The future of mobility is nigh upon us, and all signs on the shop floor point towards fully autonomous production. During this seismic transition, automotive manufacturing is becoming ever more complex and sophisticated alongside whole-scale digital transformation of multiple auto-related sectors. More than ever, automakers and their tier-one suppliers need to identify opportunities for profit in a fresh and intricate ecosystem of associated industries. Competing in new-era automotive manufacturing, therefore, requires bold, strategic vision. 

The North Star here is Deep Learning with prescriptive analytics. Why? Vehicular specifications, materials, power trains, and capabilities are evolving quickly and constantly. In the face of a radically shifting production paradigm, parameters circumscribing production will continue to proliferate, and data will be the only constant. Deep Learning with prescriptive analytics is the best and most sophisticated data-wielding tool. As such, it is the perfect vehicle to lead automotive manufacturers (whether tier-one suppliers or OEMs) towards an imminent new era of electrification, driving autonomy, and connectivity. 


Manufacturing Lighthouses have created a playbook of sorts for forward-looking legacy industrials and disruptive newcomers endeavoring to gain traction for the ride into manufacturing’s future. In practice, bold manufacturing innovation combines a proactive mindset shift with a clear, company-wide strategy. Core to this thinking is a holistic appreciation of how the paroxysms of our industrial moment—in the global economy and the environment (plus rolling technological quantum leaps) are impacting and will impact a given industry.

However, each digital transformation journey is factory (and vertical) dependent. This contextual differentiation means that any end-to-end, integrated production solutions must account for the following constraints:

  • the company’s level of maturity (in terms of merging operational and information technologies)
  • the unique production dynamics of the vertical and its various product lines.


As we stream towards manufacturing’s new era, advanced industrials contend with a destabilized global supply chain, unprecedented technological uptake & acceleration, and an international climate emergency.

Strategically, industry leaders must weigh the relative importance of rapid advancements in production materials and processing techniques and figure how they apply to current market trends and projected future realities.

They also feel mounting pressure to digitize business models thoroughly and leverage factory data with advanced analytics—to optimize lines, shorten lead times, and better meet changing customer expectations. 

From a sustainability perspective, heavy industry is simultaneously resolving to level up energy efficiency and adhere to more stringent environmental footprint metrics. What is more, industrials must manage all of these assignments in the working context of a plant with daily deadlines and KPIs.

The domain of Cost of Goods Sold (COGS) contains the most immediate potential ROI for the tech-enabled manufacturing of any modern factory. In other words, industrials will reap their most significant digital transformation gains by up-leveling the actual making of components or equipment (along with their delivery to market).


With increasingly tighter margins and the upper limit of statistical process control for advanced manufacturing reached—leveraging data for value is fundamental to surviving a disrupted, complex, and increasingly competitive landscape.

With this in mind, industry leaders should not underestimate the importance of Deep Learning prescriptions generated by advanced AI. In particular, unsupervised Deep Learning delivered by AI-as-a-Service brings significant improvement gains for fleets of production lines in any complex manufacturing operation.

Leading-edge AI deployments that use prescriptive analytics scale game-changing optimization for any advanced manufacturing process (no matter how novel or complex). Here, data can drive competitive advantage in any advanced industry ahead of production loss, equipment breakdown, or otherwise abnormal behavior. 


And yet, despite these impressive capabilities, Thilo Koslowski (Digital transformation advisor, founder, and former CEO of Porsche Digital) says this application of prescriptive analytics is just the beachhead of AI-for-manufacturing’s potential, which extends far beyond sporadic process optimization.

Koslowski believes that manufacturers need to think far more strategically about the capacity of A in the long term:

“Next-level production process capabilities will ultimately innovate holistically against the uncertainty of the supply-chain and capacity challenges. Different business units across an automaker will synthesize their individual dynamic data insights through the use of AI in manufacturing,” Koslowski stated.

Indeed, industrials might find that keeping pace with our era of unprecedented innovation is merely a matter of survival. And with so many balls in the air, visionary navigation for manufacturing’s key decision-makers is a matter of pragmatism. An example germane to the production challenges of these times is the massive, global transition to e-mobility, as Koslowski explains:

“Electric vehicles will play an increasing role in automobile production. But because of this, traditional car manufacturers are facing significant production capacity challenges. What is called for are not marginal operational improvements and incremental efficiencies, but actually reimagining how a process can be optimized at a deeper level and what that implies for the future of manufacturing.”

Fortunately, help is at hand for advanced manufacturers in all verticals—at any stage of the transition towards more autonomous production.

Years of automotive manufacturing experience at the leading edge have convinced Koslowski that Smart Manufacturing process innovations like prescriptive analytics are an underused yet critical foundation for manufacturers who want to lead in the digital era.

“Combining data insights with process automation will ultimately create a new technology paradigm: Autonomous manufacturing intelligence. Using AI-based capabilities will empower factories to leverage their production data for new performance optimization levels. The evolution of this self-optimizing manufacturing intelligence is particularly relevant to the automotive industry”, Koslowski explained. 

Factories will need to ramp up production as quickly as possible to meet demand spikes and satisfy evolving customer requirements.

As Koslowski put it:

“Ultimately, manufacturing will become autonomous. Using Deep Learning ushers in the new vision of manufacturing where technology will optimize and automate processes to achieve the highest levels of efficiency and optimization. Companies that use this approach will master agility to be able to react to changing market dynamics while reducing cost and improving production yields.”

Artificial intelligence is now the only tool powerful and adept enough to shift the needle on any advanced, multi-step process. It transforms inconceivable quantities of actual and historical industrial data into significant value. What is more, it does so timeously enough for today’s industrials to compete for the production paradigm of tomorrow.