Musk’s Manufacturing Algorithm Mirrors Engineering Digitalization

Author considers the rules through the lens of PLM strategies and engineering transformation.

Author considers the rules through the lens of PLM strategies and engineering transformation.

No book received as much publicity in 2023 as the new biography of Elon Musk by Walter Isaacson, released in September. The former editor of Time magazine has previously focused on such titans of science and technology as Isaac Newton, Albert Einstein, Alan Turing, Leonardo da Vinci and Steve Jobs. Now he turns his reporter instincts on today’s billionaire, who is a whirling dervish of innovation.

In one passage of the Musk biography, Musk shares “the algorithm”—a set of rules he religiously follows as foundational guidance. “I became a broken record on the algorithm,” Musk is quoted as saying. “But I think it’s helpful to say it to an annoying degree.”

As explained by Musk in the biography, the algorithm has five steps:

  • Step 1: Question every requirement. “Each [requirement] should come with the name of the person who made it. You should never accept that a requirement came from a department, such as the “legal department” or the “safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is.”
  • Step 2: Delete any part or process you can. “You may have to add them back later. In fact, if you do not end up adding back at least 10 percent of them, then you didn’t delete enough.”
  • Step 3: Simplify and optimize. “This should come after step two. A common mistake is to simplify and optimize a part or process that should not exist.”
  • Step 4: Accelerate cycle time. “Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted.”
  • Step 5: Automate. “[Automate] should come last. The big mistake in Nevada and at Fremont [manufacturing sites] was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs shaken out.”

Smells like PLM

It is not a stretch to consider these five steps as a concise summary of what product lifecycle management (PLM) and digital transformation experts have been trying to tell us for years. Let’s take another look at each step through the lens of engineering transformation and PLM strategies/tools.

Step 1: Question Every Requirement
Engineering Transformation Equivalent: Design Analysis

In manufacturing engineering, questioning every requirement is similar to critically evaluating design specifications. Engineers should question the “why” behind each design element before digitizing it, as a cultural imperative for the team. Musk’s insight of ‘find the person, not the department’ is a brilliant extension, moving beyond the software to humanize the process. Of course, good contemporary change management software can help, too, and ensures transparency in design decisions.

Step 2: Delete Any Part or Process You Can
Engineering Transformation Equivalents: Lean engineering; design for additive manufacturing; digital twin

Applying the concept of lean manufacturing to engineering for manufacturing means rigorously eliminating unnecessary design elements or processes that don’t add value. A modular design approach can help, making it easier to add or remove specific parts and processes. Designing as if the part were headed for additive manufacturing (AM)—even if unlikely—could lead to insights about part consolidation. Using digital twins for prototyping allows the team to virtually delete elements to assess impact before trying to create a physical prototype.

Step 3: Simplify and Optimize
Engineering Transformation Equivalents: FEA; Agile Methodology

Agile puts an emphasis on continuous improvement and simplicity; finite element analysis (FEA) provides insight into complexity. Together, they create design synergy. FEA can be performed during each Agile sprint to validate changes and improvements. Engineers are then able to make data-driven decisions more quickly, resulting in increased optimization and simplified designs in a shorter time frame.

Such processes can also lead to more collaborative decision-making. Agile encourages cross-functional teams working together. This means more frequent touchpoints among designers, engineers and shop floor personnel to find more ways to simplify and optimize.

Step 4: Accelerate Cycle Time
Digital Transformation Equivalents: Design for additive manufacturing; operational agility

Digital transformation in manufacturing often includes an increased use of AM. While AM can drastically reduce the cycle time from design to physical product, it comes with unique challenges. Do you create injection molds using 3D printing, or 3D print the final part? Is the reduction in physical waste more valuable than the upfront cost of 3D printing?

Broad operational agility strategies have to be compared to the nitty-gritty details of time versus cost, and the value of increased design freedom.

Step 5: Automate
Engineering Transformation Equivalent: Automation scalability; computer-aided manufacturing

Musk warns that automation must be the last step in the process, not the first. Only when the previous steps are rigorously applied and every unnecessary process is eliminated does it make sense to automate the remaining steps.

Automation in manufacturing engineering can be accomplished through computer-aided manufacturing (CAM) processes to automate machining processes based on the digital design. Today’s CAM software tools not only provide insight into the actual machining but offer ways to streamline the entire process.

In the context of Elon Musk’s five-step algorithm, CAM software comes into play particularly at the Automate stage, but its principles of streamlining and optimization echo throughout all the steps. Ideally, CAM is the technological layer that enables automation after the requirements have been questioned, parts and processes deleted or optimized, and cycle times accelerated. It becomes the tool of choice to finally automate a system that is lean, efficient and fully optimized.

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About the Author

Randall  Newton's avatar
Randall Newton

Randall S. Newton is principal analyst at Consilia Vektor, covering engineering technology. He has been part of the computer graphics industry in a variety of roles since 1985.

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