Simulating a manufacturing facility to test possible improvements (one at a time) suggested using tools of Lean Manufacturing

1. Hrishikesh Khamkar
Simulation Research Project
Using Arena simulation to assess the effectiveness of a future state map, in Value
Stream Mapping, by quantifying the change in non-value added time, as compared
to current state map.
Abstract:Value Stream Mapping is a technique of lean manufacturing in which the whole value stream of a
production system is plotted on paper to understand the flow of material and information, and to find out
areas with room for improvement. These improvements are done one by one, starting with making
changes to factors which are easiest to modify (Rother & Shook, 1999). However, a Value Stream Map is
a snapshot of an actual shop floor, and it may or may not give a realistic feel of the actual working
conditions. Using simulation to analyze various aspects of a working shop floor production system helps
us to assess the behavior of our production system in uncontrolled situations, and even in circumstances
with increased complexity. Also, the results obtained can be analyzed by Lean coordinators to gain a
better perspective of the operating system (Gullander, 2009). In this paper we would be studying and
documenting the effect of various changes, made to certain operating characteristics of our system, on
certain useful output parameters. Numerical values of this study will be obtained by running modeled
simulations, using ARENA simulation software.
Introduction:In the current world scenario, it has become of utmost necessity to „Continuously‟ seek improvements in
our manufacturing systems. Quick analysis of possible variations in our manufacturing systems facilitates
increase in productivity, which is the primary goal of most manufacturing companies (Gullander, 2009).
Lean manufacturing is one of the most sought after method to improve productivity of any company.
However, even when applying Lean tools it is of utmost necessity to know where to apply them and what
kind of improvement should we expect from it (to know we are doing it right). Along with this, it
becomes necessary to account for the behavior of the new system, achieved by applying lean techniques,
under the effect of system variations and uncertainty in the arrival of input variables (Amr, 2011). This
calls for a need to use powerful tools, like Simulation, to give us the liberty to study the effects of
proposed changes to the manufacturing systems. Simulation can be used to implement proposed changes
to a Value Stream Map, and then collect statistics which can be further analyzed to derive conclusions
about implementing those changes.
Various applications have shown that we require Simulation to find and study factors which couldn‟t be
seen by using Lean alone (Marvel, 2006). Lean Manufacturing, when used in Value Stream Mapping, acts
as an assurance step while we are trying to bridge the gap in between the current state map and the future
state map. This helps us to point out the crucial factors to look into, while we plan to improve our
productivity (Amr, 2011). In this paper, we try to study certain factors in detail, elimination of which can
help us reduce Non-value added time of the product. Also, we are able to analyze and observe the change
in statistical output, if we make certain changes which may seem like an obvious improvement in the first

2. look, but may affect certain performance parameters negatively. This becomes a good foundation to make
ourselves realize to tread cautiously when working with Value Stream Mapping, because it‟s not just
about making changes, but also about making the right kind of changes (Harris, 2001).
Literature Review:Since the paper is a study which consists a combination of Simulation and Lean Manufacturing, books
like „Simulation with Arena‟(Kelton, 2004) and „Creating Continuous Flow‟ (Harris, 2001) were of
immense use. Along with this, papers related to „Dynamic Value Stream Mapping‟ and doing „Value
Stream Mapping using Simulation‟ from Winter Simulation Conferences provided with literature related
to the scope of the idea about applying simulation to Value Stream Mapping. Also, research papers of
students pursuing studies related to justifying the applicability of simulation to lean manufacturing, were
of use.
Procedure:As mentioned by Gullander and Solding (2009), there are some weaknesses related to using
Simulation or Value Stream Mapping, alone, to analyze and improve the performance of any
manufacturing system. Hence, it is necessary to embed these two important tools into each other,
such that the combination of these two is able to replicate the real life situation of our
manufacturing system as well as leave sufficient room to introduce variability and randomness
into various performance parameters of the whole system. A proper gel of these two techniques
helps us to keep the system in dynamic motion, while we study the effects of randomly occurring
events on the overall system.
Here, we are using different models which have been designed to work with parameters, suitable
to replicate most common problem-causing elements in a manufacturing system. These include
effects on the output by an unbalanced line, effects of changing layout of a line to reduce
accumulation of inventory before stations, types and methodologies of using same
transportations but in different ways to reduce or completely eradicate travel times (non-value
added time) etc. The data for all the models has been assumed after referring to examples in the
book, “Creating Continuous flow” (Harris, 2001). The simulation model is designed to replicate
a large element of randomness in most common aspects of a manufacturing system like, travel
times, operator scheduling, breakdowns of a machine, inspection times etc.
According to Harris & Rother (2001), for a production line to meet the demands of the customers
the rate of production should be equal to the takt time (consumption rate) of the customers. To
get this done, the production line must be designed to have minimum or zero amount of Nonvalue adding activities. This include waste due to motion, transportation etc. (7 wastes of lean).
Hence, waiting in a queue for a long time indicates that the entity is spending time in the system
which is not contributing to its value addition, from a customer‟s point of view. Therefore, we
try to compare the effect of certain changes on the time spent by all the entities in the queue.

3. Application and analysis:We will apply simulation techniques to models developed to imitate real life situations on the shop floor.
Effect of Downtime on the waiting time in a queue:To understand the effect of downtime on waiting time of a system, we simulate a production line which
has a layout as shown in Appendix 1.We have constructed two models, one with failure as a part of the
machines operations and another without the failure. Usually, failure or breakdown of a resource is a very
common problem in manufacturing systems, and it has following disadvantages,
It is completely unpredictable
Time to restart the machine is dependent on the nature of the breakdown
During peak hours of production, it can easily lead to bottleneck formations in the production
line.
To overcome this problem most companies schedule regular checks and maintenance cycles of the
resource machines. Even though this causes a temporary stoppage in the production system, it is very
beneficial in the long run. Doing regular machine checks helps to keep up the performance levels and
prevents unprecedented delays due to machine breakdown. (Assumptions have been made about the
process times and arrival or orders to the shop floor. Also, the failure module is designed based upon
assumed values for time of occurrence of failure and time taken to get rid of the failure).
In the simulation model of Appendix 1, the resource at process assemble is given a failure schedule,
because of which it breaks down occasionally, leading to the formation of long queues in the process. To
overcome this problem, the shop floor supervisor can use Arena to simulate the regular checks of the
operating machine (in this case assembly).
To imitate a real life situation the user is given the liberty to choose the timings of machine checks. This
is basically done using the Arrival blocks and Assign elements to use keyboard keys for deciding when to
schedule a check-up of the machine ( “D” for decrease resource and “I” for increase resource)( this model
is partially based upon sample model from SMARTS folder of Arena simulation, controlling resource
capacity). Based upon a shop floor manager‟s perception combined with the performance expectations
and past usage of the machine, the user can decide when to do machine checks. The simulation is carried
on for a duration of 3 days and 3 replications. Statistics gathered are primarily about average time spent
by an entity, in the queue of assemble process, throughout the simulation.
Observations:Case 1:- Assembly process with uncertain
occurrences of breakdown
Case 2:- Assembly process with occasional
checks of machine to avoid breakdown
Time to complete Assembly process per entity
Time to complete Assembly process per entity
Average time = 2.921895 hours
Average time = 2.843519 hours
After completion of the simulation we realize that the average time taken by the entity in the
assembly process is greater when there are breakdowns due to failure as compared to the
simulation when there are frequent machine checks (3-4 every two hours). (Please refer
Appendix 3 for detailed data collection)

4. Effect of reduction in process times and changes in resource allocation on waiting time in
queue:Queue Name
Waiting time
Process A1.Queue
Number waiting in
queue
0.01767777
Process A1.Queue
0.4835
Process A2.Queue
0.01703996
Process A2.Queue
0.4887
Process A3.Queue
0.03090375
Process A3.Queue
0.8950
Process A4.Queue
0.04233805
Process A4.Queue
1.2646
Queue Name
Number waiting in
queue
Queue Name
Waiting time
Process
C1.Queue
Process
C2.Queue
Process
C3.Queue
Process
C4.Queue
0.03639
Process
C1.Queue
Process
C2.Queue
Process
C3.Queue
Process
C4.Queue
1.0168
Queue Name
Table 1
0
0.0475
0
0
1.4416
0
Table 2
Queue Name
Number waiting in
queue
Queue Name
Waiting time
Process B1.Queue
0.1803
Process B1.Queue
4.7925
Process B2.Queue
0.1978
Process B2.Queue
5.9619
Process B3.Queue
0.1585
Process B3.Queue
4.6935
Process B4.Queue
0.1799
Process B4.Queue
5.4417
Table 3

5. In Value Stream Mapping we prefer to take the current state and based upon the output
requirements of the production line we plan the production line (Rother, 2009). Based upon this
requirement the appendix 4 model is planned to show the improvements happening in a system
as we make step by step improvements. The first step indicates repositioning of the inspection
station and increase in resources. Referring table 3 we realize that there isn‟t much of a change.
While in the next step we decrease the process time for process 2, which results in decrease in
the waiting time of the queue, thereby indicating that if we want to improve the process and
decrease the overall waiting time then we should focus more on doing kaizen activities to reduce
the process times.
Optimizing the transportation time in a production system:By reducing the transportation time we can facilitate fast delivery of raw material, subassemblies etc. In this case however, when you have a shortage of resources and transporters,
then we should try to increase the performance of the transportation system by either improving
the speeds of its movements or by increasing optimization of transporter motors (i.e. making
them cover as much distance as possible in as less time as possible) or increase the number of
transporters. (Refer Appendix 5)
Conclusion and future work:Applying simulation to VSM has a its own advantages, as mentioned by Gullander (2009) like,
1. Getting a real time view of the shop floor
2. Viewing the bottlenecks in the system
3. Less cost incurred in trying new layouts on shop floor
4. Improving worker utilization (after ensuring improvements in simulation)
Keeping this in mind it seems advantageous to use simulation for assessing the effect of
randomness on the working of system. This helps us to know how a particular layout or resource
will behave if it is overloaded or provided with new resources (Donatelli. N.D). As mentioned by
Gullander (2009), it will always be useful to make proper prototypes of actual simulation layouts
rather than trying them on fictional plants. Along with this it is of utmost necessity to get
accurate data (as much as possible) to plot the input parameters and distributions for operator
times. This will make the output more realistic and even more useful when making suggestions
to a production facility regarding what changes will be needed to get what they want.
In future work, it will be good to accommodate production control plans with the actual
simulation and then make improvements to the process plans.

6. References:Donatelli, A & Harris, G. (n.d.).Combining Value Stream Mapping and Discrete Event Simulation.
Retrieved from http://www.scs.org/confernc/hsc/hsc02/hsc/papers/hsc003.pdf
Gullander, P & Solding, P. (2009). Concepts for simulation based value stream mapping,
Proceedings of the 2009 Winter Simulation Conference M. D. Rossetti, R. R. Hill, B. Johansson, A.
Dunkin and R. G. Ingalls, eds. Retrieved from http://www.informs-sim.org/wsc09papers/215.pdf
Kelton, D & Sadowski, R & Sturrock, D. (2004). Simulation with Arena
Rother, M & Shook, J. (1999). Learning to See, The Lean Enterprise Institute, Brookline,
MA.
Amr, A& Amr, M &Crowe, J. (2011). Integrating Current State and Future State Value Stream
Mapping with Discrete Event Simulation: A Lean Distribution Case Study. Proceedings of
SIMUL 2011: The Third International Conference on Advances in System Simulation.
Marvel, J & Standridge, C. (2006).WHY LEAN NEEDS SIMULATION. Proceedings of the 2006 Winter
Simulation Conference
Harris, R & Rother, M. (2001). Creating Continuous Flow: An Action Guide for Managers, Engineers
and Production Associates
Appendix:Appendix 1:- model to indicate model with failures
Appendix 2:- model to indicate model with machine checks
Appendix 3:- excel sheet comparing statistics
Appendix 4:- model to show various process and improvements made in them
Appendix 5:- Model to show transportation model
Appendix 6:- model to show improvement in transportation model
(Uploaded as separate files on RIT MyCourses portal)