LGO 2016 Research Internship Projects
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Each LGO student completes a six month internship project at one of our 27 partner companies. The projects range from energy and sustainability, lean operations, supply chain, and many more. All contain significant research which then becomes the basis for the thesis.
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LGO 2016 Research Internship Projects
- 1. MIT Leaders for Global Operations Internship Projects Fall 2015
- 2. 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.
- 3. Table of Contents National Grid Risk Analysis of Unmanned Aircraft Systems in National Airspace for Utility Applications (Jackee Mohl) Page 5 Amazon.com Inventory Management for Throughput Optimization (Jake Stowe) Page 11 Calibra Finesse Commercialization, Supply Chain Development, and Automation Implementation (Samer Haidar) Page 25 Massachusetts General Hospital Routine Post-Procedure Recovery (RPPR) Patients (Kfir Yeshayahu) Page 29
- 4. Table of Contents Continued Pacific Gas & Electric Cathodic Protection Resurvey Process – Natural Gas Distribution System (Lillian Meyer) Page 43 Amgen Streamlining and Standardizing Transcriptomic Analysis in Process Development (Kerry Weinberg) Page 56 Nike Leveraging Consumer Sales Data (Blair S. Holbrook) Page 70 Raytheon Additive Manufacturing of Metals (Andrew Byron) Page 78
- 5. 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 Applications Company Sponsors: Mike McCallan, Kara Morris Faculty Advisors: Georgia Perakis, Woody Hoburg National Grid (Mohl) – 1 of 6
- 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 ProblemProblem SolutionSolution • Implementation of UAS in utility operations National Grid (Mohl) – 2 of 6
- 7. Approach - Operational Pilot Program DevelopmentPilot Program DevelopmentFAA Approval FAA Approval Source: R4 Robotics National Grid (Mohl) – 3 of 6
- 8. 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 ProblemProblem SolutionSolution • Probabilistic risk analysis model to determine equivalent level of safety National Grid (Mohl) – 4 of 6
- 9. Approach – Regulatory Research National Grid (Mohl) – 5 of 6
- 10. 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 National Grid (Mohl) – 6 of 6
- 11. LGO internship project by Jake Stowe, LGO ’16 (MBA and SM in engineering systems) Amazon.com: Inventory Management for Throughput Optimization Company Sponsors: Brian Donato and Joanna Hicks MIT Faculty Advisors: Dr. Bruce Cameron and Dr. Roy Welsch Amazon (Stowe) – 1 of 14
- 12. 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 operations Legacy FC (LFC) Labor Constrained Robotic FC (RFC) Station Constrained Amazon (Stowe) – 2 of 14
- 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? Amazon (Stowe) – 4 of 14
- 15. How Inventory Flows in a RFC Robotic Prime Field (“Fast”) Robotic Prime Field (“Fast”) Reserve Racks (“Slow-ish”) Reserve Racks (“Slow-ish”) ReceiveReceive PackPack ShipShip Amazon (Stowe) – 5 of 14
- 16. 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? Amazon (Stowe) – 6 of 14
- 17. What kind of inventory are we storing? • Deadwood – Older than 90 days – not projected to be sold for 6 months. Amazon (Stowe) – 7 of 14
- 18. Is it getting older? Amazon (Stowe) – 8 of 14
- 19. What to do about it? Robotic Prime Field (“Fast”) Robotic Prime Field (“Fast”) Reserve Racks (“Slow-ish”) Reserve Racks (“Slow-ish”) ReceiveReceive PackPack ShipShip Amazon (Stowe) – 9 of 14
- 20. What to do about it? – Right Now Robotic Prime Field (“Fast”) Robotic Prime Field (“Fast”) Reserve Racks (“Slow-ish”) Reserve Racks (“Slow-ish”) ReceiveReceive PackPack ShipShipStore slow moving ASINs in modified reserve racks Replen when demand is generated Amazon (Stowe) – 10 of 14
- 21. What to do about it? – Ideal State Robotic Prime Field (“Fast”) Robotic Prime Field (“Fast”) Reserve Racks (“Slow-ish”) Reserve Racks (“Slow-ish”) ReceiveReceive PackPack ShipShip Amazon (Stowe) – 11 of 14
- 22. Why Does it Matter? • If you don’t increase capacity or utilization, why should this matter? • Pile-on and Pick Density Legacy Robotic Amazon (Stowe) – 12 of 14
- 23. 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 Amazon (Stowe) – 13 of 14
- 24. 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 optimization Amazon (Stowe) – 14 of 14
- 25. LGO internship project by Samer Haidar ’16 (MBA and SM in mechanical engineering) Calibra: Finesse Commercialization, Supply Chain Development, and Automation Implementation Company Sponsors: Eijiro Kawada, Hector Rodriguez, Jim Conroy MIT Faculty Advisors: Dimitris Bertsimas, Brian Anthony Calibra/Johnson & Johnson (Haidar) – 1 of 4
- 26. 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 launched in 2016. It delivers 2 units of bolus insulin per click to help diabetic patients control 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. Calibra/Johnson & Johnson (Haidar) – 2 of 4
- 27. Approach Approach Using 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 Calibra/Johnson & Johnson (Haidar) – 3 of 4
- 28. Status and Next Steps Status 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. Calibra/Johnson & Johnson (Haidar) – 4 of 4
- 29. LGO internship project by Kfir Yeshayahu ’16 (MBA and SM in electrical engineering and computer science) Massachusetts General Hospital: Routine Post-Procedure Recovery (RPPR) Patients MGH Sponsors: Dr. Peter Dunn, Bethany Daily, Cecilia Zenteno MIT Faculty Advisors: Retsef Levi, Patrick Jaillet, David Scheinker MGH (Yeshayahu) – 1 of 14
- 30. 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 toSince 1811, Massachusetts General Hospital (MGH) has been committed to delivering standard-setting medical care.”delivering standard-setting medical care.” (MGH website)(MGH website) MGH (Yeshayahu) – 2 of 14
- 31. Routine Post-Procedure Recovery (RPPR) Patients Surgical 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
- 32. 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: MGH (Yeshayahu) – 4 of 14
- 33. Approach MGH (Yeshayahu) – 5 of 14
- 34. 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 MGH (Yeshayahu) – 6 of 14
- 35. 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 MGH (Yeshayahu) – 7 of 14
- 36. Recovery Flow Scenarios: Thyroidectomy MGH (Yeshayahu) – 8 of 14
- 37. 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 MGH (Yeshayahu) – 9 of 14
- 38. Length of Stay Comparison App MGH (Yeshayahu) – 10 of 14
- 39. 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 MGH (Yeshayahu) – 11 of 14
- 40. 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 Legend: Keep in the PACU (Before 09/2011) Send to Inpatient Floors MGH (Yeshayahu) – 12 of 14
- 41. 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 MGH (Yeshayahu) – 13 of 14
- 42. Top 20 RPPR Procedures in 2014 MGH (Yeshayahu) – 14 of 14
- 43. 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 System Company Sponsors: Preston Ford, Sumeet Singh, Mallik Angalakudati MIT faculty Advisors: Herbert Einstein (CEE), Georgia Perakis (Sloan) PG&E (Meyer) – 1 of 13
- 44. Gas Operations within PG&E Source: "Natural Gas System Overview." Natural Gas System Overview. Pacific Gas & Electric Company. PG&E (Meyer) – 2 of 13
- 45. PG&E approaching industry trend Source: USA. U.S. Department of Transportation. PHMSA. Distribution, Transmission & Gathering, LNG, and Liquid Annual Data. PG&E (Meyer) – 3 of 13
- 46. Direct cost of corrosion is $276 billion per year in US Infrastructure, $22.6 Transportation, $29.7 Productionand 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 PG&E (Meyer) – 4 of 13
- 47. 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 PG&E (Meyer) – 5 of 13
- 48. Identifying probability and consequence of failure PG&E (Meyer) – 6 of 13
- 49. 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? PG&E (Meyer) – 7 of 13
- 50. 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 PG&E (Meyer) – 8 of 13
- 51. Ultimately, address riskiest areas first and reduce number of leaks PG&E (Meyer) – 9 of 13
- 52. Corrosion Cell Source: “Corrosion Basics." Corrosion Central. NACE International. PG&E (Meyer) – 10 of 13
- 53. 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. PG&E (Meyer) – 11 of 13
- 54. 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. PG&E (Meyer) – 12 of 13
- 55. Ultimately, address riskiest areas first and reduce number of leaks PG&E (Meyer) – 13 of 13
- 56. LGO internship project by Kerry Weinberg ’16 (MBA and SM in bioengineering) Amgen: Streamlining and Standardizing Transcriptomic Analysis in Process Development Company Sponsors: Brian Follstad (Amgen supervisor), Sam Guhan (Amgen champion) MIT Faculty Advisors: Doug Lauffenburger, Roy Welsch Amgen (Weinberg) – 1 of 14
- 57. 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 (Weinberg) – 2 of 14
- 58. Amgen Process Development R&DR&D PDPD CHO cell ProductivityProductivity QualityQuality Ribosome making therapeutic protein Ribosome making therapeutic protein Correct sugars on therapeutic protein Correct sugars on therapeutic protein (Image sources in notes) MfgMfg Amgen (Weinberg) – 3 of 14
- 59. Amgen Process Development • Process Development group improves and characterizes bioreactor productivity and final product quality by analyzing the cellular impact of process conditions Productivity1 F1(a,b,c)F1(a,b,c) CHO Bioreactor A B C Quality1 Productivity2 F2(a,b,c)F2(a,b,c) Quality2 Cellline1Cellline2 A B C Amgen (Weinberg) – 4 of 14
- 60. Transcriptomic Analysis in Amgen Process Development Transcriptomic Data Collection Transcriptomic Data Analysis F1(a,b,c)F1(a,b,c) CHO Bioreactor A B C Productivity1 Quality1 Pathway Analysis Pathway Analysis ClusteringClustering Principle Component Analysis Principle Component Analysis Amgen (Weinberg) – 5 of 14
- 61. Transcriptomic Analysis in Amgen Process Development + Understanding CHO Biological System Transcriptomic Data Statistical and Pathway Analysis ProductivityProductivity QualityQuality Current transcriptomic data analysis workflow Ad- hoc Ad- hoc Time consumin g Time consumin g Steep learning curve Steep learning curve Ad- hoc Ad- hoc Amgen (Weinberg) – 6 of 14
- 62. Objectives (Image sources in notes) Amgen (Weinberg) – 7 of 14
- 63. Approach Assess current state workflow Assess current state workflow Develop automated tool Develop automated tool Refine tool and develop standard work Refine tool and develop standard work PilotPilot Knowledge Transfer Knowledge Transfer CHO transcriptomic Database (Amgen) Information Systems Literature review & advisors Feedback from core users Feedback from core users Agile sprints Agile sprints Process dev R&D Publically available data (chogenome.org) Amgen (Weinberg) – 8 of 14
- 64. Status and Next Steps Assess current state workflow Assess current state workflow Develop automated tool Develop automated tool Refine tool and develop standard work Refine tool and develop standard work PilotPilot Knowledge Transfer Knowledge Transfer Publically available data (chogenome.org) Information Systems Literature review & advisors Feedback from core users Feedback from core users Agile sprints Process dev R&D Agile sprints CHO transcriptomic Database (Amgen) Amgen (Weinberg) – 9 of 14
- 65. Challenges Amgen (Weinberg) – 10 of 14
- 66. Opportunities Amgen (Weinberg) – 11 of 14
- 67. Is this sustainable? • Open source software code • Tool designed for future extensions • Highly documented source code • Process development investing in Python knowledge Amgen (Weinberg) – 12 of 14
- 68. 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 Amgen (Weinberg) – 13 of 14
- 69. References • http://www.topbritishinnovations.org/PastInnovations/Monocl , http://www.broadleyjames.com/bionet-overview.html, • http://www.rockwellautomation.com/rockwellautomation/solu , prolia.com, neulasta.com, vectibix.com Amgen (Weinberg) – 14 of 14
- 70. 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 Overson MIT Faculty Advisors: Tauhid Zaman and David Simchi-Levi Nike (Holbrook) – 1 of 8
- 71. 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 ALWAYS AVAILABLE CUSTOM Nike (Holbrook) – 2 of 8
- 72. Problem and Key Question How do we reduce bullwhipping? CUSTOMERNIKE ALWAYS AVAILABLE CONSUMER Nike (Holbrook) – 3 of 8
- 73. Objective Project & Pilot Goals Volatility Inventory Stockouts Trust Nike (Holbrook) – 4 of 8
- 74. Approach Prioritize products based on predictability Leverage point-of-sale (POS) and inventory data Forecast based on historical POS data Account for lost sales to estimate true demand Provide reorder recommendations Nike (Holbrook) – 5 of 9
- 75. 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. Nike (Holbrook) – 6 of 8
- 76. Status and Next Steps 1. Select style-colors 2. Develop statistical forecasts 3. Socialize and secure pilot commitment 4. Develop reorder policy and initiate pilot 5. Measure outcomes Nike (Holbrook) – 7 of 8
- 77. Key Challenges 1. Securing commitment from partner – Remember Barilla? 2. Executing 3. Scalability – Not overpromising Nike (Holbrook) – 8 of 8
- 78. LGO internship project by Andrew Byron ’16 (MBA and SM in aeronautics and astronautics) Raytheon: Additive Manufacturing of Metals Company Sponsors: Manuel Gamez, Teresa Clement MIT Faculty Advisors: Steven Eppinger, Brian Wardle Raytheon (Byron) – 1 of 5
- 79. 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 Raytheon (Byron) – 2 of 5
- 80. 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 Specification Equipment Qualification NDT Qualification Integration, Verification & Validation (IV&V) Statistical Sample Testing Inspection Process Control Design Feasibility Part Development Plan Proof Parts Preliminary Statistical Characterization Main Factors Units Laser Power W Scan Speed mm/s Scan Spacing µm Beam Diameter µm Feedstock Factors Flowability Go/NoGo Dose Factor % Reuse/Recycle Cycles # Particle Size (sieving) µm Coater Blade Height µm Post-Process Factors Hot Isostatic Pressing Y/N Responses Units Dimensional Accuracy % Surface Finish RMS Density (vs. wrought) % Layer Cohesion 1-5 Ultimate tensile strength ksi Yield tensile strength ksi Ductility (Elongation) ε Melt Pool Diameter µm Raytheon (Byron) – 3 of 5
- 81. • 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 Cycle Time U(3, 0.2) (Exit) Assembly Yes No Loading Cycle Time 0.6 Is Feedstock Available? Feedstock Lot Prep Cycle Time T(2, 3, 5) Baseplate Removal Cycle Time U(3, 0.75) 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 Raytheon (Byron) – 4 of 5
- 82. • 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 Raytheon (Byron) – 5 of 5
Editor's Notes
- Background about MGH and the collaboration team
- What is RPPR?
- Why is RPPR an issue?
- How are we going to attack the issues?
- What was done so far?
- What was done so far?
- Example of analysis we did (recovery flows). Mention that some flows are bad, and this is a way to catch them.
- What was done so far?
- Example of some of the work itself – not an actual result. Fits here?
- What was done so far?
- Example of an analysis we did (effects of the PACU approach)
- What are we going to do next?
- Introduce project, myself, and advisors at PG&E and MIT 10 minutes for presentation
- Introduce PG&E and its distribution pipelines In a straight line, gas network is SFO to BOS 15 times Steel distribution is SFO to BOS 6.75 times
- Walk through graph. Discuss increasing number of corrosion leaks and self-reports of unprotected pipe to the CPUC. These two factors indicate that a survey to re-evaluate the system is necessary. Mention controls from corrosion (first coatings required; then in 1970s, CP required)
- Direct costs includes monitoring, replacing, and maintaining Transmission pipelines included in infrastructure, not utilities ($7 billion) Transition from costs to project
- State problem goal within this CP survey.
- Looking at the factors that indicate the probability of failure (ie the probability that corrosion will occur) and the consequence of failure (population density, public image, cost). Both influence risk. Corrosive environment – soil resistivity, soil pH, moisture content Corrosion control conditions – impressed current or galvanic? Age, condition of coating History of pipe – material, historical installation procedures, age, pipe always protected from corrosion
- Previously have met with stakeholders. Currently in the complicated data-gathering process / exploring how other groups in the industry are evaluating risk with respect to corrosion. Discuss next steps.
- Sheer size of PG&E’s network PG&E’s service area is larger than 33 states; specifically, do we divide over 18 divisions or 4,000+ CP areas? Quality and accuracy of data in addition, data located in different systems undergoing transitions Quality control how do we make sure that CP continues to be documented accurately and promote communication between departments so that no additional unprotected pipe is accidentally created?
- Conclude by stating ultimate goals of this survey and risk model are 1) allocate resources to drive down risk in high risk areas and, as a result, 2) future results of dramatic decrease in corrosion leaks. Note that graph beyond heavy black line is idealized projection of future results Open up the floor for Q&A (approx 2 min)
- Discuss increasing number of corrosion leaks and self-reports of unprotected pipe to the CPUC. These two factors indicate that a survey to ensure system is in full compliance is necessary.
- Looking at the factors that indicate the probability of failure (ie the probability that corrosion will occur) and the consequence of failure (population density, public image, cost). Both influence risk.
- Conclude by stating ultimate goals of this survey and risk model are 1) allocate resources to drive down risk in high risk areas and, as a result, 2) future results of dramatic decrease in corrosion leaks. Open up the floor for Q&A (approx 2 min) Note that patterned part of graph is idealized projection of the results that we would like to see.
- Motivation behind this project, what’s the objective of this project and my approach. Then describe where I’m at in the project and some next steps. Close with some key challenges and interests thus far and then some q&a Be sure to explain mammalian cell culture process dev or biotech process dev
- Sources: http://www.topbritishinnovations.org/PastInnovations/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
- Drug substance Need to also mention the “knobs” you can adjust on a genetic level to the cells. i.e. overexpression of Mgat to impact high mannose. Essentially engineering cell lines to maximize productivity (not just using process conditions to impact it). Also mention that optimizing this process is key for companies that are investing in biosimilars where their cost is primarily associated with cost of production rather than cost of R&D Notes: spell out: cell line & production vessel, bioreactors, Cell line + process conditions, process conditions impact the environment, cell line is the function. F(a,b,c), f_amg123, f_amg345.
- http://www.biotech.cornell.edu/brc/genomics-facility/services/instruments,
- Images sources described in earlier slide notes. Verbally explain what the impact of this project is to Amgen (streamlining workflow to reduce time spent in analysis), identification of bio signatures import for productivity to improve bioreactor productivity. For academia: demonstration of analysis standardization for transcriptomic data, identification of biological signatures indicative of productivity (relation between process conditions and CHO cellular activity). Documentation of workflow, Figuring out how people analyzed something can be difficult Ultimate outcome: shorten time to market? Link to : shorten time to market, less lot to lot variability, more consistent product quality to focus effort on key specifications (i.e. for patients) -patient sensitivity to different levels of product quality (differences in glycosylation) Show images for each point-> i.e. code, show automation, “leverage automated workflow”: impact of variety of steps & preprocessing sensitivity (look at old work) Merge 3 & 4 together: and focus on application of tool & workflow
- Agile development: (sprints) include
- Show image of tool. Need to include significant findings so far, How to move forward to achieve project goal, CHO transcriptomic databases: CHO genome.org Knowledge Transfer: include R&D as well Main story: standard mamm cell culture process: goals are productivity & quality, need ways to control that. Transcriptomics provide understaniding, problem: this analysis is difficult/ad hoc/complex to figure out, so this solution is to provide a standard/traceable clear way of doing the analysis by automating it (creating code) vs. enterprise software tool that has many options vs. etc. Goals, omics fits in with goals, current state of omics analysis, solution ( can verbally close with this) Data structure: could be applied to other industries
- Verbally explain what the impact of this project is to Amgen (streamlining workflow to reduce time spent in analysis), identification of bio signatures import for productivity to improve bioreactor productivity. For academia: demonstration of analysis standardization for transcriptomic data, identification of biological signatures indicative of productivity (relation between process conditions and CHO cellular activity). Documentation of workflow, Figuring out how people analyzed something can be difficult Ultimate outcome: shorten time to market? Link to : shorten time to market, less lot to lot variability, more consistent product quality to focus effort on key specifications (i.e. for patients) -patient sensitivity to different levels of product quality (differences in glycosylation) Show images for each point-> i.e. code, show automation, “leverage automated workflow”: impact of variety of steps & preprocessing sensitivity (look at old work) Merge 3 & 4 together: and focus on application of tool & workflow
- Verbally explain what the impact of this project is to Amgen (streamlining workflow to reduce time spent in analysis), identification of bio signatures import for productivity to improve bioreactor productivity. For academia: demonstration of analysis standardization for transcriptomic data, identification of biological signatures indicative of productivity (relation between process conditions and CHO cellular activity). Documentation of workflow, Figuring out how people analyzed something can be difficult Ultimate outcome: shorten time to market? Link to : shorten time to market, less lot to lot variability, more consistent product quality to focus effort on key specifications (i.e. for patients) -patient sensitivity to different levels of product quality (differences in glycosylation) Show images for each point-> i.e. code, show automation, “leverage automated workflow”: impact of variety of steps & preprocessing sensitivity (look at old work) Merge 3 & 4 together: and focus on application of tool & workflow
- Write out top few questions and have backup slides
- Write out top few questions and have backup slides
- Write out top few questions and have backup slides