Forecasting notes from production planning and control -MECHANICAL

Forecasting Definition and its Methods,

FORECASTING

INTRODUCTION

The growing competition, frequent changes in customer's demand and the trend towards automation demand that decisions in business should not be based purely on guesses rather on a careful analysis of data concerning the future course of events. More time and attention should be given to the future than to the past, and the question 'what is likely to happen?' should take precedence over 'what has happened?' though no attempt to answer the first can be made without the facts and figures being available to answer the second. When estimates of future conditions are made on a systematic basis, the process is called forecasting and the figure or statement thus obtained is defined as forecast. In a world where future is not known with certainty, virtually every business and economic decision rests upon a forecast of future conditions. Forecasting aims at reducing the area of uncertainty that surrounds management decision-making with respect to costs, profit, sales, production, pricing, capital investment, and so forth. If the future were known with certainty, forecasting would be unnecessary. But uncertainty does exist, future outcomes are rarely assured and, therefore, organized system of forecasting is necessary. The following are the main functions of forecasting:

• The creation of plans of action.
• The general use of forecasting is to be found in monitoring the continuing progress of plans based on forecasts.
• The forecast provides a warning system of the critical factors to be monitored regularly because they might drastically affect the performance of the plan.

It is important to note that the objective of business forecasting is not to determine a curve or seriesof figures that will tell exactly what will happen, say, a year in advance, but it is to make analysis based on definite statistical data, which will enable an executive to take advantage of future conditions to a greater extent than he could do without them. In forecasting one should note that it is impossible to forecast the future precisely and there always must be some range of error allowed for in the forecast.

FORECASTING FUNDAMENTALS

Forecast: A prediction, projection, or estimate of some future activity, event, or occurrence.

Types of Forecasts

Economic forecasts: Predict a variety of economic indicators, like money supply, inflation rates, interest rates, etc.

Technological forecasts: Predict rates of technological progress and innovation.

Demand forecasts: Predict the future demand for a company’s products or services.

TYPES OF FORECASTING METHODS

Qualitative methods: These types of forecasting methods are based on judgments, opinions,intuition, emotions, or personal experiences and are subjective in nature. They do not rely on any rigorous mathematical computations.

Quantitative methods: These types of forecasting methods are based on mathematical (quantitative) models, and are objective in nature. They rely heavily on mathematical computations.

Forecasting Principles

There are also some general principles that should be considered when we prepare and use forecasts, especially those based on time series methods.

Oliver W. Wight in Production and Inventory Control in the Computer Age*, and Thomas H. Fuller in Microcomputers in Production and Inventory Management** developed a set of principles for the production and inventory control community a while back that I believe have universal application.

1. Unless the method is 100% accurate, it must be simple enough so people who use it know how to use it intelligently (understand it, explain it, and replicate it).
2. Every forecast should be accompanied by an estimate of the error (the measure of its accuracy).
3. Long term forecasts should cover the largest possible group of items; restrict individual item forecasts to the short term.
4. The most important element of any forecast scheme is that thing between the keyboard and the chair.

The first principle suggests that you can get by with treating a forecast method as a "black box," as long as it is 100% accurate. That is, if an analyst simply feeds historical data into the computer and accepts and implements the forecast output without any idea how the computations were made, that analyst is treating the forecast method as a black box. This is ok as long as the forecast error (actual observation - forecast observation) is zero. If the forecast is not reliable (high error), the analyst should be, at least, highly embarrassed by not being able to explain what went wrong. There may be much worse ramifications than embarrassment if budgets and other planning events relied heavily on the erroneous forecast.

The second principle is really important. A simple way to measure forecast error, the difference between what actually occurs and what was predicted to occur for each forecast time period. Here is the idea. Suppose an auto company predicts sales of 30 cars next month using Method A. Method B also comes up with a prediction of 30 cars. Without knowing the measure of accuracy of the two Methods, we would be indifferent as to their selection. However, if we knew that the composite error for Method A is +/- 2 cars over a relevant time horizon; and the composite error for Method B is +/- 10 cars, we would definitely select Method A over Method B.

QUANTITATIVE FORECASTING METHODS: TWO TYPES

Time-Series Models	Associative Models
Time series models look at past patterns of data and attempt to predict the future based upon the underlying patterns contained within those data.	Associative models (often called causal models) assume that the variable being forecasted is related to other variables in the environment. They try to project based upon those.

TIME SERIES MODELS

Model	Description
Naïve	Uses last period’s actual value as a forecast
Simple Mean (Average)	Uses an average of all past data as a forecast
Simple Moving Average	Uses an average of a specified number of the most recent observations, with each observation receiving the same emphasis (weight)
Weighted Moving Average	Uses an average of a specified number of the most recent observations, with each observation receiving a different emphasis (weight)
Exponential Smoothing	A weighted average procedure with weights declining exponentially as data become older
Trend Projection	Technique that uses the least squares method to fit a straight line to the data
Seasonal Indexes	A mechanism for adjusting the forecast to accommodate any seasonal patterns inherent in the data

DECOMPOSITION OF A TIME SERIES

Patterns that may be present in a time series

Trend: Data exhibit a steady growth or decline over time.

Seasonality: Data exhibit upward and downward swings in a short to intermediate time frame (most notably during a year).

Cycles: Data exhibit upward and downward swings in over a very long time frame.

Random variations: Erratic and unpredictable variation in the data over time with no discernable

pattern.

ILLUSTRATION OF TIME SERIES DECOMPOSITION

Hypothetical Pattern of Historical Demand

Dependent versus Independent Demand

Demand of an item is termed as independent when it remains unaffected by the demand for any other item. On the other hand, when the demand of one item is linked to the demand for another item, demand is termed as dependent. It is important to mention that only independent demand needs forecasting. Dependent demand can be derived from the demand of independent item to which it is linked.

Business Time Series

The first step in making a forecast consists of gathering information from the past. One should collect statistical data recorded at successive intervals of time. Such a data is usually referred to as time series. Analysts plot demand data on a time scale, study the plot and look for consistent shapes and patterns. A time series of demand may have constant, trend, or seasonal pattern

The forecaster tries to understand the reasons for such changes, such as,

Changes that have occurred as a result of general tendency of the data to increase or decrease, known as secular movements.

Changes that have taken place during a period of 12 months as a result in changes in climate, weather conditions, festivals etc. are called as seasonal changes.

Changes that have taken place as a result of booms and depressions are called as cyclical variations. Changes that have taken place as a result of such forces that could not be predicted (like flood, earthquake etc.) are called as irregular or erratic variations.

Quantitative Approaches of Forecasting

Most of the quantitative techniques calculate demand forecast as an average from the past demand. The following are the important demand forecasting techniques. Simple average method: A simple average of demands occurring in all previous time periods is taken as the demand forecast for the next time period in this method.

Example 1 Simple Average : A XYZ television supplier found a demand of 200 sets in July, 225 sets in August & 245 sets in September. Find the demand forecast for the month of october using simple average method. The average demand for the month of October is

SOLUTION:
SA=[D1+D2+D3]/3

SA=[200+225+245]/3

SA=223.33

SA[approx] =224 units 

Example 3 Weighted Moving Average Method : The manager of a restaurant wants to make decision on inventory and overall cost. He wants to forecast demand for some of the items based on weighted moving average method. For the past three months he exprienced a demand for pizzas as follows: