Forecasting
Forecast
• Forecast – a statement I see that you will
get an A this
about the future value of semester
a variable of interest
– We make forecasts about such
things as weather, demand, and
resource availability
– Forecasts are an important
element in making decisions
(Long-range and Short-range)
An Important Input to Decision Making
• The primary goal operations and supply
chain management is to match supply to
demand
– A demand forecast is essential for
determining how much supply will be
needed to match demand:
• Budget preparation
• Capacity decisions (e.g., staff and equipment)
• Purchasing decisions
Forecast Uses
• Plan the system
– Generally involves long-range plans related to:
• Types of products and services to offer
• Facility and equipment levels
• Facility location
• Plan the use of the system
– Generally involves short and
medium-range plans related to:
• Inventory management
• Workforce levels
• Purchasing
• Budgeting
Forecasts
• Forecasts affect different decisions and
activities throughout an organization:
– Accounting and finance
– Human resources
– Marketing
– MIS
– Operations
– Product / service design
Uses of Forecasts
Accounting Cost/profit estimates
Finance Cash flow and time & amount of
funding
Human Resources Hiring/recruiting/training/layoff
planning
Marketing Pricing, promotion
MIS IT/IS systems
Operations Schedules, capacity planning
Product/service New products and services
design
Demand Estimates
Demand estimates for products and
services are the starting point for all
the other planning in production and
operations management.
Some Reasons Why
Forecasting is Essential in POM
• New Facility Planning – It can take 5 years to design
and build a new factory or design and implement a
new production process.
• Production Planning – Demand for products vary
from month to month and it can take several months to
change the capacities of production processes.
• Workforce Scheduling – Demand for services (and
the necessary staffing) can vary from hour to hour and
employees weekly work schedules must be developed
in advance.
Features of Forecasts
• Assumes causal system
past ==> future
• Forecast accuracy decreases
as time horizon increases (uncertainties
inc.)
Elements of a Good Forecast
Timely
Reliable Accurate
Written
Elements of a Good Forecast
The forecast
• should be timely: the forecasting horizon must
cover the time necessary to implement possible
changes.
• should be accurate, state the degree of accuracy to
plan for possible errors & compare alternative
forecasts.
• should be reliable
Elements of a Good Forecast
• should be expressed in meaningful units ($,
units, machines & skills).
• should be in writing (share same information
and forecast results).
• technique should be simple to understand and
use (not too sophisticated techniques).
• should be cost effective (benefits > costs).
Steps in the Forecasting Process
“The forecast”
Step 6 Monitor the forecast
Step 5 Make the forecast report
Step 4 Obtain and analyze data
Step 3 Select a forecasting technique
Step 2 Establish a time horizon
Step 1 Determine purpose of forecast
Forecasting Approaches
• Qualitative Forecasting
– Qualitative techniques permit the inclusion of
soft information such as Personal opinions.
• Quantitative Forecasting
– Quantitative techniques involve the
estimation based on historical data.
Qualitative Approaches
• Executive opinions (group of upper level managers
in MKT, operations and finance.
• Sales force opinions (sales staff or customer service
staff, what customers would like or actually will do, but
it can be too pessimistic or too optimistic, conflict of
interest).
• Consumer surveys (natural to solicit input from them
but expensive & time consuming incase of too many
customers).
• Outside expert opinion
Quantitative Approaches
• Based on the assumption that the
“forces” that generated the past
demand will generate the future
demand, i.e., history will tend to
repeat itself.
Moving Average Method
• Moving average – A technique that averages a
number of recent actual values, updated as
new values become available. It is called
“moving” because as new demand data
becomes available, the oldest data is not used.
Ft = At-n + … At-2 + At-1
n
Moving Average Method
∑ demand in previous n periods
Moving average = n
Moving Average Example
Actual 3-Month
Month Shed Sales Moving Average
January 10
February 12
March 13
April 16 (10 + 12 + 13)/3 = 11 2/3
May 19 (12 + 13 + 16)/3 = 13 2/3
June 23 (13 + 16 + 19)/3 = 16
July 26 (16 + 19 + 23)/3 = 19 1/3
Associative Forecasting
Regression
• Forecasting an outcome based on
predictor variables (Y is dependent
and X is independent variable).
• Regression Equation or line:
y a bx
Problem 1
Break-ins Sales
• The owner of a small (X) (Y)
hardware stores noted
that the demand for 9 46
Window Locks ‒one of the 3 18
items that he sells‒ seems 3 20
5 22
to go in parallel with the
4 27
number of break-ins, 7 34
burglaries, which was 2 14
reported each week in the 6 37
local paper. 4 30
Problem 1
• Required:
1. Obtain the value of (b) and (a) then get the
regression equation for the data presented.
2. Estimate sales when the number of break-
ins is five.
3. Interpret the correlation coefficient (r) and
coefficient of determination (r2) between two
variables.
Problem 1
Regression Formulas
Problem 1
Sales (y) Break-ins(x) x2 xy y2
248 43 245 1354 7674
n xy x y
b = 1522 / 356
n x x
2 2
= 4.275281
Regression Formulas
Problem 1
Sales (y) Break-ins(x) x2 xy y2
248 43 245 1354 7674
a
y b x
= 64.162 / 9
n
= 7.129213
y a bx Regression Line:
Y = 7.129 + 4.275 X
Regression Formulas
Problem 1
• Estimate sales when the number of break-ins is
five.
• Break-ins (x) is the independent variable
because it triggers the sales for the locks (y)
which is the dependent variable.
Y = 7.129 + (4.275 * 5) = 28.50
Regression Formulas
Correlation Coefficient
Sales (y) Break-ins(x) x2 xy y2
248 43 245 1354 7674
nSxy - SxSy
r=
[nSx2 - (Sx)2][nSy2 - (Sy)2]
= .927 Very strong positive relationship
r2 = 0.859, which means 85.9% of the variability in the
sales (y) is explained by the number of break-ins (x).
Problem 2
Average number sold per day Price ($)
• The Fish Market’s Y X
operations manager was
asked to establish a 200 6
pricing policy on lobsters’ 190 6.5
188 6.75
dinners for one branch of 180 7
this famous seafood chain 170 7.25
in Egypt. Experimenting 162 7.50
with various prices, he 160 8
presented the following 155 8.25
156 8.50
data 148 8.75
140 9
133 9.25
Problem 2
• As the operations manager of The Fish Market
you are required to:
• Develop a regression equation for the
abovementioned data, after obtaining the
value of the intercept “a” and slope of the
regression “b”.
• Forecast the sales if price is $9.50.
YXXi 11982
i
2
729.75
92 .06
Problem 2
Problem 2
Problem 2
RECAP
36
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