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- 2 -
EPT1601
UNIVERSITY OF SOUTH AFRICA, SCHOOL OF ENGINEERING, CONTINUOUS ASSESSMENT
MAJOR TEST NO: 4
Semester Course
EPT1601
ENGINEERING PRODUCTION TECHNOLOGY I
Examiner:
K MASENYA
Moderator:
M SOMBANE
External Moderator:
Marks: 55
Weight of Assessment: 35%
Duration: 1.5 hours
INSTRUCTIONS TO ALL STUDENTS:
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PLATFORM.
Additional student instructions
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accepted.
- 2 -
EPT1601
[TURN OVER]
QUESTION 1
1.1
Outline the operations, finance/accounting and the marketing functions of the
following two organisations:
(4)
a.
Fashion house
b.
Bakery
1.2
Discuss how improvement in quality leads to reduced costs for a bakery like
Albany bread.
(6)
1.3
The following information has been collected at a steel manufacturing company
in Durban:
Week 1
Week 2
Production Quantity
2 000
2 500
Labour
5 persons during a 90
hour-work week at R50
per labour hour
4 persons during a 90
hour-work week at R50
per labour hour
Materials
300 Kg at R24 per Kg
290 Kg at R26-50 per Kg
Utilities
R30 000
R33 000
Capital
R5000
R7000
1.3.1 Calculate the change in multi-factor productivity between week 1 and
week 2.
(6)
[16]
QUESTION 2
2.1
Given the following causes of poor product quality at a job shop in Pretoria,
develop a Pareto chart to analyze those concerns. (Show full calculations
including the percentages and the cumulative percentages)
(7)
- 3 -
EPT1601
[TURN OVER]
Paint defects
Number of defects
Incorrect feeding speed
180
Damaged material
90
Inexperienced operator
40
Unclear instruction -
30
Worn out screws
25
Wrong drill bit
230
Other
10
2.2
A company manufacturing motorcycles and scooters has the following activities
that are required to produce an electric scooter. The times in the table are given
in minutes. Demand forecast indicate net requirements of 40 units in a 8-hour
shift.
Activity
Operating
Time (min)
Predecessor
A
3
-
B
5
-
C
2
A
D
4
A
E
3
B
F
6
D,E
G
3
C, D
H
5
D,F,G
I
3
H
J
2
I
2.2.1 Draw the precedence diagram for the assembly line.
(4)
2.2.2 Calculate the minimum number of workstations required for this assembly
line.
(3)
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EPT1601
[TURN OVER]
2.2.3 Assign the activities to workstations in order to balance the assembly line.
Show full calculations including the cumulative station times and the
unassigned station times.
(7)
2.2.4 Calculate the efficiency of the assembly line.
(2)
[23]
QUESTION 3
3.1
Quality loss function identifies all costs associated with poor quality and shows
how these costs increase as the product moves away from being exactly what
the customer wants. List four examples of costs associated with poor quality.
(2)
3.2
The sales of DSTV Explora decoders at Makro over the past year are shown in the
table below:
Month
1
2
3
4
5
6
7
8
9
10
11
12
Sales
33
28
24
31
23
36
44
40
25
43
40
34
3.2.1 Using exponential smoothing with a smoothing constant of 0.3, determine
the forecast sales for month 9 to month 12 given a forecast demand of 35
units in month 8. Round the forecasts to the nearest whole number.
(4)
3.2.2 Determine the forecast sales for month 9 to month 12 using a three-
month moving average method. Round the forecasts to the nearest whole
number.
(2)
3.2.3 On a measure of mean absolute deviation (MAD), which forecasting
method is more accurate in forecasting the sales of the Explora
decoders? Show full calculations and interpret your results. Use the table
below to help you answer the question.
(8)
Month
Actual
sales
Forecast
sales
Forecast error Absolute
forecast error
Month 9
Month 10
Month 11
Month 12
- 5 -
EPT1601
[16]
TOTAL MARKS =55
FULL MARKS = 55
©
UNISA 2023
i
EPT1601
FORMULAE SHEET:
bx
a
y
+
=
ˆ
x
b
y
a
−
=
−
−
=
2
2
x
n
x
y
x
n
xy
b
N
T
Time
Cycle
=
_
Inputs
Outputs
oductivity
=
Pr
(
)
1
1
1
−
−
−
−
+
=
t
t
t
t
F
A
F
F
=
−
i
i
i
ix
i
Q
Q
d
coordinate
x
PD
QH
Q
DS
TC
+
+
=
2
=
−
i
i
i
iy
i
Q
Q
d
coordinate
y
(
)
2
/
1
2
p
d
Q
Q
Inventory
aver
−
=
=
ss
L
d
L
d
ROP
+
=
=
C
t
n
Workstatio
Number
=
min
_
n
Forecast
Actual
MAD
i
i
−
=
CycleTime
stations
NumberWork
TaskTimes
Efficiency
actual
=
(
)
2
/
1
2
p
d
QH
Q
DS
QH
Q
DS
TC
−
+
=
+
=
−
=
=
=
=
p
d
H
DS
POQ
IP
DS
H
DS
EOQ
1
2
2
2
%
100
Pr
Pr
Pr
_
Pr
1
1
−
=
−
−
n
n
n
oductivity
oductivity
oductivity
Change
oductivity
MAD
Error
Cumulative
MAD
Forecast
Actual
Signal
Tracking
i
i
=
−
=
_
©
UNISA 2023
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