Modeling operations at pharmaceutical distribution warehouses Brian L. Heath, Ph.D. Director, Advanced Analytics December 12, 2013

Transcription

1 Modeling operations at pharmaceutical distribution warehouses Brian L. Heath, Ph.D. Director, Advanced Analytics December 12, 2013 Copyright 2012, Cardinal Health. All rights reserved. CARDINAL HEALTH, the Cardinal Health LOGO and

2 Essential Facts 2

3 Essential role We are an essential link in the healthcare supply chain 3

4 Essential role Pharmaceutical Distribution Brand pharma Generic pharma HBA/OTC Private label Cardinal Health Retail pharmacy Hospital Mail service Alternate care Physician office Surgery center Supplier partners Efficiency and demand management Contract and credit management Next day delivery to >30k locations Delivery data and product security Generating value up and down the supply chain Customers Next day delivery Access to >2,000 manufacturers with one order, one invoice Working capital management Value-added services Enable improved patient service 4 Copyright 2012, Cardinal Health. All rights reserved. CARDINAL HEALTH, the Cardinal Health LOGO and

5 Challenges in Pharmaceutical Distribution Rx Product Small in Size Limited Shelf Life Consumable Very Expensive Life critical All products are slow movers relative to traditional distribution center metrics Relatively poor manufacturing reliability Frequent supply disruptions Highly Regulated (storage & distribution) FDA & DEA Customers Frequent yet small order quantities Next day delivery (12 to 24 hour turn around) 5

6 Pharmaceutical Warehouse Design Some Key Considerations Facility layout Unit of work Flow of product Interaction between warehouse functions Order Picking Equipment Technology Efficiency Strategy/Methodology Routing Labor planning & scheduling Customer Order Requirements Congestion Analysis tools Empirical Trail & Error Flexible Expensive Risky Mathematical Modeling Cheap Optimal solutions Hard to capture unexpected dynamics (often many assumptions) Process Driven Simulation Modeling Solutions need to be generated before hand More easily captures variability Not a natural way to represent a facility, since space and interaction between workers plays a significant role Agent-Based Modeling is the next generation of Facility Design tools 6

7 Analysis Tool: Agent-Based Modeling Agent-Based Modeling Represents abstractions of distributed autonomous entities that can interact with each other and their environment through space and time Simulation Software: AnyLogic Captures Work time allocation Congestion wait time Cycle times Distances traveled Worker variability Many more metrics Designed to quickly evaluate different scenarios Reads in layout and actual location and demand data from Excel Files Distributions based on historical data Scenario Applications Comparing Layouts Picking technology Product slotting Evaluation of best methods Capability & staffing The Agent Perspective Where is my next pick? How do I get there? What is the best route? Is there anyone blocking my way? 7

8 Applications: Operation Support Tool 8

9 Applications: Layout & Flow Analysis Which layout is better? A B 9

10 Application: Elimination of Congestion Congestion is a Big Problem at Cardinal Distribution Centers Annually, congestion costs Cardinal $3-4M per year Conjecture Tested Hypotheses Technology Layout Process Design What causes congestion? Aisle Designs Management Agent-Based Facility Design Research Real World Decision Support Worker Variability 10

11 Model Demonstration 11

12 Work Hours Area Productivity/Hour The Value of ABM in Facility Design Sample Model Results Shift Length: hrs Shift Length: 9.65 hrs Grab/Put Items Walk w/o Cart QC / Tote Move Congest Wait * Shift Length: 7.73 hrs Shift Length: 7.25 hrs Scenario Current1 Current: Scenario w/o 2 Current: Scenario w/o 3 Future: Scenario w/ PDP 4 BBBC BBBC & w/ Bal. & Bal. Setup Batch No Avail Batch Cart Walk With Cart Congest Wait QC or Tote Move Walk Without Cart Grab/Put Items Discovered how to minimize congestion FTEs vs. Productivity/Hour FTEs actively picking Scenario A Scenario B Discovered Area Capacity by Scenario Using ABM in Facility Design has saved Cardinal Health Millions of Dollars 12

13 Next Generation Facility Design Models Picking Method Slotting Congestion Layout Design Wave Schedule Impact Training Ergonomics Efficiency 13

14 Concluding Remarks Agent-Based Modeling has real world applications Agent-Based Modeling is the next generation in Facility Design Tools Agent-Based Modeling saves Cardinal over $3M annually AnyLogic s agent libraries, flexible architecture, and integrated animation enables the continuing success of this project The price of light is less than the cost of darkness Arthur C. Nielsen 14

15 Questions? Brian L. Heath, Ph.D. 15