Irrepressible Tangent

May 14, 2019September 17, 2020

TECHNOLOGY TRANSFORMING TRANSPORTATION

Artificial Intelligence (AI), Internet-of-Things (IoT) and low latency 5G network connectivity are paving the way for a host of opportunities to transform the transportation industry. These are technologies that facilitate vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and vehicle to everything (V2X) communications, enabling both computer assisted driving and autonomous vehicles, both of which promise to make driving safer and more efficient.1

Safer because road hazards, traffic, environmental conditions and even the actions and future actions of other vehicles on the road can be communicated to drivers or directly to on-board driving equipment, allowing appropriate adjustments to be made in terms of speed and positioning.2 Vehicles that are aware of where they’re going, where the vehicles around them may be headed, when vehicles around them may change course, when traffic conditions may require adjustment to the route, when traffic signals are going to change, that there’s a stopped car around the corner, or a pedestrian who’s stepped out into the street complement existing LiDAR, camera and radar technology already being employed to assist drivers.3

More efficient because interconnected traffic will flow better, will allow people to use their commute times more productively and will be more fuel efficient. Further the vehicles will be more capable of predictive maintenance so fewer automobiles will be subject to unexpected failure on the roads and the same sensors that assist with maintenance can help tune performance, similar to how these same technologies are helping with the fuel efficiency of air traffic.

Car culture, particularly in the US is an impediment to uptake of the technology, but car and ridesharing have already made small inroads into changing cultural attitudes toward car ownership, and driver assistance is helping make the idea of self-driving vehicles more palatable to drivers. These technologies aren’t limited to personal transport either, commercial and public transport can leverage these same technologies to improve service delivery, safety and efficiency.

1 Hoeben, R. (2018, August 22). V2X is Here to Stay—Now Let’s Use It for Autonomous Cars. Retrieved from Electronic Design: https://www.electronicdesign.com/automotive/v2x-here-stay-now-let-s-use-it-autonomous-cars

2 Healey, J. (2013, April). If cars could talk, accidents might be avoidable. Retrieved April 25, 2019, from TED: https://www.ted.com/talks/jennifer_healey_if_cars_could_talk_accidents_might_be_avoidable

3 Fourtané, S. (2018, November 16). Connected Vehicles in Smart Cities: The Future of Transportation. Retrieved May 1, 2019, from Interesting Engineering: https://interestingengineering.com/connected-vehicles-in-smart-cities-the-future-of-transportation

May 14, 2019September 17, 2020

AI: AUGMENTED DECISION MAKING

Conversation around artificial intelligence is steeped in both utopian and dystopian promise. Currently few other technologies elicit comparable levels of emotion from high level academics, technology experts and laymen alike. Associations with Skynet from The Terminator, omniscient and omnipotent are common, but in reality, the progress in AI has been focused not on developing machine sentience, but on using massive amounts of data to facilitate automation of repetitive tasks.[1] Most successful real world applications of AI combine the technology with humans trained in its use to facilitate augmented decision making, and that’s where most experts believe the real potential of AI will be unleashed.

With proper training involving exposure to a few hundred thousand images, machines can diagnose melanoma with the same accuracy as dermatologists who’ve spent a decade in training. That knowledge is both thrilling and frightening and illuminates the real disruptive potential of AI; its impact on highly skilled labour. While past iterations of technology have automated menial physical tasks, AI is automating skilled work traditionally performed by trained professionals in medicine, science and industry. This fear response may be an overreaction, as evidence suggests the technology won’t be replacing these highly skilled individuals, but instead augmenting their skillsets elevating the work they do and the value they provide.[2]

Unfortunately, the technical expertise to leverage AI is currently in high demand and short supply. Companies from a wide variety of industries need to invest in data analytics to sustain themselves and are aggressively recruiting for individuals with strong computational data science and programming skills; skills that are not readily available.[3] User adoption is also challenging primarily because technology that’s difficult to understand is difficult to trust; particularly when it’s being used in assisted decision making. For professionals who must justify their decisions to boards, regulatory bodies or the public, a black box decision doesn’t cut it. Further, as humans are the ones training machines with human data, machine learning is susceptible to common human bias along racial, cultural and gender lines, and if you don’t know how your technology has arrived at its decision you can’t know whether it’s algorithm has been compromised. These concerns have led to the rise of explainable AI (XAI) or Transparent AI wherein machines provide an auditable accounting of the logic and rules used to arrive at a decision; a development which may in time help facilitate better adoption of the technology.[4]

[1] Thrun, S., & Anderson, C. (2017, April). What AI is — and isn’t. Retrieved May 8, 2019, from TED Ideas worth spreading: https://www.ted.com/talks/sebastian_thrun_and_chris_anderson_the_new_generation_of_computers_is_programming_itself

[2] Wilson, H J & Daugherty, P 2018, ‘Collaborative intelligence: humans and AI are joining forces’, Harvard Business Review, July–August, pp. 1–11.

[3] Henry-Nickie, M. (2016, November 16). Leveraging the disruptive power of artificial intelligence for fairer opportunities. Retrieved April 20,2019, from Brookings: https://www.brookings.edu/blog/techtank/2017/11/16/leveraging-the-disruptive-power-of-artificial-intelligence-for-fairer-opportunities/

[4] PwC Digital Services. (2018, December 06). The six priority areas to unlock AI value in 2019. Retrieved May 8, 2019, from Digital Pulse: https://www.digitalpulse.pwc.com.au/report-pwc-ai-predictions-2019/

May 14, 2019September 17, 2020

3D PRINTING: LEVERAGING THE POWER OF FLEXIBILITY, COMPLEXITY, AND EFFICIENCY

Recent developments in 3D printing are revolutionising the industry and expanding opportunities for and applications of additive manufacturing. The development of advanced simulation software is reducing the time it takes to model a 3D print design for parts that traditionally were created using combinations of multiple moulded parts that required post-production welding and brazing. The software also helps to predict defects, distortion, and stresses in designs and facilitates increased use of metals in additive manufacture.[1]

Refinements to 3D printing machines and manufacturing processes including Continuous Liquid Interface Production (CLIP), Computed Axial Lithography (CAL), Nanoparticle Jetting of metal and ceramic particles through liquid dispersion[2], Big Area Additive Manufacturing (BAAM) and mixed material printers are vastly improving production speeds, the size and structural integrity of printed items, and the types of goods that can be produced.

An ever-increasing variety of materials for use in the manufacturing process are regularly being developed. Where once only nylon and a handful of polymers were used, now high elasticity polymers, dental-grade polymers, rigid polyurethanes, silicon, epoxy, metal powders, ceramics, carbon fibre, and even biomedical photopolymers are readily available, and the options continue to expand.[3]

Further, 3D printing machine prices are breaking down barriers to entry for start-ups and reducing or eliminating switching costs for existing manufacturers. Several 3D printing machine manufacturers even offer subscription services for their machines, putting them well within reach of smaller manufacturers and allowing organisation to explore transformation opportunities like digital inventories, distributed manufacturing, and decentralised operations that offer better options for collaboration in local markets.[4]

All this means there’s never been a better time for organisations to consider how they might leverage 3D printing in their production environment. Whether you’re a start-up or a mass manufacturer, examining your business model and assessing the impacts of 3D printing should be the first step in identifying how the technology might benefit product development or supply chain optimisation.

In making these assessments it’s necessary to understand where additive outperforms traditional processes. First, additive facilitates product variation allowing greater flexibility to meet customisation demands. Advances like aerosol and nanoparticle jetting technologies make additive better at producing more complex products like those that require embedded electronics. Finally, efficiencies around material waste, transport costs and the costs of maintaining inventory can be particularly beneficial in competing with those rivals with established value chains.[5]

Armed with a strong understanding of the business model and the potential benefits of the technology an organisation can develop a life-cycle cost analysis identifying where 3D printing

[1] Hitch, J. (2019, February 01). State of 3D Printing 2019: All Grown Up & Ready to Work. Retrieved April 20, 2019, from Industry Week: https://www.industryweek.com/technology-and-iiot/state-3d-printing-2019-all-grown-ready-work

[2] D’Aveni, R. A. (2018, July-August). The 3-D printing playbook. Harvard Business Review, 1-9.

[3] Rodgers, L. (2019, May 1). The Ultimate Guide to 3D Printing Materials. Retrieved from Jabil: https://www.jabil.com/insights/blog-main/3d-printing-materials.html

[4] Yeap, M. (2019, March 1). The Future of 3D Printing – A Glimpse at the Next Generation. Retrieved April 20, 2019, from All3DP: https://all3dp.com/2/future-of-3d-printing-a-glimpse-at-next-generation-making/

[5] D’Aveni, R. A. (2018, July-August). The 3-D printing playbook. Harvard Business Review, 1-9.

April 30, 2019September 17, 2020

IOT: EMPOWERMENT THROUGH CONNECTIVITY

“Internet of Things”, if Kevin Ashton had it to do over again, he might choose a more descriptive name. It’s a vague, amorphous term that doesn’t do much to demystify a network of products equipped with embedded sensors, on-board processors and some means of communicating the data they collect with the user and or manufacturer of the product, a central system or similarly enabled products.[1] The possibilities these connected products herald correlate directly with their ability to collect and transmit data about the product, the product’s operating environment, product usage and the customer. The data facilitate a number of interesting business models the most promising of which include subscription, custom products on demand and IoT data monetisation.2

The subscription model is currently being used successfully organisations like Rolls Royce Engines with their High-Flying Platform-as-a-Service and Google Nest with their Learning Thermostat. Under this model customers pay a fee for some type of ongoing service associated with the product: typically, some form of monitoring as a service, or predictive maintenance as is the case with the Rolls Royce Engines.[3] This model allows an organisation to build ongoing relationships with their customers while collecting data about their product usage that allows for mutually beneficial future upgrades.

Custom products on demand is a service used in combination with additive manufacturing wherein product customisation and personalisation is facilitated by machine vision and other IoT sensors capturing measurement or other data and generating specs. MTailor, BMW and STYR Labs all leverage this model to provide personalised products to their customers.[4]

Finally, IoT data monetisation is essentially selling the data you collect onto a third party. Insurance companies for instance might purchase data from a car manufacturer to give them insight into driving patterns.[5]

While the business models are promising, the technology still has some issues. Currently there’s little or no standarisation in the industry which makes the technology difficult to scale. [6] There’ve even been circumstances where manufacturers have only found out after product launch that the chipsets in their embedded sensors were incompatible and their products couldn’t communicate with one another. There are also issues with a lack of suitable expertise in the industry, the complex nature of most IoT projects and the data transmission requirements and the capacity of current networks, though that particular issue should be addressed by the rollout of 5G connectivity later in the year. Still IoT is an exciting technology and opportunities abound for entrepreneurs with a vision and a plan.

[1] Porter, M E & Heppelmann, J 2014, ‘How smart, connected products are transforming competition’, Harvard Business Review, November, pp. 1–23.

[2] Elizalde, D. (n.d.). IoT Strategy for Product Teams: 7 IoT Business Models That Are Transforming Industries. Retrieved 04 30, 2019, from Daniel Elizalde: https://danielelizalde.com/monetize-your-iot-product/

[3] Forbes Insights Team. (2018, June 27). How IoT Is Spawning Better Business Models. Retrieved from Forbes: https://www.forbes.com/sites/insights-inteliot/2018/06/27/how-iot-is-spawning-better-business-models/#6808d8c15984

[4] Ibid.

[5] Elizalde, D. (n.d.).

[6] Travers, J. (2018, November 14). IoT as a Service: A new business model? Retrieved 04 20, 2019, from Ericsson: https://www.ericsson.com/en/blog/2018/11/iot-as-a-service-a-new-business-model