The Problem
Building a car is an incredibly complex process. In average a car has around 30 thousand parts. Thirty thousand parts that have to perfectly fit and work in unison for a car to work and keep us safe while traveling at 70 mph down the highway. Something that has become easy to forget given how well cars work nowadays.
This process gets more complicated when you consider that OEMs (Ford, GM, Tesla) need to build not only one car of ~30K parts, but the problem gets compounded by multiple model variants (modest to more luxurious models), to considering numerous regulations across different markets. Also, OEMs need to manage, audit, and work with their supply chain for building those 30K parts. The companies that produce such parts for OEMs are called Tier-1s. These are a definite set of companies that have been able to comply with the extremely high standards that OEMs also have to meet.
As you can imagine, sourcing 30K parts amongst hundreds of suppliers is an incredibly complex process for OEMs. One would imagine that companies in a $3T Dollar industry ($1T for Suppliers, $2T for OEMs) have sophisticated SW solutions to handle such complexity. The reality, however, is something way more sobering: neither OEM or Tier-1 systems are precisely state-of-the-art.
When an OEM is making a new car, they have to solve a huge engineering problem (they call these Manufacturing Programs ). For example, for Ford’s new F-150 program, Ford has to adapt to what current market trends are: more loading capacity, more range, and more added-in features, thus complicating the design, engineering, and manufacturing of those new thirty thousand parts. Features that aren’t aligned engineering-wise. To solve these engineering problems, OEMs work in direct contact with their supply chains, iterating through different designs until finally, everything fits according to specifications.
In this iterative process, Tier-1s have to make sense of hundreds of documents and CAD models to develop the “recipe” of how to make that part and estimate a price for OEMs. Tier-1s don’t have a specialized software tool to do that. Instead, they push Excel, SAP, and whatever other enterprise software they use to the limit, resulting in a siloed process, prone to costly human errors measured in the millions of dollars.
This process usually takes years and its done with large multidisciplinary teams ranging from engineering to finance. Not having tailor-made software has resulted in several unnecessary levels of complexity. We’ve heard stories where a Tier-1 nearly goes bankrupt because of an error in their quotation process.
Moreover, because Tier-1s usually have to continue production of such part during the next 5 to 6 years, they need to keep track of each program from the RFQ process up until End Of Production. Standard practice amongst Tier-1s is to use different systems to run their organizations:
- Enterprise Resource Planning (ERP)
- Product Lifecycle Management (PLM)
- Manufacturing Execution Systems (MESs)
- Customer Relationship Management (CRM)
However, for the most part, systems used in the industry are legacy systems. Meaning, these are large and complicated software solutions to implement, maintain, and improve. Thus, after their implementation, there is minimal incentive to change them. Given these constraints of such systems, vital Manufacturing Program information ends up being siloed across multiple systems that are nearly impossible for a company to maintain, much less make sense of, or even analyze Program data. Moreover, because programs last for years, natural employee movement around the organization makes it hard to gather anecdotal evidence from Manufacturing Programs.
This problem does not only occur on the supply; it also happens on the demand side of the equation. We’ve validated this by talking to two different OEMs. One has a team of 700 Engineers to design four different vehicle-sub-systems, and 500 purchasing employees to coordinate the purchasing efforts. This transaction between OEMs and Tier-1s has an extremely high cost for companies’ resources and time in an industry that is urgently looking for operational efficiencies.
This problem brings us to the opportunity.
Opportunity
We see the necessary conditions to build an Automotive Supply Marketplace for the Trillion Dollar Auto-Supply Industry. According to Platform Thinking, the conditions required to make one are:
- Ability to lower the transactional cost with technology
- A high number of suppliers and industry fragmentation
- Implicit unattended networks
We believe that the automotive supply industry beautifully meets these conditions. The transactional cost of the industry can definitely be lowered with technology. There are hundreds of Tier-1 Suppliers and the largest one, with $47B in sales, has 6% of the Top 100 Global Tier-1s. Lastly, each one of these companies is a massive node with thousands of potential users. The top ten Global suppliers have on average ~140k employees each.
Another complication Platform Businesses have historically needed to solve, is the Chicken or Egg Problem. Meaning that Platform companies have had the need to subsidize one side of the Platform to kickstart it, i.e., Uber paid full salaries to Drivers on their Platform to meet demand. Another historical Platform trend has been that usually, these markets are supply-side constrained.
The beauty of the Auto Industry right now, is that you can monetize the supply side of the platform by solving a huge problem for Tier-1 suppliers all while capturing that side of the market. Therefore making it possible to help speed up the transaction for both the Supply and Demand Sides.
Solution
So where do you start this super long road towards building this Platform? At the very beginning of the transaction and where the pain is felt most acutely: the RFQ process for Tier-1 Suppliers. We will start with collaborative quotation tool, that can capture the design and manufacturing “recipe” of a vehicle part that serves as the benchmark to measure against during the entire duration of Manufacturing Program. More importantly, by gathering data about the program in a single system, we’ll be able to provide Tier-1s with the analytical capabilities to apply machine learning and other state-of-the-art technologies for optimizing thus unlocking previously undetected operational efficiencies.
Why does this matter?
We hypothesize that by speeding up and giving more transparency of information and decision making to Tier-1s, we will eventually be able to provide more flexibility and speed to the current manufacturing processes while diminishing the capital expenses needed for a Manufacturing Program. If proven right, such changes will unleash unprecedented changes to the auto industry. With more flexibility, fewer capital expenditures, and more information to make better decisions, Auto-suppliers could have faster turn around cycles in between Programs giving Auto-makers the opportunity to update and change current models more often, ultimately benefiting the end-consumer.
We’re aware of both the technical and execution challenges ahead of us, but it is precisely that enormity of the problem that has us excited! So if you’re interested in joining this crazy adventure, jump on-board! Get in touch.
