Production function
describes the relationship
between inputs and output.
Output
Reasons for the existence of different production runs:
Whenever a market situation changes a firm has to make a new decision so as to maximize wealth.
The firm is uncertain if the change in the market situation
is temporary or permanent.
It will make only the minimal and necessary change
in factors to minimize cost.
Reasons for the existence of different production runs:
Even if the change is certain to be permanent,
the adjustment in factors should still be slow and
gradual because hasty change involves a larger cost.
Reasons for the existence of different production runs:
Since adjustment is gradual, according to the completeness in the adjustment in factors, three different production
Classification of production runs
Very short run (VSR) all factors are fixed (remains unchanged).
Short run (SR)
some factors are varied but some are fixed.
Long run (LR)
all factors are variable and
Variable factors versus fixed factors
Variable factors: are factors of which the employment varies with output.
Variable factor Variable
factor
Assumptions:
only two factors are involved capital & labour
Production function in the short run
Capital
Fixed Factor
____________________: is the whole amount of output produced by all the factors employed.
TP = Q
____________________: is the output per unit of the variable factor employed.
Three variables are defined to measure the output:
____________________: the change in output resulting from employing an additional unit of the variable factor.
Total product (TP)
Average product (AP)
Marginal product (MP)
L Q L
TP
AP
1
1
• National-income accounting refers to the measurement of aggregate
economic activity, particularly national income and its components.
• Gross domestic product (GDP) is the total market value of final goods
and services produced within a nation’s borders in a given time period. (Usually a year)
• GDP accounts have two sides.
• One side focuses on expenditure – the demand side. • The other side focuses on income – the supply side.
Output = Income
VALUE OF INCOME VALUE OF OUTPUT
• By charting the flow of income through the economy, we see FOR
WHOM the output is produced.
• Depreciation charges reduce GDP to the level of NDP (Net Domestic
Product) before any income is available to current factors of production.
NDP = GDP – depreciation
• Wages, interest, and profits paid to foreigners are not part of income. • They need to be subtracted from the income flow.
• Eg: Incomes earned by U.S. citizens in other nations represents an
inflow of income to U.S. households and are added.
• Once depreciation charges and indirect business taxes
are subtracted from GDP and net foreign income is added, we have national income.
• National income (NI) is total income earned by current factors of
production.
National Income
Quality Improvement and
Statistics
•
Definitions of Quality
Quality means fitness for use
- quality of design
- quality of conformance
Quality is inversely proportional to
variability.
Quality Improvement and
Statistics
•
Quality Improvement
Quality improvement
is the reduction of
variability in processes and products.
Alternatively,
quality improvement
is also
seen as “waste reduction”.
Statistical Process Control
•
Statistical process control
is a
collection of tools that when used
together can result in process stability
and variance reduction
Statistical Process Control
The
seven major tools
are
1) Histogram 2) Pareto Chart
4) Cause and Effect Diagram
5) Defect Concentration Diagram 6) Control Chart
7) Scatter Diagram 8) Check Sheet
•
A process that is operating with only
chance causes of variation
present is said
to be
in statistical control.
•
A process that is operating in the presence
of
assignable causes
is said to be
out of
control.
•
The eventual goal is the
elimination of
variability
in the process.
Basic Principles
What is Forecasting?
Process of predicting a future event based on historical data
Educated Guessing
Underlying basis of all business decisions:
ü Production
In general, forecasts are almost always wrong. So,
Why do we need to forecast?
Throughout the day we forecast very different
things such as weather, traffic, stock market, state of our company from different perspectives.
Virtually every business attempt is based on forecasting. Not all of them are derived from
sophisticated methods. However, “Best" educated guesses about future are more valuable for
• Short-range forecast
• Usually < 3 months
• Job scheduling, worker assignments
• Medium-range forecast
• 3 months to 2 years
• Sales/production planning
• Long-range forecast
• > 2 years
• New product planning
Forecasting During the
Life Cycle
Introduction Growth Maturity Decline
Sales
Time Quantitative models
- Time series analysis - Regression analysis Qualitative models
- Executive judgment - Market research
-Survey of sales force
Briefly, the qualitative methods are:
Executive Judgment: Opinion of a group of high level experts or managers is pooled
Sales Force Composite: Each regional salesperson provides his/her sales estimates. Those forecasts are then reviewed to make sure they are realistic. All regional forecasts are then
pooled at the district and national levels to obtain an overall forecast.
Market Research/Survey: Solicits input from customers
pertaining to their future purchasing plans. It involves the use of questionnaires, consumer panels and tests of new products and services.
.
Delphi Method: As opposed to regular panels where the individuals
involved are in direct communication, this method eliminates the effects of group potential dominance of the most vocal members. The group involves individuals from inside as well as outside the organization.
Typically, the procedure consists of the following steps:
Each expert in the group makes his/her own forecasts in form of statements
The coordinator collects all group statements and summarizes them The coordinator provides this summary and gives another set of
questions to each
group member including feedback as to the input of other experts.
The above steps are repeated until a consensus is reached.
.
• Collect historical data • Select a model
• Moving average methods • Select n (number of periods)
• For weighted moving average: select weights • Exponential smoothing
• Select
• Selections should produce a good forecast
To Use a Forecasting
Method
A Good Forecast
¨
Has a small error
Regression Analysis as a Method for Forecasting
Regression analysis takes advantage of the relationship between two
variables. Demand is then forecasted based on the knowledge of this
relationship and for the given value of the related variable.
Ex: Sale of Tires (Y), Sale of Autos (X) are
obviously related
If we analyze the past data of these two variables and establish a relationship between them, we may use that relationship to forecast the sales of tires given the sales of automobiles.
The simplest form of the relationship is, of course, linear, hence it is referred to as
General Guiding Principles for
Forecasting
1. Forecasts are more accurate for larger groups of items. 2. Forecasts are more accurate for shorter periods of time. 3. Every forecast should include an estimate of error.
4. Before applying any forecasting method, the total system should be understood.
5. Before applying any forecasting method, the method should be tested and evaluated.
Regression: Introduction
Basic idea:
Use
data
to
identify
Linear regression
• Linear dependence: constant rate of increase of one variable with respect to another (as opposed to, e.g., diminishing returns).
• Regression analysis describes the relationship
between two (or more) variables.
• Examples:
• Income and educational level
• Demand for electricity and the weather
• Home sales and interest rates
• Our focus:
Two main questions:
•Prediction and Forecasting
• Predict home sales for December given the interest rate for this month.
• Use time series data (e.g., sales vs. year) to forecast future performance (next year sales).
• Predict the selling price of houses in some area.
• Collect data on several houses (# of BR, #BA, sq.ft, lot size, property tax) and their selling price.
• Can we use this data to predict the selling price of a specific house?
•Quantifying causality
• Determine factors that relate to the variable to be predicted; e.g., predict growth for the economy in the next quarter: use past history on quarterly growth, index of leading economic indicators, and others.
• Want to determine advertising expenditure and promotion for the 1999 Ford Explorer.
