judgmental and are based on estimates and opinions), time series analysis, causal relationships, and simulation.

time series analysis

A type of forecast in which data relating to past demand are used to predict future demand.

(in business forecasting, short term usually refers to under three months; medium term, three months to two years; and long term, greater than two years)

Linear Regression Technique

Casual demand, assumes that demand is related to some underlying factor or factors in the environment

Simulation Models

allow the forecaster to run through a range of assumptions about the condition of the forecast.

Components of Demand

Average demand for a period of time

Trend

Seasonal element

Cyclical elements

Random variation

Autocorrelation

4 common types of trends

linear trend: straight continuous relationship. S-curve: product growth and maturity cycle. Important point where the trend changes from slow growth to fast growth or from fast to slow. asymptotic trend starts with the highest demand growth at the beginning but then tapers off. could happen when a firm enters an existing market with the objective of saturating and capturing a large share of the market. exponential curve: products with explosive growth. The exponential trend suggests that sales will continue at an ever-increasing rate—an assumption that may not be safe to make.

Simple Moving Average

A forecast based on average past demand.

(time series forecasting technique)

Weighted Moving Average

a forecast made with past data where more recent data is given more significance than older data (For example, a department store may find that in a four-month period, the best forecast is derived by using 40 percent of the actual sales for the most recent month, 30 percent of two months ago, 20 percent of three months ago, and 10 percent of four months ago.)

Exponential Smoothing

-A time series forecasting technique in which each increment of past demand data is decreased by (1 − α).

-Exponential smoothing is the most used of all forecasting techniques.

-widely used in ordering inventory in retail firms, wholesale companies, and service agencies.

Exponential smoothing techniques have become well accepted for six major 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 because of the limited use of historical data.

6.Tests for accuracy as to how well the model is performing are easy to compute.

Smoothing constant alpha (α)

The parameter in the exponential smoothing equation that controls the speed of reaction to differences between forecasts and actual demand.

(The desired response rate, or smoothing constant)

Smoothing constant delta (δ)

An additional parameter used in an exponential smoothing equation that includes an adjustment for trend.

Linear regression forecasting

A forecasting technique that assumes that past data and future projections fall around a straight line.

-major restriction using linear regression forecasting is past data and future projections are assumed to fall in a straight line. This may limit its application, if use a shorter period of time linear regression can still be used

-Linear regression is used both for time series forecasting and for causal relationship forecasting.

Decomposition

The process of identifying and separating time series data into fundamental components such as trend and seasonality.

forecast error

the difference between a forecast and the actual demand

standard error

standard error is the square root of a function, it is often more convenient to use than the function itself. This is called the mean squared error, or variance.

Mean Absolute Deviation (MAD)

The average forecast error using absolute values of the error of each past forecast.

-simple and useful in obtaining tracking signals

-It is valuable because MAD, like the standard deviation, measures the dispersion of some observed value from some expected value.

Mean Absolute Percent Error (MAPE)

the average error measured as a percentage of average demand

-This is a useful measure because it is an estimate of how much error to expect with a forecast.

Tracking Signal (TS)

A measure of whether the forecast is keeping pace with any genuine upward or downward changes in demand. This is used to detect forecast bias.

casual relationship forecasting

forecasting using independent variables other than time to predict future demand

multiple regression analysis (forecasting method)

a number of variables are considered, together with the effects of each on the item of interest.

-For example, in the home furnishings field, the effects of the number of marriages, housing starts, disposable income, and the trend can be expressed in a multiple regression equation

Qualitative forecasting techniques:

-Market research

-Panel consensus: the idea that two heads are better than one is extrapolated to the idea that a panel of people from a variety of positions can develop a more reliable forecast than a narrower group

-Historical analogy:an existing product or generic product could be used as a model.

-Delphi method: conceals identity of individuals participating in study. Everyone has the same weight. -Choose experts to participate, questionnaire (or email), obtain forecasts from all participants. Summarize results, and redistribute to participants with appropriate new questions.

4.Summarize again, refining forecasts and conditions, and again develop new questions.

Collaborative Planning, Forecasting, and Replenishment (CPFR)

An Internet tool to coordinate forecasting, production, and purchasing in a firm's supply chain.

-web-based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners.

-Although the methodology is applicable to any industry, CPFR have largely focused on food, apparel, and general merchandise industries

Collaborative Planning, Forecasting, and Replenishment (CPFR) cont.

CPFR's objective is to exchange selected internal information on shared web server in order to provide for reliable, longer-term future views of demand in the supply chain.

-uses a cyclic and iterative approach to derive consensus supply chain forecasts

5 steps of CPFR

1. creation of a front-end partnership agreement: This agreement specifies (1) objectives, (2) resource requirements (3) expectations of confidentiality concerning the prerequisite trust necessary to share sensitive company information, which represents a major implementation obstacle

2. joint business planning

3. development of demand of forecasts

4. forecast sharing

5. inventory replenishment