Everywhere analytics in the automotive industry | Deloitte US | Deloitte Analytics has been added to your bookmarks.
Everywhere analytics in the auto industry
Short takes on automotive analytics
A blog by Ashwin Patil, director and global manufacturing analytics lead, Deloitte Consulting LLP
A number of forces, including advancements in vehicle connectivity, self-driving cars, shifting consumer preferences, and more are having a significant impact on automotive original equipment manufacturers (OEMs) and suppliers. And the digital exhaust emanating from vehicles, infrastructure, consumers, and the world around them is resulting in an evolution of data in the industry that is driving a revolution in how auto companies do business. This includes how they position and market their products, interact with suppliers within the supply chain ecosystem, and ensure ongoing product quality and consumer safety.
Several technological advancements are facilitating this evolution and inspiring automotive companies to get serious about analytics. Automakers, OEMs and suppliers alike, are now able to process increasing volumes and varieties data and take advantage of specific methods for leveraging unstructured data. These advances have contributed to the rise of insight-driven organizations (IDOs) in the automotive industry.
For IDOs, the broad-yet-focused application of analytics can inform a range of processes and decisions. In an IDO, analytics insights are replicated and multiplied across decision points and functions to spawn performance and competitive improvements. Common analytics touch points in automotive IDOs include the following:
Marketing: Know thy market
Customer segmentation: Analysis of consumer and behavioral data–including attributes of motivating factors, interests, risk factors, likes, dislikes, and propensities–can inform targeted, individualized offers and incentives. These insights also help automakers offer differentiated product offerings and value propositions for each customer segment.
Marketing mix analytics: Automotive companies can apply analytics to support the creation of direct campaigns. The insights generated help marketing decision makers identify and measure incentive programs that drive profitable growth. Analytics capabilities also give OEMs and their dealers a deeper understanding of markets and customers, enabling them to reduce fixed and variable marketing costs with marketing spend analytics.
Customer retention: The application of analytics can help OEMs identify targeted communications based on call center, warranty, and sales data to protect against defection and increase customer retention. Tying in strategy planning analytics helps companies leverage price elasticity, geo mapping and simulation methodologies to drive retention and optimize profit and revenue.
Supply chain: Embed resiliency
Supply chain optimization: The analysis of supply chain data can reveal potential flaws throughout the automotive supply chain ecosystem so that timely countermeasures can be put into place quickly.
Supplier management: Applying new techniques to ever-expanding data sets, analytics helps automakers improve visibility outside the organization.
Visibility tracking: Automotive companies can leverage analytics for better tracking of open and closed product issues, investigations, and process performance. These insights drive supply chain efficiencies by bringing to light 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.
Quality and safety: Proactive problem-solving
Proactive sensing: Analytics helps identify quality and safety concerns quickly and effectively. Risk-ranked scores and alerts can better prioritize case management workload. Predictive analytics helps identify potential faults in advance, providing opportunities for counteractive repairs. For example, forecast models can help to identify the likelihood of a product recall within a specific timeframe and within certain customer demographics.
Recall Readiness: Analysis of historical data and other parameters can alert manufacturers of potential product failures before they impact production. Analytics can drive improvements in key areas, including organization and governance, data management, and recall visibility and tracking, to better prepare and protect against future recalls.
An insight-driven model for the automotive industry that embeds analytics within multiple processes and functions can not only extend the value of analytics investments for automotive companies, but also deliver insights that can create more responsive, agile, and competitive organizations.