Industry
Manufacturing
Future-Ready Manufacturing: Partnering in your journey to a sustainable manufacturing enterprise
Highlights
- Automakers are looking to boost their revenue by upgrading the technology backbone of vehicles, paving the way for new features and value-added services. The biggest challenge in doing this is whether the new features will be worthy enough to be paid for by a customer.
- To pinpoint such features will involve explicit customer feedback, coupled with intelligent sensing by the vehicle. The feedback thus gained will be used to design and deploy new features, at speed and scale.
- Identifying customer needs and turning them into monetizable features quickly can be successfully achieved with software-defined vehicles, enabled by agile enterprise processes, functional structures, and most importantly, people.
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Shifting sands
Automakers are changing their business model to a product and service-based one, moving away from focusing on just selling cars.
Software-defined vehicle (SDV) technologies have made post-sales product upgrades and value enhancement a reality, fast tracking the transition to a service-based business model. However, an automaker’s aspiration to increase digital services revenue over the near and mid-term is challenging. The industry converges on one fundamental question – what digital services to sell? Will the customer pay for them?
First-hand feedback matters
Automakers need to identify the product upgrades and features for which customers will loosen their purse strings.
To this end, they must collect firsthand feedback– through the voice of customers and the voice of the vehicle. SDVs can capture sensory inputs such as vision, sound, vibration, acceleration, pressure, and electric signals from within the vehicle and the surrounding environment.
From the environment around, sound signals are used to ‘see’ vehicle blind spots. The vehicle can ‘hear’ approaching emergency vehicles, bicycles, humans, or animals. Through noise patterns, it can detect weather condition, terrain, and road conditions. For example, Renesas’ Reality AI Automotive SWS solution provides a complete framework for audio based advanced driver assistance system (ADAS) sensing for vehicles.
There are multiple types of sensors inside the vehicle which can sense occupant activities and preferences. It can capture hand movements, use of in-vehicle infotainment (IVI), emotions from tone of voice, occupant detection (front and rear), gesture recognition (breathing, movements), and driver seat position. For example, Continental’s cabin sensing solution comprises driver monitoring, cabin monitoring, thermal comfort motoring, occupancy, child presence, and keeping a tab on other vital signs.
Besides this, there is direct explicit feedback from customers – the driver or other occupants in the vehicle – that mustn’t be ignored. All possible contact channels should be leveraged to collect direct feedback anytime during the ownership cycle. It could be through the customer app, social media platforms, the automaker’s contact center, or even retailer interaction.
Multi-sensor fusion combined with direct customer feedback collected through different channels can provide deep insights on what customers truly value and would be willing to pay for. Feedback thus collected can be classified and analyzed by region or market, frequency of occurrence, scale of impact, functionality (safety, convenience, entertainment, productivity).
Recurring feedback indicates criticality, urgency or market demand, basically, a rationale for why the automotive OEM should invest in the concerned area. The scale of impact will reveal the size of the void, in other words, the size of the opportunity for the automaker. This will also influence any monetization decision around the new feature. Functional classification will help in prioritization, for instance, safety takes precedence over the rest. Artificial intelligence (AI), with some human intervention, will classify feedback from diversified sources and data formats, synthesize it, and finally, funnel the insights into the feature backlog.
Figure 1: Reinventing mobility experience by sensing customer needs
Acting on the insights gained
Technology capabilities, complemented with human knowledge and decision-making capability, will fuel the feature design, development, and roll out.
SDVs provide the fundamental technology backbone to realize the vision of turning first-hand feedback into new vehicle features at scale and speed. The underlying electrical/electronic (E/E) architecture with high performance computing (HPC) offers capabilities to combine functionalities from different domains. Zonal control units are the hubs for cross-domain zonal management of signal, data, and power. They also host critical cross-domain real-time functions. This gives automakers the power and flexibility to deploy unique features which were never possible before. For example, Continental has implemented cross-domain HPC that combines cockpit and multiple ADAS functions like driving safety, automated parking, holistic motion control.
Once the feature is ready, automakers must decide whether it should be monetized or offered as a value-add. AI-enabled data fusion and processing will guide this critical decision. Care should be taken for monetizing safety related features. For instance, if a new ADAS feature helps the vehicle detect cyclists around a blind turn through sound alone (not the camera, radar, or usual ADAS sensors) and then apply emergency brake, should the OEM charge for this feature?
Features might have regional, functional nuances therefore only relevant features should be taken to target customers. Communicating about a recently rolled-out feature and providing the required know-how to use it are critical to ensure seamless adoption of the new feature set. Automakers, their retailers, and other business partners should have a direct communication channel with customers; only digital communication won’t be sufficient.
An important aspect in all of this is the speed to market or the time taken to roll out a new feature or upgrade. After all, what good will a product be if it gets launched long after the customer’s need has died down. Enterprise agility, therefore, lies at the very core of a successful go-to-market strategy. An enterprise that can turn sensory feedback into a new feature quickly, breaking the functional boundaries, will be the winner. A seamless thread across the enterprise connecting people, process, technology will enable this. It might demand organizational redesign, change management, enterprise technology rearchitecting. Ford, for example, went through a series of organizational changes for its Ford+ growth plan to transform the way it operates to give customers an unparalleled experience, while maximizing quality, minimizing costs, and reducing complexity.
Revolutionizing mobility, while driving the ROI
SDVs have the potential to truly revolutionize mobility.
With the advent of SDV, the ownership span of a vehicle is bound to increase due to periodic upgrades over its lifetime. This underscores the importance of service-led revenue. The question is how soon automakers will get there profitably. Many of the new features launched by automakers in the past have failed to make an impact due to incorrect hypothesis and lack of data. A combination of multiple technologies will make it possible to identify the ‘real’ needs of the customers.
All customer touchpoints should be leveraged to qualify the feature roadmap. Channels involving retailers, online or offline car clinics should be leveraged to validate any hypothesis, marketability of the new features, customer’s willingness to pay for upcoming features. Moreover, speed to market is critical not only for topline impact with new digital services revenue, but also for customer retention and attracting new customers. Even as automakers continue to invest heavily in SDV, they will be able to realize the true potential of this technology only if the concerns around the return on investment (ROI) are addressed.
