The Future of Enhanced Mobility | The 4 ACES - Connectivity
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Electric Vehicles
- Connected Intelligence
Publication | Update: Mar 2020
According to the Mckinsey perspective ‘The trends transforming mobility’s future’, the levels of disruption coming over the next dozen years are likely to exceed those of the previous 50 or more.
In this context, four trends - ACES: autonomous driving, connectivity, the electrification of vehicles, and shared mobility, are identified.
Connected cars are poised to become potent information platforms that not only provide better experiences for drivers but also open new avenues for businesses to create value.
The role of the car is shifting from a mere mode of transport to a multimedia environment where connectivity is at the heart of a new customer experience.
“We’re moving from being just a hardware provider to being a hardware, software, and experiences provider,” says Don Butler, head of Connected Vehicle and Services for Ford Motor Company in a Wired interview. “The future is going to be different, and we are embracing that difference, and we’ll continue to be a part of people’s lives.”
Connectivity is at the heart of all of this, driving a huge number of innovations that are either here now or in the works. Ford SYNC technology, for instance, lets a driver not just make hands-free phone calls but also control the entertainment, climate and navigation systems using her voice. That’s really just the beginning. New SYNC Connect technology will let you remotely start your vehicle, unlock the doors, check the fuel level, and much more from your smartphone, operating through the new FordPass app. The push for connectivity is also driving features that let you share vehicles with other people or integrate your car with a home security system.
Many manufacturers and suppliers already access a wealth of vehicle data to improve or refine their cars and services, and possibilities abound for other players to share information as new ecosystems form. Consider how connectivity-enabled services could let restaurants advertise to hungry lunchtime travelers along a given travel route. By using new forms of vehicle interactions (say, vocal commands or miniature holographic waiters) restaurants could offer menu options and preordering to save time when diners arrive.
The Mckinsey perspective ‘The trends transforming mobility’s future’, has identified five levels of connectivity, each involving incremental degrees of functionality that enrich the consumer experience, as well as a widening potential for new revenue streams, cost savings, and passenger safety and security. These levels reflect the potential for connectivity to stretch from today’s increasingly common data links between individuals and the hardware of their vehicles to future offerings of preference-based personalization and live dialogue, culminating with cars functioning as virtual chauffeurs.
The research suggests that by 2030, 45 percent of new vehicles will reach the third level of connectivity representing a value pool ranging from 450 billion to 750 billion. Surveys also indicate that 40 percent of today’s drivers would be willing to change vehicle brands for their next purchase in return for greater connectivity.
According to Wired, vehicles are already being built with driver assistance technology to help keep cars from drifting out of lane, monitor blind spots and more. But soon, your car will be able to offer more personal assistance, too—monitoring your vital signs by linking up to a fitness band or other wearable or if the car detects you’re nodding off, the vehicle can react automatically. Or, if your fitness band detects an increase in heart rate, adaptive cruise control may kick in and give you more breathing room from the car ahead of you.
As Yifan Chen, a researcher at the Ford Research Automotive Wearable Experiences Lab, explains, “We’re trying to understand wearable devices and their capability in a way that is robust, accurate, and can be used to develop sophisticated mathematical algorithms to determine the driver’s physical and mental state.”
Advances like these are paving the way for a future of autonomous vehicles, incorporating cloud-based computing technologies and smart sensors to get us where we need to go, confidently and expediently.
In the future, of course, both car owners and riders in passenger vehicles will need to be convinced of the value of new offerings—particularly those commanding a price. They will also need assurances that the data they are increasingly willing to share are secure. Meanwhile, companies will have to organize themselves around new, customer-centric business models and be open to partnerships, particularly with digital giants and innovative start-ups.
Objectives and Study Scope
This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.
The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges.
The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period.
The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players. This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.
Research Process & Methodology
We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.
We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.
Research Portfolio Sources:
Global Business Reviews, Research Papers, Commentary & Strategy Reports
M&A and Risk Management | Regulation
The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.
Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:
As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.
Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.
Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pretax revenue and its total boughtin costs (costs excluding wages and salaries).
Forecasts of GDP growth: GDP = CN+IN+GS+NEX
GDP growth estimates take into account:
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.
Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.
Industry Life Cycle Market Phase
Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:
The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.
The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.
Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from ofﬁcial sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reﬂect different assumptions about their relative importance.
The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.