Data Science Experts

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01
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Part
01

Experts in Data Science

Some examples of experts in data science, specifically in terms of a large, global supply chain that are located in the San Francisco bay area include Ayush Bharadwaj, Lu Huang, Jessica Zhang, Lütfi Uçar, and Milad Davaloo. A more detailed list with other requested information can be found in the attached spreadsheet.

Supply Chain Data Science Experts in San Francisco Bay Area

  • Ayush Bharadwaj is a Data Scientist at Postmates and did his MSc. at Case Western Reserve University.
  • Lu Huang is a Senior Data Scientist at Facebook and did her Ph.D. at Duke University.
  • Jessica Zhang is a Data Scientist at DoorDash and she did her MSc. at Stanford University.
  • Lütfi Uçar is a Senior Data Scientist at Deliv and he did his MSc. at Politecnico di Milano.
  • Milad Davaloo is a Data Scientist at ClearMetal and he did his MSc. at the University of California, Berkeley.
  • Other details, as well as additional data scientists with experience in supply chain, can be found in the attached spreadsheet.

Research Strategy

We focused on providing details for data scientists in the San Francisco Bay Area that are either working currently as a data scientist in a Logistics and Supply Chain management company or that did so in the past and/or clearly listed supply chain management as part of their expertise.

Part
02
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Part
02

Experts in Machine Learning

Some examples of experts in machine learning, specifically in terms of a large, global supply chain that are located in the San Francisco Bay Area include Jared Kruzek, John Nadvornik, David Zhan Liu, Sankara Karthik Sampath Kumar, and Xinyu Cindy Xu. A more detailed list with other requested information can be found in the attached spreadsheet.

Supply Chain Machine Learning Experts in San Francisco Bay Area

  • Jared Kruzek is a Machine Learning Engineer at Zume and did his MSc. at Stanford University.
  • John Nadvornik is a Supply Chain Technologist at Grand Canals and did his BSc. at the University of Bridgeport.
  • David Zhan Liu is a Co-founder & Chief Engineer at Bearing AI and he did his MSc. at Stanford University.
  • Sankara Karthik Sampath Kumar is a Business Consultant at WNS Global Services and he did his MSc. at the University of Illinois, Chicago.
  • Xinyu Cindy Xu is a Principal Architect, Tech Lead at JD.com and she did her Ph.D. at Arizona State University.
  • Other details, as well as additional machine learning experts with experience in supply chain, can be found in the attached spreadsheet.

Research Strategy

We focused on providing details for machine learning experts in the San Francisco Bay Area that are either working currently in a Logistics and Supply Chain management company or that did so in the past and/or clearly listed supply chain management as part of their expertise.

Part
03
of three
Part
03

Data Science and Supply Management: Trends

Some examples of current trends in data science and supply chain management include increased adoption of Blockchain technology, Artificial Intelligence, and Machine Learning, increased use of advanced analytics, and more IoT adoption and use cases.

Blockchain and Artificial Intelligence/Machine Learning

  • Although the adoption of Blockchain technology and Artificial Intelligence in supply chain management is still at the nascent stage, experts state that "interest has accelerated significantly during the past year, making blockchain a top trend for supply chain leaders to watch in 2019."
  • Companies in the supply chain sector are increasingly gathering/generating data (such as data that are being generated with the adoption of beacons, IoT solutions, and RFID technologies in supply chain) that are under used and AI and machine learning can leverage such data to make processes more efficient.
  • According to analysts, through self-learning from available data and natural language technology, "AI solutions can help automate various supply chain processes such as demand forecasting, production planning or predictive maintenance. Along with automation comes augmented human decision-making, because the human is then no longer involved in the decision-making."
  • Experts also state that "drones, autonomous intelligence and robotic automation will eventually transform warehousing and transportation, which will create networks that may look and operate very differently from those of today."

Advanced Analytics

  • More companies in the supply chain industry are increasingly adopting advanced analytics to better understand their data and increase their effectiveness.
  • McKinsey states that advanced supply chain analytics "expand the dataset for analysis beyond the traditional internal data held on Enterprise Resource Planning (ERP) and supply chain management (SCM) systems and applies powerful statistical methods to both new and existing data sources. This creates insights that help improve supply chain decision-making, all the way from the improvement of front-line operations, to strategic choices, such as the selection of the right supply chain operating models."
  • Advanced analytics are being increasingly deployed in real-time or near real-time in supply chain processes such as product quality testing, dynamic pricing, and dynamic replenishment.
  • It will also "improve organizations’ ability to gain visibility on the real-time status of their supply chain network, thus giving them the ability to not only rapidly respond to problems but more importantly, anticipate and prevent them more effectively."

IoT Adoption

  • Many companies in select supply chain industries are increasingly adopting Internet of Things in their supply chain management process and the trend is expected to continue.
  • The adoption is expected to grow in the future as businesses continue to assess more ways IoT can be used beyond the current use cases. Retailers are currently using it to track stock levels, analyze customer data, and strengthen customer relationships.
  • According to analysts, "IoT could have a broad and profound impact on the supply chain in areas such as improved asset utilization and higher uptime, improved customer service, improved end-to-end supply chain performance, or improved supply availability, supply chain visibility and reliability."

Research Strategy

To understand current trends in data science and supply chain management, we extensively searched for industry trends detailed in market research reports and industry reports. We were able to find reports from Gartner, McKinsey, Supply Chain Management Review, and Absolute Reports. We then analyzed the trends mentioned in the independent reports and selected trends that appeared in at least two of the four reports. We have detailed our findings above.
Sources
Sources