By: Jane Karla Rosita & Billy Grace Diñ0

FORECASTING & TIME SERIES DATA

Operations management deals with the design and

management of products, processes, services and supply chains. It considers the acquisition, development, and utilization of resources that firms need to deliver the goods and services their clients want.

Forecasting helps managers and businesses develop meaningful plans and reduce uncertainty of events in the future. Managers want to match supply with demand; therefore, it is essential for them to forecast how much space they need for supply to each demand.

What is the role of forecasting in operations management?

Predict the next number in the pattern:

a) 3.7, 3.7, 3.7, 3.7, 3.7, ?

b) 2.5, 4.5, 6.5, 8.5, 10.5, ?

c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, ?

Forecasting

Predict the next number in the pattern:

a) 3.7, 3.7, 3.7, 3.7, 3.7, 3.7

b) 2.5, 4.5, 6.5, 8.5, 10.5, 12.5

c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, 9.0

Forecasting

Process of predicting a future event based on historical data

Educated Guessing Underlying basis of

all business decisions Production Inventory Personnel Facilities

Forecasting

??

Short-range forecast Up to 1 year, generally less than 3 months Purchasing, job scheduling, workforce levels,

job assignments, production levels Medium-range forecast

3 months to 3 years Sales and production planning, budgeting

Long-range forecast 3+ years New product planning, facility location,

research and development

Forecasting Time Horizons

Economic forecasts Address business cycle – inflation rate,

money supply, housing starts, etc. Technological forecasts

Predict rate of technological progress Impacts development of new products

Demand forecasts Predict sales of existing products and

services

Types of Forecasts

Forecasting methods are classified into two groups:

Forecasting Approaches

Qualitative methods – judgmental methodsForecasts generated subjectively

by the forecasterEducated guesses

Quantitative methods – based on mathematical modeling:Forecasts generated through

mathematical modeling

Types of Forecasting Models

Qualitative MethodsType Characteristics Strengths Weaknesses

Executive opinion

A group of managers meet & come up with a forecast

Good for strategic or new-product forecasting

One person's opinion can dominate the forecast

Market research

Uses surveys & interviews to identify customer preferences

Good determinant of customer preferences

It can be difficult to develop a good questionnaire

Delphi method

Seeks to develop a consensus among a group of experts

Excellent for forecasting long-term product demand, technological changes, and

Time consuming to develop

Time Series Models:Assumes information needed to generate a

forecast is contained in a time series of dataAssumes the future will follow same patterns

as the pastCausal Models or Associative Models:

Explores cause-and-effect relationshipsUses leading indicators to predict the futureHousing starts and appliance sales

Quantitative Methods

Overview of Quantitative Approaches

1. Naive approach2. Moving averages3. Exponential

smoothing4. Trend projection5. Linear regression

time-series models

associative model

Set of evenly spaced numerical data Obtained by observing response

variable at regular time periods

Forecast based only on past values, no other variables important Assumes that factors influencing past

and present will continue influence in future

Time Series Forecasting

Time Series Components

Trend

Seasonal

Cyclical

Random

Time Series Patterns

MA is a series of arithmetic means Used if little or no trend

Used often for smoothing Provides overall impression of data

over time

Moving Average Method

Moving average =∑ demand in previous n periods

n

Form of weighted moving average Weights decline exponentially Most recent data weighted most

Requires smoothing constant () Ranges from 0 to 1 Subjectively chosen

Involves little record keeping of past data

Exponential Smoothing

Exponential Smoothing

New forecast = Last period’s forecast+ (Last period’s actual demand

– Last period’s forecast)

Ft = Ft – 1 + (At – 1 - Ft – 1)

where Ft = new forecastFt – 1 = previous forecast

= smoothing (or weighting) constant (0 ≤ ≤ 1)

Common Measures of Error

Mean Absolute Deviation (MAD)

MAD =∑ |Actual - Forecast|n

Mean Squared Error (MSE)

MSE =∑ (Forecast Errors)2

n

Common Measures of Error

Mean Absolute Percent Error (MAPE)

MAPE =∑100|Actuali - Forecasti|/Actuali

n

n

i = 1

Trend ProjectionsFitting a trend line to historical data points to project into the medium to long-rangeLinear trends can be found using the least squares technique

y = a + bx^

where y = computed value of the variable to be predicted (dependent variable)a = y-axis interceptb = slope of the regression linex = the independent variable

^

Thank you to those who listened!