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Smart Factory Project
Team 1
Aman Mehta
Azeez Mehta
Can Ergelmis
Elbert Iliadi
Manopat Niyomthai
Nishok Saravanan
Viswajit Mani Kumar Koyada
Background of Smart Factory
● The smart factory or factories of the future take a leap forward from
traditional automation to a fully connected and flexible system where data
constantly flows to connect production systems and operations.
● The NIST defines Smart Manufacturing as systems that are “fully-
integrated, collaborative manufacturing systems that respond in real time
to meet changing demands and conditions in the factory, in the supply
network, and in customer needs.”
● Initiatives all over the globe to promote smart factories:
○ Smart Manufacturing Leadership Coalition (SMLC) in America
○ E-Factory in Japan
○ Industrie 4.0 in Germany
○ Advanced Manufacturing in China
Retrieved from media.istock.com
● Aim: Design a smart factory that can operate without any human intervention,
make decisions and take actions based on them.
● An effective smart factory must autonomously make decisions to minimize total
system cost.
● Total system cost is the sum of two performance metrics: Avg customer waiting
time and Avg # of parts in the system.
● The goal is to improve the two performance metrics by altering various
decisions such as factory layout, inventory replenishment, job selection and
machine selection.
Problem Description
Overview of Factory Scenario
● The factory scenario is split and
allocated into 5 types of
operations, each type being
operated by 5 machines.
● 10 types of jobs are arriving with an
exponential distribution and each
type of job is processed in a
predetermined sequence of up to 5
operations depending on the job
type.
Factory Scenario Grid
Template Simulation Model
Six major parts, that mimic task and material flow of the factory -
1) Order Generation - Starting point of the factory process.
i) Arrival Time - Time when order enters system.
ii) Job Type - Different machines and operations for each type (see table).
iii) Position - Track moving progress in the system.
2) Raw Parts Storage - Check for availability of raw
materials.
3) Ordering to Supplier - Order materials (backorder).
4) Choosing Next Station - Check for readiness.
5) Machines - Processed based on shortest queue.
6) End Product Storage - Ready to be delivered. Operations required in each job type.
Alternative Designs in Model
Map Layout Changes
● Tried Trial and Error method
● Understanding Different Jobs
Map Layout Changes
● CRAFT Analysis using Excel add-in developed by University Of Texas
● Cost 662 650
Baseline Craft Layout
Map Layout Changes
● Pairwise Ranking
● Cost 662 648
Pairwise Heuristic Layout
Job Selection Changes
● Shortest Processing Time(SPT)
Reduction to 623.52
● Minimum Setup Time (MSetT)
Reduction to 609.49
● SPT + MSetT
Reduction to 593.48
● Minimum Total Processing Time
Reduction to 629.24
● MSetT special + SPT
Reduction to 588.86
Job Selection Changes
● Tried six different Job Selection Rules Changes
● Final Rule Expression (MSet + SPT + LRO + LRPT):
● Cost 648 578.74
Machine Selection Changes
● Baseline(NINQ)
● 3 changes were made.
○ Change 1 (MSet)
○ Change 2(STT)
○ Change 3(Mset+NINQ+STT)
Machine Selection Changes
● MSet
● STT
Machine Selection Changes
● MSet + NINQ + STT
Rule Expression: min((current_type(J)<>A(2))*1000+(NQ(J)*10000) +((abs(map(1, J)-map(1, M))+abs(map(2, J)-map(2,
M)))*edge))
● Cost 578.4 456.2
Inventory Model Changes
● After we have modified maps, machine selection ,and job selection we have to
determine an optimal re-order quantity and re-order point
● Trial and error
● First method: Optimizing Re-order point then Re-order Quantity
● Second method: Optimizing Re-order Quantity then Re-order point
● Cost 456.2 404.1
Conclusion
Final Layout: Final Job Rule Expression (MSet + SPT +
LRO + LRPT):
Cost 648 ---> 578.74
Final Machine Selection Rule:
Pairwise Heuristic Layout
662 → 648
The End.
Questions?

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Smart factory Presentation

  • 1. Smart Factory Project Team 1 Aman Mehta Azeez Mehta Can Ergelmis Elbert Iliadi Manopat Niyomthai Nishok Saravanan Viswajit Mani Kumar Koyada
  • 2. Background of Smart Factory ● The smart factory or factories of the future take a leap forward from traditional automation to a fully connected and flexible system where data constantly flows to connect production systems and operations. ● The NIST defines Smart Manufacturing as systems that are “fully- integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs.” ● Initiatives all over the globe to promote smart factories: ○ Smart Manufacturing Leadership Coalition (SMLC) in America ○ E-Factory in Japan ○ Industrie 4.0 in Germany ○ Advanced Manufacturing in China Retrieved from media.istock.com
  • 3. ● Aim: Design a smart factory that can operate without any human intervention, make decisions and take actions based on them. ● An effective smart factory must autonomously make decisions to minimize total system cost. ● Total system cost is the sum of two performance metrics: Avg customer waiting time and Avg # of parts in the system. ● The goal is to improve the two performance metrics by altering various decisions such as factory layout, inventory replenishment, job selection and machine selection. Problem Description
  • 4. Overview of Factory Scenario ● The factory scenario is split and allocated into 5 types of operations, each type being operated by 5 machines. ● 10 types of jobs are arriving with an exponential distribution and each type of job is processed in a predetermined sequence of up to 5 operations depending on the job type. Factory Scenario Grid
  • 5. Template Simulation Model Six major parts, that mimic task and material flow of the factory - 1) Order Generation - Starting point of the factory process. i) Arrival Time - Time when order enters system. ii) Job Type - Different machines and operations for each type (see table). iii) Position - Track moving progress in the system. 2) Raw Parts Storage - Check for availability of raw materials. 3) Ordering to Supplier - Order materials (backorder). 4) Choosing Next Station - Check for readiness. 5) Machines - Processed based on shortest queue. 6) End Product Storage - Ready to be delivered. Operations required in each job type.
  • 7. Map Layout Changes ● Tried Trial and Error method ● Understanding Different Jobs
  • 8. Map Layout Changes ● CRAFT Analysis using Excel add-in developed by University Of Texas ● Cost 662 650 Baseline Craft Layout
  • 9. Map Layout Changes ● Pairwise Ranking ● Cost 662 648 Pairwise Heuristic Layout
  • 10. Job Selection Changes ● Shortest Processing Time(SPT) Reduction to 623.52 ● Minimum Setup Time (MSetT) Reduction to 609.49 ● SPT + MSetT Reduction to 593.48 ● Minimum Total Processing Time Reduction to 629.24 ● MSetT special + SPT Reduction to 588.86
  • 11. Job Selection Changes ● Tried six different Job Selection Rules Changes ● Final Rule Expression (MSet + SPT + LRO + LRPT): ● Cost 648 578.74
  • 12. Machine Selection Changes ● Baseline(NINQ) ● 3 changes were made. ○ Change 1 (MSet) ○ Change 2(STT) ○ Change 3(Mset+NINQ+STT)
  • 14. Machine Selection Changes ● MSet + NINQ + STT Rule Expression: min((current_type(J)<>A(2))*1000+(NQ(J)*10000) +((abs(map(1, J)-map(1, M))+abs(map(2, J)-map(2, M)))*edge)) ● Cost 578.4 456.2
  • 15. Inventory Model Changes ● After we have modified maps, machine selection ,and job selection we have to determine an optimal re-order quantity and re-order point ● Trial and error ● First method: Optimizing Re-order point then Re-order Quantity ● Second method: Optimizing Re-order Quantity then Re-order point ● Cost 456.2 404.1
  • 16. Conclusion Final Layout: Final Job Rule Expression (MSet + SPT + LRO + LRPT): Cost 648 ---> 578.74 Final Machine Selection Rule: Pairwise Heuristic Layout 662 → 648

Editor's Notes

  1. The National Institute of Standards and Technology
  2. (After point 3) It is vital to minimize the waiting time for customers to keep them satisfied, which improves customer loyalty. Another critical element is that there must be a limit on the average number of parts in the system as this would reduce the investment in components and assemblies, which would, in turn, improve the company’s cash flow.