One of the evolving stories of the operational impacts of the pandemic has been port congestion. We (collectively) have been ordering more stuff — shifting consumption from services to goods — and that has meant more work flowing through the nation’s port. This is particularly true for the ports of Los Angeles and Long Beach. That has meant that there is a long, floating queue of cargo ships waiting to unload — as this WSJ video explains.

An interesting side bit here is the impact of the ever-increasing size of container ships. Oversized boats played a role in the Ever Given saga in the Suez Canal. The relevance here is that bigger boats take longer to unload. That is, wicked big boats are efficient at schlepping containers across open water but make life hard when they hit a port.

One of the biggest supply chain stories of the past week has been the saga of the Ever Given, the ginormous container ship blocking the Suez Canal. Bloomberg has a nice podcast interviewing the head of a global shipping line about the crisis (Baystate Business: Laurence Odfjell, Mar 30).

There are two interesting part to this. First, he gets into some of the physics that likely contributed to how the ship got stuck. Second, he talks about the challenges his firm faced — particularly with ships on the way to the canal and whether those should be sent to a different route.

The Wall Street Journal also has had some interesting reporting — emphasizing the knock on effects of the delays (Suez Canal Traffic Resumes Slowly as Some Ships Weigh Anchor, Others Wait, Mar 30):

Logistics experts were forecasting port congestion in Asia and Europe as some of these diverted vessels arrive at ports around the same time as the delayed vessels now making their way slowly through the canal. That is on top of regularly scheduled traffic.

“This backup risks leading to a concentration of volume,” said Luigi Bruzzone, an analyst for the port of Genoa, one of Italy’s busiest. “What we were expecting to come throughout April will now be concentrated in the last two weeks of the month.”

In short, while the grounding of the Ever Given has been a very visible event, its impact is going to be last for months and likely much less visible to those not in the industry.

I just created a review video of the celebrated Economic Order Quantity (EOQ). This video considers two key decisions in inventory management: how much and when to order? Starting from real data, we build a model to optimize the total cost, which is the sum of the setup (order + transport + receiving) cost and holding cost. The solution is the celebrated Economic Order Quantity. All in just 4min :). Please put any comments or questions in the YouTube comment section which I periodically check.

While discussing strategic capacity planning in class this morning there was a request to review the celebrated #Newsvendor model for #inventory and #capacity planning. So I figured: let’s make this 6min video for everybody who wants to review this simple, yet powerful decision model 🙂 What do you think?

Happy new year to all! I have just published a 3min video that explains what digital operations is, how to implement it, and what the benefits are. This video then directly connects to my next video on digital Control Towers for your operational network and supply chain. Please add any suggestions or questions in the comment section on YouTube.

Thanks for watching & subscribing to help the channel get to Youtube Partner status (> 1000 subscribers and > 4000 hrs watched).

Takt time is a key concept to plan and run an operation. In this video I explain what takt time is and how to calculate and use it. There are many other “times” used in operations and, in contrast to takt time, some of those other times can be confusing and have different meanings at different organizations. Cycle Time is one such example. In theory, the word takt means cycle or beat, but in practice, cycle time would better be called unit workload. Watch why 🙂

Please let me know in the YouTube comments if you have any other topic or question you would like me to address in a future video. I remain curious whether this type of #operationsmanagement content can ever get the “4,000 viewed hrs in 12 months and 1,000 subscribers” to pass YouTube requirements 🙂

The Corona crisis brings changes, including (or especially?) to academics. Marty wrote a blog again. And I got myself re-engaged in producing Youtube videos on operations. I must give credit to INFORMS: our annual international conference went virtual and all speakers were asked to record and upload their 15min video presentation. This made me rediscover the creative challenges of educational video production and I figured I may as well share my presentation on my YouTube channel.

There is joy in creativity and continuous improvement. I quickly realized that 15min videos are often too long and so I have embarked on the endless path of continuous improvement to make better videos. Let’s see how long I stick to that path; feedback and pace of improvement will matter. When you have time, check them out and leave some comments below the video. And if you like them subscribe to the channel and hit “the bell” so you receive an announcement when I upload a new video. (YouTube tracks number of subscribers and number of hours watched. Hence “YouTubers” ask you to subscribe. As we teach: metrics drive behavior 🙂

In this video I discuss why and when waiting in a single line at airport checkin, at the bank, at the supermarket is better. I explain the intuition but also quantify how much better a single queue is over service systems with two separate queues. The largest improvement stems from sharing queue length information (which leads to Join-Shortest-Queue JSQ); the second smaller improvement comes from postponing server choice (which is equivalent to allowing customers to jockey among queues).

The fine legal print: The video addresses “80% of what is important to 80% of viewers” :). It also focuses on customers waiting in line. For the mathematically inclined: The graphs consider simple M/M/1 and M/M/2 queues. There is a deep theory behind “resource pooling in heavy traffic” that shows that the insights in this video extend to Gi/G/N, but notice the required i : we must have independent inter arrival times which is fairly reasonable for customers arriving for service, but not for data network switches…

First in, first out (FIFO) is the service discipline that we are all most familiar with. If I get in line at the cafe before you do, I get to place my order first. Simple. Fair. But is that the best scheme for running the queue for covid tests?

That’s basically the question asked by a recent post on Marginal Revolution (Stack-Push-Pop COVID Testing, Aug 7). The basic complaint is that delayed test results are useless test results. Hence, there should be an emphasis on turning around results quickly while not wasting resources on past-due samples. Deviating from FIFO is one way of achieving this.

One way of thinking about this is to use a stack or last-in first-out (LIFO) model for testing. In a stack model the newest test request is pushed onto the top of the stack and the next test to be processed is popped off the top of the stack. One disadvantage of this model is that some test requests will never be processed (they should be removed from the bottom of the stack and returned as null results). Some people will be angry.

But the stack model of testing has a huge advantage over first-come, first-served. Namely, just as many tests will be completed as under the current model but the tests results will all come back faster and be much more useful.

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Take a moment to appreciate the conundrum airline pricing managers currently find themselves in. In normal times, the main task of pricing managers and the revenue management systems they oversee is to make sure that there are enough — but not too many — seats left in the days before a flight for those flyers willing to pony up big bucks. Again, in normal times, anyone could fill up a plane going between Chicago and LA at $300 per seat. The magic is selling some seats at $300 early while making sure there are seats to sell at $2,000 later.

Of course, these are not normal times. Demand has collapsed across pretty much all markets making pricing and saving seats for later irrelevant. But that shouldn’t last forever, right? And then airlines should be able to get back to business as usual. But there is a hitch. As discussed in the Wall Street Journal, revenue management systems base decisions on historical data but past data is pretty useless for the current situation and the data being collected right now is likely irrelevant for when the market recovers (Coronavirus Has Upended Everything Airlines Know About Pricing, Aug 5).

You can hear the author discuss his finding here:

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More on shifting shopping habits and how firms are responding. Specifically, we are again looking at the growth of online grocery sales. The Financial Times has a really nice story examining why the pandemic hasn’t necessarily been a boon for supermarkets (Why supermarkets are struggling to profit from the online grocery boom, Jul 22). On the one hand, stay at home orders have limited the options for dining out; that should be a good thing for supermarkets. On the other, those orders and general pandemic concerns have made people nervous about going to the store. That has led to a boom in online orders either for delivery or for pick up. According to the article, it took 20 years for online sales to account for 7% of UK sales. That percentage jumped to 13% in two months. The problem is that online sales are just not as profitable.

Sainsbury’s chief executive Simon Roberts summed the situation up, saying Covid-19 was “moving sales out of our most profitable convenience channel and driving a huge step-up in online grocery participation, our least profitable channel”.

For some numbers to back up that statement, checkout this eye candy:Screen Shot 2020-08-06 at 10.24.33 AM

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