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TRADELINE
The 2013 International Conference on Research Facilities Westin Copley Place Boston
Boston, MA May 10th, 2013
Improving Space Utilization and Operational Efficiencies in Research Labs
Niranjan S. Kulkarni, PhD CRB Consulting Engineers
Learning Objectives
Understand tools and techniques that can be used to – Improve research space utilization – Identify resource sharing opportunities – Equipment sizing – Leaning out move operations
Applying these tools and techniques – Real-life applications
Lessons learned
Agenda
Typical issues faced in the research environment
Proposed approach
Overview of relevant tools and techniques
Case studies
Typical Operational Issues Faced in the Research Environment
High amounts of variability, volatility and uncertainty
Space constraints
Equipment utilization
Collaboration – people, work and equipment
Safety
Poorly defined process sequence
Proposed Approach
Understand your Objective
• Define goals and metrics
• Select right team members
Data Collection
• Interviews, shadowing, etc.
• Mapping your process
Model / Strategy
Development
• Physical or Computer
• Static or Dynamic • Model verification
and validation
Scenario Analysis
• ‘What-if’ cases • Optimization and
recommendation
Documentation
• Reporting • Lessons learned
• Brainstorming • Improvement
solutions
Ideation
Tools and Techniques
Understanding and describing variability – Histogram – Stem and Leaf plots – Gage R&R – Design of Experiments
Stem Leaf1 2, 3, 72 1, 3, 3, 4, 73 0, 3, 94 5, 65 0
0-10 11-20 21-30 31-40 41-50 51+
0
3
5
3
2
0
X
Y
Z
X
Y
+ +
- -
2k Factorial Design
k = 2 k = 3
Tools and Techniques (cont’d)
Mapping and flow diagramming – Value stream maps – Spaghetti diagrams – Role-centric mapping
Tools and Techniques (cont’d)
Simulations (static vs. dynamic) – Monte Carlo Simulations – Discrete Event Simulation (DES) – Agent Based Simulation – Continuous Simulation – Hybrid Simulations
DES Overview
Time based, Discrete Time, Unequal Intervals Processes, Queuing and Delays Deterministic (Static) or Stochastic (Dynamic) Incorporate effects of variability Perform scenario analysis
Tools and Techniques (cont’d)
Group formation – Descriptive procedures – Mathematical programming – Cluster analysis – Graph partitioning – Artificial intelligence techniques
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Attributes shared by more than one cluster
Case Studies
Case study 1 – Expected increase in demand – Facility was a part of a consolidation effort to re-purpose an existing warehouse
space into research laboratories – Sizing storage chambers under demand uncertainty – Required to design a lean move strategy
• Reducing number of product touches • Reducing instances for OOS conditions during consolidation
Case Study 1
Need more chamber space to accommodate increased demands Design a lean move strategy
Number of current studies Temperature and humidity conditions Current chamber sizes
Understand your Objective
Data Collection
Model and Strategy Development
Forecasting bin requirement – Probabilistic model developed – Number of bins used to size chambers – Bins move from one storage condition to another
Condition
(Temp/RH)
Number of Bins
2012 2013 2014 2015 2016
25/60 743 1530 899 756 721 30/65 464 767 427 314 372 40/75 645 358 270 231 218 2-8C 83 376 291 195 135
30/75 32 20 123 87 152 25/75 32 4 12 0 1
Scenario Analysis
Location 1Chambers 1, 2 and 3
Location 2Chambers 9, 10 and 11
Location 3Chambers 4 – 8
Step 1
Move bins to new chambers
Location 1Chambers 1, 2 and 3
Location 2Chambers 9, 10 and 11
Location 3Chambers 4 – 8
Step 2
Move bins from Location 2 to chambers in Location 3
Location 1Chambers 1, 2 and 3
Location 2Chambers 9, 10 and 11
Location 3Chambers 4 – 8
Step 3
Move bins from Location 1 to chambers in Location 2
Location 1Chambers 1, 2 and 3
Location 2Chambers 9, 10 and 11
Location 3Chambers 4 – 8
Step 1
Move bins to new chambers
Step 2
Move bins from Location 2 to chambers in Location 3
Location 1Chambers 1, 2 and 3
Location 2Chambers 9, 10 and 11
Location 3Chambers 4 – 8
Move bins from Location 1 to chambers in Location 2
Move bins to new chambers
Scenario 1 Scenario 2
• 3 step move strategy • Longer schedule • More efficient use of space and resources • Minimizes capital requirement • No shutdowns
• 2 step move strategy • Shorter schedule • Increases equipment redundancy • Larger capital requirement • Potential disruption ~ 1 week
Model and Strategy Development (cont’d)
Summary Case Study 1
Use of appropriate technique to forecast bin requirement Outputs from model, inputs to chamber sizing Lean move strategy Reduced need for new chambers Savings in equipment $2.9M
Case Studies (cont’d)
Case study 2 – Department consists of 3 groups spread across two floors – Inefficient use of space and equipment – Need to improve workflow – No major capital projects can be undertaken – Plan for increased headcount
Case Study 2
Improve space and equipment utilization Reduce researchers' travel distances and enhance
workflow productivity Plan for increase in headcount
Survey questionnaire Shadowing efforts Space assessment
Understand your Objective
Data Collection
Survey Findings
• Lab Culture • Rarely used equipment occupying
space • Increased local storage by individual
scientists
• Nature of experiments – need to check every 10-20 minutes
• Break room location • Safety issues
Shadowing
Scientists traveling 0.6 miles – 2.1 miles every day
Forming Work Groups Group ID Work Group Location ID
1
KG Gene Therapy 630-03 NS Biologics 626-03 JM Biologics 622-02 MT Pharmacology 626-02 AN Biologics 636-02 LB Biologics 622-01
MA Biologics 626-01 SG Biologics 622-03 YW Biologics 622-05 AC Biologics 622-06
2
DL Pharmacology 602-03 VK Biologics 630-08 YQ Biologics 618-02 LF Gene Therapy 618-01 SP Pharmacology 610-02 LY Pharmacology 616-01 EU Pharmacology 638-05 HM Pharmacology 602-05
3
MS Pharmacology 616-02 MC Pharmacology 606-03 OD Pharmacology 602-07 NB Pharamcology 602-08 SS Pharmacology 602-01 AD Pharmacology 602-06 HL Gene Therapy 618-04 SA Pharmacology 610-06 YZ Biologics 610-09 JD Pharmacology 618-06 TD Pharmacology 638-07
4
TM Gene Therapy 903-07 VC Gene Therapy 630-05 AG Gene Therapy 903-06 NN Gene Therapy 903-10 AnG Operations 638-11
KG NS JM MT LB SG YW AC DL LF SP LY EU HM MS MC OD NB SS AD SAE1 x x x xE3 x x x xE4 xE5 x x x x x xE6 x x xE8A x x x x xE13 x x xE15 x xE20 x xE1AE2 x x x x xE9 x x x xE7 xE8 x x x x xE8B x x x xE10 x x x xE11 x x x x xE12 x x xE14 xE16 xE17 x x x x x x xE18 x x x x x xE19 x x
Group 3Equipment #
Group 1 Group 2
Developing High Level BFDs
Prioritizing Improvement Options
Summary Case Study 2
Root cause analysis of issues Shadowing activities to understand most traveled paths and distance
traveled Selective relocation of people, cold storage space and equipment for more
efficient travel paths 5S event to reduce clutter in the space and better organize the department’s
assets
Lessons Learned
Lab culture is a significant factor, not to be ignored
Researchers are worried about contamination and safety
Crucial to get the right team members, may want to include external SMEs
Important to get researchers’ buy-in
The Tradeline Three
Variability and uncertainty drives most of the inefficiencies
Understand your science (goal) and select the right team and problem
solving technique/tool; build in flexibility that you can afford
Thoroughly evaluate / test changes on system prior to implementation
Niranjan S. Kulkarni, PhD niranjan.kulkarni@crbusa.com
617-475-3050
Thank You!
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