Benefits of Systematic Part Identification in AM
Imagine this: your manufacturing facility is under pressure to reduce costs, minimize lead times, and stay competitive in an evolving market. Additive manufacturing (AM) could be a solution, but where do you start? How do you identify which parts are best suited for AM? How can you be sure this decision you make will yield results?
What is Systematic Part Identification?
Systematic Part Identification is a data-driven approach that enables manufacturers to pinpoint which components are best suited for on-demand manufacturing. By focusing on the right parts, organizations can optimize costs, improve efficiency, and maximize the value of their resources.
At SelectAM, we help organizations address these very questions. After processing millions of estimates and assisting numerous clients in their AM transformations, we’ve found that success lies in systematic part identification. Here’s how this approach is reshaping manufacturing operations worldwide:
Key Benefits of Systematic Part Identification:
- Strategic cost savings by targeting the right parts
- Data-backed decision-making that goes beyond intuition
- Extended life for legacy equipment
- Consolidated production for simpler workflows
- Innovative designs unrestricted by traditional manufacturing constraints
- Sustainable, eco-friendly manufacturing practices
Strategic Cost Optimization: Looking Beyond Immediate Savings
When manufacturers first explore AM, they often compare costs directly with traditional methods. However, systematic part identification can highlight hidden cost efficiencies too. By pinpointing specific parts that benefit most from AM, companies can unlock significant savings and operational improvements beyond their initial analysis.
An example from the aerospace industry demonstrates this well: while only a small percentage of parts were suitable for AM, these components represented a substantial portion of overall manufacturing costs. By focusing on these high-impact/high-value parts, the company achieved major reductions in processing times and considerable cost savings per project.
Data-backed decision-making that goes beyond intuition
In today’s complex manufacturing landscape, gut feelings are no longer enough. With a solution like ours, customers can access data from over 200 industrial AM systems, enabling them to make highly informed decisions based on robust analysis.
A great example from the automotive sector illustrates this well. By analyzing its entire parts inventory, one manufacturer uncovered opportunities in its aftermarket supply chain, leading to reduced lead times and optimized inventory, ultimately enhancing operations and achieving substantial cost savings on the TCO for their components.
Extended life for legacy equipment
Systematic part identification can be a game-changer for maintaining legacy equipment. In industries like defense and aerospace, finding parts for aging systems can be a challenge, especially if the original manufacturers are no longer in operation. Through systematic part identification, manufacturers can create digital inventories of critical components, enabling on-demand production and extending equipment life while avoiding the costs of premature replacements.
Innovative designs unrestricted by traditional manufacturing constraints
Systematic part identification goes beyond simply identifying components for AM, it reveals opportunities for weight reduction and optimization that can enhance efficiency. By targeting high-impact parts, companies can reduce material use and improve overall performance.
For example, in a recent project for a customer, we identified a component with potential for significant weight reduction. After a thorough redesign, we achieved a 70% weight reduction, resulting in substantial material savings and a boost in productivity. This transformation made the component an ideal use case for on-demand manufacturing, demonstrating how targeted optimization can unlock new levels of efficiency and resourcefulness.
In one instance from the consumer electronics industry, systematic identification led to a redesign of certain components to minimize weight without compromising functionality, resulting in a more efficient and cost-effective product.
Sustainable, eco-friendly manufacturing practices
In an era where environmental responsibility is both a moral imperative and a business advantage, systematic part identification reveals unexpected opportunities for sustainability improvements. By identifying parts suitable for local production, lightweight designs, and on-demand manufacturing, organizations can significantly reduce their environmental footprint.
A recent analysis for a heavy machinery manufacturer revealed that transitioning just 3% of their identified parts to AM could reduce their annual carbon emissions by 15% through localized production and optimized designs.
Making it Work: Your Journey to AM Excellence
At SelectAM, we understand that every organization's path to AM success is unique. That's why we've developed flexible solutions to support your journey:
For those taking their first steps, our Open-Access version provides essential tools and insights to begin identifying AM opportunities. As your needs grow, our AM Launchpad offers expert guidance to accelerate your progress. Also, for organizations ready to maximize their AM potential, our Expert plan delivers comprehensive optimization capabilities.
The Road Ahead
The future of manufacturing belongs to those who can effectively identify and leverage AM opportunities. Our data shows that while typically only 5% of analyzed parts make strong business cases for AM, these parts often represent the difference between market leadership and falling behind.
Think of systematic part identification as your GPS in the AM journey – it not only shows you where you're going but helps you find the most efficient path to get there. The technology is ready. The benefits are clear. The only question is: Are you ready to take the first step?
Contact SelectAM today to discover how our platform can illuminate your path to AM excellence. Let's transform your manufacturing operations together.
SelectAM is a leading provider of on-demand manufacturing optimization solutions, helping businesses streamline their processes through advanced part identification, qualification, and ordering systems. With proven success across industries and a commitment to continuous innovation, we're here to guide your on-demand manufacturing journey every step of the way.
References
- Åhlström, P., et al. (2021). Is lean a theory? Viewpoints and outlook. International Journal of Operations & Production Management, 41(12), 1852-1878.
- Andersen, P. H., et al. (2019). Supplier value creation configurations in high-cost countries. Journal of Global Operations and Strategic Sourcing, 12(3), 429-448.
- Ballardini, R. M., Flores, I. I., & Pei, E. (2018). Printing spare parts through additive manufacturing: Legal and digital business challenges. Journal of Manufacturing Technology Management, 29(6), 958-982.
- Banaś, A., Olejarczyk, M., & Wojtuszewski, R. (2019). Selecting parts for additive manufacturing in aerospace sector: Multi-criteria analysis. 75th Annual Vertical Flight Society Forum and Technology Display, 2360-2366.
- Baumers, M., Dickens, P., Tuck, C., & Hague, R. (2016). The cost of additive manufacturing: Machine productivity, economies of scale and technology-push. Technological Forecasting and Social Change, 102, 193-201.
- Beltagui, A., Rosli, A., & Candi, M. (2020). Exaptation in a digital innovation ecosystem: The disruptive impacts of 3D printing. Research Policy, 49(1), Article 103833.
- Boothroyd, G., Dewhurst, P., & Knight, W. A. (2010). Product design for manufacture and assembly (3rd ed.). CRC Press.
- Bracken, J., Holmström, J., & Partanen, J. (2020). Exploring the adaptation of product architectures and business models enabled by additive manufacturing for spare parts supply. Computers in Industry, 115, Article 103174.
- Carter, W. T., et al. (2014). The GE aircraft engine bracket challenge: An experiment in crowdsourcing for mechanical design concepts. 25th Solid Freeform Fabrication Symposium, 1402-1411.
- Chaudhuri, A., et al. (2021). Selecting spare parts suitable for additive manufacturing: A design science approach. Production Planning & Control, 32(8), 670-687.
- Chaudhuri, A., et al. (2020). Should hospitals invest in customised on-demand 3D printing for surgeries? International Journal of Operations & Production Management, 41(1), 55-62.
- Chekurov, S. (2019). Categorization of design for additive manufacturing concepts. ASME International Mechanical Engineering Congress and Exposition.
- Chen, Z., Wang, L., & Dul, S. (2019). Applying data mining to identify potential aerospace spare parts for additive manufacturing. Robotics and Computer-Integrated Manufacturing, 59, 1-9.
- Coatanéa, E., et al. (2021). Systematic manufacturability evaluation using dimensionless metrics and singular value decomposition: A case study for additive manufacturing. International Journal of Advanced Manufacturing Technology, 115(1-4), 715-731.
- Conner, B. P., et al. (2015). Making sense of 3-D printing: Creating a map of additive manufacturing products and services. Additive Manufacturing, 1-4, 64-76.