The automotive sector has witnessed a sea change over the last decade, disrupting the conventional ecosystem of automotive players. Car connectivity growth is predicted by Gartner to reach one billion in 2020, which is expected to have some major impact on product features like safety, infotainment, mobility and vehicle management that will enable new revenue streams and business models for automotive OEMs.
Another major change has come across the manufacturing plants where every piece of information is getting tracked with all the sensors and high-tech devices placed around. With a few clicks of a button, the entire value chain can be informed about the inventory status & sourcing needs whereas the shop floor has been made more efficient with predictive maintenance and downtime schedules.
But all these technological advancements are fuelling the newer set of challenges where buying channels and consumer buying behaviour has grown complicated. They need an extra push in the form of offers, discounts or add-on accessories which leads to depleting margins. It is been furthered by the industry shifting towards a global supply chain, so auto manufacturers are exposed to not only local but global competitors as well.
This hostile environment requires you to leverage all the resources available at your disposal and for that nothing is more crucial than the vast amount of big data in the form of structured data stored across your ERPs, CRMs and unstructured data from sensors and digital space.
This pool of data will ultimately power disruptive technologies, creating multiple possibilities for the players across the value chain.
Predictive & Advanced Analytics: Product Quality, Recall & Customer Satisfaction
The quality management team has to look out for a whole gamut of aspects before and after any launch ranging from customer satisfaction, regulatory requirements to cost control. A growing number for automakers are deploying advanced analytics solutions for quicker and proactive responses to such events.
With predictive analytics, the quality management teams can process a large set of information (historical) to reveal the underlying cause. It allows for the early detection of issues and minimises the probability of its occurrence in the future.
For example, a transmission system may be performing below the expected level, indicating the need for early repair work (and consequently refuting the need for a costly replacement job).
As these automobiles move towards a smarter and connected environment, the aggregated information is going to increase at a rapid pace. With big data analytics on structured & unstructured data, a more satisfying user experience can be delivered. It includes listening and analysing the customers’ data from their social media profiles with relevant tags, call centers queries and parts sales data from the ERP to act well in advance.
Product recall is a commonplace menace for the automotive industry that forecasting tools and predictive analysis are actively combating to mitigate risks of product recalls. All this data can be used to find patterns and resolve quality issues either in the nick of time or prevent them from happening altogether. This leads to both customer satisfaction and quality management at a cost-effective level.
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Automobiles are moving towards global production networks. With variability in automobile models, there is a lot of uncertainty in the demand from auto manufacturers. It requires better visibility and analysis for investments as well as operation and to avoid the risk of inventory stock-outs.
The advancements in supply chain analytics models transform the decision-making from reactive to proactive. It allows the management team to get a real-time snapshot of the inventory stock. The primary supply chain use cases of analytics can be categorized as:
Supply chain optimization: A comprehensive system for supply chain analytics can reveal potential flaws throughout the automotive supply chain ecosystem so that measures can be taken proactively to safeguard.
Supplier management: Applying new techniques to an ever-expanding data set, analytics helps auto-manufactures improve visibility outside the organization. The modern supply chain management is taking a more collaborative approach, wherein the inventory information is shared with a partner like suppliers, procurement, operations, sales teams. Hence making them prepared to sense and act quickly.
Visibility tracking: Automotive companies can leverage analytics for better tracking of resolved and unresolved product issues, investigations, and performance. These insights can help drive supply chain efficiencies by highlighting issues related to shared suppliers, parts, and technologies.
Governance and oversight: Analytics insights inform the ownership of supply chain governance and oversight responsibilities, improve communications and reporting among stakeholders, and create more resilient supply chains.
Streamlining customer information and using it to your advantage is key for any business and the automotive industry is no exception to this. Customers undergo thorough research before making the final buying decision. It generates a huge volume of information the automakers can leverage to both understand the competition and trends that are driving the industry.
This information is generated from all categories of sources which makes it difficult to collate and interpret the available information. Using big data analytics, sales and marketing teams can understand the levers that have worked in the past and what situation. Once done correctly, automakers can enhance the customer engagement and interaction with their brand through more targeted, controlled and informed sales and marketing initiatives.
With the use of software and sensors in automobiles, the automakers can accurately identify new purchase opportunity by considering factors like the number of repairs on the current vehicle, current mileage, age of the vehicle and information culled from social media to identify likely buyers.
Cars contain about 50 or more sensors accumulating data like speed, emissions, fuel consumption, and security. Leading automotive players are using predictive analytics and collaborate with the government to predict and identify high traffic congestion zones based on data collected from automobiles for town planning and building smart cities.
Telematics is enabling automotive manufacturers, sellers, and insurers to gather information from GPS, satellites, and cell phone data and usage patterns. OEMs (Original Equipment Manufacturers) collect their customers’ data even after a sale is complete.
The data generated by sensor-driven cars about driving behaviour, speed, braking habits, turning styles, acceleration, and abidance with the traffic rules in a country is then used to create a driver profile. Insurance companies vary premiums basis the driver profiles.
There are several moving parts across a plant in the form of goods from suppliers, inventories, semi-finished and finished goods, cost of labors and machinery involved on the shop floor. The information of the cost associated with each is spread across files, logs and support systems.
A financial analytics system can help decision-makers to understand the full picture of their company’s costs and revenues beyond accounting information. These systems can easily connect to any number of cloud or on-premise sources and databases, combine tables from different sources and deliver answers to any ad-hoc questions quickly. The governance teams can set permissions at the database, dashboard and user level, ensuring every user is only exposed to the figures that they require.
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Modern automobiles manufacturers are tracking their products and machinery using RFID, or radio-frequency identification tags proximity and heat sensors. From the above use cases, we can see that it becomes pivotal to harness the data from the supply chain of the automotive industry to unlock benefits like increased margins, lesser downtime risks and a lean supply chain. Good automotive analytics practice can go a long way in attaining a sustainable competitive advantage.
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