SIMULATION MODEL IN LEAN MANUFACTURING SYSTEM.pptx

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1. SIMULATION MODEL IN LEAN MANUFACTURING SYSTEM

2. CONTENTS Introduction Literature Review Problem Definition Objectives Methodology Results And Discussion conclusion Scope Of Future Work References

3. INTRODUCTION Lean has been defined in different ways. “ A systematic approach to identifying and eliminating waste(non-value-added activities) through continuous improvement by flowing the product at the pull of the customer in pursuit of perfection.” “Simulation modelling is used to help designers and engineers understand whether, under what conditions, and in which ways a part could fail and what loads it can withstand.”

4. INTRODUCTION Weight bag production system which of different process shown in below process flow chart Weight Bag Arrival Assign weight bag attributes Determine client number based on order size Generate order Orders gathered Zip tie stage process Weight bag fill and weigh cup station process Pour into bag process Tie weight bag process Glue bag process shipping Assign under 2000, 4000 and above 4000 capacity

5. Title of paper Author Review Simulation analysis for managing and improving productivity A case study of an automotive company Raed El-Khalil The purpose is to identify and resolve inefficiencies within the car assembly system utilizing discrete simulation modeling and analysis in order to improve productivity at one of the original equipment manufacturers Simulation Intelligence And Modeling For Manufacturing Uncertainties Sk Ahad Ali To improve the system’s performance in the high-mix low-volume manufacturing systems. Fuzzy rule based machine, labour and logistics uncertainties are addressed. A combination of product mix and production volume is analysed using intelligent simulation model for an optimal designing of the production system to meet future customer Demands. Simulation Modeling for Operational and Business Processes in Lean Manufacturing Jie Chen, Sarah S. Lam, Sreekanth Ramakrishnan Integrated of Lean principles and discrete simulation modeling for decision making in operational and business processes. Simulation is advocated as a necessary tool for continuous improvement and waste elimination in Lean. Lean techniques are incorporated into each stage during the design and development of simulation models. LITERATURE REVIEW

6. Simulation Systems Supporting Lean Manufacturing Methods Implementation Magdalena Baczkowicz , Damian Krenczyk Lean Manufacturing methods have been implemented to shorten the production cycle and reorganized existing production department. To that end, plans and feasible indicators have been used to improve the entire production and show how to use simulation modelling and computer visualization to help achieve those goals. Using Discrete System Simulation to Model and Illustrate Lean Production Concepts Phillip B. Coleman, 2012 The purpose of this research project, and for that matter this paper, is not to serve in itself as a comprehensive introduction to lean manufacturing concepts, but rather to provide a means to better illustrate some of the waste elimination principles of lean to production associates to encourage and understand. Simulation-based Optimisation Model for the Lean Assessment in small and medium enterprises Amr Mahfouz, John Shea , Amr Arisha 2011 Lean principles are considered as effective improvement approach to eliminate system’s waste and inefficiencies. Although much of the academic materials have addressed the lean practices into large, global companies, they can still be adjusted to SMEs. Simulation can be successfully used to predict the impact of the proposed changes ahead of the implementation which helps to mitigate risks. Simulation And Optimization Of Production Control For Lean Manufacturing Transition Sean M. Gahagan , Jeffrey W. Herrmann Push production control to pull is not well understood or studied. This dissertation explores the events of a production control transition, quantifies its costs and develops techniques to minimize them. Simulation models of systems undergoing transition from push to pull are used to study this transient behaviour. It is shown that, except when backlog costs are high, it is better to transform the system quickly. It is also demonstrated that simulation based optimization is a viable tool to find the optimal transition strategy.

7. PROBLEM STATEMENT Number of kanbans depends on the size of the Kanban, these parameters affect system performance and level of WIP. By minimizing the number of kanbans through the system, the level of inventory is also minimized. To over come push production system.

8. OBJECTIVES To determine the Kanban size in a manufacturing system with variable workforce production and variable demand pattern . To construct Kanban simulation model to analyse weight bag production process.

9. SIMULATION METHODOLOGY Start Identifying process flow and work stations Identifying resources at each work stations Collecting task time data randomly at each work station Collecting time and data for an input Input all task time data at each workstation For arena analysis Activity duration and type of probability time distribution at each work station Build, verify, and validate process of arena simulation model Manufacturing cycle time and resource utilisation stop

10. DATA COLLECTED, ASSUMPTIONS AND INFERENCES Empirical dat a collected from weight bag production from J ournal paper Production is between the hours of 9AM and 1:30PM. The first break is 15min and second is 45min. Total 1Hr break between 9AM to 1:30PM. Total three and half hours of production per shift .

11. Problem formulation Setting of objectives and overall project plan Model conceptualization Data collection Model translation verified validated Experimental design Production runs and analysis More runs Documentation and reporting implementation 1 2 3 4 5 6 7 8 9 10 11 12 NO NO NO YES YES YES NO SIMULATION STEPS

12. ARENA MODEL OF CURRENT WEIGHT BAG PROCESS

13. Name Time in sec For 1500 Time in sec For 3000 Time in sec For 5000 weight bag fill and weigh cup station process U(10-20) U(15-25) U(18-28) pour into bag process N(51,0.3) N(52,1) N(55,0.2) tie weight bag process N(60,0.2) N(61,0.2) N(63,1) glue bag process N(60,0.2) N(61,0.2) N(64,1) zip tie stage process N(40,1) N(43,1) N(48,0.5) Read write N(20,0.1) N(20,0.1) N(20,0.1 ) DETAILS OF THE SIMULATION PROCESS

14. ARENA MODEL OF WEIGHT BAG PRODUCTION WITH KANBAN SYSTEM

15. Name Time in sec For 1500 Time in sec For 3000 Time in sec For 5000 weight bag fill and weigh cup station process N(15,0.2) N(17,0.2) N(20,0.4) pour into bag process N(55,1) N(57,1) N(59,0.8) tie weight bag process N(52,0.9) N(55,0.9) N(57,0.6) glue bag process N(45,1) N(48,1) N(51,0.3) zip tie stage process N(38,0.2) N(41,0.2) N(44,0.7) Read write U(0.2-0.4) U(0.2-0.4) U(0.2-0.4) DETAILS OF THE SIMULATION PROCESS

16. Name Time In Sec For 1500 Time In Sec For 3000 Time In Sec For 5000 seize KB 1 U(5-10) U(5-10) U(5-10) seize KB 2 U(5-10) U(5-10) U(5-10) release KB 1 U(5-8) U(5-8) U(5-8) seize KB 3 U(5-10) U(5-10) U(5-10) Release KB 2 U(5-8) U(5-8) U(5-8) seize KB 4 U(5-10) U(5-10) U(5-10) Release KB 3 U(5-8) U(5-8) U(5-8) seize KB 5 U(5-10) U(5-10) U(5-10) Release KB 4 U(5-8) U(5-8) U(5-8) Release KB 5 U(5-8) U(5-8) U(5-8) Hold production U(7-9) U(7-9) U(7-9) match 1 U(3-4) U(3-4) U(3-4) signal for proceeding U(2-5) U(2-5) U(2-5)

17. RESULTS AND DISCUSSION Based on results obtained cycle time reduction is a key component to success in lean manufacturing system. To determine K anban size for a manufacturing system with variable production rates in order to reduce cycle time.

18. Different order size Weight bag fill and weigh cup station process Pour into bag process and tie weight bag process Glue bag process and zip tie stage process ORDER SIZE 1500 ORDER SIZE 3000 ORDER SIZE 5000 GRAPHICAL REPRESENTATION OF CURRENT WEIGHT BAG PRODUCTION PROCESS

19. Order size Time available per shift in minutes Production VAT per day Production NVAT Total Production Lead time per day 1500 210 374.42 min 0.339 min 103.87 min 3000 210 410.33 min 0.334 min 116.90 min 5000 210 477.86 min 0.330 min 168.24 min RESULTS OBTAINED FROM SIMULATION

20. Different order size Weight bag fill and weigh cup station process Pour into bag process and tie weight bag process Glue bag process and zip tie stage process ORDER SIZE 1500 ORDER SIZE 3000 ORDER SIZE 5000 GRAPHICAL REPRESENTATION OF WEIGHT BAG PRODUCTION PROCESS WITH KANBAN SYSTEM

21. Order size Time available per shift in minutes Production VAT per day Production NVAT Total Production Lead time per day 1500 210 280.46 min 5.820 min 101.21 min 3000 210 246.12 min 5.847 min 109.43 min 5000 210 306.05 min 5.898 min 140.43 min RESULTS OBTAINED FROM SIMULATION WITH KANBAN SYSTEM

22. Order size Total Production Lead time without Kanban Total Production Lead time with Kanban 1500 103.87 min 101.21 min 3000 116.90 min 109.43 min 5000 168.24 min 140.43 min COMPARING TOTAL PRODUCTION LEAD TIME FOR DIFFERENT ORDER SIZE

23. COMPARING REDUCED CYCLE TIME WITH KANBAN SYSTEM AND WITHOUT KANBAN SYSTEM

24. CONCLUSION Discrete event simulation and mathematical programming were shown to be powerful tools in determining Kanban size for the manufacturing process to reduce cycle time. A model was constructed to compare the current state and with integrated kanbans to simulation and compare the operations flow.

25. SCOPE OF FUTURE WORK Small manufacturing facilities could follow the same process and utilize a similar Kanban production flow seize Kanban 1, process, seize Kanban 2, release Kanban 1, process 2. Could alter the model to test different manufacturing process for JIT system. Concepts such as Continuous Work-In-Process (CONWIP) can be used as an alternative to achieve minimum inventory . Other forms of Kanbans like Electronic Kanbans and transport Kanbans can also be tried.

26. REFERENCE Determining Kanban Size Using Mathematical Programming and Discrete Event Simulation for a Manufacturing System with Large Production Variability. Simulation based optimisation model for the lean assessment in SME: a case study K.V . Sreenivasa Prasad (2008) Some stochastic inventory models with deterministic variable lead time. European Journal of Operational Research, 113 , 42–51 T.R. Srinivas (2010) The Quantum Leap in Speed-To-Market. Denver, CO: John Costanza Institute of Technology, Inc S . S. Abuthakeer (2010) Production Flow Analysis and Simplification Toolkit (PFAST). International Journal of Production Research, 38(8), 1855-1874 P.V . Mohanram (2010) Theory of Constraints and Lean Manufacturing: Friends or Foes? Chesapeake Consulting, Inc : www.chesapeake.com . G. Mohan Kumar (2008) Learning to See. Version1.2. Brookline, MA: Lean Enterprise Institute. Ben Gazzara , VisualiseIT - http://community.visualize-it.co/knowledgebase/what-are-the-various-modelling-approaches-available/

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