Automotive’s New “ICE” Age: From Combustion to Chips

In the new world, vehicles will be differentiated not by stylish sheet metal or powerful engines but by advanced driver assistance systems, infotainment, telematics, LiDAR sensors, image-recognition systems, 5G communications and electric powertrains.


For 100 years, the automotive industry has been refining its processes around the Internal Combustion Engine. The historic pace of change for OEMs and suppliers will seem like a Sunday stroll compared to the simultaneous surge of disruptions it is facing beyond the current pandemic. A recent McKinsey study estimated that by 2030, 100% of all new vehicles will be connected, 50% of passenger vehicles will be autonomous, 30% of all miles driven will be shared and electric vehicles (EVs) will secure greater than 30% of the market. These innovations are summarized as C.A.S.E. – Connected, Autonomous, Shared Mobility and Electric – which together are turning cars into supercomputers on wheels. The impact is profound. The good news is that the overall size of the automotive market is increasing from 3.6T to 7.2T, a tsunami of change representing an opportunity as well as a threat. 

In the new world, vehicles will be differentiated not by stylish sheet metal or powerful engines but by advanced driver assistance systems (ADAS), infotainment, telematics, LiDAR sensors, image-recognition systems, 5G communications and electric powertrains. CASE depends on ingesting increasing amounts of data that needs to be processed at ever faster speeds. For example, aftermarket OEMs can leverage data like vehicle usage, telematics, wear and tear, diagnostics and miles driven from connected vehicles to create a feedback loop which make proactive recommendations on parts and services.

These capabilities are enabled by semiconductors and electronics, which will effectively usher in a new ICE age of automotive (Internal “Chips” Engine). In this paradigm, the hard lines between industry sub-verticals such as automotive, industrial, hi-tech and consumer electronics will blur as the sectors gravitate toward each other in a phenomenon called "Industry Morphing." A clear conclusion is that the new automotive winners will operate as technology companies, integrating best practices from both the high-tech and consumer industries. Early evidence of the effect of this convergence can be seen from the recent chip shortage that has plagued OEM assembly lines.

Supply chain risk mitigation

The auto industry is a complex ecosystem of dealers, OEMs, multiple tiers of suppliers, third-party logistics providers (3PLs) and electronic manufacturing services. Any disruption in one node, including a Tier 2 or Tier 3 semiconductor wafer provider, can send a bullwhip effect through the entire network. The chip shortage has led to dramatic impacts. IHS Markit anticipates 672,000 fewer vehicles will be produced in the first quarter of 2021. Therefore, it is important to be able to identify risks and deviations not just at the node level, but also across the entire network, as a single kink in the supply chain during any part of the process can have a tremendous ripple effect across production.

If this was not enough, the pre-existing strategic disruptions, including Brexit, the Coronavirus disease (COVID-19) pandemic and the Suez Canal crisis have catalysed manufacturers' actions into reworking top-level network design. It is not surprising that building agility and resilience into supply chain design has become a top priority. Measures include reconfiguring global and regional supply chain flows, sourcing critical components from local suppliers, flexible supplier contracts and conducting trade-offs according to cost, service and risk analysis. 

Scenario planning

Amidst such uncertainty, complexity and volatility, auto manufacturers should develop a range of “what-if” simulations and contingency plans based on demand realization and supply disruption scenarios. Access to agile and accurate artificial intelligence (AI)-powered scenario modelling enables organizations to understand the impact on service levels, cash flow, profit and loss and balance sheet.

For example, automating sales and operations planning (S&OP) processes allow companies to move from sequential, static and siloed processes to an integrated, collaborative, financial-driven process that looks at trade-offs, risks and what-if scenarios. During continued uncertainty around demand volume/mix and supply disruption, companies should be running several scenarios every week based on different economic recovery outlooks. 

Amidst the sheer pace of change combined with complexity, leading manufacturers are introducing artificial intelligence techniques to sense changes in demand patterns sooner and then to autonomously correct in real-time. Boundaryless scenario planning means integrating the long, medium-term and real-time perspectives to create an always-on, digital twin of the business that is a holistic representation of the logistic and financial state of the business. The benefit is that the company gets ahead of disruption and opportunity to optimize the outcome for customers and business results.

End-to-end supply chain visibility and agility

In the corporate world, agility is often described as the ability to be responsive to change. The digital world adds an important caveat. It is no longer enough to react quickly after an event. Damage limitation needs to be replaced by system-wide optimization. To usher in this capability, a digital control tower scans the ecosystem beyond the traditional walls of the business to provide AI-powered visibility and real-time reaction. For example, capacity and forecast collaboration must be extended beyond Tier 1 manufacturers out to Tier 2 and Tier 3 suppliers of semiconductor wafers. In the event of a missed part delivery, the control tower can weigh such options as expediting a replacement from the Tier 1 supplier or shipping from an alternate source. It can evaluate the speed of different carriers and route to minimize any negative impact caused by the disruption, thereby enabling the most cost-efficient outcome, improving customer service levels and increasing revenue and margin. The result is a more agile and resilient supply chain.

Aftermarket omnichannel fulfillment

In a C.A.S.E. world, service level requirements will vary greatly across channels, including dealers, distributors, e-commerce and fleet. For example, the e-commerce channel expects speed, responsiveness, convenience, real-time inventory visibility, different fulfillment options and the ability to track shipments throughout the lifecycle of the order. Expectations of clients of the service-oriented, shared vehicle market will be quite different from those still purchasing traditionally. This means orchestrating personalized and even experience-aware supply chains across a complex structure of products, assemblies, parts families and SKUs. Again, AI and automation offer an answer by capitalizing on machine learning (ML) to recognize the digital signatures of natural and emerging micro-segments across complex product hierarchies. Crucially, the capability goes on to dynamically optimize service and business outcomes across all segments. This dynamic segmentation capability can enable automotive manufacturers to analyze data at scale and autonomously optimize their supply chain for segment specific profitability goals. Outcomes can include increased service levels, reduced response times, and increased forecast accuracy along with reduced inventory days.


The three Cs of COVID-19, C.A.S.E. and Convergence along with the chip shortage have demonstrated that the pace of disruption and evolution is, if anything, accelerating. The increased complexity and velocity of the market means that existing techniques no longer scale. So, it is no surprise that auto manufacturers are looking to AI and ML for solutions. After all, the sector has been at the forefront of this technology for in-car features such as lane control and autonomous driving. Clearly, the introduction of predictive, prescriptive, and autonomous technology is also the future of end-to-end supply chains as ecosystem-level “predict-and-pivot” capabilities become the new competitive advantage. 

The future supply chain will be designed to adapt from first principles. It is an intelligent self-learning system designed to deliver the optimum outcomes for businesses and customers, ready to predict and adapt to whatever the future may bring.