Educational White Paper on Bi-Objective Optimzaiton

IBM just published an educational white paper by Pete Cacioppi on bi-objective optimization.

This white paper provides details for the interested business user as well as the researcher into the latest thinking in solving problems with more than one objective.

The paper discusses the business benefit of viewing the trade-off curve between two objectives and how this can give valuable insights into your problem.

It also explains some of the science behind this bi-objective optimization and how  massively parallel cloud computing can effectively solve these problems.

Bring Network Design To Your Classroom: Latest Trends and Commercial Software

On Thursday, April 18 at 12 PM ET, (11 CT, 10 MT, and 9 PT), we will be hosting a 30-minute webinar for professors interested in bringing network design into the classroom.

The topic can be a full class or part of a class in supply chain, operations, or optimization.

Network design continues to play an important role in the running of a supply chain. Future supply chain managers need to know about this topic. This webinar event will cover the latest trends in network design and tie these trends to case studies. Co-authors Sara Lewis and Michael Watson will also cover how you can bring free commercial network design software into your classroom to enhance your student’s learning and expose them to real-world sized problems. Additionally, they will talk about how you can position network design into a variety of different classes. The 30-minute webinar will conclude with a Q&A session.


Registration Details
Date: Thursday, April 18, 2013, 12:00 pm, Eastern Daylight Time
Meeting Number: 840 023 266
Meeting Password: 0418

Click here for the webinar link (same as the link at the top of the post).

Classic Network Design Case Study With a Twist for Air Shipments

When locating a warehouse, there is usually a trade-off between the number of warehouses and service level.  That is, as you add more warehouses, are you are closer to your customers.

However, if you can ship your product via air (like with UPS), there is a twist to this trade-off.  You have the choice to pay for much more expensive air services, but you can avoid the extra cost of warehouses and inventory.  For light, high-value items, it may make perfect sense to ship via air services.

I was able to publish a nice case study on this with Jim Morton (then of UPS).  Click here for the article.

Top Three Reasons Supply Chain Models Can Go Wrong (From SC Digest)

Dan Gilmore of Supply Chain Digest has written a few articles on the value of supply chain modeling this year.  Then, a reader asked ”if the fear of bad models leading to terrible supply chain decisions wasn’t a key factor in some companies avoiding modeling.”

This was a great topic that comes up enough to address.  You can check out the full response in my blog post at SC Digest.  But, here are the top three reasons a model can go wrong:

  1. The results come in, but do not suggest a change
  2. You never get results and have to cancel the modeling project
  3. You implement the results and they turn out to be wrong

It is good to understand these reasons so you can avoid them.

Going Beyond Traditional Network Design at Ahold

I was recently reminded of a talk that Ahold and IBM gave in 2011 at the Material Handling Conference in Park City Utah.

It is a good network design case.  Here the details from the IBM blog site:

Ahold is an international retailing group based in the Netherlands.  Ahold had revenues of 29.5B Euros in 2010.   Ahold USA is a wholly owned subsidiary of Ahold.  Ahold USA operates Stop & Shop, Giant, and Giant Martin’s stores as well as the on-line grocer Peapod.

The title of the talk is “Beyond the Traditional Network Analysis.”

So you are challenged with managing a large portfolio of products and a complex set of vendors, customers and distribution locations. How do you make sense of this all and streamline your supply chain? This session takes you beyond the pin-on-a-map network analysis and examines factors such as sourcing strategies, inventory optimization, route planning and more. We will also review a grocery case study that involved the analysis of sourcing effectiveness, evaluation of DC investment opportunities, and relocation of legacy facilities to get the most out of their supply chain network. “



Book Reviewed by Crabtree Analytics

Andrew Gibson at Crabtree Analytics recently reviewed our network design book:

Here are a few quotes from the review:

I’ve done a lot of  supply chain network design projects and consider myself to be an expert. Had I had this book from the start, I may have got to expert status a lot faster…


…What attracted me was that it goes beyond the theory and has lots of details around project execution:.  the need for sensitivity analysis; the difficulty of getting reliable transportation rates ; sensible data aggregation strategies; why you must have an optimized “baseline”; and numerous others.  These are all areas that analysts get wrong – as I did.  Some learn from the experience, others send out the results anyway…


…If you have a network design project in mind and a plane journey coming up – make the investment.”

Network Design at Schwan’s Home Services

Inbound Logistics ran an article on network design at Schwan’s Home Services.

Schwan’s Home Services runs a direct to consumer delivery business for food products.  Before network optimization, they served 90% of the continental US with 475 company owned depots.  The depots each had around 10 to 15 trucks to make the deliveries.  The depots were replenished from their central warehouse in Marshall, MN.

According to their VP of Supply Chain, Jeff Modica, their fixed costs were too high and their supply chain was outdated.  They needed “a lower cost to serve” and “had to be more flexible.”

They used Deloitte Consulting to help with the modeling and to bring and outside perspective.

The article does a nice job in explaining the unique nature of Schwan’s Home Services’s labor and sales intensive business.  It also does a nice job of pointing out the the fact that the network modeling team had to communicate and get the buy-in from many different stakeholders within the company.   These projects are always a mix of quantitative analysis (and they used IBM’s LogicNet Plus) and qualitative factors.

Deloitte initially recommended closing a dozen facilities to start– to get some quick wins (always a good idea in projects like this).  Eventually, the closed 54 sites.  And, they changed the structure of their supply chain:

Additionally, Schwan’s has migrated to a hybrid model that uses existing company-owned depots, master depots with hub-and-spoke shuttle systems, and 3PL-managed facilities. As the company removes and/or re-tasks nodes, the network continues to change—setting the stage for further optimization in 2013.

The article doesn’t tell us the savings, but it does mention:

While the financial impact of Schwan’s network optimization is under wraps, it achieved marked operational and asset appreciation savings, in addition to avoiding some cost and gaining revenue from the sale of assets.

The article is worth a read.  This is a nice case study of a network design project.

Cost-To-Serve Modeling and Network Design

Earlier this week, I published an article on cost-to-serve on SupplyChainDigest.

Network design plays a nice role in cost-to-serve modeling:

Network design modeling software can complete the analysis by allocating those cost that simply cannot be allocated with Excel.  For example, it is not trivial to allocate your inbound transportation costs or costs of raw materials to your customers.  When using your network modeling software for this, you may want to model every customer and every product, but limit the amount of optimization (you don’t want to consider opening and closing facilities with this use of the tool.).  Then, when you run, the tool returns details on the cost to get each product to each customer.  In effect, this tool allocates all your transportation costs, your production costs, and your warehousing costs to a customer and product combination.  You get all this information as part of a standard network optimization run—it requires no special features.

This is another good reason to have the capability to run a network modeling tool.

Plant Location in a Global Economy

The Economist recently published an survey on Offshoring and Outsourcing.  If you follow manufacturing trends, this article is great read.

The survey covers trends in both manufacturing and services.  It goes in-depth into the manufacturing trend we are now seeing with many companies bringing manufacturing back to the US or to the market where the demand is.

The survey highlights the fact that companies are discovering the “all the disadvantages of distance.”  This includes the high transportation costs along with the extra risks.  But, it also points out that the wage gap is shrinking between China and the US, natural gas is driving down energy costs, and automation is removing a lot of labor anyway.

The article quotes one consultant who claims that if total labor costs are less than 15% of the product’s cost, then it is not worth it to pursue cheap labor.

Also, there was a nice quote that reminds us of the value and limitations of network design:

Choosing the right location for producing a good or a service is an inexact science, and many companies got it wrong.

They are correct, that network design is not an exact science, but using network design tools can help you better narrow your choices and pick good solutions.  With network design, you have a better chance of getting it right.  And, if you continually model your supply chain, you can better adjust as conditions change.

Some Basics of Modeling Multiple Time Periods

Adding extra time periods to a network design model turns out to be a bit harder than it would seem.

First, since network design is a computationally hard problem, when we create a 12 time period model, the size grows by 12X, but run time could easily increase by 100X.  So, keep that in mind as you add multiple time periods.

Second, you need to think harder about how the model works.  One key idea is to think about what links the time periods together.  In a model with multiple years (say an annual model for the next 10 years), the locations link one time period to the next.  That is, if you open a new plant in Year 2, it will be there in Year 3 unless you close it.  In a model with multiple weeks or months, the locations still link the time periods.  But, inventory also links the time periods.  So, I can build extra inventory in March that will be used to satisfy demand in July (the graph at the top is showing this inventory build).

Finally, you need to think about the data.  There is nothing unique about a multi-year model.  But, with a monthly model, you need to think about the starting inventory (there is product in the system at time period 0) and the ending inventory (you don’t want the model to drain all the inventory from the system in the last time periods).  Collecting data on starting and ending inventory can be problematic.  If you can do it, it is sometimes helpful to start your model at the very end of the season when inventory is at its lowest.  This can prevent some of the data issues.