Select a topic:
All
Data Science
Operations and Supply Chain
  • The Four Myths of Supply Chain Risk

    Larry Snyder, Ph.D. Sep 30th, 2016
    In over 15 years of studying supply chain risk, there is a common set of comments that I often hear from both supply chain researchers and practitioners. Whether they would admit it or not, the people making these comments are basically using them as an excuse to say, “Meh. I’m not going to bother dealing... Read More »
  • On LinkedIn, I came across this short, one and a half minute, video on how Amazon arranges items in its prime warehouses.  These are the warehouses where Amazon stocks items in local markets for their 2- and 1-hour deliveries.  The warehouses store items randomly throughout the warehouse to minimize the time to pick.  The video... Read More »
  • Imagining the Supply Chain of 2030

    Kristen Daihes Sep 15th, 2016
      I had the opportunity to join the Supply Chain Insight Global Summit last week in Arizona.  As expected, this summit did not disappoint.  Lora Cecere has a knack for pushing practitioners, vendors, and consultants outside of their comfort zone.  She uses data to weave a story about reality that we cannot refute, and then... Read More »
  • A couple of years ago while working on my PhD and before joining Opex Analytics, I had the privilege of working with some amazing people as part of solving a customer product delivery and transportation problem for GE Appliances & Lighting. The results of this collaboration is published in a journal paper and we believe... Read More »
  • The Newsvendor Doesn’t Tell the Whole Story

    Larry Snyder, Ph.D. Aug 5th, 2016
  • (We recently were interviewed on weekly The Supply Chain Television Channel and CSCMP– click on the above picture and go to the 3:55 mark for the interview.  The interview was based on the following blog post which also appeared on SCDigest.  We received a lot of feedback on the post and will write more. Without further... Read More »
  • Non-linear models and the value they can create

    Mohsen Moarefdoost Ph.D. Jan 18th, 2016
    Operations research analysts and modelers try to avoid non-linear models in network and supply chain design. This has led many of us to believe that most of the problems in the area of supply chain are inherently mixed-integer linear problems. In fact, there are many problems that require non-linear modeling; remember that the most basic... Read More »
  • Why python can make your decision making process easier

    Mohsen Moarefdoost Ph.D. Oct 22nd, 2015
    If you are dealing with complex, large scale and multi layered systems, you may end up analyzing and solving multiple, even nested, optimization problems (LP- linear programs, MIP- mixed integer programs, QP- quadratic programs, etc.) in your decision making process. So, you need to design an analytical algorithm that calls optimization problems multiple times, use... Read More »
  • Two Supply Chain Design Lessons from Starbucks CEO

    Michael Watson Ph.D Oct 19th, 2015
    The Starbucks CEO, Howard Schultz, spoke at the annual CSCMP event in San Diego a few weeks ago. He made two key observations about the the supply chain: First, you won’t be able to scale your business unless your supply chain can handle it.  (As a side note, he also mentioned that if your HR department... Read More »
  • (This article first appeared in Supply Chain Digest) Let’s start with an example of multi-objective optimization.  The map you see is all the towns and cities in Germany where your product is sold.  Let’s assume you want to to determine how many warehouses you need so that you are within 75km of every customer. This... Read More »
  • Northwestern Professor and Partner at Opex Analytics, Diego Klabjan recently authored the following column on same-day delivery and its impact on the design supply chain in Supply Chain Digest. The article highlights past failed attempts of a decade ago of firms like Webvan and Kozmo with recent efforts by companies ranging from start-ups such as Deliv... Read More »
  • IBM put together a nice 3-minute video explaining mathematical optimization.