Research Paper: OEE Analysis, Time Randomness, and IE Applications

This research paper delves into the application of Overall Equipment Effectiveness (OEE) analysis within an industrial engineering context. The paper addresses two key questions: firstly, it utilizes the Ranked Positional Weight (RPW) method to solve a task scheduling problem, commenting on the solution's quality and considering both deterministic and variable task times. The second question focuses on OEE calculations for three machines, analyzing factors such as availability, performance, and quality to determine each machine's OEE. The report provides detailed calculations for each machine, including net available time, ideal operating time, and lost operating time, and proposes strategies for performance improvement based on the OEE results. The analysis includes the impact of unplanned downtime and scrap rates on overall efficiency. The research paper concludes with a bibliography of relevant sources.

Industrial Engineering 1
RESEARCH PAPER ON INDUSTRIAL ENGINEERING
A Research Paper on OEE By
Student’s Name
Name of the Professor
Institutional Affiliation
City/State
Year/Month

Industrial Engineering 2
Question 1
Task Time Immediate predecessors
A 20 -
B 18 -
C 6 A
D 10 A
E 6 B
F 7 C,D
G 6 E,F
H 14 G
a. Solve using RPW with C = 30. Comment (quantitatively) on your solution quality.
Task Time Immediate
predecessors
PW RPW
A 20 - 87 1
B 18 - 87 2
C 6 A 47 3
D 10 A 43 4
E 6 B 33 5
F 7 C,D 27 6
G 6 E,F 20 8
H 14 G 14 9
Predecessors Diagram
Station Time remaining Tasks Time used
1 30, 10 A 20
2 30, 18, 6 B, C, 24
3 30, 20, 14, 7, 1 D, E, F, G 29
4 30, 14 H 16
LB = Summation of ti/c = 87/30 = 2.9
LE = Summation of ti/kc = 87/(4*30) = 0.725
SI = {(30-20)2 + (30-24)2 + (30-29)2 + (30-16)2}1/2

Industrial Engineering 3
SI = {(10)2 + (6)2 + (1)2 + (14)2}1/2
SI = {100+36+1+196}1/2
SI = 3331/2
SI = 18.25
Approaches of Processing Time Randomness
Depending of the properties of the samples, there are numerous approaches of addressing
time randomness, these approaches include branching approach, renewal approach, Gaussian
approach, Levy approach, Markov approach, martingales, and random walks (Karadgi,
2012).
b) Solve using RPW, with considering variation. Treat the task times as normally distributed
with a coefficient of variation equal 0.2. Assume that 95% of the time all tasks must be
completed. Comment on other approaches to address processing time randomness.
Coefficient variation = 0.2
Task completed = 95%
Actual time = previous time*coefficient of variation
Time (Task 1) = 20*0.2 = 4
By taking into consideration the coefficient of variation of 0.2 and 95% task completion, the
fallowing table of RPW can be gotten:
Task Time Immediate
predecessors
PW RPW
A 4 - 17.4 1
B 3.6 - 17.4 2
C 1.2 A 9.4 3
D 2 A 8.6 4
E 1.2 B 6.6 5

Industrial Engineering 4
F 1.4 C,D 5.4 6
G 1.2 E,F 4 8
H 2.8 G 2.8 9
With C=6
Station Time remaining Tasks Time used
1 6, 2 A 4
2 6, 2.4, 1.2 B, C, 4.8
3 6, 4, 2.8, 1.4, 0.2 D, E, F, G 5.8
4 6, 3.2 H 2.8
LB = Summation of ti/c = 17.4/6 = 2.9
LE = Summation of ti/kc = 17.4/(4*30) = 0.145
SI = {(6-4)2 + (6-4.8)2 + (6-5.8)2 + (6-2.8)2}1/2
SI = {(2)2 + (1.2)2 + (1)2 + (3.2)2}1/2
SI = {4+1.44+1+10.24}1/2
SI = 16.681/2
SI = 4.084
Question 2
An 8 hour shift is scheduled to produce three parts as shown below. The shift has two 10
minute breaks and a 5 minute clean up period. Calculate the OEE for each machine and
discuss how each might be improved.
M/C Cycle Production Scrap Unplanned
downtime
A 10 min 2240 50 32 min
B 45 min 450 25 18 min
C 70 min 229 11 22 min

Industrial Engineering 5
The first step is to determining the time factor for every machine.
Net Available Time: Every machine is expected to run for the full 8 hour shift hence the Net
Available Time for every machine is determined as follows:
Scheduled Time = 8 hours = (8*60) = 480 minutes
Planned Down Time = 2 breaks * 10 minutes + clean-up 5 minutes = 25 minutes
Net Available Time (NAT) = 480 – 25 = 455 minutes
Machine A
Unplanned Downtime = 32 minutes
Net Operating Time (NOT) = Net Available Time – Unplanned Downtime
NOT = 455-32 = 423 minutes
Ideal Operating Time (IOT): 2240 total parts *10 seconds = 22400/60 = 373.333 minutes
Lost Operating Time (LOT): 50 scrap parts * 10 seconds = 8.33 minutes
OEE of machine A can be determined as follows:
Availability: NOT/NAT = (423/455) *100 = 92.97%
Performance: IOT/NOT = (373.33/423)*100 = 88.26%
Quality: (IOT-LOT)/IOT = (373.33-8.33)/373.33*100 = 97.11%
OEE = A*P*Q = 92.97%*88.26%*97.77% = 80.22%
Machine A can be improved through improving the performance of the machine whose
percentage is the lowest among the other two factors.
Machine B
OEE factors can be determined as shown below:

Industrial Engineering 6
Net Operating Time (NOT) = Net Available Time – Unplanned Downtime
NOT = 455-18 = 437 minutes
Ideal Operating Time (IOT): 450 total parts *45 seconds = 20250/60 = 337.5 minutes
Lost Operating Time (LOT): 25 scrap parts * 45 seconds = 1125/60 = 18.75 minutes
OEE of machine A can be determined as follows:
Availability: NOT/NAT = (437/455) *100 = 96.044%
Performance: IOT/NOT = (337.5/437)*100 = 77.23%
Quality: (IOT-LOT)/IOT = (337.5-18.75)/337.5*100 = 94.44%
OEE = A*P*Q = 96.044%*77.23%*94.44% = 70.05%
Machine A can be improved through improving the performance of the machine whose
percentage is the lowest among the other two factors.
Machine C
OEE factors can be determined as shown below:
Net Operating Time (NOT) = Net Available Time – Unplanned Downtime
NOT = 455-22 = 433 minutes
Ideal Operating Time (IOT): 299 total parts *70 seconds = 20930/60 = 348.833 minutes
Lost Operating Time (LOT): 11 scrap parts * 70 seconds = 770/60 = 12.833 minutes
OEE of machine A can be determined as follows:
Availability: NOT/NAT = (433/455) *100 = 95.16%
Performance: IOT/NOT = (348.833/433)*100 = 80.56%
Quality: (IOT-LOT)/IOT = (348.833-12.833)/337.5*100 = 99.70%
OEE = A*P*Q = 95.16%*80.56%*99.70% = 76.433%

Industrial Engineering 7
Machine A can be improved through improving the performance of the machine whose
percentage is the lowest among the other two factors.
Bibliography
Karadgi, S., 2012. A Reference Architecture for Real-Time Performance
Measurement: An Approach to Monitor and Control Manufacturing Processes.
Colorado: Springer.
Lee, S., 2013. Frontiers of Assembly and Manufacturing: Selected papers from
ISAM'09'. New York: Springer Science & Business Media.
Vallespir, B., 2015. Advances in Production Management Systems: New
Challenges, New Approaches. Bordeaux: Springer.