Category Archives: Logistics

Congrats Dr. Unnu

Today Kaanu Unnu defended his PhD dissertation on “Optimization Models and Frameworks for On-demand Warehousing Systems.”  Congrats Dr. Unnu!

As illustrated by Kaan’s slide, his research is timely, with on-demand warehousing starting in 2013 with Flexe and has grown to become a global business model.

The main purpose of this research is to develop decision-support models and frameworks given the advent of on-demand distribution systems. On-demand distribution platforms match companies with underutilized warehouse and distribution center (DC) capacities with customers who need extra space or fulfillment services. These systems provide unique advantages, but also have different cost structures and risks. Therefore, a company interested in adopting these new systems must consider new dynamics, which requires new knowledge and methods for the design and operations of distribution networks.

In this dissertation, first, different options to acquire warehouse and distribution capacities, which have varying benefits but also have varying cost structures, are analyzed. Next, new dynamic facility location models are developed to simultaneously consider acquiring three distribution center types (self-distribution, lease, on-demand) which have different capacity granularity, commitment granularity, and access to scale properties. Then, a heuristic approach is designed to solve such dynamic facility location models for large-scale and national level distribution networks. Finally, from a warehouse owner perspective, new decisions for deploying on-demand distribution are explored, and a framework summarizing its impact on facility design and operations is created.

If you are interested in the on-demand warehousing business model, I would encourage you to check out his research, available at his Research Gate page.  Specifically, he has a cost calculator that industry may find useful.  His paper, currently under review and available here  answers “Is there a business case to be made for the use of on-demand systems, and if so, in what environments?”  There’s a lot of great insights in the paper, into when on-demand warehousing models make sense.  The short answer, is it depends (sometimes it’s useful, sometimes not as much).  Three interesting findings:

  1. On-demand warehousing is a good supplement to more traditional forms of acquiring fulfillment services.  Specifically, incorporating on-demand warehousing into a company’s network design strategy improves the capacity utilization of the build and lease DCs a company uses.
  2. If on-demand availability is not guaranteed and response requirements are relaxed (e.g., a 250-mile maximum range), lower distribution costs can be achieved with designs not incorporating on-demand warehousing. However, if the response requirement is tight, like for same day delivery, regardless of the on-demand alternative’s capacity availability, on-demand warehousing is cost effective.
  3. The flexibility provided by 3PL/lease capacity expansion with a premium cost performs worse than using the on-demand alternatives. Thus, 3PLs should offer more granular solutions to their customers – without the overcapacity penalty – to be competitive in the market.

His committee was impressed by his use of a diverse set of tools, combined with  data, to provide insights.  As a proud advisor, I echo these comments and would like to commend Dr. Unnu on a dissertation I am immensely proud of.  It’s been fun to work with him, learn from him, and see him grow.

I wish we could be in person to celebrate his tremendous accomplishments.  Be assured, when we are allowed to do so, we’ll host a party in his honor. Until then, congrats!

New Research: When is it beneficial to provide freelance suppliers with choice?

Think about the things you or your business own.  Are those things highly utilized?  When you’re at a stoplight, count the empty seats in the vehicles around you.  When you’re in a parking lot, think about the fact that most of the surrounding cars get an hour of use a day.  Or consider the duplication of effort when both you and your neighbor make individual grocery trips.  Now think of resources your company owns.  Is your lab equipment used all the time?  What about your warehouse – is it full to capacity 12 months a year?  How many empty miles does your fleet of trucks accumulate each month?  These example all represent underutilized capacity, and with the right systems, data, and technology, individuals and companies could start accessing these underutilized resources.

Attempts to build these systems are all the rage these days (platform companies such as Uber, Lyft, Flexe all claim to increase underutilized resources).  Existing systems to match supply and demand in platforms can be organized broadly into two categories.

The first is a centralized model, which prioritizes meeting demand commitments and enabling a quick time to match.  However, centralized approaches do not allow for decision making by the resource owners, so it dampens their participation. Examples of a centralized approach are the default settings in ride sharing.  For example, did you know that Uber drivers don’t know where they’re taking you until you get in the car?  The driver instead has 15 seconds to accept a recommended request (again without knowing its destination). This means drivers are not provided with enough information to make decisions based on their needs and schedule.  This limits who can participate as a driver and utilizing underutilized capacity is not prioritized.  If you’re interested in learning more about drivers’ experiences in ride sharing applications – I highly recommend a recent book on the topic: Uberland by Alex Rosenblat

The other approach is a “jobs board” system, where all requests are displayed to all suppliers.  This can lead to myopic decision making: no one supplier has the whole picture of the marketplace, which results in reduced system performance because some requests receive multiple selections and others are left unfulfilled.  Also, this is a passive system – in which individuals must take the initiative (and invest the time) to find preferable demand requests. Recently, a number of interesting empirical work captures reduced systematic performance in such systems, see Cullen and Farronato (2018); Fradkin (2017) and Horton (2014).

Our research is interested in a new way to match underutilized capacity owned by freelance suppliers to demand requests that combines the advantages of the two approaches. This is a hard problem, because we have two different sets of stake-holders tugging us in different directions.  On the one hand, we want to entice the owners of these resources to provide access, this means creating pro-supplier policies, taking into account individual preferences (for example, that these freelance suppliers may have planned tasks that we want to tag along for).  However, on the other, we need to ensure high levels of service for the demand-side.

Millennial Millie

Our innovation is an approach enabling “Supplier Choice”.  The platform – without control nor perfect knowledge of suppliers’ preferences  – uses choices estimated from suppliers’ past behavior to increase participation and resource utilization.  To illustrate, meet Millennial Millie. Millennial Millie has gone through training and signed up to be an on-demand volunteer. She gets a notification on her phone asking if she’d like to deliver groceries to shut-in residents. Millie clicks yes. Two requests appear. She chooses the one that fits with her plans that day.

Before this example can become a reality, research is needed to discover new ways to provide choices and quantify the impact of those choices on suppliers and demand requests. Our research, led by Shahab Mofidi, recently published in Transportation Research Part B: Methodological is an important starting point for platforms that must coordinate demand requests with decentralized owned resources. For all the details, please check out our paper:

Mofidi, Shahab, and Pazour, Jennifer A., “When is it Beneficial to Provide Freelance Suppliers with Choice? A Hierarchical Approach for Peer-to-Peer Logistics Platforms,” to appear in Transportation Research Part B: Methodological. [PrePrint]

This link provides free access until July 23, 2019:

In what follows, I walk you through why I am excited about this research, and why I believe it can start to improve crowdsourced delivery and rider sharing, but also impact volunteer management and other applications.

As displayed in the above figure, our research recasts the platform’s role as one providing personalized recommendations (i.e., a menu of requests) to a set of suppliers.  This means that to match supply and demand, two decisions processes take place.

  1. First, the platform must decide how multiple, simultaneous recommendations are made.  The same request may be recommended to multiple suppliers to decrease the time to match and to hedge against suppliers’ autonomy to decline recommendations.
  2. Second, suppliers have autonomy to select requests (if any) from the personalized menus, resulting in some requests not selected and others selected by more than one supplier.

This hierarchical approach enables a quick time to match, and does not require suppliers to explicitly provide preference information for all requests. Suppliers retain autonomy to select requests that can be interleaved with their planned activities.  And as the number of choices increases, suppliers have a higher chance to be recommended a request they are willing to select (increasing participation). However, due to misalignment between the suppliers and the platform’s preferences, a larger number of choices may lead to suppliers selecting a request with lower platform benefit. Also, as the number of choices increases, less systematic coordination occurs, and the chance for rejected requests increases. Therefore, we are interested in understanding when is it beneficial for a platform to use personalized recommendations to a set of freelance suppliers to coordinate demand requests? We also want to quantify this benefit to the platform, the suppliers, and the demand requests under different environmental factors.

To be able to answer these questions, we develop a new bi-level optimization framework to explicitly model both the platform’s decisions and the suppliers’ selection decisions.  What’s challenging about the model is that the platform’s objective function is influenced by both the platform’s recommendation, but also the suppliers’ interdependent selection outcomes. Our model assumes the platform uses expected values of agents’ utilities to make recommendations. We have a single period model and assume the model is re-solved iteratively with a given set of available requests and available suppliers. Because bi-level optimization problems are computationally hard to solve, we exploit the problem’s structure to reformulate as an equivalent single-level optimization problem.  For mathematical and computational details, please see our journal article.

Choice is only useful if the platform does not fully understand what the freelance suppliers want to do.  This is typically the case – because eliciting utility values of all requests is time consuming and tedious for suppliers.  Instead, in practice a platform is able to only partially estimate suppliers’ selection outcomes. Therefore, we develop a computational study – based on ride-sharing – to investigate whether personalized recommendation sets can be used as a coordination mechanism for distributed resources when the platform has neither perfect knowledge nor control over suppliers. The computational experiments have four phases (see Figure below), which generates 1140 instances and solves 10,260 optimization problems (see journal paper for details).

The goals of our computational experiments are to:

(1) compare the performance of our proposed hierarchical model with existing platform matching methods, namely, centralized, decentralized, and many-to-many stable matching mechanisms.

(2) quantify the platform’s value of providing choices in on-demand environments;

(3) determine under what scenarios it is better to recommend only one alternative, a few alternatives, or all alternatives, and to investigate when it is useful for the recommendations to contain overlapping alternatives.


First, we find that providing choices and recommending alternatives to more than one supplier simultaneously provide value, on average, to the platform, suppliers, and demand requests. When the platform is only partially able to estimate suppliers’ utilities, the hierarchical approach creates recommendation sets with higher average platform performance than centralized, decentralized, and many-to-many stable matching approaches. A centralized approach does not perform well due to a higher chance of suppliers electing not to participate in the platform’s assignment. However, too much choice can be problematic even for the suppliers themselves. As excess discretion creates collisions, the decentralized approach had the worst angry driver rate and lowest serviced ride rate. Thus, in uncertain environments, the proposed hierarchical approach can benefit the platform, drivers, and ride requests.

Second, a platform with uncertainty about suppliers’ selection outcomes can sometimes improve its performance by offering choices to suppliers, but not always. In platforms where uncertainty over suppliers’ selection exists, choices help the platform when (1) suppliers are inflexible with a high no-choice utility; and (2) suppliers’ utility values have higher variance than the platform’s utility values. Choices increase the chance of enticing supplier participation, and in both cases, the increase in participation likelihood outweighs the match’s reduced platform benefit likelihood. However, when (1) the platform’s benefit values have higher variance than the suppliers’ expected utilities or (2) suppliers are flexible, offering additional choices, on average, can reduce the platform’s benefit.

We view the current paper as a “proof of concept” that providing freelance suppliers with choices can sometimes be beneficial.  However, this is really just a jumping off point, and much more research is needed.  Thankfully I have smart students and funding support to keep researching. Specifically, Hannah Horner (a math PhD student), John Mitchell (Professor in Mathematics at RPI) and I are explicitly capturing the stochastic nature of suppliers’ utilities directly in the optimization formulation, which allows us to extend our analysis beyond answering if choice is beneficial, and to determine optimal menu characteristics (i.e., menu sizes and request overlaps).  Rosemonde Ausseil (PhD student in ISE) and I are working to extend the optimization model from a single-period model to one that considers the dynamic properties of the problem directly in the optimization formulation. Safron Smith (MS student at RPI) is interested in application specific formulations for volunteer management. Talented undergrads are also contributing to this work, including Tina Nazario, Brooke Ramlakhan, Wenlin Gong, Parker Shawver, and Karthik Dusi.

We greatly appreciate funding of this work from the National Science Foundation Award# 1751801:  CAREER: Distribution Resource Elasticity: A New Hierarchical Approach for On-Demand Distribution Platforms. I would like to acknowledge Dr. Shahab Mofidi – who is the lead author on this work.  This was part of his dissertation, which was awarded the 2019 Del and Ruth Karger Dissertation Prize. Also, thank you to John Backman, who wrote up some of this work for the RPI ISE department newsletter

We would appreciate feedback on our work, send questions and inquiries to

Blockchain Technology – Guest Blog Post


Happy (almost) End of the Semester.  I am happy to share a guest blog post by two undergraduate researchers, Mara and Jake, about their adventures into blockchain technology.

I’m hoping to get more undergraduate students blogging about their research interests, and so added them under the people heading.  Click here for Mara and Jake’s blog post about blockchain technology for supply chain applications.

I’m super excited about this technology and think it has great potential in supply chains.  Specifically, I believe supply chains and logistics are becoming more democratized, which results in a need for decentralized and distributed decision making.  Another area of my research has been in improving supply chain visibility.  In theory, visibility should be solved by now, but I have a number of data points that show in practice it isn’t.  My hypothesis for this discrepancy is that a top-down approach to visibility is hard to achieve in practice.  This is because to have visibility into your supply chain requires decentralized entities to agree to connect data bases and systems, share data, etc., and this is a challenging management and technology problem.  One promising technology to improve visibility is blockchain technology, which uses decentralized protocols capturing and validating information transactions between multiple users via a distributed ledger system.  My hope is to report more on this research in future posts.

IIE Transactions Best Paper Honorable Mention


ISERC 2016 Repositioning Rental Vehicles Presentation

My co-authors and I received an honorable mention designation in the IIE Transactions Focused Issue on Design and Manufacturing Best Applications Paper Award Competition for 2016. (The award is selected by an examining committee from all papers published from July 1, 2014 through June 30, 2015, issues 46:7 through 47:6).

Roy, Debjit, Jennifer A. Pazour, and René De Koster. “A novel approach for designing rental vehicle repositioning strategies.” IIE Transactions 46.9 (2014): 948-967.

While the paper can be downloaded here  I also had the opportunity to present our research at the Industrial and Systems Engineering Research Conference.  If you weren’t able to attend my talk, here’s the cliff notes version.


The rental car industry has experienced “the Amazon effect” where customers place requests with little or no warning.


An important tactical decision for vehicle rental providers is the design of a repositioning strategy to balance vehicle utilization with customer wait times due to vehicle unavailabilities.



To address this problem, this article analyzes alternative repositioning strategies: a no-repositioning strategy, a customer repositioning strategy, and a vehicle repositioning strategy, using queuing network models that are able to handle stochastic demand and vehicle unavailabilities.


Optimization models are formulated to determine the repositioning fractions for alternate strategies that minimize the rental provider’s cost by balancing repositioning costs with customer waiting penalty costs. The nonlinear optimization problems are challenging to solve because the objective functions are non-differentiable and the decision variables (such as effective arrival rates and customer repositioning fractions) are interrelated.


Therefore, a two-phase sequential solution approach to estimate the repositioning fractions is developed. Phase 1 determines the effective arrival rates by developing an approximate network model, deriving structural results, determining a high-quality solution point, and refining the solution. Phase 2 determines the repositioning fractions by solving a transportation problem.


Numerical experiments are used to evaluate the efficacy of the proposed solution approach, to analyze alternate repositioning strategies, and to illustrate how the developed techniques can be adopted to create a better readiness at a depot.




Outlook 2016


As one of five industry thought leaders, I share my observations and insights on potential critical issues likely to impact the warehouse/DC sector this year in the January/February 2016 issue of the WERC Sheet.

Contributors to WERCSheet’s Outlook 2016 panel include: Steve Johnson, managing principal, Johnson Stephens Consulting; Jennifer Pazour, Ph.D., assistant professor of industrial and systems engineering, Rensselaer Polytechnic Institute; Norman Saenz, managing director, St. Onge Company; Geoff Milsom, director, enVista; and Lawrence Dean Shemesh, president-CEO, OPSdesign Consulting.

My Outlook for 2016 is provided below:

The millennial generation
Jennifer Pazour, Ph.D.

The millennial generation has a major stake in defining both what the warehouse industry needs to do, as well as who will help get it done.  Specifically, the millennial generation is the warehouse industry’s current and future customers, as well as its workforce.

From a customer perspective, millennials are eager to do everything on their smart phones, and have very little patience for non-valued added activities, such as waiting.  This has implications for the warehousing industry as it changes order profile structures and lead time expectations.  Thus, distribution and logistics operations will need to be designed to be agile and responsive.

The millennial workforce, who are interested in making an impact, skilled in technology, and natural at identifying non-valued added processes, seem like a great solution to meet such dynamic customer demands.

For a warehouse to be responsive to dynamic customer demands, as well as profitable, utilization of both physical and human resources is a high priority.  An emerging way to achieve effective resource utilization in a dynamic environment is through the use of on-demand peer-to-peer logistics systems.

These systems use a business model for the movement and storage of goods that matches resources owned by a group of independent users to demand requests.  These systems are part of the “sharing economy” and utilize technology platforms that are able to provide wide reach visibility into untapped resource capacity (such as warehouse space, transport space, and delivery services).

A variety of such companies have sprung up in all aspects of the supply chain.  These include companies like FLEXE that connects companies with underutilized warehousing capacity to companies that need space, as well as companies that facilitate crowdsourced transport and delivery, like Deliv, Instacart, Amazon Flex, and Cargomatic.

On-demand peer-to-peer logistics systems have the ability to improve resource efficiency by increasing visibility and accessibility of existing, idle resource capacities.  They can reduce the costs associated with changing resource capacity, which allows companies to be more flexible.

In addition, these system, which require supply chain visibility and security, will also influence traditional warehousing and logistics operations.   Initiatives that improve supply chain visibility, create increased transparency and security, and embrace technology, will create new capabilities and business opportunities for traditional warehousing and logistics operations as well.

As an industry, we should position ourselves as proactively leading the charge to provide increased customer service capabilities by embracing new business models, technologies, and the changing workforce.

I’m excited to continue this discussion while I moderate a panel on “Crowdsourcing and Collaborative Warehousing and Logistics” at the 2016 Warehousing and Education Research Council Conference in Providence, RI in May.  To check out the conference preview and read the other through leaders’ thoughts on 2016, check out the WERC website.

Successful Defenses



Congrats to Dr. Faraz Ramtin, who successfully defended his Ph.D. dissertation thesis entitled, “Modeling and Analysis of Automated Storage and Retrieval Systems with Multiple in-the-aisle Pick Positions,” and to Patrick Reilly, who successfully defended his M.S. thesis entitle, “Propagation of Unit Location Uncertainty in Dense Storage Environments.”

I am super proud of both students, who are excellent researchers and human beings.



Faraz’s dissertation consists of three contributions all focusing on a special type of case-level order fulfillment technology – an “Automated Storage and Retrieval System with Multiple in-the-aisle pick positions.” These semi-automated systems are common in temperature-controlled warehouses.  Our first contribution includes the first study to analyze AS/RS with multiple in-the-aisle outputs. We develop expected travel time models for random storage policies and provide design insights into these systems.  In our second contribution, we considered the use of MIAPP-AS/RS to fulfill orders for non-identical items’ demand, which relaxed some of the assumptions we made in the first contribution. Specifically, we focused on an important practical design decision, the optimal SKU assignment problem. We studied the impact of different pick position assignments on system throughput, as well as system design trade-offs that occur when the system is running under different operating policies and different demand profiles. We developed optimization models to find the optimal assignment that minimizes the expected travel time.  Finally, we developed optimization models for the SKU-to-pick position assignment problem for dedicated and class-based storage policy for MIAPP-AS/RS.  By exploiting the structure of these optimization models, we decomposes the problem using Benders decomposition.


The first two contributions of Faraz’s dissertation work has been accepted for publication:

  • Ramtin F., Pazour J. A. “Analytical Models for an Automated Storage and Retrieval System with Multiple in-the-Aisle Pick Positions”. IIE Transactions, 46(9), 968-986.
  • Ramtin F., Pazour J. A. “Product Allocation Problem for an AS/RS with Multiple in-the-Aisle Pick Positions”. IIE Transactions, Accepted Manuscript.

He is working on the manuscript of his third contribution, which explores a dedicated storage policy in these systems.

Patrick’s work focuses on dense storage environments and adds an additional dimension to the warehousing literature in that area, specifically item location uncertainty.  Effective space utilization is an important consideration in logistics systems and is especially important in dense storage environments. Dense storage systems provide high-space utilization; however, because not all items are immediately accessible, storage and retrieval operations often require shifting of other stored items in order to access the desired item, which results in item location uncertainty when asset tracking is insufficient. Given an initial certainty in item location, we use Markovian principles to quantify the growth of uncertainty as a function of retrieval requests and discover that the steady state probability distribution for any communicating class of storage locations approaches uniform. Using this result, an expected search time model is developed and applied to the systems analyzed. We also develop metrics that quantify and characterize uncertainty in item location to aid in understanding the nature of that uncertainty. By incorporating uncertainty into our logistics model and conducting numerical experiments, we gain valuable insights into the uncertainty problem such as the benefit of multiple item copies in reducing expected search time and the varied response to different retrieval policies in otherwise identical systems.IMG_1745

Material Handling Education Foundation Scholarships


I’m honored to be featured in the Where are they now? article in the MHI Solutions Magazine.  [PDF] You have to read through to the bottom to find my favorite quote from the article, which is

“I find the work extremely rewarding,” Pazour said.  “One of the aspects that I really like about my job is that I get paid to learn.  I’m both creating knowledge and disseminating knowledge to my students, and that’s very rewarding.”

I’m even more excited to announce that two of the students in my research group are recipients of a 2015/2016 Material Handling Education Foundation Scholarship.  The Material Handling Education Foundation provides scholarships and educational opportunities to students studying in the field of material handling, logistics and supply chain.

  •  Shahab Mofidi was awarded the Lee Wood Scholarship for the 2015/2016 academic year from the Material Handling Education Foundation, Inc.
  • Catherine Ninah was awarded the Crane Manufacturers Association of America Honor Scholarship for the 2015/2016 academic year from the Material Handling Education Foundation, Inc.

Shahab Mofidi is a Ph.D. student in the IEMS department, and his research focuses on logistical decision making in environments that exhibit item location uncertainty.  Some examples include sea-based logistics, as well as ship-from-store fulfillment operations for e-commerce orders.  Catherine Ninah is an undergraduate student in the IEMS department, who has conducted research on sea-based logistics and healthcare logistics.  In addition, Catherine will participate in an REU (research experience for undergraduates) this summer at Duke University.  She’ll be working with The Center for the Environmental Implications of NanoTechnology (CEINT) on Risk Assessment and Modeling.



Catherine Research Poster

What Supply Chain Means to Me? #SC4ME


April 10th, 2014 is Supply Chain Day.  And to celebrate, Eye for Transport is posting a daily quote on what supply chain means to individuals within the industry.  Given my primary research focus is applying operations research methodologies to logistic challenges, I thought I would offer my opinion on what supply chain means to me.

Supply chain and logistics enables us to experience the things that make us happy and healthy.  Here are three personal, tangible reasons why I think supply chain and logistics play an important role in our day-to-day lives and our economy.

  1. FacebookLogistics
    The picture above is a screen shot of my mother-in-law’s Facebook post around her birthday.  Logistics is THE modern marvel that enables her to enjoy beautiful tulips in frigid Wyoming in February.

  2. PazourFamilyFeedersMy family is in the beef business.  They produce a great product that they believe should be consumed around the world.  Great supply chains and logistics are what enable me to enjoy Pazour Beef in Florida.  On the flipside, logistics is also what enables my Dad to enjoy Lobster in South Dakota.  Without logistics, we would be confined to experiencing only the products that we could produce in front of us.
  3. Given that in the U.S. almost 40 percent of the drugs we take are made somewhere else, logistics plays a vital role in getting the medications that save lives to the patients that need them. 

To understand what supply chains have done for others, follow the twitter hashtag #SC4ME.

In Good Company


Today I stumbled upon the following press release announcing the winners of the 2013 Material Handling Institutes’ Material Handling and Logistics Research Grants.  Hector Vergara and I are recipients of the Start Up Grant.  Dean Jensen and Adam Piper are recipients of a Spark Grant.  I am honored to be in such good company, especially because I was an undergraduate student in Dean’s statistical quality control course at South Dakota School of Mines & Technology and went to graduate school at the University of Arkansas with Hector.  What a Small World!

One of the joys of an academic career is the many “excuses” to see old friends at conferences (as well as the chance to meet new ones).  I am excited that I will get to reconnect with Hector and Dean at the 2014 International Material Handling Research Colloquium (IMHRC) in Ohio this summer.

A Random Aside:

Based on Ugander,  et al. and Backstrom et al. , I guess it really is a small world — any two people are separated by only four degrees of friendship on average.

Four Degrees of Separation

The Exciting World of Logistics

There has been a lot of buzz about logistics lately — more specifically, the future of logistics.  Two big stories from two big tech companies hit this week.


The first is about Amazon testing a drone delivery system they call “Prime Air” for last-mile delivery.  The technology is still under development and has some regulation hurdles to overcome, but it is a solution to a difficult logistics feat — same hour delivery.  The idea of being able to order something one minute and a half hour later being able to enjoy that something is pretty exciting.  Of course this service wouldn’t be for all items or all customers — instead it would be focused on delivering products less than 5 pounds in urban areas in close proximity of distribution centers.  But, that would still cover my household and we order everything from Amazon.  I really mean everything: in the last month alone, we have had cases of soup, cases of cereal bars, a bobble-head doll of Walter White, toothbrush heads, hair gel, numerous CD’s, a few video games, a handful of books, printer toner, plus other essential items delivered to our door.  Just think what we would order if it would arrive in a half hour!

The second story comes from Google — who has been acquiring companies with a focus on automation and many of the applications are in supply chain and logistics.  The recent New York Times article, “Google Puts Money on Robots, Using the Man Behind Android” makes me think that Google thinks logistics is an exciting field with lots of opportunities — I do too!

Some quotes from the article:

“A realistic case, according to several specialists, would be automating portions of an existing supply chain that stretches from a factory floor to the companies that ship and deliver goods to a consumer’s doorstep.”

“The opportunity is massive,” said Andrew McAfee, a principal research scientist at the M.I.T. Center for Digital Business. “There are still people who walk around in factories and pick things up in distribution centers and work in the back rooms of grocery stores.”

Logistics is pretty cool, huh!