During my 17-year career in manufacturing, I’ve experienced many technology adoption journeys, giving me a front row seat to the good, the bad, and the ugly. Each of them came with their own interesting technical and business lessons, but they all had one common denominator: People. I am often asked, “Why is Additive Manufacturing (AM) not being adopted faster?” My simple answer is that we (the people) have a trust issue. To which a critic might say, “Trust? That seems like an emotional response. I thought AM adoption was all about the data?”
Firm belief in the reliability, truth, ability, or strength of someone or something.
Well, now look at that! We use some of those same words to describe the qualification process. Building trust in AM is a multi-faceted challenge that involves both data AND people. Let’s dive a little deeper into 5 key factors for building trust in AM.
1) Develop a 2-way Business Case: To build a resilient supply chain, everyone must benefit. AM adoption is not easy, so both parties need to have a compelling reason to participate. There is a challenging chicken and egg situation here, as a strong supply chain takes time and investment; many OEMs won’t adopt technology that is from a single source supplier. However, it is unfair for OEMs and Tier 1 suppliers to ask their supply chain to build a field of dreams in case they possibly want to source AM parts in the future.
A good example for building a 2-way business case is Neighborhood 91, an AM production campus at Pittsburgh International Airport, where the Allegheny County Airport Authority, along with the Commonwealth of Pennsylvania, has invested in infrastructure. Tenants are attracted based on a 2-way business case, with the tenant achieving cost benefits owing to recycled Argon, a dedicated powder storage facility, high quality power, and co-location of the supply chain; the speed of trust is increased as trading partners are just a walk away, which reduces complexity and speeds conflict resolution. The neighborhood benefits with the creation of the backbone of the AM supply chain with suppliers of materials, printing, heat treatment, post-processing, and testing and inspection, all of whom solidify future success for the campus.
2) Be Honest About Your Capabilities: It is imperative for suppliers to be truthful about their capabilities when speaking with a customer or investor. I’ve seen so many bad examples of over selling a technology that ends poorly. In some cases, early adoption failures gave the AM technology a black eye; the risks were not adequately communicated, and leaders decided, “AM does not work!” At The Barnes Global Advisors, we advocate for a crawl, walk, run approach starting with applications with lower criticality and building the skills and knowledge to later take on more rigorous product requirements, as showcased in our TBGA AM Maturity Model (Figure 1). While equipment builders are making great strides in robustness, many users have experienced new equipment that is not industrially ready (e.g. a machine nicknamed “Sparky”) or has an immature maintenance network. On the investor front, millions are lost when a company fails to meet projected revenue targets.
Figure 1: The TBGA AM Maturity Model charts progressing skills and knowledge to increasing product requirements and greater business risk and reward.
3) Protect the Data – This category has both cyber security and intellectual property (IP) implications, with the key questions: Who owns what, and how is it protected? One hotly contested area for AM IP is build file ownership. The build file contains data on part orientation, stacking, and support structures, with approaches that are often unique to the printing supplier. Therefore, it is crucial to nail down IP ownership early in the contracting process. Additionally, AM is a type of digital advanced manufacturing making it more connected and available, so AM users are inherently exposed to greater cyber security risk. Recently, I heard of a supplier of both AM and traditional manufacturing who was shut down with a $1M ransom from cyber attackers. As this risk grows, stronger protection mechanisms are necessary.
4) Understand the Qualification Path – Data takes center stage as we ask, “What performance, stability, repeatability, robustness, producibility, and material performance is required to meet the final application requirements?” Secondarily, what is your role in the process? For printing suppliers, have you sufficiently characterized the material and process to support your customer’s journey? For equipment suppliers, is your equipment robust and process repeatable? In-process defect detection is a key advancement that enables earlier knowledge and decisions regarding the quality of a build. Standards are progressing that simplify the qualification, and eventual certification, process with the creation of a common foundation.
5) Know the Requirements – As always, requirements are king. It is critical to look at the end product requirements and account for the full value chain from material down to final quality inspection and testing (Figure 2). Furthermore, what are the business requirements for adoption? Are we optimizing for schedule, scope, or cost? One unfortunate example is a group who was laser focused on the printing requirements and built a business case based on cost reduction. When final application requirements were considered, an expensive computed tomography scan was necessary for serial production; the business case was completely blown.
Figure 2: The AM value chain from feedstock production through to final product testing.
Both data and people play a critical role in building trust in AM. For AM adoption, the biggest challenge is often not the technology; physics already knows the answer. The most important factor is getting the people focused on asking the right question of physics. Additionally, building trust does not mean never failing. In fact, AM is a great vehicle to “fail” fast, which speeds up the learning cycle. Instead of churning hour upon hour of analysis, multiple designs can be built and tested to generate performance data and quickly iterate designs. Building trust means all parties bring data and represent their capabilities honestly. This way everyone accurately conveys the level of risk and reward with their partners. It harkens the Reagan Cold War era concept of, “Trust, but verify”.
You can have faith in people, but trust in AM is built on a foundation of data.