Design of Map to Build Carts and Allocate

Design of Map to Build Carts and Allocate Products Final Report April 17, 2018 IOE 481 - 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 18 W 9 -final-report

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

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

Example Cart

Body System Orientation

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

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

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

Linear Program Issues and Challenges: 1. Needed constraints with binary variables Linear Program Attempted Mitigation: 1. Moved to an Integer Program model

Integer Program Issues and Challenges: 1. Changing shelf height 2. Two-bin kanban system 3. Objective function formulation Mitigation: 1. Created two decision variables 2. Created a binary parameter 3. Minimize wasted space Issues and Challenges: 4. Variable with four indices Linear Program Integer Program Attempted Mitigation: 4. Divided the program into 2 parts

Greedy Algorithm and Integer Program Issues and Challenges: 1. Size of the dataset and constraints 2. Spreadsheet not user-friendly 3. Body system constraint Linear Program Integer Program Attempted Mitigation: 1. Experimented in open solver 2. Brainstormed simplifications 3. Potentially 11 objective functions Greedy Integer Algorithm Program

Greedy Algorithm and VBA Program Issues and Challenges: 1. VBA program input arrays 2. VBA program body system order 3. VBA program shelf height Linear Program Integer Program Mitigation: 1. GSI assistance and research 2. Recorded a sorting macro 3. Created max bin height variable Greedy Integer Algorithm Program Greedy VBA Algorithm Program

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

Design Methods: Complete Model Description

Design Method: Greedy Algorithm

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

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 Last Shelf Next Shelf and Next Bin Next Cart

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

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

Design Requirements: Hard Constraints Greedy Algorithm VBA Program Output a bin assignment for every Output a map to help the PAR product in list 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

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

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

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

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

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

Recommendations for Future Development 1. Supply room drop-down menu Supply Room Auto Load 1. Identify flexible products 2. Incorporate item footprints

Expected Impact

Standard Work

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