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    A new way to integrate data with physical objects

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    To get a sense of what StructCode is all about, says Mustafa Doğa Doğan, think of Superman. Not the “faster than a speeding bullet” and “more powerful than a locomotive” version, but a Superman, or Superwoman, who sees the world differently from ordinary mortals — someone who can look around a room and glean all kinds of information about ordinary objects that is not apparent to people with less penetrating faculties.

    That, in a nutshell, is “the high-level idea behind StructCode,” explains Doğan, a PhD student in electrical engineering and computer science at MIT and an affiliate of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). “The goal is to change the way we interact with objects” — to make those interactions more meaningful and more meaning-laden — “by embedding information into objects in ways that can be readily accessed.”

    StructCode grew out of an effort called InfraredTags, which Doğan and other colleagues introduced in 2022. That work, as well as the current project, was carried out in the laboratory of MIT Associate Professor Stefanie Mueller — Doğan’s advisor, who has taken part in both projects. In last year’s approach, “invisible” tags — that can only be seen with cameras capable of detecting infrared light — were used to reveal information about physical objects. The drawback there was that many cameras cannot perceive infrared light. Moreover, the method for fabricating these objects and affixing the tags to their surfaces relied on 3D printers, which tend to be very slow and often can only make objects that are small.

    StructCode, at least in its original version, relies on objects produced with laser-cutting techniques that can be manufactured within minutes, rather than the hours it might take on a 3D printer. Information can be extracted from these objects, moreover, with the RGB cameras that are commonly found in smartphones; the ability to operate in the infrared range of the spectrum is not required.

    In their initial demonstrations of the idea, the MIT-led team decided to construct their objects out of wood, making pieces such as furniture, picture frames, flowerpots, or toys that are well suited to laser-cut fabrication. A key question that had to be resolved was this: How can information be stored in a way that is unobtrusive and durable, as compared to externally-attached bar codes and QR codes, and also will not undermine an object’s structural integrity?

    The solution that the team has come up with, for now, is to rely on joints, which are ubiquitous in wooden objects made out of more than one component. Perhaps the most familiar is the finger joint, which has a kind of zigzag pattern whereby two wooden pieces adjoin at right angles such that every protruding “finger” along the joint of the first piece fits into a corresponding “gap” in the joint of the second piece and, similarly, every gap in the joint of the first piece is filled with a finger from the second.

    “Joints have these repeating features, which are like repeating bits,” Dogan says. To create a code, the researchers slightly vary the length of the gaps or fingers. A standard size length is accorded a 1. A slightly shorter length is assigned a 0, and a slightly longer length is assigned a 2. The encoding scheme is based on the sequence of these numbers, or bits, that can be observed along a joint. For every string of four bits, there are 81 (34) possible variations.

    The team also demonstrated ways of encoding messages in “living hinges” — a kind of joint that is made by taking a flat, rigid piece of material and making it bendable by cutting a series of parallel, vertical lines. As with the finger joints, the distance between these lines can be varied: 1 being the standard length, 0 being a slightly shorter length, and 2 being slightly longer. And in this way, a code can be assembled from an object that contains a living hinge.

    The idea is described in a paper, “StructCode: Leveraging Fabrication Artifacts to Store Data in Laser-Cut Objects,” that was presented this month at the 2023 ACM Symposium on Computational Fabrication in New York City. Doğan, the paper’s first author, is joined by Mueller and four coauthors — recent MIT alumna Grace Tang ’23, MNG ’23; MIT undergraduate Richard Qi; University of California at Berkeley graduate student Vivian Hsinyueh Chan; and Cornell University Assistant Professor Thijs Roumen.

    “In the realm of materials and design, there is often an inclination to associate novelty and innovation with entirely new materials or manufacturing techniques,” notes Elvin Karana, a professor of materials innovation and design at the Delft University of Technology. One of the things that impresses Karana most about StructCode is that it provides a novel means of storing data by “applying a commonly used technique like laser cutting and a material as ubiquitous as wood.”

    The idea for StructCode, adds University of Colorado computer scientist Ellen Yi-Luen Do, “is “simple, elegant, and totally makes sense. It’s like having the Rosetta Stone to help decipher Egyptian hieroglyphs.”

    Patrick Baudisch, a computer scientist at the Hasso Plattner Institute in Germany, views StructCode as “a great step forward for personal fabrication. It takes a key piece of functionality that’s only offered today for mass-produced goods and brings it to custom objects.”

    Here, in brief, is how it works: First, a laser cutter — guided by a model created via StructCode — fabricates an object into which encoded information has been embedded. After downloading a StructCode app, an user can decode the hidden message by pointing a cellphone camera at the object, which can (aided by StructCode software) detect subtle variations in length found in an object’s outward-facing joints or living hinges.

    The process is even easier if the user is equipped with augmented reality glasses, Doğan says. “In that case, you don’t need to point a camera. The information comes up automatically.” And that can give people more of the “superpowers” that the designers of StructCode hope to confer.

    “The object doesn’t need to contain a lot of information,” Doğan adds. “Just enough — in the form of, say, URLs — to direct people to places they can find out what they need to know.”

    Users might be sent to a website where they can obtain information about the object — how to care for it, and perhaps eventually how to disassemble it and recycle (or safely dispose of) its contents. A flowerpot that was made with living hinges might inform a user, based on records that are maintained online, as to when the plant inside the pot was last watered and when it needs to be watered again. Children examining a toy crocodile could, through StructCode, learn scientific details about various parts of the animal’s anatomy. A picture frame made with finger joints modified by StructCode could help people find out about the painting inside the frame and about the person (or persons) who created the artwork — perhaps linking to a video of an artist talking about this work directly.

    “This technique could pave the way for new applications, such as interactive museum exhibits,” says Raf Ramakers, a computer scientist at Hasselt University in Belgium. “It holds the potential for broadening the scope of how we perceive and interact with everyday objects” — which is precisely the goal that motivates the work of Doğan and his colleagues.

    But StructCode is not the end of the line, as far as Doğan and his collaborators are concerned. The same general approach could be adapted to other manufacturing techniques besides laser cutting, and information storage doesn’t have to be confined to the joints of wooden objects. Data could be represented, for instance, in the texture of leather, within the pattern of woven or knitted pieces, or concealed by other means within an image. Doğan is excited by the breadth of available options and by the fact that their “explorations into this new realm of possibilities, designed to make objects and our world more interactive, are just beginning.” More

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      Researchers develop novel AI-based estimator for manufacturing medicine

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      When medical companies manufacture the pills and tablets that treat any number of illnesses, aches, and pains, they need to isolate the active pharmaceutical ingredient from a suspension and dry it. The process requires a human operator to monitor an industrial dryer, agitate the material, and watch for the compound to take on the right qualities for compressing into medicine. The job depends heavily on the operator’s observations.   

      Methods for making that process less subjective and a lot more efficient are the subject of a recent Nature Communications paper authored by researchers at MIT and Takeda. The paper’s authors devise a way to use physics and machine learning to categorize the rough surfaces that characterize particles in a mixture. The technique, which uses a physics-enhanced autocorrelation-based estimator (PEACE), could change pharmaceutical manufacturing processes for pills and powders, increasing efficiency and accuracy and resulting in fewer failed batches of pharmaceutical products.  

      “Failed batches or failed steps in the pharmaceutical process are very serious,” says Allan Myerson, a professor of practice in the MIT Department of Chemical Engineering and one of the study’s authors. “Anything that improves the reliability of the pharmaceutical manufacturing, reduces time, and improves compliance is a big deal.”

      The team’s work is part of an ongoing collaboration between Takeda and MIT, launched in 2020. The MIT-Takeda Program aims to leverage the experience of both MIT and Takeda to solve problems at the intersection of medicine, artificial intelligence, and health care.

      In pharmaceutical manufacturing, determining whether a compound is adequately mixed and dried ordinarily requires stopping an industrial-sized dryer and taking samples off the manufacturing line for testing. Researchers at Takeda thought artificial intelligence could improve the task and reduce stoppages that slow down production. Originally the research team planned to use videos to train a computer model to replace a human operator. But determining which videos to use to train the model still proved too subjective. Instead, the MIT-Takeda team decided to illuminate particles with a laser during filtration and drying, and measure particle size distribution using physics and machine learning. 

      “We just shine a laser beam on top of this drying surface and observe,” says Qihang Zhang, a doctoral student in MIT’s Department of Electrical Engineering and Computer Science and the study’s first author. 

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      A physics-derived equation describes the interaction between the laser and the mixture, while machine learning characterizes the particle sizes. The process doesn’t require stopping and starting the process, which means the entire job is more secure and more efficient than standard operating procedure, according to George Barbastathis, professor of mechanical engineering at MIT and corresponding author of the study.

      The machine learning algorithm also does not require many datasets to learn its job, because the physics allows for speedy training of the neural network.

      “We utilize the physics to compensate for the lack of training data, so that we can train the neural network in an efficient way,” says Zhang. “Only a tiny amount of experimental data is enough to get a good result.”

      Today, the only inline processes used for particle measurements in the pharmaceutical industry are for slurry products, where crystals float in a liquid. There is no method for measuring particles within a powder during mixing. Powders can be made from slurries, but when a liquid is filtered and dried its composition changes, requiring new measurements. In addition to making the process quicker and more efficient, using the PEACE mechanism makes the job safer because it requires less handling of potentially highly potent materials, the authors say. 

      The ramifications for pharmaceutical manufacturing could be significant, allowing drug production to be more efficient, sustainable, and cost-effective, by reducing the number of experiments companies need to conduct when making products. Monitoring the characteristics of a drying mixture is an issue the industry has long struggled with, according to Charles Papageorgiou, the director of Takeda’s Process Chemistry Development group and one of the study’s authors. 

      “It is a problem that a lot of people are trying to solve, and there isn’t a good sensor out there,” says Papageorgiou. “This is a pretty big step change, I think, with respect to being able to monitor, in real time, particle size distribution.”

      Papageorgiou said that the mechanism could have applications in other industrial pharmaceutical operations. At some point, the laser technology may be able to train video imaging, allowing manufacturers to use a camera for analysis rather than laser measurements. The company is now working to assess the tool on different compounds in its lab. 

      The results come directly from collaboration between Takeda and three MIT departments: Mechanical Engineering, Chemical Engineering, and Electrical Engineering and Computer Science. Over the last three years, researchers at MIT and Takeda have worked together on 19 projects focused on applying machine learning and artificial intelligence to problems in the health-care and medical industry as part of the MIT-Takeda Program. 

      Often, it can take years for academic research to translate to industrial processes. But researchers are hopeful that direct collaboration could shorten that timeline. Takeda is a walking distance away from MIT’s campus, which allowed researchers to set up tests in the company’s lab, and real-time feedback from Takeda helped MIT researchers structure their research based on the company’s equipment and operations. 

      Combining the expertise and mission of both entities helps researchers ensure their experimental results will have real-world implications. The team has already filed for two patents and has plans to file for a third.   More

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      Report: CHIPS Act just the first step in addressing threats to US leadership in advanced computing

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      When Liu He, a Chinese economist, politician, and “chip czar,” was tapped to lead the charge in a chipmaking arms race with the United States, his message lingered in the air, leaving behind a dewy glaze of tension: “For our country, technology is not just for growth… it is a matter of survival.”

      Once upon a time, the United States’ early technological prowess positioned the nation to outpace foreign rivals and cultivate a competitive advantage for domestic businesses. Yet, 30 years later, America’s lead in advanced computing is continuing to wane. What happened?

      A new report from an MIT researcher and two colleagues sheds light on the decline in U.S. leadership. The scientists looked at high-level measures to examine the shrinkage: overall capabilities, supercomputers, applied algorithms, and semiconductor manufacturing. Through their analysis, they found that not only has China closed the computing gap with the U.S., but nearly 80 percent of American leaders in the field believe that their Chinese competitors are improving capabilities faster — which, the team says, suggests a “broad threat to U.S. competitiveness.”

      To delve deeply into the fray, the scientists conducted the Advanced Computing Users Survey, sampling 120 top-tier organizations, including universities, national labs, federal agencies, and industry. The team estimates that this group comprises one-third and one-half of all the most significant computing users in the United States.

      “Advanced computing is crucial to scientific improvement, economic growth and the competitiveness of U.S. companies,” says Neil Thompson, director of the FutureTech Research Project at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who helped lead the study.

      Thompson, who is also a principal investigator at MIT’s Initiative on the Digital Economy, wrote the paper with Chad Evans, executive vice president and secretary and treasurer to the board at the Council on Competitiveness, and Daniel Armbrust, who is the co-founder, initial CEO, and member of the board of directors at Silicon Catalyst and former president of SEMATECH, the semiconductor consortium that developed industry roadmaps.

      The semiconductor, supercomputer, and algorithm bonanza

      Supercomputers — the room-sized, “giant calculators” of the hardware world — are an industry no longer dominated by the United States. Through 2015, about half of the most powerful computers were sitting firmly in the U.S., and China was growing slowly from a very slow base. But in the past six years, China has swiftly caught up, reaching near parity with America.

      This disappearing lead matters. Eighty-four percent of U.S. survey respondents said they’re computationally constrained in running essential programs. “This result was telling, given who our respondents are: the vanguard of American research enterprises and academic institutions with privileged access to advanced national supercomputing resources,” says Thompson. 

      With regards to advanced algorithms, historically, the U.S. has fronted the charge, with two-thirds of all significant improvements dominated by U.S.-born inventors. But in recent decades, U.S. dominance in algorithms has relied on bringing in foreign talent to work in the U.S., which the researchers say is now in jeopardy. China has outpaced the U.S. and many other countries in churning out PhDs in STEM fields since 2007, with one report postulating a near-distant future (2025) where China will be home to nearly twice as many PhDs than in the U.S. China’s rise in algorithms can also be seen with the “Gordon Bell Prize,” an achievement for outstanding work in harnessing the power of supercomputers in varied applications. U.S. winners historically dominated the prize, but China has now equaled or surpassed Americans’ performance in the past five years.

      While the researchers note the CHIPS and Science Act of 2022 is a critical step in re-establishing the foundation of success for advanced computing, they propose recommendations to the U.S. Office of Science and Technology Policy. 

      First, they suggest democratizing access to U.S. supercomputing by building more mid-tier systems that push boundaries for many users, as well as building tools so users scaling up computations can have less up-front resource investment. They also recommend increasing the pool of innovators by funding many more electrical engineers and computer scientists being trained with longer-term US residency incentives and scholarships. Finally, in addition to this new framework, the scientists urge taking advantage of what already exists, via providing the private sector access to experimentation with high-performance computing through supercomputing sites in academia and national labs.

      All that and a bag of chips

      Computing improvements depend on continuous advances in transistor density and performance, but creating robust, new chips necessitate a harmonious blend of design and manufacturing.

      Over the last six years, China was not known as the savants of noteworthy chips. In fact, in the past five decades, the U.S. designed most of them. But this changed in the past six years when China created the HiSilicon Kirin 9000, propelling itself to the international frontier. This success was mainly obtained through partnerships with leading global chip designers that began in the 2000s. Now, China now has 14 companies among the world’s top 50 fabless designers. A decade ago, there was only one. 

      Competitive semiconductor manufacturing has been more mixed, where U.S.-led policies and internal execution issues have slowed China’s rise, but as of July 2022, the Semiconductor Manufacturing International Corporation (SMIC) has evidence of 7 nanometer logic, which was not expected until much later. However, with extreme ultraviolet export restrictions, progress below 7 nm means domestic technology development would be expensive. Currently, China is only at parity or better in two out of 12 segments of the semiconductor supply chain. Still, with government policy and investments, the team expects a whopping increase to seven segments in 10 years. So, for the moment, the U.S. retains leadership in hardware manufacturing, but with fewer dimensions of advantage.

      The authors recommend that the White House Office of Science and Technology Policy work with key national agencies, such as the U.S. Department of Defense, U.S. Department of Energy, and the National Science Foundation, to define initiatives to build the hardware and software systems needed for important computing paradigms and workloads critical for economic and security goals. “It is crucial that American enterprises can get the benefit of faster computers,” says Thompson. “With Moore’s Law slowing down, the best way to do this is to create a portfolio of specialized chips (or “accelerators”) that are customized to our needs.”

      The scientists further believe that to lead the next generation of computing, four areas must be addressed. First, by issuing grand challenges to the CHIPS Act National Semiconductor Technology Center, researchers and startups would be motivated to invest in research and development and to seek startup capital for new technologies in areas such as spintronics, neuromorphics, optical and quantum computing, and optical interconnect fabrics. By supporting allies in passing similar acts, overall investment in these technologies would increase, and supply chains would become more aligned and secure. Establishing test beds for researchers to test algorithms on new computing architectures and hardware would provide an essential platform for innovation and discovery. Finally, planning for post-exascale systems that achieve higher levels of performance through next-generation advances would ensure that current commercial technologies don’t limit future computing systems.

      “The advanced computing landscape is in rapid flux — technologically, economically, and politically, with both new opportunities for innovation and rising global rivalries,” says Daniel Reed, Presidential Professor and professor of computer science and electrical and computer engineering at the University of Utah. “The transformational insights from both deep learning and computational modeling depend on both continued semiconductor advances and their instantiation in leading edge, large-scale computing systems — hyperscale clouds and high-performance computing systems. Although the U.S. has historically led the world in both advanced semiconductors and high-performance computing, other nations have recognized that these capabilities are integral to 21st century economic competitiveness and national security, and they are investing heavily.”

      The research was funded, in part, through Thompson’s grant from Good Ventures, which supports his FutureTech Research Group. The paper is being published by the Georgetown Public Policy Review. More

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      Companies use MIT research to identify and respond to supply chain risks

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      In February 2020, MIT professor David Simchi-Levi predicted the future. In an article in Harvard Business Review, he and his colleague warned that the new coronavirus outbreak would throttle supply chains and shutter tens of thousands of businesses across North America and Europe by mid-March.

      For Simchi-Levi, who had developed new models of supply chain resiliency and advised major companies on how to best shield themselves from supply chain woes, the signs of disruption were plain to see. Two years later, the professor of engineering systems at the MIT Schwarzman College of Computing and the Department of Civil and Environmental Engineering, and director of the MIT Data Science Lab has found a “flood of interest” from companies anxious to apply his Risk Exposure Index (REI) research to identify and respond to hidden risks in their own supply chains.

      His work on “stress tests” for critical supply chains and ways to guide global supply chain recovery were included in the 2022 Economic Report of the President presented to the U.S. Congress in April.

      It is rare that data science research can influence policy at the highest levels, Simchi-Levi says, but his models reflect something that business needs now: a new world of continuing global crisis, without relying on historical precedent.

      “What the last two years showed is that you cannot plan just based on what happened last year or the last two years,” Simchi-Levi says.

      He recalled the famous quote, sometimes attributed to hockey great Wayne Gretzsky, that good players don’t skate to where the puck is, but where the puck is going to be. “We are not focusing on the state of the supply chain right now, but what may happen six weeks from now, eight weeks from now, to prepare ourselves today to prevent the problems of the future.”

      Finding hidden risks

      At the heart of REI is a mathematical model of the supply chain that focuses on potential failures at different supply chain nodes — a flood at a supplier’s factory, or a shortage of raw materials at another factory, for instance. By calculating variables such as “time-to-recover” (TTR), which measures how long it will take a particular node to be back at full function, and time-to-survive (TTS), which identifies the maximum duration that the supply chain can match supply with demand after a disruption, the model focuses on the impact of disruption on the supply chain, rather than the cause of disruption.

      Even before the pandemic, catastrophic events such as the 2010 Iceland volcanic eruption and the 2011 Tohoku earthquake and tsunami in Japan were threatening these nodes. “For many years, companies from a variety of industries focused mostly on efficiency, cutting costs as much as possible, using strategies like outsourcing and offshoring,” Simchi-Levi says. “They were very successful doing this, but it has dramatically increased their exposure to risk.”

      Using their model, Simchi-Levi and colleagues began working with Ford Motor Company in 2013 to improve the company’s supply chain resiliency. The partnership uncovered some surprising hidden risks.

      To begin with, the researchers found out that Ford’s “strategic suppliers” — the nodes of the supply chain where the company spent large amount of money each year — had only moderate exposure to risk. Instead, the biggest risk “tended to come from tiny suppliers that provide Ford with components that cost about 10 cents,” says Simchi-Levi.

      The analysis also found that risky suppliers are everywhere across the globe. “There is this idea that if you just move suppliers closer to market, to demand, to North America or to Mexico, you increase the resiliency of your supply chain. That is not supported by our data,” he says.

      Rewards of resiliency

      By creating a virtual representation, or “digital twin,” of the Ford supply chain, the researchers were able to test out strategies at each node to see what would increase supply chain resiliency. Should the company invest in more warehouses to store a key component? Should it shift production of a component to another factory?

      Companies are sometimes reluctant to invest in supply chain resiliency, Simchi-Levi says, but the analysis isn’t just about risk. “It’s also going to help you identify savings opportunities. The company may be building a lot of misplaced, costly inventory, for instance, and our method helps them to identify these inefficiencies and cut costs.”

      Since working with Ford, Simchi-Levi and colleagues have collaborated with many other companies, including a partnership with Accenture, to scale the REI technology to a variety of industries including high-tech, industrial equipment, home improvement retailers, fashion retailers, and consumer packaged goods.

      Annette Clayton, the CEO of Schneider Electric North America and previously its chief supply chain officer, has worked with Simchi-Levi for 17 years. “When I first went to work for Schneider, I asked David and his team to help us look at resiliency and inventory positioning in order to make the best cost, delivery, flexibility, and speed trade-offs for the North American supply chain,” she says. “As the pandemic unfolded, the very learnings in supply chain resiliency we had worked on before became even more important and we partnered with David and his team again,”

      “We have used TTR and TTS to determine places where we need to develop and duplicate supplier capability, from raw materials to assembled parts. We increased inventories where our time-to-recover because of extended logistics times exceeded our time-to-survive,” Clayton adds. “We have used TTR and TTS to prioritize our workload in supplier development, procurement and expanding our own manufacturing capacity.”

      The REI approach can even be applied to an entire country’s economy, as the U.N. Office for Disaster Risk Reduction has done for developing countries such as Thailand in the wake of disastrous flooding in 2011.

      Simchi-Levi and colleagues have been motivated by the pandemic to enhance the REI model with new features. “Because we have started collaborating with more companies, we have realized some interesting, company-specific business constraints,” he says, which are leading to more efficient ways of calculating hidden risk. More

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      The reasons behind lithium-ion batteries’ rapid cost decline

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      Lithium-ion batteries, those marvels of lightweight power that have made possible today’s age of handheld electronics and electric vehicles, have plunged in cost since their introduction three decades ago at a rate similar to the drop in solar panel prices, as documented by a study published last March. But what brought about such an astonishing cost decline, of about 97 percent?

      Some of the researchers behind that earlier study have now analyzed what accounted for the extraordinary savings. They found that by far the biggest factor was work on research and development, particularly in chemistry and materials science. This outweighed the gains achieved through economies of scale, though that turned out to be the second-largest category of reductions.

      The new findings are being published today in the journal Energy and Environmental Science, in a paper by MIT postdoc Micah Ziegler, recent graduate student Juhyun Song PhD ’19, and Jessika Trancik, a professor in MIT’s Institute for Data, Systems and Society.

      The findings could be useful for policymakers and planners to help guide spending priorities in order to continue the pathway toward ever-lower costs for this and other crucial energy storage technologies, according to Trancik. Their work suggests that there is still considerable room for further improvement in electrochemical battery technologies, she says.

      The analysis required digging through a variety of sources, since much of the relevant information consists of closely held proprietary business data. “The data collection effort was extensive,” Ziegler says. “We looked at academic articles, industry and government reports, press releases, and specification sheets. We even looked at some legal filings that came out. We had to piece together data from many different sources to get a sense of what was happening.” He says they collected “about 15,000 qualitative and quantitative data points, across 1,000 individual records from approximately 280 references.”

      Data from the earliest times are hardest to access and can have the greatest uncertainties, Trancik says, but by comparing different data sources from the same period they have attempted to account for these uncertainties.

      Overall, she says, “we estimate that the majority of the cost decline, more than 50 percent, came from research-and-development-related activities.” That included both private sector and government-funded research and development, and “the vast majority” of that cost decline within that R&D category came from chemistry and materials research.

      That was an interesting finding, she says, because “there were so many variables that people were working on through very different kinds of efforts,” including the design of the battery cells themselves, their manufacturing systems, supply chains, and so on. “The cost improvement emerged from a diverse set of efforts and many people, and not from the work of only a few individuals.”

      The findings about the importance of investment in R&D were especially significant, Ziegler says, because much of this investment happened after lithium-ion battery technology was commercialized, a stage at which some analysts thought the research contribution would become less significant. Over roughly a 20-year period starting five years after the batteries’ introduction in the early 1990s, he says, “most of the cost reduction still came from R&D. The R&D contribution didn’t end when commercialization began. In fact, it was still the biggest contributor to cost reduction.”

      The study took advantage of an analytical approach that Trancik and her team initially developed to analyze the similarly precipitous drop in costs of silicon solar panels over the last few decades. They also applied the approach to understand the rising costs of nuclear energy. “This is really getting at the fundamental mechanisms of technological change,” she says. “And we can also develop these models looking forward in time, which allows us to uncover the levers that people could use to improve the technology in the future.”

      One advantage of the methodology Trancik and her colleagues have developed, she says, is that it helps to sort out the relative importance of different factors when many variables are changing all at once, which typically happens as a technology improves. “It’s not simply adding up the cost effects of these variables,” she says, “because many of these variables affect many different cost components. There’s this kind of intricate web of dependencies.” But the team’s methodology makes it possible to “look at how that overall cost change can be attributed to those variables, by essentially mapping out that network of dependencies,” she says.

      This can help provide guidance on public spending, private investments, and other incentives. “What are all the things that different decision makers could do?” she asks. “What decisions do they have agency over so that they could improve the technology, which is important in the case of low-carbon technologies, where we’re looking for solutions to climate change and we have limited time and limited resources? The new approach allows us to potentially be a bit more intentional about where we make those investments of time and money.”

      “This paper collects data available in a systematic way to determine changes in the cost components of lithium-ion batteries between 1990-1995 and 2010-2015,” says Laura Diaz Anadon, a professor of climate change policy at Cambridge University, who was not connected to this research. “This period was an important one in the history of the technology, and understanding the evolution of cost components lays the groundwork for future work on mechanisms and could help inform research efforts in other types of batteries.”

      The research was supported by the Alfred P. Sloan Foundation, the Environmental Defense Fund, and the MIT Technology and Policy Program. More

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      End-to-end supply chain transparency

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      For years, companies have managed their extended supply chains with intermittent audits and certifications while attempting to persuade their suppliers to adhere to certain standards and codes of conduct. But they’ve lacked the concrete data necessary to prove their supply chains were working as they should. They most likely had baseline data about their suppliers — what they bought and who they bought it from — but knew little else about the rest of the supply chain.

      With Sourcemap, companies can now trace their supply chains from raw material to finished good with certainty, keeping track of the mines and farms that produce the commodities they rely on to take their goods to market. This unprecedented level of transparency provides Sourcemap’s customers with the assurance that the entire end-to-end supply chain operates within their standards while living up to social and environmental targets.

      And they’re doing it at scale for large multinationals across the food, agricultural, automotive, tech, and apparel industries. Thanks to Sourcemap founder and CEO Leonardo Bonanni MA ’03, SM ’05, PhD ’10, companies like VF Corporation, owner of brands like Timberland, The North Face, Mars, Hershey, and Ferrero, now have enough data to confidently tell the story of how they’re sourcing their raw materials.

      “Coming from the Media Lab, we recognized early on the power of the cloud, the power of social networking-type databases and smartphone diffusion around the world,” says Bonanni of his company’s MIT roots. Rather than providing intermittent glances at the supply chain via an auditor, Sourcemap collects data continuously, in real-time, every step of the way, flagging anything that could indicate counterfeiting, adulteration, fraud, waste, or abuse.

      “We’ve taken our customers from a situation where they had very little control to a world where they have direct visibility over their entire global operations, even allowing them to see ahead of time — before a container reaches the port — whether there is any indication that there might be something wrong with it,” says Bonanni.

      The key problem Sourcemap addresses is a lack of data in companies’ supply chain management databases. According to Bonanni, most Sourcemap customers have invested millions of dollars in enterprise resource planning (ERP) databases, which provide information about internal operations and direct suppliers, but fall short when it comes to global operations, where their secondary and tertiary suppliers operate. Built on relational databases, ERP systems have been around for more than 40 years and work well for simple, static data structures. But they aren’t agile enough to handle big data and rapidly evolving, complex data structures

      Sourcemap, on the other hand, uses NoSQL (non-relational) database technology, which is more flexible, cost-efficient, and scalable. “Our platform is like a LinkedIn for the supply chain,” explains Bonanni. Customers provide information about where they buy their raw materials, the suppliers get invited to the network and provide information to validate those relationships, right down to the farms and the mines where the raw materials are extracted — which is often where the biggest risks lie.

      Initially, the entire supply chain database of a Sourcemap customer might amount to a few megabytes of spreadsheets listing their purchase orders and the names of their suppliers. Sourcemap delivers terabytes of data that paint a detailed picture of the supply chain, capturing everything, right down to the moment a farmer in West Africa delivers cocoa beans to a warehouse, onto a truck heading to a port, to a factory, all the way to the finished goods.

      “We’ve seen the amount of data collected grow by a factor of 1 million, which tells us that the world is finally ready for full visibility of supply chains,” says Bonanni. “The fact is that we’ve seen supply chain transparency go from a fringe concern to a broad-based requirement as a license to operate in most of Europe and North America,” says Bonanni.

      These days, disruptions in supply chains, combined with price volatility and new laws requiring companies to prove that the goods they import were not made illegally (such as by causing deforestation or involving forced or child labor), means that companies are often required to know where they source their raw materials from, even if they only import the materials through an intermediary.

      Sourcemap uses its full suite of tools to walk customers through a step-by-step process that maps their suppliers while measuring performance, ultimately verifying the entire supply chain and providing them with the confidence to import goods while being customs-compliant. At the end of the day, Sourcemap customers can communicate to their stakeholders and the end consumer exactly where their commodities come from while ensuring that social, environmental, and compliance standards are met.

      The company was recently named to the newest cohort of firms honored by the MIT Startup Exchange (STEX) as STEX25 startups. Bonanni is quick to point out the benefits of STEX and of MIT’s Industrial Liaison Program (ILP): “Our best feedback and our most constructive relationships have been with companies that sponsored our research early on at the Media Lab and ILP,” he says. “The innovative exchange of ideas inherent in the MIT startup ecosystem has helped to build up Sourcemap as a company and to grow supply chain transparency as a future-facing technology that more and more companies are now scrambling to adopt.” More