1. Design of Map to Build Carts and Allocate Products
Final Report
April 17, 2018
IOE Team 9
Stephen Criel
Maggie Hafers
Taylor Martell
Carrianna Voellm
Ms. Kristine Komives, Associate Director Supply Chain/Materiel Services, Michigan Medicine
Mr. Arnold Yin, Industrial Engineer, University of Michigan Health System
Ms. Yuting Ding, Performance Improvement Fellow, Michigan Medicine
Dr. Mark Van Oyen, Professor, Industrial and Operations Engineering
Ms. Mary Duck, Staff Specialist, UMH QI Michigan Quality System, Michigan Medicine
18W9-final-report

2. Project Background, Goals, and Scope
Model Iterations and Challenges
Model Description
Model Description
Live Demo
Design Requirements
Limitations, Findings, and Conclusions
Findings and Conclusions
Recommendations and Impact

3. Background
Done manually based on individual knowledge by members of the PAR Team

4. Example Cart

5. Body System Orientation

6. Goals and Objectives
Goal: Design a model that will output a standardized map to build supply carts
Objectives:
Gather product identifiers and product information from the PAR Team
Use a Greedy Algorithm in VBA code to allocate products to bins
Design a VBA program that allocates bins to shelves and shelves to carts
Identify high-risk and large products in the end map

7. Project Scope Includes: Utilizing data from the Cardiovascular Center
Designing a program generalized to all supply rooms
Excludes:
Specific department constraints
Changing cart layout in the supply room
Maggie

8. Project Background, Goals, and Scope
Model Iterations and Challenges
Model Description
Model Description
Live Demo
Design Requirements
Limitations, Findings, and Conclusions
Findings and Conclusions
Recommendations and Impact

9. Linear Program Needed constraints with binary variables
Issues and Challenges:
Attempted Mitigation:
Needed constraints with
binary variables
Moved to an Integer Program model
Linear Program

10. Integer Program Changing shelf height Two-bin kanban system
Issues and Challenges:
Mitigation:
Changing shelf height
Two-bin kanban system
Objective function formulation
Created two decision variables
Created a binary parameter
Minimize wasted space
Issues and Challenges:
4. Variable with four indices
Attempted Mitigation:
Divided the program into 2 parts
Linear Program
Integer Program

11. Greedy Algorithm and Integer Program
Issues and Challenges:
Attempted Mitigation:
Size of the dataset and constraints
Spreadsheet not user-friendly
Body system constraint
Experimented in open solver
Brainstormed simplifications
Potentially 11 objective functions
Linear Program
Integer Program
Greedy
Algorithm
Integer
Program

12. Greedy Algorithm and VBA Program
Issues and Challenges:
Mitigation:
VBA program input arrays
VBA program body system order
VBA program shelf height
GSI assistance and research
Recorded a sorting macro
Created max bin height variable
Linear Program
Integer Program
Greedy
Algorithm
Integer
Program
Greedy
Algorithm
VBA
Program

13. Project Background, Goals, and Scope
Model Iterations and Challenges
Model Description
Model Description
Live Demo
Design Requirements
Limitations, Findings, and Conclusions
Findings and Conclusions
Recommendations and Impact

14. Design Methods: Complete Model Description

15. Design Method: Greedy Algorithm

16. Design Method: VBA Program
Begin with body system 14, bin 1, shelf 1, and cart 1
Loop through all bins in a supply room
If bin fits on current shelf
Assign bin to current shelf and current cart
Update sum of bin widths on current shelf
If bin doesn’t fit on shelf and a new shelf fits on current cart
Increment to next shelf
Assign bin to new shelf
Update cart height to include new shelf height

17. Design Method: VBA Program
If bin doesn’t fit on shelf and new shelf exceeds cart height
Increment to the next cart
Assign bin and shelf to next cart
If last cart is full
Assign leftover bins as “overflow”
Repeat until all bins have been assigned to a shelf
Next Cart
Last Shelf
Next Shelf and Next Bin

18. Project Background, Goals, and Scope
Model Iterations and Challenges
Model Description
Model Description
Live Demo
Design Requirements
Limitations, Findings, and Conclusions
Findings and Conclusions
Recommendations and Impact

19. Project Background, Goals, and Scope
Model Iterations and Challenges
Model Description
Model Description
Live Demo
Design Requirements
Limitations, Findings, and Conclusions
Findings and Conclusions
Recommendations and Impact

20. Design Requirements: Soft Constraints
Greedy Algorithm
VBA Program
Easily utilized by PAR Team
Allow for countermeasure identification
Student team has experience in language used
Understandable, visual format
Data easily pulled from Excel
Flagged products are identifiable
Output easily incorporated into VBA Program as inputs

21. Design Requirements: Hard Constraints
Greedy Algorithm
VBA Program
Output a bin assignment for every product in list
Output a map to help the PAR team easily build supply carts
Adaptable to product lists for any supply room
Assign every bin to a shelf and cart
Bin size accounts for quantity needed at two standard deviations
Number of carts in room can be adjusted

22. Project Background, Goals, and Scope
Model Iterations and Challenges
Model Description
Model Description
Live Demo
Design Requirements
Limitations, Findings, and Conclusions
Findings and Conclusions
Recommendations and Impact

23. Limitations to the Greedy Algorithm
Flexible Items
Item Footprints
Large vs. large quantity

24. Limitations to the VBA Program
Items must be in a bin
May leave blank space

25. Findings and Conclusions: Complete Model
Greedy Algorithm
VBA Program
Assigning products a bin type by volume was the best approach
Assigning tallest bins within a body system first and building from the bottom up was the most effective approach
Products were correctly assigned to bins of larger volume
Additional carts decrease overflow volume
Conclusion: Output of both programs was sufficient to meet the needs of the PAR Team

26. Project Background, Goals, and Scope
Model Iterations and Challenges
Model Description
Model Description
Live Demo
Design Requirements
Limitations, Findings, and Conclusions
Findings and Conclusions
Recommendations and Impact

27. Recommendations for Future Development
Supply room drop-down menu
Identify flexible products
Incorporate item footprints
Supply Room
Auto Load

28. Expected Impact

29. Standard Work

30. Thank you for listening!
Questions?
Maggie