In 2009, Ford’s former CEO Alan Mulally made a breakthrough statement about the ongoing evolution in the automotive industry – “We are a car company, but we are learning how to think like an electronics company”. True to his word, manufacturers, dealers, suppliers and even consumers have been expeditiously broadening their horizons and adapting new technology across functions in the life cycle of an automobile. In the paper Connected car, automotive value chain unbound, leading management consulting firm McKinsey & Company notes that ‘Manufacturers and suppliers are looking to reposition themselves with customers as specific strategy patterns are emerging in response to the wake of the connected car’.
As a result, IoT/software enabled automotive have been hitting the road, and features like autonomous driving, faster, over-the-air updates from a central location, predictive maintenance etc., have become a palpable reality. But it does not end there. According to Business Insider’s connected car report, the market is growing at a five-year compound annual growth rate of 45% – 10 times as fast as the overall car market. Therefore, 75% of the estimated 92 million cars shipped globally in 2020 is to be inbuilt with internet-connection hardware. This is as opposed to the current scenario where consumers still rely on a secondary device to connect to the network.
But the future of automotive technology wanes towards the former, as embedded connections will have two essential advantages in store for consumers, dealers and manufacturers.
Firstly, the accumulation/analysis of data from across the life cycle becomes affable.
Secondly, aided by personalization, updates/patches are endeavored faster to individual customers, avoiding recalls in the software or frequent repairs in the vehicle.
Since software features and sensors are making automotive companies data richer by the hour, the need to be insight driven has become absolutely essential. By drawing relevant inputs, one can provide the stakeholders with improved products at all stages as illustrated above.
But most companies (both premium OEMs and automotive suppliers) still struggle with lack of insights, and not to mention the conundrum surrounding security/privacy. Fortunately, we believe we have a way to ‘harmonize’ (if you know what we mean) data extraction, analysis and incorporation in models, in addition to the privacy/personalization that we enable with Azure ML.
With dynamic multi-dimensional analysis, Harman’s ATLAS (Analytics for Total Life-cycle Management of Automotive Systems) analyses multiple data types emerging from all stages and stakeholders, while generating insights that can be incorporated in the modeling, manufacturing, marketing and service repair of the vehicle.
The hallmark feature of ATLAS lies with the CAR model (Analytical Hierarchy Process) which flows down the significance of the various criteria (prioritized by occurrence/severity) through the hierarchy of the car. This ensures effective failure mode analysis and renders useful business insights to do the following:
Reduces component failure and the time to next service, by improving the performance of the car.
Render useful business insights for ERP, MES or warranty cost modelling.
Both of which contributes to the scope of improvisation that can be reaped by all those involved in the process.