Over the years, numerous analytical and empirical methods have been developed to prevent machine failure, reduce costs, and increase production capacity. In the present age, companies like M2M Data Corporation and Senseye started out by developing physical models of machines based on data collected from their customers’ facilities. The data consists of parameters such as pressure, speed of motors, particle concentration in lubricants, acoustic data, temperature in the friction generating part of the equipment, and machine unique data sheets. For example, each pump has its unique “pump curves” that correlate pump rotation speed with discharge pressure and flow. A deviation of these above parameters is used to flag the plant personnel of possible machine issues, and could also suggest a specific malfunction. Continue reading
We’re just beginning to get our heads around Artificial Intelligence, but the machines are already making their next move: creativity. While we still think of imagination as an innately human capability, advances in computing power are making arts as diverse as architecture, music, movies, and material design into easily-accessible, programmable spaces. In some areas, machines have already surpassed human originality and quality – as rated by other humans – and more fields are likely to fall. Continue reading
Google Glass is back. Last week, X, a subsidiary of Google parent Alphabet, announced a revival of its most embarrassing wearable mishap with a new focus on the enterprise market. In the past couple years, Google Glass Enterprise Edition (EE) has been silently tested in pilot programs with companies such as GE, DHL, Boeing, Volkswagen, and Sutter Health. After last week’s announcement of Glass EE, the wearable device will now be more widely available via a network of partners. As of now, there are no further plans to bring back the original consumer edition. Continue reading
Earlier this week, healthcare IT firm Change Healthcare became the newest member of the Hashed Health Blockchain Consortium, a distributed ledger consortium whose goal is to advance the use of blockchain protocols in healthcare. The expansion of this group and the quest for the establishment of standards for implementation of blockchain in healthcare are not surprising – the last year has witnessed a sharp uptick in developers looking to bring blockchain to the industry. However, while the number of companies starting to apply blockchain – a distributed ledger technology that claims to offer several benefits over traditional databases, such as improved trustworthiness and automated smart contracts – to healthcare is growing, and while there certainly is a lot of hype surrounding this activity, there still remains confusion on the specific challenges these companies are looking to tackle and on the value they promise to deliver. In the table below, we synthesize currently sought-after use cases for blockchain in health, outline tech developers’ claims, and highlight players in each solution category.
We have expressed previously that predictive maintenance (PdM) is one of the most promising areas of the industrial internet of things (IIoT) and that there are a number of startups developing innovative sensor-based and/or software solutions. To identify where these technology developers are innovating, we took a look at the patent activity on PdM. During the past 20 years, there have been approximately 4,400 patents focused on PdM applications, and companies are filing more patent applications every year to differentiate themselves and protect their models, algorithms, or hardware-based solutions. In our “Predictive Maintenance: The Art of Uptime” report, we mentioned that innovations in connected sensing technologies and analytics are driving better operations, enabling users to gather and process real-time data on machine health to decrease downtime. For example, a study related to the oil and gas (O&G) industry revealed that performing PdM using a data-driven approach with sensors experienced 36% less unplanned downtime. There is still significant space for companies to decrease their unplanned downtime, and one method to do that is by adding more data streams from sensors. Therefore, we filtered our search to focus only on developments in PdM using sensing technologies, and we can see in Figure 1 that the IP space has been increasingly more active since 2014. Nonetheless, the patents aiming to protect sensor-based PdM are roughly 30% of the total amount of IP seeking protection for PdM applications.
Several days ago, Rolls-Royce announced a brand-new “Airline Aircraft Availability Center.” According to a press release from the company, this facility is aimed at ensuring “every aircraft it powers departs and lands on time, every time.” The company monitors about 4,500 jet engines powering commercial airliners, which operate for a combined 14 million hours per year. The company said its new center will employ data analytics to optimize operations and plan and manage maintenance activities. Unsurprisingly, its SVP of Civil Aerospace Services said, “We are entering a new era of digital connectivity and new services technology which allows us to greatly expand the type of services we can offer….” Rolls-Royce also disclosed plans to make this availability center a hub for innovation, deploying new technologies such as “remote surgery” engineering tools; it is aiming to be using robots within the next five years. Continue reading
On June 16, Amazon announced it intends to buy U.S. grocery retailer Whole Foods for $13.7 billion. Whole Foods is a relatively young (founded in 1980), small chain with about 450 stores in mostly affluent neighborhoods (as well as nine in the U.K.), and focuses on organic, healthy, and relatively expensive products – earning it the nickname “Whole Paycheck.” In comparison, grocery giants like Albertsons and Kroger were founded in 1939 and 1883 respectively, and have more than 2,000 stores each. For its part, Amazon’s foray into grocery, Fresh, has not had much impact on the $700 billion industry. Still, this move is absolutely a big deal, and one that will reshape not only retail (news of the deal destroyed nearly $40 billion in market value of competing retailers) but also consumer packaged goods (CPG) categories like food, personal care, and home care, and impact industries beyond. We discussed five strategic implications with retail technology expert Jim Crawford of RED-LAB to ascertain what will happen now, what’s next for these industries, and where this positions Amazon to go in the future: Continue reading
For centuries, the way in which consumers interacted with food had been dictated by a set of fixed factors. With evolving consumer demand and growing innovation, however, thousands of tech developers are now trying to help a much more complex consumer choose, buy, and measure the impact of food. Continue reading
We’ve expressed in the past that accurate and reliable early-stage disease detection combined with non-invasive sample collection is the holy grail of molecular diagnostics. Previously, we discussed the growing popularity of non-invasive saliva-based diagnostics in the context of this theme (see the insight “Digital IVD sample of the future: Saliva” [client registration required]). While less mature, sweat based tests, too, present a compelling avenue for non-invasive sensing in medical, enterprise, and consumer applications. To gauge the state of innovation in sweat sensing, we surveyed the evolving landscape of sweat sensors patents. In total, we identified 1,009 patents for the search terms “sweat sensor” and “perspiration sensor” published in the past decade. As evident by Figure 1 below, sweat sensing technologies have seen consistent increase in patents applications. 2016 saw most activity, with a total of 194 patent grants and applications.
Lux Research recently attended the 2017 Open Data Science Conference (ODSC) in Boston, a large, multi-day event with speakers ranging from Amazon’s Data and Analytics Practice Lead to the U.S. EPA’s Chief Data Scientist. The conference presented a solid mix of technical topics and strategic advice, covering trends in applied data science and specific techniques within analytics, big data, and machine learning. Although the agenda included a variety of technical deep-dives, four particular strategic themes stood out for us, each speaking to some of the bigger challenges and opportunities facing big data and analytics today: Continue reading