System modeling using ERG and Petri Nets

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craftinginnovationtogether
c r a f t i n g i n n o v a t i o n t o g e t h e r
•Modeling Causality with Event
Relationship Graphs
•Petri Nets for Dynamic Event
driven system modeling
By Navneet Bhushan
Crafitti Consulting Private
Limited
Talks on 21st January
2016

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SYSTEM AND FUNCTION
2
• System - from fundamental principles point of view
– set of elements working together to achieve an
objective or perform a function.
– set of elements (Energy-matter organized in space-time) working together
(exchanging energy and information) to achieve an objective or perform a
function (Create Change - in Matter, Energy and Information in Space-
time).
• When the system is achieved by thought and
consciousness we make systems artificial – A
TECHNICAL SYSTEM
System

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Three Types of Certainty – Leonhard Euler, 1761
February 1, 2016© Crafitti Consulting Private Ltd. 3
• Perceptual certainty
– “I saw it with my own eyes”
• Demonstrative certainty
– Deductive logic/ Logical certainty
• Moral certainty
– Told by others – with some established
authority

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Sources of Uncertainty and Science
John L. Casti (ref) – “Searching for Certainty” – two main
sources of uncertainty – randomness and imprecision
Science has and can deal with randomness to a great extent –
but needs precision or least vagueness in language and
expression
Observation Empirical
Laws
Laws of
nature Theories
Experiment
Theory
Process of Science

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Paradigm level issues in Modeling and
Simulation (Rand, Paul K. Davis)
• Models as Tools Vs Models as representation of knowledge
• About uncertainty
– Soft Factors
– Complexity
– Uncertainty
• Parametric – Point scenarios (insufficient) – Spanning set of scenarios/Capabilities based
planning
• Structural
Models
• Record and communicate knowledge of
complex and complicated systems
• Computational experiments for generative
models
Reality

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How to Evaluate a System (4 Scientific Ways)
• DIRECT OBSERVATION AND MEASUREMENT
• EXPERT JUDGEMENT / GROUP DECISION MAKING
• ANALYTICAL/MATHEMATICAL MODEL (deterministic/stochastic – but a
closed form solution)
• SIMULATION
• Emergent/agent based simulations

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Everything is not Software!!
February 1, 20167 ©
… But we can
make a Model
and Simulate
the model of
nearly
everything,
potentially … as
everything has
Information

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Decisions must be made and actions must be taken today, but
the results are not clear until tomorrow, at best.
The uncertainty of the situation at hand makes every decision a
burden
Handbook of Foreign Policy Analysis
Play
ˀ “If only I
know …”
ˀ “If only I had
known …”
Decisi
on
Dealing with
UNCERTAINTY
To minimize uncertainty, … by taking
pains to get more information about
the environment

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Situation
Assessment Explanation
Forecasting
Options
Generation
Making
Decision -
Choice
Science is not an oracle – but it can help to
reduce uncertainty
Situation
Assessment
Data Collection
Data Cleansing
Data Collation
Classification
Observation
Explanation
Causal
Analysis
Cognitive
Mapping
Systems
Analysis
Forecasting
Historical
Analogies
General
Analogies
Prediction
Projection
Forecasting
“What if”
Analysis
Options
Generation
Decision
Trees
Scenario
Writing
Alternatives
Brainstormin
g
 Solution
Choice
Optimization
Decision
Making under
uncertainty and
partial
information

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Situation Assessment – Where do we stand?
Simple Indicators and Checklists/ Complex Indicators/ Scaling (R-factor Analysis)/
Typologies (Q-factor Analysis) /Cluster Analysis /Multidimensional Scaling/ Artificial
Neural Networks (ANN)/ Value Stream Mapping / TRIZ – 9 Windows/ TRIZ- Ideal Final
Result
Explanation – Why are things as they are?
Correlation Analysis/ Regression Analysis/Analysis of Non-Linear Relationships/ Partial
and Multiple Correlation Analysis/ Multiple Regression Analysis/ Path Analysis
Forecast – What will happen?
Systematic Expert Judgment/ Decision Matrix/ Analytic Hierarchy Process/ Bayesian
Inference/ Cross-Impact Analysis/ Early warning Indicators/ Extrapolation with Moving
Averages/ Trend Analysis/ Time Series Analysis/ Spectral Analysis/Combined Trend and
Time Series Analysis /Trend Impact Analysis
Preparation of Decisions – What are the Options?
Game Theory/ Gaming/ Computer Simulation/ Cellular Automata/ Petri Nets/ Econometric
Models/Mathematical Modeling / TRIZ
Choice – What to do?
Decisional Trees/ Decisional Matrix/ Linear Partial Information (LPI) Analysis/
Linear/Integer/Non-Linear Programming/ Heuristic Optimization Techniques – Genetic
Algorithms, Simulated Annealing, Tabu Search, Artificial Life / AHP
Techniques & Methodologies

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Discrete-Event Systems and Dynamic
Models
• Elements/Entities of a system may be physical or mathematical
• Entities may be “resident” or “transient”
• In a Barber Shop – Barbers are resident, customers are
transient
• System is an abstraction in some sense of reality
• Entities will have attributes – which can be static or dynamic,
deterministic or stochastic

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Discrete-Event Systems and Dynamic
Models

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System Execution – Discrete Event

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Event-Relationship Graphs

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Event-Relationship Graphs model of a single
server queuing system

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Event-Relationship Graphs

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Verbal ERGs

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Parametric ERGs

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PETRI NETS
19
• Graph Models of System behaviour ( a bi-partite graph)
• Abstract and Formal
• Description and Analysis of – Info and Control Flow
• Asynchronous and Concurrent activities can be modeled
System State: Holding of a set of conditions
State Change: End of some conditions and Start of some conditions
Event: Elementary state change (atomic)
Definition: PNs are graph models for system description using notions of
conditions and events

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PETRI NET Model of a marriage !
Man
P1
Woman
P2
P3
Pundit/Qazi
/Minister/
Judge
t1
Husband
P4
Wife
P5

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(a) Before Ceremony
(b) After Ceremony
PETRI NET Model of a marriage !

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Petri Nets (Bi-partite graph models of a system)
Formally a PN is defined as a 5 tuple
PN = { P, T, F, W, M0} where
P = {p1 ,p2 ,...,pm} is a finite set of places.
T = {t1,t2,...,tn} is a finite set of transitions.
F  {PT}  {T  P} is a set of arcs (flow relation).
W : F {1,2,3,...} is weight function.
M0: P  {0,1,2,3,...} is the initial marking.
Also, PT =  and PT  .
Dynamic Behaviour
• Use of Tokens
• # of tokens in a place => #
of data items/conditions
• Token Distribution over
places is the system state
• M0 is the initial marking
• Transition Rules – simulate
system dynamics
• Enabled Transition
• Firing of Transition
• Result of Transition (state
change) => change in
marking

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Transition Firing

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State machines and Marked Graphs are special
cases of Petri Nets
State Machine
- PN in which For all T
- i/p place is 1
- o/p place is 1
- Modeling of all sequential
programs
- Represent decisions
- Can’t Represent Concurrency
Marked Graphs
- PN in which
- For all P
- i/p transition is 1
- o/p transition is 1
- Can’t represent decisions
- Can represent concurrency

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Modeling Power of
Petri Nets

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Modeling Power of Petri Nets

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Properties and Analysis Methods of Petri Nets
Ability to support analysis of many
properties and problems associated
with concurrent systems
• Reachability
• Boundedness
• Safeness
• Liveness
• Persistence
• Coverability
• Reversability
Analysis Methods
• CoverabilityTree
• Matrix State Equations
• Reduction or Decomposition
techniques
System Simulation

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A PETRI NET BASED SIMULATION
APPROACH FOR EVALUATING BENEFITS OF
TIME TEMPERATURE INDICATOR AND
WIRELESS TECHNOLOGIES IN PERISHABLE
GOODS RETAIL MANAGEMENT
Navneet Bhushan and Kishore
17-18 June 2002, Cork, Ireland

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TALK ORGANISATION
• PERISHABLE GOODS RETAIL
MANAGEMENT- CURRENT SCENARIO AND
PROBLEMS
• TIME TEMPERATURE INDICATOR AND
WIRELESS TECHNOLOGIES – THE
PROPOSED SOLUTION
• PETRI NETS (PN) FOR MODELING &
SIMULATION
• PN BASED SIMULATION OF PERISHABLES
RETAIL MANAGEMENT
• TEST SCENARIO
• PN MODEL OF THE SYSTEM
• SIMULATION RESULTS
• ANALYSIS
• CONCLUSIONS AND FURTHER WORK

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PERISHABLE GOODS RETAIL MANAGEMENT
• Perishable Goods (PG) – Fruits, Meat Products, Medicines,
Chemicals, etc. need Sufficient Cold Storage from the Production
stage to the Consumption Stage so as to remain fresh
• Presently a Sell by Date label is fixed by the vendor
• No way to find out whether the cold chain was maintained on the
way to retail store or not.
• Problem lies in limitations of technology to ascertain the freshness
of the products.

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Apply a
Barcode with
“Sell By Date”
Maintain the
cold chain
Manage inventory
based on FIFO or
the “Sell-By-Date
Maintain the
cold chain
Manage inventory
based on FIFO or
the “Sell-By-Date”
Vendor
Transportation
Warehousing
Distribution
Retail
The Current Scenario
PERISHABLE GOODS RETAIL MANAGEMENT

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CAN TECHNOLOGY SOLVE THE PROBLEM ? – A Proposal
• Combining Two Key Technologies
• TIME TEMPERATURE INDICATORS (TTI) are capable of
measuring the life of temperature sensitive products - An
adhesive label consisting of an enzyme and a substrate
filled ampoule separated by a breakable seal. The colour of
the ampoule changes from green at the start to yellow at
the end of product life cycle. An increase in temperature
beyond the specific temperature hasten the color change.
Change in color if captured can tell the remaining life of the
product.
• WIRELESS LOCAL AREA NETWORKING (WLAN) IEEE 802.11b
standard, is a wireless networking technology that can
integrate mobile devices to the wired infrastructure as well
as to each other through wireless links. WLANs are already
being deployed in large stores and organizations.

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Vendor
Transportation
Warehousing
Distribution
Retail
Apply a
Barcode with
“Sell By Date”
Manage inventory
based revised “Sell-
By-Date”
Update the Sell
By date based
on remaining
life
Inform Vendor
& transporter
in case of
reduced life
Find cause &
take action
Find cause &
take action
Manage the
cold chain
Manage the
cold chain
THE PROPOSED SOLUTION

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Update
Inventory
Information
Inventory
movement based
on remaining life
Vendor Rating
systems updated
Category Management
System
New price and
sell by date
information
New label printed
to enable sale at
an optimal price
THE PROPOSED
SOLUTION – At The Retail
Store

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MODELLING AND SIMULATION – PETRI NETS
• A powerful modelling framework
for information flow.
• Major use is for modelling
Concurrent occurrences with
constraints, precedence or
frequency of occurrences.
• Carl Adam Petri 1962
 Directed, weighted, bipartite graph - two
kinds of node, places and transitions.
 Places represent conditions and transition
represent events.
 A transition has input and output places
representing pre-conditions and post-
conditions of events
Formal Definition: Petri Net (PN)
PN = { P,T,F,W,Mo)
P = { p1,p2,…,pm } is finite set of places.
T = { t1,t2,…,tn } is finite set of transitions.
F  { P X T }  { T X P } is a set of arcs (flow relation).
W : F-> {1,2,3,…} is weight function.
Mo : P-> {0,1,2,3,…} is the initial marking.
Also, P  T = 
P U T ≠ 

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c r a f t i n g i n n o v a t i o n t o g e t h e rA state or marking in a PN is changed according to following transition rule:
•A transition t is said to be enabled if each input place p of t marked with at
least w(p,t).
•An enabled transition may or may not fire.
•A firing enabled transition t removes w(p,t) tokens from each input place p
of t, adds w(p,t) tokens to each output place p of t.
Simulation of dynamic behavior of systems
Timed Transition Petri Nets
•Transition have firing time associated with them.
•Time may be Deterministic or Stochastic
•The stochastic transition may follow an exponential distribution with
parameter 

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Petri Net: Food Selling illustration
Customers in Store
Apples on Shelf
Oranges on Shelf
4
2
Occurrence of Purchase
Satisfied Customer
Firing
Customers in Store
Apples on Shelf
Oranges on Shelf
4
2
Occurrence of
Purchase
Satisfied Customer

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THE SIMULATION SCENARIO
• The Demand: In multiple Retail Stores there has been observed an average demand for 200 Boxes of Perishable
Goods per day. Further this demand has been observed to follow a Poisson probability distribution, i.e., the
inter arrival time between two demands is exponentially distributed with mean 200 boxes per day.
• The Perishable Goods: The Perishable Goods are labeled with Sell by Date at the Vendor Place. This Sell by Date
is computed assuming average cold storage conditions on the way from the vendor to the store. The boxes
having the least Sell by Date are picked in a FIFO manner.
• The Vendor: The vendor sends on an average 2000 boxes every 10th day. This is assuming that retail stores
have a capability of storing 2000 boxes for 10 days after which the goods expire. These boxes are sent in 20
trucks each carrying 100 boxes. These trucks reach the respective stores on an average in 2 days starting from
the vendor to the stores. On an average 10% of these trucks per 10 days do not meet the cold storage
requirements. 10% actually exceed the cold storage required by Sell by Date. And 80% meet the cold conditions
required by Sell by Date. Also, we assume that the goods not meeting the required cold conditions (we call
them Category C) perish on an average in 2 days of reaching the retail outlets. The goods meeting the cold
storage (Category B) perish on an average in 8 days of reaching the retail outlets. While goods exceeding the
cold storage (Category A) perish on an average in 10 days of reaching the retail outlet.
• Present Scenario: There is no way presently to distinguish between the three categories at the retail outlets.
Hence the Boxes are randomly picked (average 200 boxes per day) from Category A, Category B or Category C.
This leads to possible customer dissatisfaction if they buy Category C and loss by the store if they sell Category
A goods much before their actual Sell by Date.
• Proposed Solution: In the Proposed Solution because of TTI labels, it is possible to distinguish between the
three categories and schedule their selling based on Least Shelf Life First Out (LSFO) scheduling

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PN MODEL OF THE SYSTEM
ColdChainMaintained p = 0.8
BoxesWithVendor
Requested
Boxes
200
Transportation
CheckColdChain
ReachingStore
ColdChain ColdChainNotMaintained p= 0.1
VeryWellMaintained p = 0.1
100 ActuallyPerished
ExceedSellByDate
SellByDate
DemandCreated
Demand
200
PerishedPut
On Shelf
CategoryCSold
CategoryBSold
GoodToS
ell
CategoryASold
TotalPerished
G1
G2
G3
BoxesSent
BoxesNeed
ed

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SIMULATION RESULTS AND RELATIVE CHANGE IN
PARAMETERS
Parameters Present
Case (E)
Proposed
Solution (N)
Relative Change
(N/E)
Total Boxes Sent by
Vendor (X)
69231 71224 1.029
Sold Category B (Y) 48972 61512 1.256
Sold Category C 5999 - -
Sold Category A 6061 - -
Perished (P) 220 87 0.395
Left (L) 7979 9625 1.206
% Category B sold (100
* Y/X)
70.7 86.4 1.222
% Perished (100 * P/X) 0.318 0.122 0.384
Category B Sales (% terms) have increased by 22%, perished goods have decreased by 60%.

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ANALYSIS OF THE SIMULATION RESULTS
Parameter Present Proposed Change (%)
Boxes Sold (S) 61032 (Category A, B
and C)
61512 (Category B
only)
0.786
Revenue (S * $ 120) 61032 * 120 = 7323840 61512 * 120 = 7381440 0.786
Profit (S * $20) (P) 61032 * 20 = 1220640 61512 * 20 = 1230240 0.786
Loss Due to selling
Category C (1)
5999 * $10 = 59990 0
Loss Due to Selling
Category A (2)
6061 * $5 = 30305 0
Loss Due to
Perished Boxes
(3)
220 * $100 = 22000 87 * $100 = 8700 -60.455
Loss Due to Loss in
Profit because of
Perished Goods
(4)
220* $20 = 4400 87 * $20 = 1740 -60.455
Total Loss (L=
1+2+3+4)
116695 10440 -91.054
Net Profit (P –L) 1103945 1219800 10.495

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CONCLUSIONS AND FURTHER WORK
• The present perishable goods retail management results in considerable loss
for the retailer due to inability of the system to predict the storage conditions
on the way from the vendor point to the retailer
• Combining two recent technologies, i.e., Time Temperature Integrators (TTI)
and Wireless LAN (WLAN), we propose a solution that will considerably solve
the problem.
• To evaluate the benefits of proposed solution vis-à-vis existing situation we
developed a simulation algorithm based on a well-established technique
called PETRI NETS.
• The results of the simulation analysis clearly shows the benefits of the
proposed solution. In the assumed scenario, it has been shown that the Loss
of perishable goods can be reduced up to 90% in monetary terms and Net
profit for the retailer can grow by 10%.
• The study indicates the power of using SIMULATION in analyses of
conceptualized solutions before designing the actual solution.
• Next step is to design and develop the solution as the benefits are clearly
quantified using the simulation model described here.

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Case Study: Methodology paper (Petri Net
based simulation of Air Defence System)
Performance Evaluation of an Air Defence System Modeled as a Petri Net
System analysis symposium (CASSA , Bangalore, 1997)
Abstract
Petri Nets (PN) are one of the powerful models of information flow. The major use
of PNs has been in the modelling of systems of events in which it is possible for
some events to occur concurrently but there are constraints on the occurrence,
precedence or frequency of these occurrences. This paper presents a novel
approach to model an Air Defence System (ADS) as a PN for performance
evaluation. The ADS modeled as a PN is simulated to estimate various
performance parameters such as throughput, penetration probability and
response time of the system. This performance evaluation tool can be
successfully adapted to other systems if the corresponding PN models of such
systems are available.

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The problem – Air Defence System
One of the major factors contributing to Allied victory over Iraq in 1991 Gulf War
was the failure of Iraqi Air Defence System. The results of any future war will
depend to a large extent upon the performance of Air Defence Systems (ADS) of
respective countries. How the ADS of any country will perform under a given
threat is a difficult question to answer. For this purpose there is a need to
develop a performance evaluation tool for finding out the effectiveness of the
ADS. There are four main techniques usually considered for performance
evaluation namely, expert judgement, direct measurement, analytical modelling
and simulation. Out of these, simulation provides a flexibility and efficiency that
is not possible in other techniques. However, describing a system for simulation
purposes requires a much involved modelling exercise [3,5].

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Uses of Air Power
• Traditional Use
• Counter Air/Deep Strike
• Close Air Support
• Extensive Air Bombardment
followed by ground action
(1991 Gulf War)
• Winning Future wars with
Air Force only

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1991 Gulf War (Force Package Concept)
A W A C S
would ensure A/c coming from different bases would arrive
at the target in a pre-decided manner
Defence Suppression A/c
EF-111 jam Iraqi Long
Range Radar forcing
SAM Crews to turn on
their radar
F-4G wild weasel fires
Msl( HARM/Shrike) to
knock out SAM radar
Time
A
c
t
i
o
n
s
Fighters
/Interceptors
F-15 or F-16
would come
as AD
Escorts
Actual
Bombers
Delivery of
bomber
payload

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BOR DERBOR DERBOR DER
Enemy Aircraft
Airborne radar
Mobile radar
Radar
Sensor Fusion Post (SFP)
ACC1
ACC2
SFP2
SFP3
Central Air Operation
Centre (CAOC)
AIR CONTROL
CENTRE
(ACC)
SAM
OP
Centrel
SHORAD
OPERATION
CENTRE
FIGHTER
SQUADRON
OP CENTRE
SAM
Site
Air Defence
Guns
Fighter
Squadron
The system description – Air Defence System
Modeled on

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Petri Nets
• Petri Net (PN) is one of the most powerful formal models of information flow.
The major use of PN has been the modelling of systems of events in which it is
possible for some events to occur concurrently but there are constraints on
the occurrence, precedence or frequency of these occurrences. PNs are a
graphical and mathematical modelling tool applicable to many systems. As a
graphical tool, PNs can be used as a visual communication aid similar to
flowcharts, block diagrams and networks. In addition, tokens are used to
simulate the dynamic and concurrent activities of systems. As a mathematical
tool, it is possible to set up state equations, algebraic equations and other
mathematical models governing the behaviour of systems. The concept of PN
was originated in the year 1962 when Carl Adam Petri presented his
dissertation. Some time later the work of Petri came to the attention of A.W.
Holt who applied these concepts to various concurrent processing concepts.

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Petri Nets
Formally a PN is defined as a 5 tuple
PN = { P, T, F, W, M0} where
P = {p1 ,p2 ,...,pm} is a finite set of places.
T = {t1,t2,...,tn} is a finite set of transitions.
F  {PT}  {T  P} is a set of arcs (flow relation).
W : F {1,2,3,...} is weight function.
M0: P  {0,1,2,3,...} is the initial marking.
Also, PT =  and PT  .

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Petri Nets (the dynamic behaviour model of a
system)
The behaviour of many systems can be described in terms of system states and
their changes. In order to simulate the dynamic behaviour of a system, a state or
marking in a PN is changed according to the following transition (firing) rule :
• A transition t is said to be enabled if each input place p of t is marked with
atleast w(p,t) tokens, where w(p,t) is the weight of the arc from p to t.
• An enabled transition may or may not fire ( depending upon whether or not
the event actually takes place)
• A firing of an enabled transition t removes w(p,t) tokens from each input
place p of t, and adds w(t,p) tokens to each output place p of t, where w(t,p)
is the weight of the arc from t to p.
• A transition without any input place is called a source transition, and one
without any output place is called the sink transition. A timed transition PN is
one in which the transitions have a firing time associated with them. This time
may be deterministic or stochastic. If the deterministic transition has zero
time it is called immediate. The stochastic transition may follow an
exponential distribution with parameter .

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Simulation Methodology
• Modeling of the hypothetical ADS as a Time Transition Petri Net (TTPN).
• Modeling of each ACC-SFP pair in the ADS as a petri net object. The ACC-SFP
pairs are modeled in such a way that they maintain a recursive structure and
hence are easily extended to a series of ACC-SFP pairs connected through
CAOC.
• The simulator is modeled as an object interacting with the petrinet objects
and obtaining information about transitions that are about to fire.
• Event driven simulation of the events that were generated by the petrinet
objects is carried out
• A sensitivity analysis of the simulation results towards various input
parameter is carried out.

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Delay Delay Start
Stop Arrival
Limit
300 .Arrivals
Arrival
Threats
DetectedUndetected
Back Sensor
Undetected
Penetrations
Penetration
TX1
SFP
TL
Gone1
Gone2
CAOC
ACC
NS
Plane
No response
ACCL
TR
ACCR
SAM
Gun
Sqdn
SAMOC
Fighter Plane
SAM
Gun
Pres
Sres
Gres
SHORADOC
NG
NP
ACC
TX2
Transition with exponential firing
Transition with determinstic firing
Fig. 3 : TTPN Model of an AD System
Timed-transition Petri Net Model of an AD
system

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Delay
Arrival
Penetration
No response
Fighter
SAM
Gun
AD1 AD2 AD3
CAOC CAOC
ACC
ACC ACC
CAOC
TL3
TR2
TR1
TL3
PN Model of a CAOC linking
three AD Systems

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Penetration probability variation wrt Arrival rate
Table 3 : Variation of Throughput & Penetration probability with arrival rate
parameter (a)
Arrival Rate
Parameter
0.008 0.0167 0.042 0.1 1.0 2.0 5.0
Arrival 3963 5457 7281 9000 9900 9900 9900
Responded 3678 5057 6750 8110 8882 8871 8873
no Response 3 4 11 237 317 318 334
Penetrated 25 36 48 53 60 63 62
Fighter
Response
2833 3886 5198 6195 6811 6778 6804
SAM
Response
525 724 960 1172 1279 1295 1273
GUN
Response
321 448 592 743 792 798 796
Throughput 0.93 0.93 0.93 0.90 0.897 0.888 0.896
Penetration
Probability
0.007 0.007 0.008 0.032 0.038 0.038 0.04
Using Petri Nets
methodology it is
possible to study a large
number of system
parameters. The
technique described in
this paper can be used to
carry out performance
modelling and system
analysis of any system
that can be described as a
discrete event dynamic
system.

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S-400 Missile system (Triumf) from Russia – can we simulate it and
see how effective it will be in Indian scenarios? Compare it with the
existing air defence system?
The S-400 uses three different missiles to cover
its entire performance envelope. These are the
extremely long range 40N6, long range 48N6
and medium range 9M96 missile. Each one has
different capabilities.
Structure
30K6E administration system: manages 8 divisions (battalions)[9][10][11]
55K6E command and control centre based on Ural-532301.
91N6E[12] Panoramic radar detection system (range of 600 km) with protection against
jamming. Mounted on an MZKT-7930. 300 targets. Decimetric band (S).[13]
6 battalions of 98ZH6E Surface-to-air missile systems consisting of (an independent
combat system for autonomous operation):[14] Each battalion can hit no more than 6 goals on
their own.[15]+2 another battalions if they are within range 40 km.
92N6(or 2)E Multi-functional radar (range of 400 km). 100 targets.[16]
5P85TE2 launchers and/or 5P85SE2 on the trailer (up to 12 launchers).
Surface-to-air missiles allowed by Russian Presidential decree: 48N6E, 48N6E2,
48N6E3, 48N6DM, 9M96E, 9M96E2 and ultra distance 40N6E.[17]
Own the radars system S-400 this is Active electronically scanned array (official
government statement)[18]
The Petri Net
based Air
Defence
modeling and
simulation
methodology
can be used

Confidential
HPSim Live Tool demo
Model of a Tank Operation

Confidential
Simulation Results

Confidential
Contents
February 1, 2016
CRAFITTI: Your Global Innovation and IP Think Tank
Crafitti offers active collaboration to craft ideas
in multiple innovation contexts.

Confidential
Crafitti Consulting Private Limited
February 1, 201659
Crafting INNOVATION Together
• We co-craft end-customer value
with every mind working with us by
– Enabling Emergence of NEW
– Maximizing the LIFE of EACH
IDEA
– Empowering IDEAS
– Making Innovation Happen
– Future-proofing by creating
Future insights and forecasts

Confidential
WHAT YOU TOLD US…
February 1, 201661
• WE DON’T KNOW WHAT’S NEXT
• WE DON’T HAVE IDEAS
• WE HAVE LOTS OF IDEAS BUT DON’T KNOW WHAT TO DO
WITH THEM
• CAN YOU HELP US INVENT
• WE HAVE LOTS OF INVENTIONS BUT WE DON’T KNOW IF
THEY CAN BE PATENTED
• WE HAVE PATENTS BUT WE DON’T KNOW HOW STRONG
THEY ARE
• WE HAVE PATENTS BUT WE DON’T KNOW THEIR VALUE
• WE HAVE PRODUCT BUT WE DON’T KNOW THE MARKET
• WE HAVE PRODUCT BUT WE DON’T KNOW TO
COMMUNICATE ITS VALUE
• WE DON’T NEED INNOVATION BUT CAN YOU BRING US
CLIENTS
WE SAID
WE ALSO DON’T
KNOW
BUT TOGETHER
WE CAN FIND
OUT
AND TOGETHER
WE CREATED
ANSWERS
Crafting
Innovation
Together

Confidential
Crafitti acts through
active collaboration to
craft innovation in
multiple contexts.
February 1, 201662 © Crafitti Consulting Private Ltd.
CRAFITTI: Your Global Innovation ThinkTank
Increasing Innovation Momentum in the Enterprise
• A systematic, time-
bound, flexible,
initiative to transform
the Enterprise into
Invention Empowered
Enterprise
• Unlike other such
initiatives we work closely
with each and every mind
of the Enterprise based
on scientific methods
honed through years of
practice
• The Enterprise is self-
enabled and
empowered to adapt to
and in fact architect
positive change
continuously

Confidential
Crafitti Consulting: Background
63
• INNOVATION RESEARCH AND
CONSULTING in business, science and
technology contexts
• Started in June 2008 and was incubated
at the NSR Centre for Entrepreneurial
Learning at IIM Bangalore
• Crafitti’s frameworks provide potent
platforms to innovate in crafting
strategies, breakthrough products, new
services, technological alternatives,
patent portfolios, process design and
embedding successful change in
organizations.

Confidential
February 1, 201664
Thank You!
Navneet Bhushan
Navneet.bhushan@crafitti.com
+91 9902766961
INVENT ! TOGETHER

System modeling using ERG and Petri Nets

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