Presenting at the Lanner predictive simulation conference, 2016, Justyna Rybicka from Cranfield University explores the use of Lanner simulation software WITNESS to model support decision making tool for flexible manufacturing system optimisation.

1. Use of WITNESS software to model support
decision making tool for flexible
manufacturing system optimisation
Justyna Rybicka, PhD Researcher
Lanner User Group Event
28th of April, MTC, Coventry

2. AGENDA
• WITNESS role in research
• Background
• FMS definition
• Problems in modelling FMS
• Methods of data collation- for better modelling
• FMS case study on optimisation of flexible production line
• Acknowledgements

3. WITNESS role in research
Simulation as reconfiguration capability
development tool for FMS based production
Provision of a simulation environment to test
complex FMS configuration where:
• “Black box” activities need to be accurately
modelled
• Mix-model production needs to be
addressed
• Production requirements change rapidly

4. Background
• Customisation and product diversification
is becoming standard
• Manufacturers seek solutions to unique
capabilities where there is a need for
product range diversification providing line
efficiency and production flexibility
• Flexible manufacturing systems (FMS)
provide a unique capability to
manufacturing organisations where there is
a need for product range diversification by
providing line efficiency through production
flexibility
• Discrete event simulation is a simulation
approach considered as successful in
addressing real world problems in
manufacturing sector

5. Flexible Manufacturing System
• A flexible manufacturing system (FMS) is a
group of numerically controlled machine
tools, interconnected by a central control
system.
• Operational flexibility is enhanced by the
ability to execute all manufacturing tasks
on numerous product designs in small
quantities and with faster delivery.
Flexible manufacturing system basic layout

6. Data driven DES modelling for FMS
Due to the logic being proprietary to the system
designer, some of the behaviour of the system’s
hardware components cannot be accurately
replicated in coding. To overcome this, system
behaviour has been observed and the logic
inferred in the model
Limited understanding of the machine behaviour
and therefore inaccurate modelling can affect
the results of the simulation run
The quality of data fed in to the simulation
affects the quality of outputs which in
consequence translates to the trust that the
simulation is reliable source of analysis
MHS - Logic process flow

7. Approach for data collection
Observe the
behaviour
Identify
distinctive
actions
Collect the
data related
to distinctive
actions
Convert data
into
simulation
friendly
format
Use data as
simulation
input
CHALLENGE
Obtaining algorithms for FMS stacker crane not impossible due to IP
SOLUTION
Method for collection of primary data from shop floor through videoing

8. FMS challenges
Flexibility of FMS is a major argument for its
benefits to industry
Joseph (2011) defines flexibility as the ability
of a system to respond effectively to changes
[…]
GAP: limited insight into systems that assume
total flexibility in FMS
This research…
…investigation into optimal production set-up
with total flexibility on CNC machines in FMS
context is explored.

9. Case Study
FMS
• PLC with 2 types of CNC machines
• Parts on pallets
• Two types of parts processed
• 68 storage spaces
Modelling Approach
Full control over the process flow – functions and rules
Flexibility on:
• Key production elements (no of machines, no of
pallets)
• Cycle times
• Product mix in production
• Further plans: levels of flexibility in routing

10. Part Sequencing
• Stage and location
• 5 operations in CNC and manual operations
• 44 steps in production

11. Conceptual Model
Included in the model Excluded from the model
FMS and surrounding it manual
operations
Total flexibility of FMS operation
Two parts are machined on each
pallet
Shift time– 24/5
4 type 1 machines (M1)
1 type 2 machines (M2)
Manual operations dedicated to
stations (no flexibility)
Raw material is always available
Labour
Statistical Breakdowns
Transportation of parts
Set-up times
Robinson (2011)

12. Experimentation
Experimental Factors Responses
Sequence of parts (S1, S2)
Number of pallets
(N2,N3,N4)
Machine breakdown
(M4,M3)
Machine utilisation
Throughput
Summary of the model experimental factors and
responses
Experiment set-up
• Deterministic model
• 1 simulation run per experiment
• Warm-up period: 10 weeks
• Run time: 52 weeks

13. Design of Experiments
Scenario Parameters
No. Sequence (S) Number of pallets (N) Number of machines (M)
Base Case 1 3 4
1 2 3 4
2 1 2 4
3 2 2 4
4 1 4 4
5 2 4 4
6 1 3 3
7 2 3 3
8 1 2 3
9 2 2 3
10 1 4 3
11 2 4 3
The design of experiments set-up.

14. N - parameter
0
100
200
300
400
500
600
2 3 4
TotalUtilisation
N- parameter
Sum of M2 Utilisation % Sum of M1 Utilisation %
720
740
760
780
800
820
840
860
880
900
2 3 4
AverageThroughput
N- parameter

15. M - parameter
0
100
200
300
400
500
600
700
800
900
3 4
TotalUtilisation
M - parameter
Sum of M2 Utilisation % Sum of M1 Utilisation %
1100
1150
1200
1250
1300
1350
3 4
AverageThroughput
M- parameter

16. Combined scenarios utilisation
0
20
40
60
80
100
120
140
160
180
200
3 4 3 4 3 4 3 4 3 4 3 4
2 3 4 2 3 4
1 2
Totalutilisation
M, N, S - parameters
Sum of M2 Utilisation % Sum of M1 Utilisation %

17. Combined scenarios throughput
0
50
100
150
200
250
300
350
3 4 3 4 3 4 3 4 3 4 3 4
2 3 4 2 3 4
1 2
AverageThroughput
M,N,S- parameters

18. Conclusions
The developed modeling demonstrate how WITNESS can support flexible set-up in flexible
manufacturing system
General conclusions can be drawn about the FMS behavior to support flexibility:
• The sequence of operation around the FMS had impact on the FMS performance
• Optimisation of the number of pallets in the system is key as its shortage can lead to FMS
starvation and its oversubscription creates bottlenecks in the system affecting throughput

19. Acknowledgement
Many thanks to Advanced Manufacturing Supply Chain Initiative (AMSCI) for supporting and funding
the research in automotive industry. Also, great thanks to the industry collaborators who supported
us in this work - Cosworth.

20. Thank You
Justyna Rybicka
PhD in Manufacturing Systems
Cranfield University
Cranfield, MK430AL
Email: j.e.rybicka@cranfield.ac.uk