ACQUIRED PREVIOUS KNOWLEDGE

• define the term operations management;

• identify the major functional areas of organizations
and describe how they interrelate;
• identify similarities and differences between
production and service operations;
• briefly describe the historical evolution of operations
management; and
• characterize current trends in business that impact
operations management.
SCOPE OF OPERATIONS
MAGEMENT
UNIT II
By: Joycelyn P. Ituriaga, MBA
ACQUIRED PREVIOUS KNOWLEDGE

At the end of the unit, the student can:

1. describe the interrelated activities of forecasting,
capacity planning, facility layout, and scheduling under
the operations function;
2. explain the steps involved in the operating activities of
forecasting, capacity layout, facility layout, and
scheduling; and
3. apply the various tools used in forecasting, capacity
planning, facility layout, and scheduling of operations.
TOPIC OUTLINE

Unit II.
• Forecasting
Scope of • Capacity Planning
Operations • Facility Layout
Management
• Scheduling
FORECASTING

TOPIC 1
FORECAST
A statement about the
future value of a variable of
interest.
FEATURES COMMON TO ALL FORECASTS

Forecasting techniques generally assume that the same underlying causal system that
existed in the past will continue to exist in the future.

Forecast are not perfect.

Forecast for groups of items tend to be more accurate than forecasts for individual items
because forecasting errors among items in a group usually have a canceling effect.

Forecast accuracy decreases as the time covered by the forecast – the time horizon –
increases.
ELEMENTS OF A GOOD FORECAST

TIMELY ACCURATE RELIABLE MEANINGFUL IN WRITING

UNITS

SIMPLE TO COST-
UNDERSTAND EFFECTIVE
AND USE
 STEPS IN THE FORECASTING PROCESS

Determine
• Determine the purpose of the forecast.
Establish
• Establish a time horizon.
Obtain, clean,
and analyze
• Obtain, clean, and analyze appropriate data.
Select
• Select a forecasting techniques.
Make
• Make the forecast.
Monitor
• Monitor the forecast errors.
FORECAST ACCURACY

Forecast accuracy is a significant factor when deciding among

forecasting alternatives.

Accuracy is based on the historical error performance of a

forecast.

Forecast error is the difference between the value that occurs

and the value that was predicted for a given time. Hence, Error =
Actual – Forecast.
THREE COMMONLY USED MEASURES FOR
SUMMARIZING HISTORICAL ERRORS
• Mean Absolute Deviation (MAD) is the absolute error
• Mean Squared Error (MESE) is the average of squared errors
• Mean absolute percent error (MAPE is the average absolute percent
error
 𝐴𝑐𝑡𝑢𝑎𝑙1 −𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡1
• 𝑀𝐴𝐷 =
𝑛

 𝐴𝑐𝑡𝑢𝑎𝑙1 −𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡1 2
• 𝑀𝑆𝐸 =
𝑛 −1

|𝐴𝑐𝑡𝑢𝑎𝑙𝐴𝑐𝑡𝑢𝑎𝑙
1
−𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 |1𝑥 100
• 𝑀𝐴𝐷 = 1
𝑛
EXAMPLE 1: COMPUTING MAD, MSE, and MAPE

• Compute MAD, MSE, and MAPE for the following data, showing actual
and forecasted numbers of accounts serviced.
Period Actual Forecast (A-F) Error |Error| Error2 [|Error|  Actual] x 100
1 217 215 2 2 4 0.92%
2 213 216 -3 3 9 1.41
3 216 215 1 1 1 0.46
4 210 214 -4 4 16 1.90
5 213 211 2 2 4 0.94
6 219 214 5 5 25 2.28
7 216 217 -1 1 1 0.46
8 212 216 -4 4 16 1.89
-2 22 76 10.26%
• Compute for MAD, MSE, and MAPE
APPROACHES TO FORECASTING

Qualitative Quantitative
QUALITATIVE METHODS

Consist mainly of subjective inputs, which often defy precise

numerical description.

Permits inclusion of soft information (e.g., human factors,

personal opinions, hunches) in the forecasting process.

Those factors are often omitted or downplayed when quantitative

techniques are used because they are difficult or impossible to
quantify.
QUANTITATIVE METHODS

Involve either the projection of historical data or the development of associative

models that attempt to utilize causal (explanatory) variables to make a forecast.

Consist mainly of analyzing objective, or hand, data.

They usually avoid personal biases that sometimes contaminate qualitative

methods.

In practice, either approach or a combination of both approaches might be used to

develop a forecast.
FORECASTING TECHNIQUES

Judgmental forecasts – forecasts that use subjective inputs

such as opinions from consumer surveys, sales staff, managers,
executives, and experts.

Time-series forecasts – forecasts that project patterns identified

in recent time-series observations.

Associative model – forecasting technique that uses

explanatory variables to predict future demand.
QUALITATIVE FORECASTS

Executive Opinions

Salesforce Opinions

Consumer Surveys

Other Approaches
• Delphi method – an iterative process in which managers and staff
complete a series of questionnaires, each developed from the previous
one, to achieve a consensus forecast.
FORECASTS BASED ON TIME-SERIES DATA
• Time series – a time-oriented sequence of observations taken at
regular intervals.
1. Trend – a long-term upward or downward movement in data.
2. Seasonality – short-term regular variations related to the calendar
or time of day.
3. Cycle – wavelike variations lasting more than one year.
4. Irregular variation – caused by unusual circumstances, not
reflective of typical behavior.
5. Random variations – residual variations after all other behaviors
are accounted for.
NAÏVE METHODS
A forecast for Period Actual
Change from
Forecast
any period 1 50
Previous Value

that equals 2
3
53
+3 53 + 3 =
the previous 56

period’s
actual value.
TECHNIQUES FOR AVERAGING

Moving average – technique that averages several recent actual

values, updated as new values become available.

Weighted average – more recent values in a series are given

more weight in computing a forecast.

Exponential Smoothing – a weighted averaging method based

on previous forecast plus a percentage of the forecast error.
EXAMPLE 2: COMPUTING A MOVING AVERAGE

• Compute a three-period moving average forecast given

demand for shopping carts for the last five periods.

Period Demand
1 42
2 40
3 43
4 40 The 3 most recent demands
5 41
SOLUTION
Period Demand
1 42
2 40
3 43
4 40 The 3 most recent demands
5 41

43+40+41
• 𝐹6 = = 41.33
3
• If actual demand in period 6 turns out to be 38, the moving average
forecast for period 7 would be
43+40+38
• 𝐹7 = = 39.37
3
EXAMPLE 3: COMPUTING A WEIGHTED MOVING
AVERAGE
• Given the following data,
a. Compute a weighted average forecast using a weight of .40 for the
most recent period. .30 for the next most recent, .20 for the next, and
.10 for the next.
b. If the actual demand for period 6 is 39, forecast demand for period 7
using the same weights as in part a.
Period Demand
1 42
2 40
3 43
4 40
5 41
SOLUTION
Period Demand
1 42
2 40
3 43
4 40
5 41

• 𝐹6 = .10 40 + .20 43 + .30 40 + .40(41) = 41

• 𝐹7 = .10 43 + .20 40 + .30 41 + .40(39) = 40.20
EXAMPLE 3: COMPUTING EXPONENTIAL
SMOOTHING
• New forecast = Previous forecast + (Actual – Previous forecast)
• Where (Actual – Previous forecast) represents the forecast error and  is a
percentage of the error. More concisely,
• 𝐹𝑡 = 𝐹𝑡 − 1 + 𝛼(𝐴𝑡 − 1 + 𝐹𝑡 − 1)
where:
• 𝐹𝑡 – Forecast for period t
• 𝐹𝑡 − 1 – Forecast for the previous period (i.e., period t – 1)
• 𝛼 − 𝑆𝑚𝑜𝑜𝑡ℎ𝑖𝑛𝑔 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 (𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒)
• 𝐴𝑡 − 1 – Actual demand or sales for the previous period
EXAMPLE 3: COMPUTING EXPONENTIAL
SMOOTHING
• The smoothing constant  represents a percentage of the forecast error.
Each new forecast is equal to the previous forecast plus a percentage of
the previous error. For example, suppose the previous forecast was 42
units, actual demand was 40 units, and  = .10. the new forecast would
be computed as follows:
• 𝐹𝑡 = 42 + .10 40 − 42 = 41.80
• The, if the actual demand turns out to be 43, the next forecast would be
• 𝐹𝑡 = 41.80 + .10 43 − 41.8 = 41.92
TRY THIS: EXPONENTIAL SMOOTHING

Period (t) Actual Demand  = .10 Forecast  = .40 Forecast

1 42
2 40
3 43
4 40
5 41
6 39
7 46
8 44
9 45
10 38
11 40
12
 EXAMPLE 4: COMPARING FORECAST ERRORS
• Compare the error performance of these three forecasting techniques using MAD, MSE, and MAPE;
a naïve forecast, a two-period moving average, and exponential smoothing with  = .10 for periods 3
through 11. Complete the table.
Naïve Two-period MA Exponential Smoothing
Period, t Demand Forecast Error Forecast Error Forecast Error
1 42 - -
2 40 42 42.00
3 43 40 41.0 41.80
4 40 43 41.5 41.92
5 41 40 41.5 41.73
6 39 41 40.5 41.66
7 46 39 40.0 41.39
8 44 46 42.5 41.85
9 45 44 45.0 42.07
10 38 45 44.5 42.36
11 40 38 41.5 41.92
MAD
MS
MAPE
OTHER FORECASTING METHODS

• Focus forecasting – using the forecasting method that

demonstrates the best recent success.
• Diffusion models – when new products or service are introduced,
historical data are not generally available on which to bae
forecasts. Instead, predictions are based on rate of product
adoption and usage spread form other established products, using
mathematical diffusion models. These models consider such
factors as market potential, attention from mass media, and word
of mouth. Although the details are beyond the scope of this text, it
is important to point out that diffusion models are widely used in
marketing and to assess the merits of investing in new
technologies.
REFERENCE:
Operations Management with Total Quality Management McGraw-Hill
Education
End of Presentation
Thank you.
SCOPE OF OPERATIONS
MAGEMENT
UNIT II
FORECASTING