5G: The Impact on Us, Cars and OEM Business Models
Recently TechRadar published a post touting the advent of 5G coming to a town near you soon. The writer clearly indicated that throughput of the various payloads will greatly improve; as will power consumption and; most importantly, latency. After all, you do not want to rely on a vehicle braking fifty yards in front of you on the highway syncing your vehicle this fact 40 instead of 4 milliseconds later. Imagine the infamous download bar or swirling hour glass right before….KaBoooom! Sorry, it already happened.
The article also indicated that Apple and Android’s near-field-communication (NFC) protocols can make your car talk directly to other equipment (cars, traffic lights, too booths, etc.) without crowding the wireless spectrum. After all, as cars may become more autonomous and you lean back in your mobile work/leisure space of your driver seat to consume the latest blockbuster movie. By doing so you are helping to bring the projected video consumption of total traffic to 75% of all mobile data traffic by 2020, making NFC communication a required peak traffic alternative and similar to solar power in the energy space.
All this always-on communication many of us enjoy in our homes already bringing us increased productivity and entertainment will also surface a few challenges. Here are a few:
- Reduced privacy
A lot of data will be stored in the vehicle, local databases supporting outside NFC equipment, e.g. traffic monitoring centers, service providers delivering pre, during and post trip functionality. Route and behavioral patterns will become apparent and pose a security risk making corporate and vehicle cyber defense mechanisms even more important.
- Communication overload
The amount of data coming through a variety of protocols at different speeds, quality (latency, completeness and relevance) will increase the total number of software and processing components to approach 1/3 of the overall cost of the vehicle despite the continuing decrease in hardware cost.
- Decision complexity
AI will have to come to the rescue here as the volume, veracity, etc. of the data coming into your vehicle will be overwhelming for any human to interpret in a timely fashion. Humans may have to give up some, if not all, of the controls in the vehicle and become (just like pilots) entry points for destinations and preferences and a sanity check in mid-flight.
Standardization around architectures, communication protocols, data quality as well as privacy safeguards are being worked on by a variety of cross-industry consortia. However, the automotive industry has previously voiced concern about the consumers’ and their own readiness to adopt this technological leap as regulation and the finer technical points are still in their infancy in most jurisdictions.
Hey, Prepper, What is Your Bug Out Route?
As a participant in such a consortium, your organization needs to ramp up some basics first before embarking on the most advanced autonomous vehicle use cases. A key strategic move is to centralize and harden data management to provide a trusted reporting (and transaction) backbone, which can be used by divisional, agile self-service analytics users. Before embarking on this centralization train, think about these aspects first:
- Level of Uncertainty (regulatory, technological, demand) in your native sector and the one(s) you are trying to get into
- The Similarity of your various divisions’ business models, e.g. customer/supplier base, transaction type, products, etc.
- The ability to Plan your organization’s value-add under the current state of your support infrastructure (people, process, technology)
MIT’s Chander Velu and Stuart Madnick et al wrote an interesting paper in 2013 around this. I wanted to use their methodology to assess the above challenges in this context.
Please follow me down the rabbit hole, if you will.
- The auto sector’s future is increasingly uncertain compared to the last twenty years and especially before globalization kicked in. This uncertainty creates a positive dynamism allowing for innovation, which will benefit us all. On the flip side, it creates negative dynamics as regulators and whole ecosystems need to catch up or sync to allow for a safe and productive roll out of the innovation, e.g. ADAS features.
- As more innovative use cases around the vehicle are explored and productized, the dissimilarity between an OEM (and likely also supplier’s) business units will increase. However, while their products and services as well as go-to-market routes (dealer, digital, PAYD) will differ, the underlying platform and its user (vehicle, driver) will remain the same. Not knowing vehicle and driver profiles and history across these business models is a commercial death sentence.
- As OEMs spin-off or spin-in COEs or fully functional software development units, executives should first analyze the current state of affairs: the software quality track record, speed of innovation (new releases), funding, IT support infrastructure, internal vs external development focus and history to monetize this investment. How close is the organization to a B2B or B2C software paradigm? Are you more like an Apple or Google or a SAP or IBM? Both approaches have their appeal and pitfalls.
So what is an automotive OEM (or supplier) to do? What business model is the lowest risk with the highest payday or at least the most predictable?
HBR published a Gartner Quadrant ™-like graph from Compustat data showing the relative position of various sectors as it relates to technological and demand uncertainty. Unsurprisingly, software and pharma were at the top right (highly uncertain) as they are heavily influenced by technological changes, low barriers of entry, long R&D cycles or alike. CPG (and automotive) were also nearby as they are considered discretionary spend items.
The fairly certain sectors (bottom left quadrant) are the usual suspects: utilities (we all need power, water, gas all the time), metals, insurance and banking.
So given the above trifecta (uncertainty, similarity an my addition of “planability”), I am not surprised to see automotive firms going further towards the 8 o’clock position to improve these three factors, e.g. Nissan’s xStorage.
Staying or increasing their level of uncertainty; however, by going into software and transportation logistics (see GM’s stake in Lyft), is strategically quite a departure. This can only be explained by their executives’ assumption that the business model is fairly close and the uncertainty is lower than anticipated. Uber’s global success may play a role here: the allure of a more profitable service model, the high frequency transaction stream flattening their traditional demand cycles and its ability to consistently overcome legal and wage disputes.
So, here is my question given the above: If OEMs want to de-risk their future business model or it’s all a wash anyway, why are OEMs not following the Monsanto approach and provide logistics and productivity B2B services? Why are they not providing transportation and traffic management services like some software vendors provide to cities globally?