LGO 2016 Research Internship Projects

MIT Leaders for Global Operations Internship ProjectsFall 2015

LGO Internships

One of the hallmarks of the LGO program is the six month internship project, completed at one of our 27 partner companies. Internship projects vary broadly in industry and function, but all contain a significant research

component which then becomes the foundation of the student’s thesis.

Included in this slide deck are eight internship projects from the Class of 2016.

Table of Contents

National Grid Risk Analysis of Unmanned Aircraft Systems in National Airspace for Utility Applications (Jackee Mohl)

Page 5

Amazon.comInventory Management for Throughput Optimization (Jake Stowe)

Page 11

CalibraFinesse Commercialization, Supply Chain Development, and Automation Implementation (Samer Haidar)

Page 25

Massachusetts General HospitalRoutine Post-Procedure Recovery (RPPR) Patients (Kfir Yeshayahu)

Page 29

Table of Contents Continued

Pacific Gas & ElectricCathodic Protection Resurvey Process – Natural Gas Distribution System (Lillian Meyer)

3

AmgenStreamlining and Standardizing Transcriptomic Analysis in Process Development (Kerry Weinberg)

Page 56

NikeLeveraging Consumer Sales Data (Blair S. Holbrook)

Page 70

RaytheonAdditive Manufacturing of Metals (Andrew Byron)

Page 78

LGO internship project by Jackee Mohl, LGO ’16(MBA and SM in aeronautics and astronautics)

National Grid:Risk Analysis of Unmanned Aircraft Systems in National Airspace for Utility ApplicationsCompany Sponsors: Mike McCallan, Kara MorrisFaculty Advisors: Georgia Perakis, Woody Hoburg

National Grid (Mohl) – 1 of 6

Objective - Operational

Operational Goals• FAA approval for UAS operations• Instructions for pilot program• Safety, training and operations plans

• Inspection access and safety

• Helicopter operations cost and schedule

Problem Solution

• Implementation of UAS in utility operations


Approach - Operational

Pilot Program Development

FAA Approval

Source: R4 Robotics


Objective – Regulatory Research

Regulatory Research Goals• Recommendations for loosening of restrictions• Additional data for utility pushback to FAA• Recommendations for additional safety

technology or restrictions required

• FAA regulations too restrictive

• Limits potential use cases

Problem Solution• Probabilistic risk

analysis model to determine equivalent level of safety


Approach – Regulatory Research


Status and Next Steps

Next Steps• Further research data and gain access required for

models• Run probability models and define recommendations• Further develop pilot program

FAA Section 333 Exemption Ground Collision Model Midair Collision Model


LGO internship project by Jake Stowe, LGO ’16(MBA and SM in engineering systems)

Amazon.com:Inventory Management for Throughput OptimizationCompany Sponsors: Brian Donato and Joanna Hicks MIT Faculty Advisors: Dr. Bruce Cameron and Dr. Roy Welsch

Amazon (Stowe) – 1 of 14

Context• In 2012 Amazon purchases Kiva Systems Inc.• Amazon has built several fulfillment centers reliant on

Kiva technology• Kiva dramatically changes production model of

warehouse operationsLegacy FC (LFC)

Labor ConstrainedRobotic FC (RFC)

Station Constrained


Terminology

Pod

Drive

Bin

StationAmazon (Stowe) – 3 of 14

Fundamental Questions• Cost structure of RFCs is substantially less than LFCs• Natural question: How do we increase throughput of a Robotics FC? • A few things to focus on:

– Capacity – More stations? More pods? More floor space? >> Expensive and complex

– Utilization – Increase stow rates, increase pick rates >> Hard

– Inventory – Are we stocking the right stuff in the right places, in the right way?


How Inventory Flows in a RFC

Robotic Prime Field(“Fast”)

Reserve Racks(“Slow-ish”)

Receive Pack

Ship


Further Fundamental Questions• What kind of inventory are we storing in

our robotic field?• How fast does it move?• Is it getting older, or younger on

average?• How should we deal with slow moving

inventory?• Why does it matter?


What kind of inventory are we storing?• Deadwood – Older than 90 days – not projected

to be sold for 6 months.


Is it getting older?


What to do about it?



Receive Pack

Ship


What to do about it? – Right Now



Receive Pack

Ship Store slow moving ASINs inmodified reserve racks

Replen when demand is generated


What to do about it? – Ideal State



Receive Pack

Ship


Why Does it Matter?• If you don’t increase capacity or utilization,

why should this matter?• Pile-on and Pick Density

Legacy Robotic


Next Steps• Create a realistic model of how consolidation

activities will affect utilization• Use of optimization techniques to determine an

optimal number of station hours to devote to consolidation

• Pilot and scale up storage in the reserve racks (beginning this week)

• Larger Impact for Industry– Greater understanding of the dynamics of

inventory in automated warehousing operations– Exploring the applications of legacy vs.

automated operations


Key Challenges and Interests• Challenges

– Complexity of the systems and stakeholder interests

– Siloed knowledge about the technology and human processes

• Three major knowledge areas are necessary: – Human Processes: Leadership of the FC and line

Workers– Kiva Technology: Kiva engineers and systems

engineers tasked with optimizing system– LGO Knowledge – Time series analysis and

mathematical optimizationAmazon (Stowe) – 14 of 14

LGO internship project by Samer Haidar ’16(MBA and SM in mechanical engineering)

Calibra:Finesse Commercialization, Supply Chain Development, and Automation ImplementationCompany Sponsors: Eijiro Kawada, Hector Rodriguez, Jim Conroy MIT Faculty Advisors: Dimitris Bertsimas, Brian Anthony

Calibra/Johnson & Johnson (Haidar) – 1 of 4

Objective

What I am hoping to learn:•How to manage a 20-member cross-functional project team within a very large healthcare company•How to design and optimize a customer-centric end-to-end supply chain for a new product launch

Background•Calibra was a venture-backed startup that Johnson & Johnson acquired in 2012. •The Calibra Finesse is a wearable insulin delivery patch that will be launchedin 2016. It delivers 2 units of bolus insulin per click to help diabetic patientscontrol blood glucose at mealtime. Objective and Benefits•The internship will develop a 7-year strategic plan for a lean, end-to-end supply chain for the Calibra Finesse.


ApproachApproachUsing the Process Excellence tools at J&J and the Lean/Six-Sigma training we received at LGO, we are following the DMADV process design framework:

Resources that I will need•Guidance from faculty advisors and project leader and champion•Functional leaders within project team to define current state processes and metrics•Gartner supply chain benchmarking insights

-Project Charter-Identify & segment customers

-Voice of Customer-Define metrics

-Current State Analysis-Benchmarking-High level design/modelling

-Develop detailed design elements-Develop validation and control plans

-Verify against business case-Validate against VOC


Status and Next StepsStatus Update•Collected and analyzed Voice of Customer (VOC) data from interviews and surveys with persons with diabetes, healthcare professionals, wholesale distributors and retailers.•Translated VOC needs into supply chain’s functional requirements.•Developed modelling framework for manufacturing facility location-allocation problem.Findings•Customer journey mapping reveals critical steps in insulin device training, ordering, use, and support. •Affinity mapping of VOC needs statements uncover strategic supply chain design requirements focused on visibility, collaborative planning, and order management.

Next steps•Develop current state value stream map highlighting different functions’ processes.•Benchmark best-in-class companies based on critical functional requirements. •Develop future state design concepts and evaluate against functional requirements.•Expand manufacturing facility location-allocation model to incorporate supplier selection and distribution scenarios.


LGO internship project by Kfir Yeshayahu ’16(MBA and SM in electrical engineering and computer science)

Massachusetts General Hospital:Routine Post-Procedure Recovery (RPPR) PatientsMGH Sponsors: Dr. Peter Dunn, Bethany Daily, Cecilia ZentenoMIT Faculty Advisors: Retsef Levi, Patrick Jaillet, David Scheinker

MGH (Yeshayahu) – 1 of 14

Massachusetts General Hospital

• MGH annually handles ~1.5 million outpatient visits and admits ~48,000 inpatients

• More than 37,000 surgeries annually, in 70 Operating Rooms spread across five buildings on three floors

Dr. John Collins Warren performs the first surgery without pain as William Morton administers ether

““Since 1811, Massachusetts General Hospital (MGH) has been committed to Since 1811, Massachusetts General Hospital (MGH) has been committed to delivering standard-setting medical care.” delivering standard-setting medical care.” (MGH website)(MGH website)


Routine Post-Procedure Recovery (RPPR) PatientsSurgical patients categories:

• RPPR is an internal category in MGH and is not used by insurance companies.

• In practice, a hospital bed is being reserved for all RPPR patients.MGH (Yeshayahu) – 3 of 14

Project Objectives & RPPR Challenges

Project Goal: Determine optimal patient flow strategies for RPPR patients by analyzing the trade-offs in resource utilization of alternative pathways.

RPPR Challenges:


Approach


Status• Studied RPPR booking considerations:

– bed assignments, procedure types, insurance reimbursement, etc.

• Generated a Recovery Flow Charts for certain surgical procedures

• Created a Length of Stay comparison tool (web application)

• Analyzed patterns over time– Example: Implications of approach change after the Lunder

building opened







building opened


Recovery Flow Scenarios: Thyroidectomy







building opened


Length of Stay Comparison App







building opened


Study Different Approaches: Keep in the PACU vs. Send to Floors

Length of stay definition: from entering the PACU to discharging from the hospital

Patient population: RPPR who were discharged home directly from the PACU

Time frame: CY2010-CY2013 Data sources: Operating

Rooms & Admitting department (CBED)

Legend:Keep in the PACU (Before 09/2011)Send to Inpatient Floors


Next Steps• Map & acquire additional necessary data to

analyze wasted bed-time

• Model alternative mechanisms of handling RPPR patients in terms of recovery location and surgery scheduling

• Create a predictive model for patients’ length of stay in the hospital

• Generate operational recommendations


Top 20 RPPR Procedures in 2014


LGO internship project by Lillian Meyer ’16(MBA and SM in civil and environmental engineering)

Pacific Gas & Electric:Cathodic Protection Resurvey Process – Natural Gas Distribution SystemCompany Sponsors: Preston Ford, Sumeet Singh, Mallik AngalakudatiMIT faculty Advisors: Herbert Einstein (CEE), Georgia Perakis (Sloan)

PG&E (Meyer) – 1 of 13

Gas Operations within PG&E

Source: "Natural Gas System Overview." Natural Gas System Overview. Pacific Gas & Electric Company.


PG&E approaching industry trend

Source: USA. U.S. Department of Transportation. PHMSA. Distribution, Transmission & Gathering, LNG, and Liquid Annual Data.


Direct cost of corrosion is $276 billion per year in US

Infrastructure, $22.6

Transportation, $29.7

Production and manufacturing,

$17.6 Government, $20.1

Electrical utilities, $6.9

Gas distribution, $5.0

Drinking water and sewer systems,

$36.0 Utilities, $47.9

Cost of Corrosion in billions

Source: Corrosion Costs and Preventive Strategies in the United States. McLean, VA: Federal Highway Administration, 2002. NACE International. Note: Cost of corrosion includes monitoring, replacing, and maintaining


Prioritizing a system-wide survey based on risk• How do we properly allocate resources? Where

can we start that eliminates the most risk?

• Goal: a systematic way of designating where leaks due to corrosion are most likely to occur throughout PG&E’s distribution pipeline system


Identifying probability and consequence of failure


Significant amount of work still to be completed

• Currently– Gathering data about the current pipeline system– Researching relevant corrosion models

• Next steps– Perform regression analyses and compare predictive

factors to historical leak data– Develop risk-ranking model and identify high-risk

areas– Perform system-wide survey based on risk analysis;

were high-risk areas addressed first?


3 key challenges to consider• Sheer size of PG&E’s network

• Communication between different databases and data storage methods over time

• Sustainability of maintenance practices


Ultimately, address riskiest areas first and reduce number of leaks


Corrosion Cell

Source: “Corrosion Basics." Corrosion Central. NACE International.


PG&E has an increasing number of corrosion leaks repaired

Source: USA. U.S. Department of Transportation. PHMSA. Distribution, Transmission & Gathering, LNG, and Liquid Annual Data.


Identifying probability and consequence of failure

Source: Ogosi, Eugene, and Stephen McKenny. "Indexing Model for Pipeline Risk Assessment and Corrosion Management." NACE Corrosion 2014 (2014): OnePetro.


Meyer, Lillian

re-do graphic and include what data I am usingevents tree or bow-tie graphic?

Ultimately, address riskiest areas first and reduce number of leaks


LGO internship project by Kerry Weinberg ’16(MBA and SM in bioengineering)

Amgen:Streamlining and Standardizing Transcriptomic Analysis in Process DevelopmentCompany Sponsors: Brian Follstad (Amgen supervisor), Sam Guhan (Amgen champion)MIT Faculty Advisors: Doug Lauffenburger, Roy Welsch

Amgen (Weinberg) – 1 of 14

Agenda • Amgen biotech process development • Current complex data analysis requires streamlining

workflow and developing internal tools• Current Status: Beta version of tool developed• Next Steps: Application of beta tool to historical

datasets• Key challenges and opportunities• Q&A


Amgen Process Development

R&D PD

CHO cell

Productivity

Quality

Ribosome making

therapeutic protein

Correct sugars on

therapeutic protein

(Image sources in notes)

Mfg


Amgen Process Development• Process Development group improves and

characterizes bioreactor productivity and final product quality by analyzing the cellular impact of process conditions

Productivity1F1(a,b,c)

CHO Bioreactor

A

B

C Quality1

Productivity2F2(a,b,c)

Quality2

Cell

line

1Ce

ll lin

e 2 A

B

C


Transcriptomic Analysis in Amgen Process Development

Transcriptomic Data Collection

Transcriptomic Data Analysis

F1(a,b,c)

CHO Bioreactor

A

B

C

Productivity1

Quality1

Pathway Analysis

Clustering

Principle Component

Analysis


Transcriptomic Analysis in Amgen Process Development

+

Understanding CHO Biological System

Transcriptomic Data Statistical and Pathway Analysis Productivity

Quality

Current transcriptomic data analysis workflow

Ad-hoc

Time consumin

g

Steep learning

curve

Ad-hoc


Objectives

(Image sources in notes)


Approach

Assess current state workflow

Develop automated

tool

Refine tool and develop

standard workPilot Knowledge

Transfer

CHO transcriptomicDatabase (Amgen)

Information Systems

Literature review & advisors

Feedback from core users


Agile sprints

Agile sprints

Process dev

R&D

Publically availabledata

(chogenome.org)


Status and Next Steps

Assess current state workflow

Develop automated

tool

Refine tool and develop

standard workPilot Knowledge

Transfer

Publically availabledata

(chogenome.org)Information

Systems

Literature review & advisors



Agile sprints

Process dev

R&D

Agile sprints

CHO transcriptomicDatabase (Amgen)


Challenges


Opportunities


Is this sustainable?• Open source software code• Tool designed for future extensions• Highly documented source code• Process development investing in Python

knowledge


What is the business impact?• Simpler data analysis workflow drives future

use of omic analysis• Cost of omic analyses ~$1k vs. Cost of

shutdown bioreactor run ~$1M• Understanding link between process inputs and

process outputs = key for comparability


References• http://www.topbritishinnovations.org/PastInnov

ations/MonoclonalAntibodies.aspx, http://www.broadleyjames.com/bionet-overview.html,

• http://www.rockwellautomation.com/rockwellautomation/solutions-services/oem/process-bioreactors.page, prolia.com, neulasta.com, vectibix.com


http://www.topbritishinnovations.org/PastInnovations/MonoclonalAntibodies.aspx

http://www.topbritishinnovations.org/PastInnovations/MonoclonalAntibodies.aspx

http://www.broadleyjames.com/bionet-overview.html

http://www.broadleyjames.com/bionet-overview.html

http://www.rockwellautomation.com/rockwellautomation/solutions-services/oem/process-bioreactors.page



LGO internship project by Blair S. Holbrook ’16(MBA ad SM in engineering systems)

Nike:Leveraging Consumer Sales Data

Company Sponsors: Jon Frommelt and Mike OversonMIT Faculty Advisors: Tauhid Zaman and David Simchi-Levi

Nike (Holbrook) – 1 of 8

Company Context

CORE ESSENTIALS

LONG LIFECYCLE

1 WEEK ORDERS

SHORT LEAD TIME

SEASONAL

INNOVATIVE

6 MONTH ORDERS

CUSTOMIZED

PERSONALIZED

2 WEEK ORDERS

QUICK

RESPONSIVE

3 MONTH ORDERS

SEASONAL QUICK TURN

ALWAYSAVAILABLE CUSTOM


Problem and Key Question

How do we reduce bullwhipping?

CUSTOMERNIKE ALWAYS AVAILABLE CONSUMER


Objective Project & Pilot Goals Volatility Inventory Stockouts Trust


Approach

Prioritize products based on

predictability

Leverage point-of-sale (POS) and inventory data

Forecast based on historical

POS data

Accountfor lost sales to

estimate true demand

Provide reorder

recommendations


Findings To Date

POS based forecasts can be much more accurate.

…yet current forecasts can be highly inaccurate .

POS data can be consistent…

Some products are better suited for forecasting.


Status and Next Steps1. Select style-colors

2. Develop statistical forecasts

3. Socialize and secure pilot commitment

4. Develop reorder policy and initiate pilot

5. Measure outcomes


Key Challenges1. Securing commitment from partner – Remember Barilla?

2. Executing

3. Scalability – Not overpromising


LGO internship project by Andrew Byron ’16(MBA and SM in aeronautics and astronautics)

Raytheon:Additive Manufacturing of MetalsCompany Sponsors: Manuel Gamez, Teresa ClementMIT Faculty Advisors: Steven Eppinger, Brian Wardle

Raytheon (Byron) – 1 of 5

Why develop additive manufacturing?

• Raytheon produces precision weapons using advanced manufacturing

• Additive manufacturing (AM) can help develop and produce complex products faster

• Industry has begun to define practices and characteristics, but most is primary research or proprietary

• Aerospace applications need reliability and repeatability

Image source: Raytheon Product Information, Standard Missile-3

Courtesy of TWI Ltd

Image source: Wikimedia


How to drive change• We are developing a

business process programs and factories can use to ensure qualified, predictable results for AM parts

• Part of that effort involves understanding the effect of process inputs – materials, controls, procedures

– Initiated through a designed experiment on primary metals AM control parameters

Process Capability Material

SpecificationEquipment

QualificationNDT

Qualification

Integration,Verification &Validation (IV&V) Statistical

Sample Testing Inspection Process Control

Design FeasibilityPart

Development Plan

Proof PartsPreliminaryStatistical

Characterization

Main Factors UnitsLaser Power WScan Speed mm/sScan Spacing µmBeam Diameter µmFeedstock Factors

FlowabilityGo/NoGo

Dose Factor %Reuse/Recycle Cycles #Particle Size (sieving) µmCoater Blade Height µmPost-Process FactorsHot Isostatic Pressing Y/N

Responses UnitsDimensional Accuracy %Surface Finish RMSDensity (vs. wrought) %Layer Cohesion 1-5Ultimate tensile strength ksiYield tensile strength ksiDuctility (Elongation) εMelt Pool Diameter µm


• First level of qualification process nearly developed– Builds required, testing and estimated schedule

Availability 100

Units 1

AM Specialist

AM Build

Cycle Time N(18, 0.6)

Mold Breakout/Powder Cleanup

Cycle Time U(2.5, 0.3)

Welding

Cycle Time U(1.1, 0.8)

Is Part Welded?

Surface Finishing

Cycle Time U(2, 0.5)

HIP


(Exit)

Assembly

Yes

No

Loading

Cycle Time 0.6

Is Feedstock Available?

Feedstock Lot Prep

Cycle Time T(2, 3, 5)

Baseplate Removal


Inspection

Cycle Time 0.6

Availability 100

Units 1

Machinist

Status and next steps

ProModel Process Simulation diagram of model used to represent AM parts build process and duration


• Initial screening experiment complete

Next Steps:• Final product will be a recipe for any material

or process: a set of steps to qualify AM for a new part

• Experimental results will be analyzed and used to create a more specific factored test on targeted parameters

• End goal is to develop a process “recipe” that can be used for any material or AM technology


LGO 2016 Research Internship Projects

Education

Transcript of LGO 2016 Research Internship Projects