1. By: Jane Karla Rosita & Billy Grace Diñ0

3. 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.

4. 
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.

5.  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, ?

6.  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

7.  Process of predicting a future event based on
historical data
 Educated Guessing
 Underlying basis of
all business decisions
 Production
 Inventory
 Personnel
 Facilities
??

8.  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

9.  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

10.  Forecasting methods are classified into two groups:

11.  Qualitative methods – judgmental
methods
 Forecasts generated subjectively by the
forecaster
 Educated guesses
 Quantitative methods – based on
mathematical modeling:
 Forecasts generated through mathematical
modeling

12. Type
Executive
opinion
Characteristics
Strengths
Weaknesses
A group of managers Good for strategic or One person's opinion
meet & come up with new-product
can dominate the
a forecast
forecasting
forecast
Market
research
Uses surveys &
Good determinant of It can be difficult to
interviews to identify customer preferences develop a good
customer preferences
questionnaire
Delphi
method
Seeks to develop a
consensus among a
group of experts
Excellent for
Time consuming to
forecasting long-term develop
product demand,
technological
changes, and

13.  Time Series Models:
 Assumes information needed to generate a
forecast is contained in a time series of data
 Assumes the future will follow same patterns as
the past
 Causal Models or Associative Models:
 Explores cause-and-effect relationships
 Uses leading indicators to predict the future
 Housing starts and appliance sales

14. Naive approach
2. Moving averages
3. Exponential smoothing
4. Trend projection
1.
5.
Linear regression
time-series
models
associative
model

15.  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

16. Trend
Cyclical
Seasonal
Random

18. 
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 =
∑ demand in previous n periods
n

19.  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

20. New forecast = Last period’s forecast
+ (Last period’s actual demand
– Last period’s forecast)
Ft = Ft – 1 + (At – 1 - Ft – 1)
where
Ft =
Ft – 1 =
=
new forecast
previous forecast
smoothing (or weighting)
constant (0 ≤ ≤ 1)

21. Mean Absolute Deviation (MAD)
MAD =
∑ |Actual - Forecast|
n
Mean Squared Error (MSE)
MSE =
∑ (Forecast Errors)2
n

22. Mean Absolute Percent Error (MAPE)
n
MAPE =
∑100|Actuali - Forecasti|/Actuali
i=1
n

23. Fitting a trend line to historical data points to
project into the medium to long-range
Linear trends can be found using the least
squares technique
^
y = a + bx
where ^ = computed value of the variable to be
y
predicted (dependent variable)
a = y-axis intercept
b = slope of the regression line
x = the independent variable