Category: Module E

Posts related to Module E

OM in the News: AI and the Learning Curve

Scale drives efficiency—for almost a century, industrial planners have relied on this simple principle. In 1936 aeronautical engineer Theodore Wright discovered that costs fell in a predictable way every time production doubled. The more you produce, the cheaper things become, in part because the learning cost per unit declines. This is the topic of Module E in your text.

Artificial intelligence has accelerated this principle, writes The Wall Street Journal (Oct. 23, 2025). It is rewriting Wright’s Law, which assumes that experience follows production: You make mistakes, learn from them and improve. AI makes it possible for experience to come before production. Simulation can happen millions of times before a single box is shipped. Experience scales almost instantly at no real cost. The learning curve doesn’t only steepen. It collapses.

That means knowledge that once took decades of human trial and error can emerge in weeks, days, even hours. In a supply chain, this is a profound shift. Decisions about capacity, warehouse space, routing, technology adoption and risk management can be modeled, tested and optimized in advance. The costs of imprecise planning shrink dramatically.

AI is breaking Wright’s Law because the learning cycle is no longer physical but computational. Models can test, fail and improve millions of times faster than any team of human engineers. Experience can be generated in advance, and at negligible cost.

The implications for logistics are extraordinary. AI agents will negotiate, reroute and optimize flows of goods in real time. Traditional ownership models, fleets, warehouses and even labor could be replaced by dynamic orchestration of perfectly used assets.

This new golden age of logistics will unveil solutions to problems we may not even know exist. Wright’s Law still matters, but perhaps AI has broken it.  The challenge will be not building the tools but surviving the pace of their consequences.

Classroom discussion questions:

  1. Why can AI have this impact on learning curves?
  2. Besides logistics, which is mentioned in this article, can AI impact operations?

OM in the News: Learning Curves and Solar Power

Recent history shows that climate policies such as taxes, subsidies and mandates matter most by catalyzing a virtuous cycle of higher demand that leads to more innovation, learning-by-doing and economies of scale that lower costs and further boost demand. In solar power, the results have been spectacular. Between 1980 and 2012, the cost of a photovoltaic module made from crystalline silicon fell 96%. Roughly 30 percentage points of this is attributed to public and private research and development, which among other things, led to more efficient modules and larger, thinner silicon wafers. Another 60 points came from “learning-by-doing”—improvements to the manufacturing process, such as less waste, that came with experience—and economies of scale, reports The Wall Street Journal (Aug. 25, 2022). The average plant capacity grew roughly 200-fold.

Similar, though less dramatic, dynamics have been at work in wind power and battery storage. They all hewed to “Wright’s Law,” named for the 1930s aeronautical engineer Theodore Wright. In Module E, Learning Curves, we explain that each doubling of production is accompanied by a roughly constant percentage decline in cost, known as the learning rate. “Over the long term these learning rates appear to be the best way to predict the future cost of technology that we know of,” said one industry expert.

One implication is that as a technology matures, production takes longer to double and so costs fall more slowly. In solar power, for instance, PV module factories are now so large and the manufacturing process so efficient that incremental improvements are much harder to come by. Sure enough, the cost of solar-generated power has fallen an average of 6% annually from 2018 through 2021, compared with 21% in the previous nine years.

Greater potential lies in replicating the experience of solar in other technologies that are currently too costly for widespread adoption. Emission reductions in the coming decade are the low hanging fruit, achievable with technologies that are already competitive or nearly so with fossil fuels such as wind, solar and batteries. Getting the rest of the way to net zero depends on hard-to-decarbonize sectors such as aviation, industrial processes and agriculture for which commercially viable technology to eliminate emissions doesn’t yet exist.

Classroom discussion questions:

  1. Where were learning curves first applied? (See Table E.1 in your Heizer/Render/Munson text)
  2. Why do costs fall more slowly as a product matures?

Guest Post: Learning Curves, Batteries and Product Life Cycles

Our Guest Post comes from Prof. Howard Weiss, the developer of Excel OM and POM.  These 2 software packages (that we provide for free) have helped our books become number 1 in U.S. and global markets.

In the figure below, you can see that a 1 kWh lithium-ion battery that cost over $1,100 in 2010 now costs less than $160. Batteries are critical especially as more and more car models are electric or hybrid.

Module E in your Heizer/Render/Munson textbook explains: “… if the learning curve is an 80% rate, the second unit takes 80% of the time of the first unit, the 4th unit takes 80% of the time of the 2nd unit, the 8th unit takes 80% of the time of the 4th unit, and so forth.” Learning curve unit times or costs are based on the volume doubling.

The formula for the time or cost of the Nth unit is TN = T1(Nb)

where TN is the time/cost for the Nth unit and b = (log of the learning rate)/ (log 2)

Using Excel’s Goal Seek we determine that to have the cost reduced from $1160 to $153 would require production in 2019 to be 1183 times the number of units produced in 2010. The steep increase in volume agrees with the introduction stage of product life cycles displayed in text Figure 5.2 (see p. 164).

Classroom discussion questions:
1. What products have had their costs decline as steeply as the batteries in this article?
2. What is the current stage in the product life cycle of Zoom?

OM in the News: The Navy’s Learning Curve Problem

Huntington Ingalls., the sole U.S. builder of aircraft carriers, continues to fall short of the Navy’s demand to cut labor expenses to stay within an $11.39 billion cost cap mandated by Congress on the second in a new class of warships, reports Industry Week (Aug. 17, 2018). With about 47% of construction complete on the USS John F. Kennedy, the Navy figures show the contractor isn’t yet meeting the goal it negotiated with the service: reducing labor hours by 18% from the first carrier, the USS Gerald Ford, which at $13 billion has become the costliest warship ever. They’re the first two of a planned, 4-vessel, $55 billion program.

USS Gerald R. Ford at Newport News Shipyard

It took about 49 million hours of labor to build the Ford. The Navy’s goal for the Kennedy is to reduce that to about 40 million hours. Huntington Ingalls’s performance “remains stable at approximately 16%” less, said spokesman for the Navy. “Key production milestones and the ship’s preliminary acceptance date remain on track” and there are “ample opportunities for improvement with nearly 4 years until contract delivery and over 70% of assembly work remaining on the vessel.” Navy officials have cited what they describe as progress on the Kennedy as one justification for buying the 3rd and 4th Ford-class carriers under a single contract.

The Navy assesses that, although difficult, the shipbuilder can still attain the 18% reduction goal, said a spokesman. The Navy Secretary, who’s been closely monitoring the carrier program, said that Huntington Ingalls has been on “an impressive learning curve” in reducing labor costs. But a director with the GAO, who monitors Navy shipbuilding, said “with so much of the program underway, it is unlikely that the Navy will regain efficiency. In later phases of a shipbuilding contract, performance typically degrades, not improves.”

Classroom discussion questions:

  1. Why are learning curves so important in ship construction?
  2. What learning curve is the goal? What is the current rate?

 

OM in the News: The Pentagon’s F-35 Push

Lockheed's F-35 assembly line in Fort Worth, Texas
Lockheed’s F-35 assembly line in Fort Worth, Texas

When I worked as a design engineer at McDonnell Douglas in the late 1960s, the F-4 Phantom fighter jet assembly line was one floor above my basement office. We rolled out one Phantom a day, a very efficient line, with volume stable and constant. This has not been the case with our nation’s latest fighter jet, the F-35. Lockheed’s mile-long assembly plant in Fort Worth currently produces only four F-35s a month.

But now “the Pentagon plans to push Congress to approve a deal for more than 400 F-35  jets, worth $34 billion, in what would be the largest-ever weapons’ contract,” writes The Wall Street Journal (May 30-31, 2015). The Pentagon said that committing to buy that many jets over 3 years starting in 2018 could yield cost savings as suppliers would be able to plan with more certainty, buy materials in bulk and triple production from existing levels to about 150 planes a year.

Boosting production is crucial to cutting the cost of the F-35 from the $108 million average paid for the jet in a 43-plane deal agreed last November. Lockheed recently submitted proposals for the next 2 batches of aircraft, and alongside other suppliers have pledged to cut the average cost to $80-$85 million by 2019. Even a rise in output to 150 jets a year would fall short of the 200-plane capacity of the Lockheed plant. Analysts believe official projections of demand for more than 3,000 jets won’t be realized. (Italy and Japan also plan to assemble some jets). Lockheed’s earlier F-16 fighter jet had more than 4,500 orders, and experts expect the F-35 to secure at most 2,000.

Classroom discussion questions:

1. This cost savings plan requires knowledge of learning curves (see Module E). What is the typical learning rate in this industry and how does it impact the analysis?

2. Why will increasing production rates decrease unit costs?

Video Tip: Watching the Boeing 787 Being Built–in 3 Minutes

boeing 787Jay and I have followed the Boeing 787 project closely for the past decade. The Global Company Profile that opens Chapter 2 details the plane’s design, supply chain, technology, and construction. The 787 has become one of Boeing’s most popular models due to its lightweight carbon composite airframe and the resulting lower fuel burn. Boeing continues to lose money on each Dreamliner it builds, but expects to reach the break-even point on the 787 program this year. The program’s deferred production cost, an accounting measure of how efficient an assembly program becomes over time, rose to $25.2 billion last year, topping the $25 billion cap Boeing had forecast for the 787.

Of course, the 787′s assembly costs will continue to drop over time as workers improve the efficiencies of the line and the rate at which they can build new planes. We discuss this issue on page 768, in Module E, noting the far-reaching consequences of learning curves. Boeing has a backlog of about 850 Dreamliner orders, on sales of 1,072 planes. It builds 10 each month at two plants and plans to boost output gradually to a dozen per month in 2016 and to 14 by 2020.

Your students will enjoy this 3-minute video showing the assembly line in Charleston S.C.  The amazing thing about the building is there are no uprights supporting the roof. Six planes in various stages of completion are under the one roof. When completed, the plane is towed to the paint shop. Boeing has a runway that connects with the Charleston airport, and from here that the planes are delivered to customers.

You might show this video with Chapter 2, OM in a Global Environment (Boeing is one of the U.S.’s largest exporters), Chapter 9, Layout, or Module E.

Teaching Tip: Using Knee Surgery to Illustrate Learning Curves

One of my favorite OM topics is learning curves (covered in Module E).  It takes only an hour of class time to cover well and it’s also a subject that is very motivational. When I tell students that understanding learning curves may save their lives one day, it catches their attention.

While  learning curves originated in the aircraft industry– and continue to drive production rates at Boeing today– an equally important application is in surgical procedures. I usually mention my friend the urologist, who has done thousands of kidney transplants. Who would you rather have operating on you?  A rookie on his or her 3rd or 6th transplant–or someone who is well down the experience curve?

In the text, we discuss one -year death rates for heart transplant patients, which follow  a 79% learning rate. Yesterday’s Wall Street Journal (Feb.14, 2012) provides a second medical example, with an article titled “Study Shows Knee Surgeons Have a Learning Curve”.  Here we find that if a patient’s ACL (anterior cruciate ligament) surgery was among the first 10 such cases of a surgeon’s career, the patient had 5 times the risk of having another ACL repair within a year as a patient whose doctor had performed more than 150 of the operations.

While it isn’t surprising that there is a learning curve, “it was striking to see the figures shown in such a dramatic way,” says the orthopedic professor who did the study.  ACL surgery, while routine, he adds, “is fairly complex.”  Potential pitfalls include incorrectly placing the graft, not fixing it solidly, and not dealing with other damaged ligaments in the area. Given that the learning curve is inevitable, what can be done? Two solutions are: have an experienced doctor supervise surgeries early in a new surgeon’s career, or use medical simulators in training.

Meanwhile, my 1st question is always: “How many times have you done this procedure?”

Good OM Reading: Analytics–The Widening Divide

Why don’t more managers embrace the business analytic tools we use in so many aspects of our OM courses?  A new report by MIT Sloan Management Review (Nov.8, 2011) answers the question with a survey of 4,500 executives regarding the integration of analytics in their enterprises. The report,  Analytics: The Widening Divide,  concludes that cultural biases, such as the need for new management competencies and organizational resistance to new ideas –more than technological hurdles–are the primary barriers.

First, a definition of business analytics: “the use of statistical, quantitative, predictive, and other models to drive fact-based planning, decisions, execution, management, measurement, and learning. Analytics may be descriptive, predictive. or prescriptive”.

The MIT Sloan report breaks companies down into 3 categories: Transformed (heavy users), Experienced (moderate users), and Aspirational (companies least experienced in the use of business analytics). The good news is that the number of firms in the 1st two categories, who use analytics for competitive advantage, has surged by 57% in the past year. The Aspirational group’s use of analytics actually declined by 5%. Transformed organizations have set the pace in expanding use of analytics and were found to be 2.2 times more likely to substantially outperform industry peers.

The Transformed group keenly appreciates the value of precise and real-time decisions, and is 3 times more likely to focus on speed of decision-making than Aspirational firms. This means managing operations and improving output levels based on real-time supply and demand management. Inventory replenishment processes, for example, are automated and production is optimized in these companies. As a case study, the report follows McKesson, which processes 2 million hospital orders per day. McKesson does so by embedding algorithms into customer orders to manage the inventory process without human intervention.

When students ask you why analytics are important in your OM course, this report provides a ready response.

Teaching Tip: Learning Curves and the Boeing 787

I first heard of the importance of learning curves when working at McDonnell Douglas right out of college during the peak of the Viet Nam War. While I toiled designing the wing of the DC-10 jumbo jet in the basement of the St. Louis plant, over 30 F-4 Phantom jets were rolling off the assembly line on the ground floor per month. Each one took less time than the one before it–and we could compute to the hour how long completion would take, as the learning curve effect is well-known in the airplane industry (see Table E.1 in Module E of OM, 10th ed.).

But the F-4 is old news and we want to provide you with a more current example. Businessweek (Aug.23-30, 2011) tells the story of Boeing’s Dreamliner 787, the world’s fastest selling jet, racking up more than 800 orders before  it even flew. The planes have an average “catalog price” of $202 million, and Boeing plans to assemble 10 a month by 2013– a record for wide-body jets.

But here comes the bad news. As you know, Boeing is running 3 years behind schedule because of supply chain problems (that we have blogged about several times). The company has amassed $16.2 billion worth of inventory in the form of 35 almost-finished  jets  scattered at parking spaces from Washington to Texas. Some are waiting for seats, some lavatories, and others engines. Boeing has spent an average of over $250 million to build each of the 44 planes it has “completed” so far.  The 45th plane will cost $184 million. To reach a break-even point at 1,000 planes, the unit cost must drop to $113 million. And this can only be accomplished with a very aggressive learning curve of 76%.

Based on the plane (no.45) currently being completed, the rate sits at 82.5%, not far from the 84% learning curve the Boeing 777  jumbo jet  has followed. At this rate, the loss will be $4 billion per year through 2015. Is the learning curve critical to Boeing? Absolutely!

Teaching Tip: Helping Your OM Students Find Jobs with Business Analytics

Just a day or two ago, I got a nice email from our colleague Barry Spraggins, who is Chair of the Managerial Sciences Department at the University of Nevada-Reno.  Barry uses our text Operations Management, 10th ed.,  and noted that he teaches heavily from all the Quantitative Modules, including Decision Making Tools (Module A), LP (Mod. B), Transportation (Mod.C), Queuing (Mod.D), Learning Curves (Mod.E), and Simulation (Mod.F). He writes: “I still think these are relevant components”. Not by coincidence, we find that The Wall Street Journal (Aug. 4,2011), Jay and I,  and IBM all agree.

The Journal reports that finding qualified graduates in business analytics has proven difficult–and colleges are finally stepping up to meet industry demand. IBM, which spent more than $14 billion since 2005 to buy a flock of analytics companies, has now teamed up with over 200 colleges to develop analytics courses. Fordham and Indiana U. are unveiling analytics curricula, as well as certificate and degree programs. Indiana, for example, is offering certificate programs in business analytics to both Deloitte and Booz Allen employees. Fordam has a required course called Marketing Analytics for MBAs. U. of Virginia, also working with IBM, is introducing an analytics track this fall.

“Analytics is certainly one of the top five things executives are worried about and investing in heavily”, says the president of Teradata. “Industry is going to demand it. Students are going to demand it”. As IBM and other big firms drive the software implementation process to the board room, we in academia may very well see a resurgence of demand for the very topics Prof. Spraggins has long espoused. In a tough job market, this is one way to help our students gain competitive advantage.