Earlier this year, Annie Wang and Zach Simkin, Co-Presidents of SENVOL, a services firm that provides analytics to the additive manufacturing industry, presented a case study at RAPID, Long Beach, California, titled Selecting the Most Cost-Effective Location for Additive Manufacturing (AM) Production.
SENVOL prepared the case study for the Government of Canada. However, the case study is relevant to any business entity with interest in setting up AM production in-house, establishing a service bureau or purchasing parts from service bureaus. Annie Wang commented at RAPID that it is very important for businesses to understand the most cost-effective location for AM production, as it can provide a competitive edge.
AMazing is pleased to present some notable highlights and video from SENVOL’s presentation at RAPID.
Case Study: Selecting the Most Cost-Effective Location for Additive Manufacturing (AM) Production
Case Study: Scenario and Parameter Selection
The scenario and parameter selection for the cost study involved additively manufacturing a specific part, using a specific machine and material, at five locations around the world including: Canada (Mississauga, Ontario), United States (Dayton, OH), United Kingdom (Warton, Lancashire), Mexico (Tijuana) and China (Beijing).
Case Study: AM and Non-AM Cost Variables (Inputs)
The case study involved a number AM and non-AM cost variables:
- Non-AM cost variables included energy costs, real estate costs, labor costs, shipping costs and foreign exchange rates
- AM cost variables included machine costs, other equipment costs, material costs, ancillary equipment costs, annual services costs, processing costs, consumable costs, post-processing costs
SENVOL utilizes a proprietary algorithm to evaluate variable cost contributions and develop ‘what if’ scenarios. For this case study, SENVOL performed the initial analysis and then re-evaluated their findings by varying energy costs at the different locations.
Case Study: Total Cost per Part Summary (Outputs)
For this specific case, a 14.2% cost differential was realized between additively manufacturing a part in Mississauga, Ontario, with a mean of $6,552 per part, versus additive manufacturing the same part in Tijuana, Mexico, with a mean of $5,738 per part. As stated by Zach Simkin, “When we talk about lean manufacturing, a 14% cost differential is quite substantial.”
Case Study: Reasons for the Cost Differential
The notable variable cost contributions that affected the overall total cost per part value were real estate, post-processing, energy and labor. (See Total Rent/part, Total Cost of Heat-Treatment & HIP/part, Total AM Energy Cost/part and Total Labor Cost/part in the graph below)
It is important to note, this specific case study only focused on direct costs associated with producing the part and not the cost of delivering the part to the customer.
Case Study: Summary
- In the base scenario analyzed, the AM parts cannot be cost-effectively manufactured in Mississauga, ON, Canada compared to Tijuana, Mexico and Beijing, China.
- Mean energy and real estate costs are the lowest in the Canada location, but the mean labor costs are the highest in Canada.
- Generally speaking, the Canada location is the most expensive production option because its high labor costs outweigh its cost-advantage on energy and real estate costs.
- If the AM process (including post-processing) were to use 8.1x more total energy, then Mississauga, ON, Canada would have the lowest total cost per part.
- If the AM process (including post-processing) were to require 90% fewer total labor hours, then Mississauga, ON, Canada would have the lowest total cost per part.
Case Study: Key Takeaways
- Relative costs change over time
- It is important to track all of the variables and continually re-analyze
- Direct cost is just one of many factors that manufacturers consider when choosing where to manufacture. Other factors include:
- Stability of the energy grid
- Availability of trained AM machine operators
- Ability to protect IP
- Availability of equipment required for post-processing
- Location of customers
- What Senvol presented is a framework that any company can use to analyze and determine the most cost-effective location for AM production
- This framework is not just for comparing AM costs across different countries, but can also be used to compare AM production costs across different cities within the same country
SENVOL at RAPID 2015
Video courtesy of SENVOL
About Zach Simkin
Zach is passionate about business strategy and educating business leaders on additive manufacturing. He is published in top industry journals, has presented at numerous conferences, and has written papers and business cases on how companies can incorporate additive manufacturing into their long term business strategy.
Zach is also a member of the ASTM International F42 Committee on Additive Manufacturing Technologies. Prior to co-founding Senvol, Zach was the Director of Corporate Development at HYSO, a manufacturer of public health and hygiene products. Zach received a BA in Economics from the University of Pennsylvania, where he graduated Summa Cum Laude, and an MBA in Operations & Information Management from the Wharton Business School.
About Annie Wang
Annie’s particular area of expertise is supply chain process engineering and financial analysis for the additive manufacturing (i.e. 3D printing) industry. She helps companies understand how specific choices along the product lifecycle (e.g. design, manufacturing, supply chain) impact the company’s bottom line.
Annie is a member of the ASTM International F42 Committee on Additive Manufacturing Technologies. Prior to co-founding Senvol, Annie worked for an operations consulting firm where she assisted clients in the manufacturing, finance, telecom, and healthcare industries. Annie received a BA in Chemistry from the University of Pennsylvania, where she graduated Cum Laude, and an MBA from the Wharton Business School and an MA in International Studies from The Lauder Institute.
Senvol is a services firm that conducts analytics exclusively for the additive manufacturing industry.Senvol has worked with a variety of Fortune 500 companies and government agencies in industries such as aerospace, oil & gas, consumer products, and automotive. Senvol also provides the Senvol Database, the industry’s first and only free, searchable database for additive manufacturing machines & materials.
About Senvol Database
In January 2015, SENVOL launched the first searchable 3D printing database for industrial additive manufacturing machines and materials. SENVOLS’s free, online database contains over 350 machines and 500 materials, and is searchable by over 30 fields including machine build size, material type, and mechanical properties. www.senvol.com/database
To learn more about Senvol, visit http://www.senvol.com, or follow @Senvol on Twitter. For media inquiries, please contact: info(at)senvol(dot)com.
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