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!