1. Investigation on the impact of certain
parameters on Kanban-based system’s
performance
Linnan Zhuang
Yue Gui

2. Introduction and Objectives
Kanban is a Japanese word for “signboard” which means viewing board. It basically
works as a scheduling system which signals the workstations when to work, how
much to produce, when to stop, etc. It was first invented by Taiichi Ohno, an
industrial engineer at Toyota[1], who utilized this in TPS (Toyota Production System)
to improve manufacturing efficiency. Kanban is a great method to achieve
Just-in-time production, which aims to reduce inventory, maximize productivity,
reduce rework and improve throughput. It’s an epitome of lean manufacturing.
Because of its efficiency, kanban was so successful that it gradually transcended
manufacturing industry and became widely used in other areas such as software/IT[2].
Figure 1[3] shows the simple layout of a typical kanban system. On the production
floor, the product flows like what happens on the floor of a push system. However,
each workstation does not start working unless it receives kanban signal, which is the
key difference between a pull system and its counterpart.
Figure 1: Layout of Kanban System
The advantages of kanban and pull system likewise are obvious. It is capable of
limiting WIP (work-in-process) and optimizing inventory along the production line.
Through reducing waste and scrape and inspecting work items, it makes the
production line leaner[4]. And because of all these virtues, it leads to total cost
reduction.
Due to its wide applicability and practical application, we are immensely interested
and decide to take a look at this and try to make sense of kanban system by simulation
and analysis through simple models based on kanban.
In this paper, we are trying to investigate how variability in processing time, customer
demand and kanban number affects system’s WIP and cycle time using simulation
models based on SIMUL8.
Research Methodology
The research study was carried out first by conducting a literature review, which is a

3. usual method to dive into a research subject. And for the project proposal the
literature review was done and explained in the report. After the literature research, a
deeper analysis of those papers was conducted and targeted issues and questions were
identified.
Simulation models based on kanban system was then formulated according to models
built in a research paper from F.T.S Chan[5]. And design parameters, variables and
relevant performance measures were then identified.
Multiple simulations were then run under different parameters. Simulation results
were obtained which led to further analysis and discussion. Finally, a conclusion was
drawn based on analysis results.
Limits and restriction was found and pointed out based on a reevaluation of the whole
research process, which includes the understanding and realization of the design
concept, simulation modelling, parameter setting, result obtaining and analysis, etc.
Further recommendation was made following the reevaluation process, questions to
be asked of this research process in terms of how to improve it are as follows:
1) How to improve the simulation model to be more reflective of what happens
in a manufacturing plant?
2) What can be done to improve the whole analysis process?
3) What is the future direction from this system?
Simulation Model
Simulation models were built in SIMUL8. As shown in figure 2 and figure 3, the
system has six workstations and five queues used as buffers. Those red bins at the
bottom of the figure are kanban posts. The number of kanban in both systems changes
in different case scenarios. Visual logic is used to simulate the kanban signal.
1) In the first model below, the processing time at each workstation is normally
distributed with an average of 2 minutes and standard deviation of 0.1, 0.3,
0.5, 0.7, 0.9. Because of the change in standard deviation, this simulation
model is variable and that’s what we try to find out-the impact of variability
on the level of WIP and the system’s cycle time. A set of kanban number is
chosen: 5, 10, 20, 40, 60, 80, 100. To ensure that each workstation would not
starve, arrival at the starting point is unlimited. Simulation is run for 1 month.
Figure 2: Simulation model 1
2) In the second model shown below, the processing time at each workstation is
2 minutes. To add one more variable in this model, another starting point is
added here to simulate customer demand, which is set as fixed inter-arrival
time: 0.5, 1, 2, 4, 10, 20, 50, 100(minute). This model aims to investigate the

4. influence of customer demand on the average utilization rate, level of WIP
and cycle time. And like the first model, a set of kanban number is chosen: 5,
10, 20, 40, 60, 80, 100. Likewise, simulation is run for 1 month.
Figure 3: Simulation model 2
Design parameters, variables and performance measures
The design parameters in this report are variability in processing time, kanban number
and customer demand. The performance measures are level of WIP and cycle time.
The objective of this report is to investigate how change in such design parameters
would affect the performance measures.
Results and Analysis
1) In the first simulation model, the impact of variability in processing time on
average WIP is shown in figure 4. Since the processing time is normally
distributed in this simulation, the standard deviation is the variability factor.
And it is obvious that as the standard deviation increases, level of WIP in the
system goes up. However, when there is not plenty of kanbans (case scenario
of 5, 10, 20) in the system, the increase in WIP is not marked. Number of
kanban seems to become the main contributor to WIP under these
circumstances.
Figure 4: Impact of variability in processing time on average WIP
0
10
20
30
40
50
60
0.1 0.3 0.5 0.7 0.9
WIP/item
Variability/Standard deviation
WIP-Variability in processing time
100
80
60
40
20
10
5
Kanban
number

5. The trend of change in cycle time is basically the same as WIP. As standard
deviation goes up, cycle time in the system increases. When number of kanban
is low, cycle time does not change markedly in relation to standard deviation.
Figure 5: Impact of variability in processing time on cycle time
2) In the second simulation model, kanban number and customer demand both
contribute to the change of average WIP and cycle time. Figure 6, figure 7 and
figure 8 shows the impact of kanban number on WIP, cycle time and
utilization rate, respectively.
a) As shown in the figure below, WIP generally increases as the kanban
number goes up. What stands out in the figure is the blue curve at the
bottom. When the customer demand is significantly high (inter-arrival
time is 0.5 minute), WIP goes up when the number of kanban increases.
However, it levels off when kanban is abundant enough.
10
20
30
40
50
60
70
80
90
100
110
0.1 0..3 0.5 0.7 0.9
Cycletime/minute
Variability(Standard deviation)
Cycle time-Variability in processing time
100
80
60
40
20
10
5
Kanban
number

6. Figure 6: Impact of kanban number on average WIP
The impact of kanban number on system’s cycle time shown below is
similar. Cycle time increases as number of kanban goes up. And the
higher the customer demand, the less impact of kanban number there is
on system’s cycle time.
Figure 7: Impact of kanban number on cycle time
What is shown in figure 8 indicates that the higher customer demand, the
higher the average utilization rate is. But with a fixed customer demand
in each case scenario, number of kanban does not affect utilization rate
too much.
0
50
100
150
200
250
300
350
400
450
500
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
WIP/item
Kanban number
WIP-Kanban number
0.5
1
2
4
10
20
50
100
Customer
demand
(Inter arrival
time)/minute
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Cycletime/minute
Kanban number
Cycle Time-Kanban number
0.5
1
2
4
10
20
50
100
Customer
demand
(Inter arrival
time)/minute

7. Figure 8: Impact of kanban number on utilization rate
b) In order to fully investigate the impact of customer demand on WIP and
utilization rate, different figures were plotted using customer demand as
the variable. The impact of customer demand on level of WIP is shown in
the figure below.
Figure 9: Impact of customer demand on WIP
As seen in figure 9, the fewer kanban number, the flatter the WIP curve is.
And the impact of customer demand on level of WIP is not significant
when there is not many kanban in the system.
The impact of customer demand on utilization rate is shown in figure 10,
which clearly indicates how change in customer demand affects
utilization rate in the system. What is interesting about the trends in the
0
10
20
30
40
50
60
70
80
90
100
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Utilizationrate/%
Kanban number
Utilization rate-Kanban number
0.5
1
2
4
10
20
50
100
Customer
demand
(Inter arrival
time)/minute
0
50
100
150
200
250
300
350
400
450
500
0.5 1 2 4 10 20 50 100
WIP/item
Customer demand(Inter arrival time)/minute
WIP-Customer Demand
100
80
60
40
20
10
5
Kanban
number

8. figure is that at both ends of the curves, utilization rate gradually levels
off. When the inter arrival time is 0.5, the rates are approaching 100 %,
which means all the workstations are very busy and efficient in
processing the work items. While it is just the opposite that utilization
rates are immensely low when inter arrival time is 100 minutes. Both
ends of curves seem to approach their respective limit values.
Figure 10: Impact of customer demand on utilization rate
Conclusion
Based on the relevant research papers and simulation results, conclusion was
drawn:
1) The more variable processing time is, the more WIP and cycle time there is in
the system.
2) When there is not plenty of kanbans in the system, increases in WIP and cycle
time is mainly restricted by the number of kanban rather than variability.
3) The higher customer demand at the end, the more WIP there is in the system.
However, a low number of kanban plays a greater role as it limits the growth
in WIP regardless of the change in customer demand.
4) With a fixed customer demand, utilization rate is not sensitive to change in
kanban number. It barely changes in spite of the sharp change in kanban
number
5) Utilization rate is greatly influenced by customer demand. And the higher the
customer demand, the higher the utilization rate is.
Therefore, in order to make a kanban-based system work efficiently, each
workstation should work at a similar pace to reduce the variability. In addition, the
kanban number should not be too low.
0
10
20
30
40
50
60
70
80
90
100
0.5 1 2 4 10 20 50 100
Utilizationrate/%
Customer demand(Inter arrival time)/minute
Utilization rate-customer demand
100
80
60
40
20
10
5
Kanban
number

9. Limits and Recommendation
One of the limits about our simulation analysis is that the simulation models are
simple, results obtained from the models can only indicate patterns to a limited degree.
To improve the simulation model and build a more authentic one reflective of
real-world manufacturing environment, study could be done in terms of building more
workstations with multiple servers, adding potential downtimes, adding variability in
quality of work items, etc.
To verify simulation results mathematically, equations could also be added to the
analysis. This is another limitation of our report. It would have been more effective
and valid with explanation and verification of math formula.
In addition, comparison of push system, pull system and a hybrid system could
be done based on this. Future work could be done in this direction.
Reference
[1] “Kanban.” Wikipedia.
[2] What is Kanban?, http://www.swiftkanban.com/kanban/what-is-kanban/
[3] Kanban replenishment cycle.
http://functionalguy.blogspot.ca/2011/06/kanban-replenishment-cycle.html#axzz4
5JaHILX8
[4] Advantages and disadvantages of Kanban.
http://functionalguy.blogspot.ca/2007/04/advantages-and-disadvantages-of-kanba
n.html#axzz43nnZgRTT
[5] F.T.S Chan, Effect of kanban size on just-in-time manufacturing systems,
Journal of Materials Processing Technology, Volume 116, Issues 2–3, 24 October
2001, Pages 146-160, ISSN 0924-0136.