How to achieve sustainable mobility using sustainable software development

 The automotive industry is changing right before our very eyes. Today, services based on the CASE model are looming on the horizon. They are capturing an increasing market share and gaining more and more each year in total dollar value. What's in store for the automotive sector and how automotive enterprises can seize these opportunities?

New mobility services are emerging rapidly

By 2030, over 30 percent of the projected increase in vehicle sales due to urbanization and macroeconomic growth will be unlikely to happen owing to the shared mobility expansion.

In China, the European Union, and the United States, which are countries supporting  shared mobility solutions , the mobility market  could reach 28 percent annual growth from 2015 to 2030 . Of course- this would be the most optimistic scenario. FutureBridge specialists expect the shared mobility market to grow significantly over the next five to seven years at a CAGR of 16 percent from 2018, reaching  180 billion dollars by 2025 . How can the growing demand for new mobility services be explained?

On the one hand,  the automotive industry deals with  changing consumer preferences . One travels by car covering shorter distances, but much more frequently. And it doesn’t have to be by car at all, as new means of transportation are becoming more accessible.

On the other hand, soaring car prices (though cars lose their value a few months after the purchase) prompt us to search for other, cheaper alternatives that provide optimal driving comfort anyway.

How will companies relying on the traditional car ownership model respond to this trend? They will provide new services such as substitution models, in which, for a once-off monthly payment, you can have a new car with insurance, maintenance, roadside assistance, etc.  Subscriptions will soon account for about 15% of new car sales and should have risen to 25% by 2025. In this context, new mobility in the form of rental and ride-sharing services, which are also part of the transformation on the roads, also becomes significant.

The third thing is growing technology,  based on the CASE model(Connectivity, Autonomous driving, Shared mobility, Electrification,) that empowers the development of new mobility services on an unprecedented scale. According to Microsoft experts, by 2030 virtually all new cars will have been connected devices, functioning as data centers on wheels.

6 leading new mobility services

Carsharing

A short-term car rental model that allows users to choose a vehicle and pick-up/drop-off location. Users can determine vehicles and flexible rent times. Operators gain high ROI with high utilization and minimal staffing.

 Examples: citybee, E-VAI, fetch

Ride-hailing

A form of cab rental in which the drivers are usually contractors using their private vehicles rather than direct employees. The user has immediate availability and payment is handled through the operator. The benefits are also the ability to track and monitor journeys. For operators instead, traditional fleet costs must be handled by the drivers. It’s an easily scalable service.

 Examples: Uber, Lyft, Bolt, marcel, OLA

P2P Sharing

this service allows vehicle owners to rent their vehicles when they are not currently in use. BMW-run ReachNow is piloting a version of this type of service, which allows Mini owners to offer their currently unused vehicles for rent. The benefits for users are the lower costs than traditional vehicle rental. Meanwhile, the operator has no fleet to manage and gets access to an easily scalable model of business.

 Examples: HoppyGo, SnappCar

Carpooling

Allows users to join an already scheduled trip. The operating company acts as an "intermediary" through which rides can be announced and joined. Carpooling can apply both to people taking a trip alone and to those who want to share rides to reduce the total cost of the trip for a single passenger. It’s a cheap and environmentally friendly service. What is more, the operator has a higher margin per ride and no fleet to manage.

 Examples: BlaBlaCar, GoMore, liftshare

Car rental

The evolution of the traditional car rental by the day, allowing users to rent cars for different periods without the traditional hassle associated with this type of service. From the user's point of view, such new services enable an easier and quicker process of vehicle rental. Also, it’s possible to choose a vehicle before finalizing the rental. In turn, the operator has less staffing than a traditional rental and can utilize already existing fleets.

 Examples: Audi Silvercar, Hertz, Sixt, PORSCHE DRIVE, UBEEQO

Multimodal

An integrator of public transport mobility services, as well as other modes of transportation, such as public transportation, rail networks, and even cabs. The goal of such services is to get people from their starting point to their destination in the fastest, cheapest, or most efficient way, depending on individual needs. In this model, the operator gets access to additional potential users and has relatively low costs of deployment due to a lack of physical assets.

 Examples: FREE2MOVE, whim, Google Maps

Which new mobility services are growing the fastest?

Of the 55 providers of the aforementioned new mobility services operating in European countries, the most popular are those in the area of  carsharing  (51%) . The second most popular are  car rental services  (20%) , followed by  P2P sharing  (13%) .

In terms of ownership, most new mobility services were OEM owned (over 36%), although many of them were independent (over 38%). Also included were OEM invested services (31%).

Technologies and functionalities fueling the development of new mobility services

Mobility services are based on advanced software that uses, at least, the Internet of Things, to transfer data from the vehicle to the cloud. Then the individual information is available on the user's mobile application.

For services based on unmanned vehicle rental, modern security features have been considered when it comes to opening and closing the car.

With a view to minimizing possible problems, the developers of digital new mobility services are also introducing a  fault reporting option.

 Below is a selection of the most common functionalities and technologies in detail for each new mobility service in Europe.

All of these and other options provide guidance and a certain pattern of behavior for future developing OEMs.

Key factors crucial for the development of new mobility services

CASE trends provide new opportunities for the vehicles of the future. However, the interrelationships between software, in-car sensors, and electronic systems require a  huge amount of resources , especially when we are talking about reliable operations that translate into a competitive advantage of new mobility services and popularity among potential users.

 Therefore, if you want to develop in this area, consider at least these few factors.

1.     Cybersecurity    . In addition to creating huge amounts of code, what also matters is that your user data tracking processes comply with the standards and regulations that apply in your geographic region.

2.  Careful listening to user needs. In order to compete with technology start-ups, OEMs should focus on innovative digital solutions oriented towards     actual consumer expectations    . What matters is flexibility, when it comes to the portfolio of functionalities.

3.  Certainly,     emotion    is a factor that must be taken into account. Solution providers should care about providing unique experiences and sensations that will make the user eager to re-use a particular service, and in the process, spread it to their community.

4.     Flexibility and scalability    . You need to be prepared not only to meet the changing expectations of customers who come in with feedback but also to expand functionality to include those that competitors already have (or to offer completely innovative solutions).

5.     Being ready to expand the offering    . For example, with new types of vehicles: not only internal combustion but also hybrid, electric; not only cars but also city scooters, etc.

If you want to deal with the challenges that come with developing new mobility services and are considering the above and other growth factors, contact Grape Up. We can help you expand your business in terms of features and values appreciated by today's conscious consumers.

Our  initial article on predictive maintenance covered the definition of such a system, its construction, and the key implementation challenges. In this part, we'll delve into how PdM technology is transforming different facets of the automotive industry and its advantages for OEMs, insurers, car rental companies, and vehicle owners.

Best predictive maintenance techniques and where you can use them

In the first part of the article, we discussed the importance of sensors in a PdM system. These sensors are responsible for collecting data from machines and vehicles, and they can measure various variables like temperature, vibration, pressure, or noise. Proper placement of these sensors on the machines and connecting them to IoT solutions, enables the transfer of data to the central repository of the system. After processing the data, we obtain information about specific machines or their parts that are prone to damage or downtime.

The automotive industry can benefit greatly from implementing these top predictive maintenance techniques.

Vibration analysis

 How does it work?

Machinery used in the automotive industry and car components have a specific frequency of vibration. Deviations from this standard pattern can indicate "fatigue" of the material or interference from a third-party component that may affect the machine's operation. The PdM system enables you to detect these anomalies and alert the machine user before a failure occurs.

 What can be detected?

The technique is mainly applied to high-speed rotating equipment. Vibration and oscillation analysis can detect issues such as bent shafts, loose mechanical components, engine problems, misalignment, and worn bearings or shafts.

Infrared thermography analysis

 How does it work?

The technique involves using infrared cameras to detect thermal anomalies. This technology can identify malfunctioning electrical circuits, sensors or components that are emitting excessive heat due to overheating or operating at increased speeds. With this advanced technology, it's possible to anticipate and prevent such faults, and even create heat maps that can be used in predictive models and maintenance of heating systems.

 What can be detected?

Infrared analysis is a versatile and non-invasive method that can be used on a wide scale. It is suitable for individual components, parts, and entire industrial facilities, and can detect rust, delamination, wear, or heat loss on various types of equipment.

Acoustic analysis monitoring

 How does it work?

Machines produce sound waves while operating, and these waves can indicate equipment failure or an approaching critical point. The amplitude and character of these waves are specific to each machine. Even if the sound is too quiet for humans to hear in the initial phase of malfunction, sensors can detect abnormalities and predict when a failure is likely to occur.

 What can be detected?

This PdM technology is relatively cheaper compared to others, but it does have some limitations in terms of usage. It is widely used in the Gas & Oil industry to detect gas and liquid leaks. In the automotive industry, it is commonly used for detecting vacuum leaks, unwanted friction, and stress on machine parts.

Motor circuit analysis

 How does it work?

The technique works through electronic signature analysis (ESA). It involves measuring the supply voltage and operating current of an electronic engine. It allows locating and identifying problems related to the operation of electric engine components.

 What can be detected?

Motor circuit analysis is a powerful tool that helps identify issues related to various components, such as bearings, rotor, clutch, stator winding, or system load irregularities. The main advantage of this technique is its short testing time and convenience for the operator, as it can be carried out in just two minutes while the machine is running.

PdM oil analysis

 How does it work?

An effective method for Predictive Maintenance is to analyze oil samples from equipment without causing any damage. By analyzing the viscosity and size of the sample, along with detecting the presence or absence of third substances such as water, metals, acids or bases, we can obtain valuable information about mechanical damage, erosion or overheating of specific parts.

 What can be detected?

Detecting anomalies early is crucial for hydraulic systems that consist of rotating and lubricating parts, such as pistons in a vehicle engine. By identifying issues promptly, effective solutions can be developed and potential damage to the equipment or a failure can be prevented.

Computer vision

 How does it work?

Computer vision is revolutionizing the automotive industry by leveraging AI-based technology to enhance predictive maintenance processes. It achieves this by analyzing vast datasets, including real-time sensor data and historical performance records, to rapidly predict equipment wear and tear. By identifying patterns, detecting anomalies, and issuing early warnings for potential equipment issues, computer vision enables proactive maintenance scheduling.

 What can be detected?

In the automotive industry, computer vision technology plays a crucial role in detecting equipment wear and tear patterns to predict maintenance requirements. It can also identify manufacturing defects such as scratches or flaws, welding defects in automotive components, part dimensions and volumes to ensure quality control, surface defects related to painting, tire patterns to match with wheels, and objects for robotic guidance and automation.

Who and how can benefit from predictive maintenance

Smart maintenance systems analyze multiple variables and provide a comprehensive overview, which can benefit several stakeholders in the automotive industry. These stakeholders range from vehicle manufacturing factories and the supply chain to service and dealerships, rental companies, insurance companies, and drivers.

Below, we have outlined the primary benefits that these stakeholders can enjoy. In the OEMs section, we have provided examples of specific implementations and case studies from the market.

Car rentals

 Fleet health monitoring and better prediction of the service time

Managing service and repairs for a large number of vehicles can be costly and time-consuming for rental companies. When vehicles break down or are out of service while in the possession of customers, it can negatively impact the company’s revenue. To prevent this, car rental companies need constant insight into the condition of their vehicles and the ability to predict necessary maintenance. This allows them to manage their service plan more efficiently and minimize the risk of vehicle failure while on the road.

Car dealerships

 Reducing breakdown scenarios

Car dealerships use predictive maintenance primarily to anticipate mechanical issues before they develop into serious problems. This approach helps in ensuring that vehicles sold or serviced by them are in optimal condition, which aids in preventing breakdowns or major faults for the customer down the line. By analyzing data from the vehicle's onboard sensors and historical maintenance records, dealerships can identify patterns that signify potential future failures. Predictive maintenance also benefits dealerships by allowing for proactive communication with vehicle owners, reducing breakdown scenarios, and enhancing customer satisfaction

Vehicle owners

 Peace of mind

Periodic maintenance recommendations for vehicles are traditionally based on analyzing historical data from a large population of vehicle owners. However, each vehicle is used differently and could benefit from a tailored maintenance approach. Vehicles with high mileage or heavy usage should undergo more frequent oil changes than those that are used less frequently. By monitoring the actual vehicle condition and wear, owners can ensure that their vehicles are always at 100% and can better manage and plan for maintenance expenses.

Insurance companies

 Risk & fraud

By using data from smart maintenance systems, insurance companies can enhance their risk modeling. The analysis of this data allows insurers to identify the assets that are at higher risk of requiring maintenance or replacement and adjust their premiums accordingly. In addition, smart maintenance systems can detect any instances of tampering with the equipment or negligence in maintenance. This can aid insurers in recognizing fraudulent claims.

OEMs successful development of PdM systems

BMW Group case study

The German brand implements various predictive maintenance tools and technologies, such as sensors, data analytics, and artificial intelligence, to prevent production downtime, promote sustainability, and ensure efficient resource utilization in its global manufacturing network. These innovative, cloud-based solutions are playing a vital role in enhancing their manufacturing processes and improving overall productivity.

The BMW Group's approach involves:

•     Forecasting phenomena and anomalies using a cloud-based platform.    Individual software modules within the platform can be easily switched on and off if necessary to instantly adapt to changing requirements. The high degree of standardization between individual components allows the system to be globally accessible. Moreover, it is highly scalable and allows new application scenarios to be easily implemented.
•     Optimizing component replacements       (this uses advanced real-time data analytics).  
•     Carrying out maintenance and service work in line with the requirements of the actual status of the system.  
•     Anomaly detection using advanced AI predictive algorithms.  

Meanwhile, it should be taken into account that in BMW's body and paint shop alone, welding guns perform some 15,000 spot welds per day. At the BMW Group's plant in Regensburg, the conveyor systems' control units run 24/7. So any downtime is a huge loss.

→  SOURCE case study.

FORD case study

Predictive vehicle maintenance is one of the benefits offered to drivers and automotive service providers as part of Ford's partnerships with CARUSO and HIGH MOBILITY.  In late 2020, Ford announced two new connected car agreements to potentially enable vehicle owners to benefit from  a personalized third-party offer.

CARUSO and HIGH MOBILITY will function as an  online data platform that is completely independent of Ford and allows  third-party service providers secure and compliant access to vehicle-generated data. This access will, in turn, enable third-party providers to create personalized services for Ford vehicle owners. This will enable drivers to benefit from smarter  insurance, technical maintenance and roadside recovery.

Sharing vehicle data (warning codes, GPS location, etc.) via an  open platform is expected to be a way to maintain competitiveness in the connected mobility market.

→  SOURCE case study.

Predictive maintenance is the future of the automotive market

An effective PdM system means less time spent on equipment maintenance, saving on spare parts, eliminating unplanned downtime and improved management of company resources. And with that comes more efficient production and customers’ and employees’ satisfaction.

As the data shows, organizations that have implemented a PdM system report an average  decrease of 55% in unplanned equipment failures.  Another upside is that, compared to other connected car systems (such as infotainment systems), PdM is relatively easy to monetize. Data here can remain anonymous, and all parties involved in the production and operation of the vehicle reap the benefits.

Organizations have come to recognize the hefty returns on investment provided by predictive maintenance solutions and have thus adopted it on a global scale. According to Market Research Future, the global Predictive Maintenance market is projected to grow to  111.30 billion by 2030 , suggesting that further growth is possible in the future.

Connected, autonomous, and electric cars are changing the automotive industry. Yet, the massive amount of data they generate often remains siloed across different systems, making management and collaboration challenging.

This article examines how data platforms unify information, connecting teams across departments - from engineering to customer support - to analyze trends, address operational challenges, and refine strategies for success.

How are data platforms transforming the automotive industry?

 Data platforms resolve fragmentation issues by consolidating data from various sources into a unified system. This structure not only improves data accessibility within departments but also enables secure collaboration with trusted external partners

The impact of this approach is clear: improved safety through fewer accidents, better performance thanks to real-time analytics, and quicker development of features supporting solutions such as advanced driver assistance systems and  personalized in-car experiences .

As the demand for effective data solutions accelerates, the  global automotive data management market , valued at $1.58 billion in 2021, is projected to grow by 20.3% annually through 2030. This rapid development underscores how essential platforms are for addressing the increasing complexity of modern automotive operations, making them vital tools for staying competitive and meeting customer expectations.

Defining data platforms in automotive

Combined with a structured data architecture that defines how information is ingested, stored, and delivered, the platform acts as the operational backbone that transforms this architecture into a functional system. By removing duplications, cutting down on storage expenses, and making it easier to  manage data , the platform helps OEMs spend less time on technical hassles and more time gaining meaningful insights that drive their business forward.

In an industry where data flows through multiple departments, this centralized approach ensures that knowledge is not only easily available but also readily applicable to innovative solutions.

Data platforms as the engine for data-driven insights

Unlike standalone systems that only store or display information, automotive data platforms support the processing and integration of information, making it analysis-ready.

Here's a closer look at how it works:

Data ingestion

Automotive platforms handle a variety of inputs, categorized into real-time and batch-processed data. Real-time information, such as CAN bus telemetry, GPS tracking, and ADAS sensor outputs, supports immediate diagnostics and safety decisions.

Batch processing, on the other hand, involves data that is collected over time and processed collectively at scheduled intervals. Examples include maintenance records, dealership transactions, and even unstructured feedback logs.

Many platforms offer hybrid processing to meet specific operational and analytical needs.

Moreover, there are some unique methods used in the automotive industry to gather data, including:

•  Over-the-air (OTA) updates: remotely deliver software or firmware updates to vehicles to improve performance, fix bugs, or add features without requiring a service visit.

•  Vehicle-to-Everything (V2X) communication: capture real-time data on traffic, infrastructure, and environmental conditions.

These industry-focused techniques enable companies to obtain data critical for operational and strategic insights.

Data processing and storage

Processing involves cleansing for reliability, normalizing for consistency, and transforming data to meet specific requirements, such as diagnostics or performance analytics. These steps ensure the information is accurate and tailored for its intended use.

The processed information is stored in centralized repositories: data warehouses for structured records (e.g., transactions) and data lakes for semi-structured or unstructured inputs (e.g., raw sensor data or feedback logs). Centralized storage allows quick, flexible access for teams across the organization.

                   Fundamental principles for a modern data platform                
   
    -          Scalability and simplicity:         Easily expandable to accommodate growing data needs.    
   
    -          Flexibility and cost-efficiency:         Adaptable to evolving requirements without high overhead costs.    
   
    -          Real-time decision-making:         Providing immediate access to critical information.    
   
    -          Unified data access:         Breaking down silos for a complete organizational view.          

Data platforms in automotive: Key applications for efficiency and revenue

Many companies recognize the importance of data, but only a few use it effectively to gain meaningful insights about their business and customers. Better use of information can help your company drive more informed decisions about products and operations. Consider this:

-> Is data being used to improve the customer experience in tangible ways?

-> Are your teams focused on creating new solutions, or are they spending too much time preparing and organizing data?

Data platforms serve as the foundation for specific use cases:

Customer services and new revenue opportunities

Data on vehicle usage and driver behavior supports personalized services and drives innovative business models. Examples include:

•     Maintenance reminders    : Platforms analyze usage data to alert drivers about upcoming service needs.

•     Third-party partnerships    : For example, insurers can access driving behavior data through controlled platforms and offer tailored policies like pay-as-you-drive.

•     Infotainment    : Secure data-sharing capabilities allow developers to design custom infotainment systems and other features, creating new revenue opportunities for companies.

Operational efficiency

Let’s look at where else the platforms are used to solve real-world challenges. It’s all about turning raw information into revenue-growing results.

In  predictive maintenance , access to consistent sensor data helps identify patterns, reduce vehicle downtime, prevent unexpected breakdowns, and ensure proactive safety measures.

 Ford’s data platform illustrates how unifying data from over 4,600 sources - including dealership feedback, repair records, and warranty services - can drive new business models. By centralizing diverse inputs, Ford demonstrates the potential for predictive insights to address customer needs and refine operational strategies.

In  supply chain management , integrating data from manufacturing systems and inventory tools supports precise resource allocation and production scheduling.

 Volkswagen 's collaboration with AWS and Siemens on the Industrial Cloud is a clear example of how data platforms optimize these operations. By connecting data from global plants and suppliers, Volkswagen has achieved more precise production scheduling and management.

 Product development benefits from data unification that equips engineers with the visibility they need to resolve performance challenges faster, ensuring continuous improvement in vehicle designs. This integrated approach ensures better collaboration across teams. Aggregated data highlights frequent problems in vehicle components, while customer feedback guides the creation of features aligned with market demands, driving higher-quality outcomes and user satisfaction.

 Fleet management also sees significant improvements through the use of data platforms. Real-time information collected from vehicles allows for improved route planning while reducing fuel consumption and delivery times. Additionally, vehicle usage data helps optimize fleet operations by preventing overuse and extending vehicle lifespans.

Regulatory compliance

Another key advantage of centralizing data is easier compliance with regulations such as GDPR and the EU Data Act. A unified system simplifies managing access, tracking usage, and securely sharing information. It also supports meeting safety and environmental standards by providing quick access to the data required for audits and reporting.

What’s next for automotive data platforms

While some data platforms' capabilities are already in place, the following represent emerging trends and transformational predictions that will define the future:

 AI-powered personalization

Platforms are evolving to deliver even more sophisticated personalization. In the future, they’ll integrate data from multiple sources - vehicles, mobile apps, and smart home devices - to create a unified profile for each driver. This will enable predictive services, like suggesting vehicle configurations for specific trips or dynamically adjusting settings based on the driver’s schedule and habits.

 Connected ecosystems

Future platforms may process data from smart cities, energy grids, and public transport systems, creating a holistic view for better decision-making. For example, they could optimize fleet operations by aligning vehicle usage with real-time energy availability and urban traffic flow predictions, expanding opportunities for sustainability and efficiency.

 Real-time data processing

The next generation of platforms will handle larger volumes of information with greater speed, supporting developments like autonomous systems and advanced simulations. By combining historical data with real-world inputs, they will improve predictive capabilities; for instance, refining AI algorithms for better safety outcomes or optimizing fleet routes to reduce emissions and costs.

 Enhanced cybersecurity

Looking ahead, data platforms will incorporate more advanced security measures, such as decentralized systems like blockchain to safeguard data integrity. They will also provide proactive threat detection, using AI to identify and mitigate risks before breaches occur. This will be critical as vehicles and ecosystems become increasingly connected.

These advancements will not only address current challenges but also redefine how vehicles interact with their environment, improving functionality, safety, and sustainability.

Ready to change your automotive data strategy?

As the industry evolves with connectivity, autonomy, and electrification, the need for dependable and flexible systems grows.

Need a secure, scalable platform designed for automotive requirements?

Whether you're creating one from scratch or improving an existing system, we can help provide solutions that improve operational efficiency and create new revenue opportunities.

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