Transcript MIE 754 Manufacturing & Engineering Economics

MIE 754 - Class #13
Manufacturing & Engineering
Economics
• Term Project
– Next week guest lecture
• Concerns and Questions
• Quick Review
• Today’s Focus:
Chap 5 Estimating for Economic Analyses
(continued)
Concerns and Questions?
 Mid-Term
Exam - to be determined
(approx 2-3 weeks), following Chap 17
 Supplemental
reading for Chap 17 to be
distributed shortly
 Reminder
class
- Chap 5 homework due next
Quick Recap of Previous Class

What is a cost estimate?

What’s the purpose of a cost
estimate?

Sources of errors in cost estimating

Sources of data

Quantitative estimating techniques
Quantitative Estimating
Techniques
1. Time-series - when cost (revenue)
elements are a function of time.
Collect data; study underlying
relationships.
• Regression - estimating causal
relationships within time-series data
• Exponential Smoothing - estimating
future extensions to historical data
patterns
Quantitative Estimating
Techniques
2. Subjective - expert judgment is
applied to the results of time-series
techniques (how future might differ
from the past)
• Delphi Technique - voice opinions
anonymously and through an
intermediary
• Technology Forecasting - procedures
for data collection and analysis to
predict future technological
developments and their impacts
Quantitative Estimating
Techniques
3. Cost Engineering - identify and
utilize various revenue/cost drivers
to compute estimates
Exponential Smoothing



Assumes trends and patterns of the
past will continue into the future
More weight on current data
No assumption of linearity
with ’= smoothing constant,
St = ‘xt + (1 - ‘)St-1
(0’1)
Usually (0.01’0.30)
(Forecast for period t+1,
made in period t) =
’(Actual data point in period t)
+ (1-’)(Forecast for period t,
made in period t-1)
’ = 1 implies?
’ = 0 implies?
Example Problem 5-8

Actual sales of a firm were 500 units for year 1
and 600 for year 2. You forecsted it would be
550 units for year 2, and now you wish to
forecast for year 3 and beyond.
• What would be your forecast for year 3 if
your smoothing constant was 0.1, 0.5, and
0.97?
• Suppose actual sales for years 3 - 7 are
700, 800, 700, 600, 600 respectively. What
would have been the forecast for each year
4-7 using the 3 smoothing constants?
• Desirability of low or high smoothing const?
Example Problem 5-8
Period #
Actual
Forecast @ ’ = 0.1
Error (Forecast Actual)
Forecast @ ’ = 0.5
Error (Forecast Actual)
Forecast @ ’ =
0.97
Error (Forecast Actual)
3
700
555
4
800
569.5
5
700
592.55
6
600
603.3
7
600
602.97
-145
-230.5
-107.45
3.3
2.97
575
637.5
718.75
709.38
654.69
-125
-162.5
18.75
109.38
54.69
598.5
696.96
796.9
702.1
603.1
-101.5 -103.04
96.9
102.1
3.1
Sources of Data

Accounting Records

Other Sources Within the Firm

Sources Outside the Firm

Research & Development
Quantitative Estimating
Techniques
3. Cost Engineering - identify and
utilize various revenue/cost drivers
to compute estimates
Manufacturing Cost Estimating
Objective:
To make a product that can be sold at a competitive price
yet yields a reasonable profit.
Determining Selling Price:
SP/unit = (Total Mfg. Costs for X units)(1 + profit)
X
To estimate the cost of a manufactured product, you need
a product design and process plan to compute direct
labor hours and material costs. A cost estimating
worksheet or spreadsheet is a useful tool for this.
Estimating Manufacturing Costs - Breakdown of Product Costs
Direct Costs
direct labor
materials
Product
Costs
Indirect Costs
plant overhead
general and administrative
engineering design/devlpmt.
supervision
quality control
etal
Developing Cash Flows for Feasible Alternatives
Basic Components
I. Work Breakdown Structure
II. Cost and Revenue Structure (classification)
III. Estimating Techniques (models)
I. Work Breakdown Structure (WBS)
The WBS technique organizes activities
into a hierarchical structure based on
either functional or physical categories
WBS - Organizational Schemes
• Functional
• Physical
WORK BREAKDOWN STRUCTURE
Functional
 Logistical Support
 Project Management
 Marketing
 Engineering
 System Integration
Physical
 Labor
 Material
 Energy
 Capital
WBS for Commercial Building Project (text or tabular format)
1.0 Commercial Building Project
1.1 Site Work and Foundation
1.1.1 Site Grading
1.1.2 Excavation
1.1.3 Sidewalks/Parking
1.1.4 Footing/Foundation
1.1.5 Floor Slab
1.2 Exterior
1.2.1 Framing
1.2.2 Siding
1.2.3 Windows
1.2.4 Entrances
1.2.5 Insulation
1.3 Interior
1.3.1 Framing
1.3.2 Flooring/Stairways
1.3.3 Walls/Ceilings
1.3.4 Doors
1.3.5 Special Additions
II. Cost and Revenue Structure
•
Investment Costs (Fixed and Working
Capital)
•
Labor Costs
•
Material Costs
•
Maintenance Costs
•
Property Taxes and Insurance
•
Quality (and Scrap) Costs
•
Overhead Costs
•
Disposal Costs
•
Revenues from all Potential Sources
•
Salvage or Market Values
III. Estimating Models (techniques)
• Indexes
• Unit Method
• Exponential Costing
• Learning Curves
Cost Indexes
 A dimensionless
number that indicates
how costs and prices change with time
 Used
to estimate present or future costs
based on past costs.
 In-class
 Also
notes and examples follow
refer to Virtual Classroom web site
for examples
Unit Method
Utilizes a “per unit” factor that can be
estimated effectively.
- construction cost per square foot
- operating cost per mile
- maintenance cost per hour
Refer to on web site for example calculations
Exponential Costing
This estimating model assumes that
cost varies as some power of the
change in size or capacity.
X
C A  SA 
    
CB  SB 
X
S A 
CA  CB  
SB 
C = cost, S = size, X = cost capacity factor
In-class example
Sources and Limitations of Indexes
 Sources
• Engineering News Record
• Producer Prices and Price Indexes
• Consumer Price Index Report
 Limitations
of Indexes
• They represent composite data
• They average data
• Various base periods are used for different
indexes
• Accuracy is limited for periods greater than
10 years
Learning Curves
As a task is performed repeatedly, learning occurs
and the number of input resources (labor, material)
required to complete successive units decreases.
Most learning curves are based on the assumption
that the number of input resources needed
decreases by a constant percentage each time the
number of units produced doubles.
Zu = Kun
Zu = time to make uth unit
K = time to make first unit
s = learning curve parameter
(e.g., s = 0.8 for an 80% learning curve)
log (s)
n =
log (2)
EXAMPLE
It has been determined that a 90% learning curve
applies to a particular assembly operation. It takes 30
minutes to assemble the first unit. How many
minutes are required to produce the 5th unit? the 30th
unit?
n = log (0.9) / log (2) = -0.152
Z5
=
Z30 =
30(5)-0.152
= 23.49 minutes
30(30)-0.152 = 17.89 minutes
Other computations related to learning curves:
Tu = cumulative time to produce u units
= K[1n +2n +...+un]
Cu = cumulative average for u units = Tu/u
Continuing example,
T5 = 30[1-0.152 + 2-0.152 + 3-0.152 + 4-0.152
+ 5-0.152] = 130.18 minutes
C5 = T5 /5 = 130.18/5 = 26.04 minutes per unit
Learning Curves - Example
A batch of 50 throttle assemblies defines one output unit
and 36.48 factory labor hours is based on the 16th output
unit. What was the number of factory labor hours
required for the first batch of 50 throttle assemblies, and
what is your estimate of the labor hours needed for the
64th and 100th output units, assuming a 90% learning
curve?
How many units must be produced until only 24.85
factory labor hours are required?
Worked In-class