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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 r
•Modeling Causality with Event...
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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 r
SYSTEM AND FUNCTION
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• System...
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Three Types of Certainty – Leo...
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Sources of Uncertainty and Sci...
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Paradigm level issues in Model...
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How to Evaluate a System (4 Sc...
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Everything is not Software!!
F...
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Decisions must be made and act...
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Situation
Assessment Explanati...
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Situation Assessment – Where d...
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February 1, 2016© Crafitti Con...
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February 1, 2016© Crafitti Con...
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PETRI NETS
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• Graph Models o...
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PETRI NET Model of a marriage ...
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February 1, 2016© Crafitti Con...
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Petri Nets (Bi-partite graph m...
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Transition Firing
February 1, ...
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State machines and Marked Grap...
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Modeling Power of
Petri Nets
F...
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February 1, 2016© Crafitti Con...
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Properties and Analysis Method...
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A PETRI NET BASED SIMULATION
A...
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TALK ORGANISATION
• PERISHABLE...
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PERISHABLE GOODS RETAIL MANAGE...
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Apply a
Barcode with
“Sell By ...
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CAN TECHNOLOGY SOLVE THE PROBL...
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Vendor
Transportation
Warehous...
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Update
Inventory
Information
I...
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MODELLING AND SIMULATION – PET...
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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 c...
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Petri Net: Food Selling illust...
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THE SIMULATION SCENARIO
• The ...
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PN MODEL OF THE SYSTEM
ColdCha...
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SIMULATION RESULTS AND RELATIV...
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ANALYSIS OF THE SIMULATION RES...
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CONCLUSIONS AND FURTHER WORK
•...
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Case Study: Methodology paper ...
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The problem – Air Defence Syst...
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Uses of Air Power
• Traditiona...
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1991 Gulf War (Force Package C...
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Petri Nets
• Petri Net (PN) is...
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Petri Nets
Formally a PN is de...
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Petri Nets (the dynamic behavi...
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Simulation Methodology
• Model...
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System modeling using ERG and Petri Nets

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"System Modeling" Course - talks are on

Event Relationship Graphs and Petri Nets for dynamic event driven system modeling

at Center for Artificial Intelligence and Robotics (CAIR), DRDO, at Bengaluru, 21st January 2016.

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System modeling using ERG and Petri Nets

  1. 1. Confidential 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
  2. 2. Confidential 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 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
  3. 3. Confidential 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 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
  4. 4. Confidential 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 Sources of Uncertainty and Science February 1, 2016© Crafitti Consulting Private Ltd. 4 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
  5. 5. Confidential 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 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 February 1, 2016© Crafitti Consulting Private Ltd. 5 Models • Record and communicate knowledge of complex and complicated systems • Computational experiments for generative models Reality
  6. 6. Confidential 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 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 February 1, 2016© Crafitti Consulting Private Ltd. 6
  7. 7. Confidential 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 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
  8. 8. Confidential 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 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
  9. 9. Confidential 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 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
  10. 10. Confidential 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 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
  11. 11. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 11 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
  12. 12. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 12 Discrete-Event Systems and Dynamic Models
  13. 13. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 13 System Execution – Discrete Event
  14. 14. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 14 Event-Relationship Graphs
  15. 15. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 15 Event-Relationship Graphs model of a single server queuing system
  16. 16. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 16 Event-Relationship Graphs
  17. 17. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 17 Verbal ERGs
  18. 18. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 18 Parametric ERGs
  19. 19. Confidential 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 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
  20. 20. Confidential 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 PETRI NET Model of a marriage ! Man P1 Woman P2 P3 Pundit/Qazi /Minister/ Judge t1 Husband P4 Wife P5
  21. 21. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 21 (a) Before Ceremony (b) After Ceremony PETRI NET Model of a marriage !
  22. 22. Confidential 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 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  . February 1, 2016© Crafitti Consulting Private Ltd. 22 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
  23. 23. Confidential 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 Transition Firing February 1, 2016© Crafitti Consulting Private Ltd. 23
  24. 24. Confidential 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 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 February 1, 2016© Crafitti Consulting Private Ltd. 24 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
  25. 25. Confidential 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 Power of Petri Nets February 1, 2016© Crafitti Consulting Private Ltd. 25
  26. 26. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 26 Modeling Power of Petri Nets
  27. 27. Confidential 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 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 February 1, 2016© Crafitti Consulting Private Ltd. 27 Analysis Methods • CoverabilityTree • Matrix State Equations • Reduction or Decomposition techniques System Simulation
  28. 28. Confidential 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 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
  29. 29. Confidential 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 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
  30. 30. Confidential 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 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.
  31. 31. Confidential 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 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
  32. 32. Confidential 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 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.
  33. 33. Confidential 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 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
  34. 34. Confidential 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 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
  35. 35. Confidential 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 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 ≠ 
  36. 36. Confidential 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 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 
  37. 37. Confidential 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 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
  38. 38. Confidential 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 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
  39. 39. Confidential 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 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
  40. 40. Confidential 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 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%.
  41. 41. Confidential 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 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
  42. 42. Confidential 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 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.
  43. 43. Confidential 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 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. February 1, 2016© Crafitti Consulting Private Ltd. 43
  44. 44. Confidential 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 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]. February 1, 2016© Crafitti Consulting Private Ltd. 44
  45. 45. Confidential 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 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
  46. 46. Confidential 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 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
  47. 47. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 47 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
  48. 48. Confidential 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 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. February 1, 2016© Crafitti Consulting Private Ltd. 48
  49. 49. Confidential 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 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  . February 1, 2016© Crafitti Consulting Private Ltd. 49
  50. 50. Confidential 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 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 . February 1, 2016© Crafitti Consulting Private Ltd. 50
  51. 51. Confidential 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 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. February 1, 2016© Crafitti Consulting Private Ltd. 51
  52. 52. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 52 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
  53. 53. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 53 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
  54. 54. Confidential 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 Penetration probability variation wrt Arrival rate February 1, 2016© Crafitti Consulting Private Ltd. 54 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.
  55. 55. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 55 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
  56. 56. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 56 HPSim Live Tool demo Model of a Tank Operation
  57. 57. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. 57 Simulation Results
  58. 58. Confidential 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 Contents February 1, 2016 CRAFITTI: Your Global Innovation and IP Think Tank Crafitti offers active collaboration to craft ideas in multiple innovation contexts.
  59. 59. Confidential 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 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
  60. 60. Confidential 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 February 1, 2016© Crafitti Consulting Private Ltd. Our Clients (Representative list)
  61. 61. Confidential 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 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
  62. 62. Confidential 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 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
  63. 63. Confidential 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 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.
  64. 64. Confidential 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 February 1, 201664 Thank You! Navneet Bhushan Navneet.bhushan@crafitti.com +91 9902766961 INVENT ! TOGETHER

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