Monthly Archives: August 2012

Earlier this year, The Economist published an article on Honeywell’s dramatic plant floor improvements through their lean efforts.

The article quoted the improvements at one plant:

It used to take 42 days to make and deliver a sophisticated toxic-gas detector, for clients including Intel and Samsung; now it takes ten. The production process used to consume the factory floor; now, it uses merely a quarter of it. This has freed up the rest of the factory to make lots of other products.

The factory therefore makes more stuff, generating more revenue, with essentially the same headcount, square footage and energy consumption.

The article mentions that Honeywell developed their own version of Toyota’s production system after visiting Toyota for two weeks.

Here are my top three lessons learned from this article:

  1. Toyota was the pioneer of Lean.  It is good to understand what they did and why it worked.
  2. It is important to adapt Lean to your environment.  Toyota created a great system for themselves. Other firms needs to build their own system to suit their needs.
  3. You need to take Lean deep into your organization.  To make Lean work, you need to spend time and effort educating everyone in your organization.



Many practical business problems (scheduling, network design, routing) can be solved with mathematical optimization techniques (namely combinations of linear and integer programming).  Optimization allows you to make better decisions with the data you have.

With this benefit comes the fact that these problems are very difficult.  They fall into a class of problems known as NP-Complete.  Here is a quote from Wikipedia’s entry on NP-Complete:

“…there is no known efficient way to locate a solution in the first place; indeed, the most notable characteristic of NP-complete problems is that no fast solution to them is known. That is, the time required to solve the problem using any currently known algorithm increases very quickly as the size of the problem grows. As a result, the time required to solve even moderately sized versions of many of these problems easily reaches into the billions or trillions of years….is one of the principal unsolved problems in computer science today.”

Note the frequent reference to no fast solution and solve times in the billions or trillions of years.

So, of course, in practice you are likely to run into slow run-times when you implement these solutions.

In a post from earlier this month, Jean Francios Puget of IBM mentions that complaints about run times stem from the fact that customers realize the “…optimization results are great;  much better than they thought.  They now want to apply the magic stick to all of their problem!” or more problems.  This is an interesting take on the situation from an expert in optimization.

Over 10 years ago, the economist published a long article on mass customization.

I find that the article still rings true today and covers the main principles.  Since the article’s publication, more firms are getting better at mass customization, but some firms have tried it and failed.

It is interesting to discuss how mass customization principles align (or don’t align) with lean principles.  In some ways these two contradict each other and in some way you can’t implement a mass customization system without being lean.

Every supply chain manager must deal with variability.  Of course, there are techniques for reducing variability, but even the best program cannot eliminate it completely.

In Feb of 2012, we were able to publish a guest blog post at LogisticsViewPoints that addressed this topic.

The idea is that safety stock can help you buffer against this variability.  What is interesting is that that is really not a solution about inventory, it is about reducing lost sales, reducing late shipments (and upset customers), and reducing expediting expenses (and headaches).  Here is quote from the article:

Safety stock allows you to seamlessly meet unpredictable spikes in demand, and it allows you to protect your customers from production breakdowns, supplier failures, or unusually long shipment times. Safety stock helps increase sales, reduce late shipments to your customers (keeping them happy or avoiding contractual penalties), and reduce the cost of expediting by minimizing the times you are short on stock.

In the end, you may not be able to control variability, but you can control what you do about it.  The blog takes an idea from Hopp and Spearman’s book, Factory Physics:

In this book, they mention that you can either choose how you will buffer against variability or it will be “chosen” for you. When it is “chosen” for you, it shows up as lost sales, late shipment penalties, expediting, and a more chaotic supply chain.

In April of 2012, BusinessWeek published a nice article on how lean techniques are being used to help turn around troubled manufacturing firms.  In this article, they were focusing on manufacturing firms with around $100 million in revenue.

The article discusses how they were able to turn around these firms.

Earlier this year, I was talking to a manager at a major retailer.  He mentioned how they are now seeing vendors in the US offering better prices than the vendors from China.  He attributed this trend to the US manufacturers becoming more efficient by using lean techniques.

The field of lean is very rich.  There are very simple techniques that can be used, their are more advanced principles (around understanding variability), and there is a big effort to apply these concepts outside of the manufacturing sector (where they were pioneered by Toyota).

For a Web Application that relies on recurring revenue from customers (that is, customers who continue to pay monthly for service), it is especially important to keep your customers.  (This, of course, applies to many types of businesses.

The CEO of a Treehouse wrote a short article on how they used analytics and statistics to better predict when a customer might cancel.  They could then use this information to reach out to the customer to see if there was a problem they could fix to keep the customer.

This article mentioned a few of the tools they used, but did not provide too many details.  I think it was interesting that the article and comments talked about the false positives.  I think this is important.  We often forget that these tools can never be 100% accurate.  We need to understand these issues when making decisions with models.

Of course, this type of analytics applies to many different types of firms.

In 2010, the Wall Street Journal reported on foldable shipping containers (you may need to subscribe to WSJ to access this) and I posted a blog entry on IBM’s site on this topic.

As a quick summary, the main benefit of a foldable container is in more efficiently moving empty containers.  The empty containers must be moved around because their is not an exact match of supply and demand.  The ports that collect the empty containers are not necessarily the ones the have demand for empty containers.

Without a folding container, the empty containers use as much space as a full one (but weigh less).  The quote from the article is telling:

“It’s a huge expense, a huge headache for the industry,” says Neil Davidson of London-based Drewry Shipping Consultants. The net cost of moving empties is around $7 billion a year, say analysts.

This is a nice case of using design and optimization together to solve a problem.   The foldable container designs away some of the $7 billion cost.

With or without the foldable containers, firms use optimization to help minimize the cost of getting the containers to where they need to be.

This problem pops up in anywhere where you have a closed-loop supply chain.  You need to be able to get the empty or used product back where it is needed.