By Kevin Ettwein and David Prestin
In aerospace engineering, knowledge is one of the most important components in designing and managing complex systems. However, capturing that knowledge can be difficult since it is often undocumented or hard to access. Digital twins provide a solution by creating digital replicas of existing systems that allow engineers to simulate,capture and learn from their production system’s knowledge base. This allows for upstream knowledge management during design stages as well as downstream when implementing an existing product or production system.
Let’s look at how digital twins can help capture and simulate this valuable knowledge.
You can capture, simulate, and learn from the undocumented knowledge within the production system. It can be done upstream during design stages as well as in capturing an existing production systems knowledge base. Without digitizing production and product knowledge within a network of interdependencies, you fail to truly capture the knowledge. Knowledge, at its most fundamental form, is connection and insight drawn from interrelationships among information, and topics. Knowledge is also organic and dynamic and updates over time. Following a static plan or schedule is like basing your daily activity as a human on static and never changing knowledge base about the world. That’s not likely to help you survive or thrive, so why do we do this as a regular practice in today's A&D Production Systems?
A funny but simple example of how this works is talking about hotdogs and hamburgers. I might get hungry as I type this but here we go.
Hotdogs are orderly, easy, repeatable. Hamburgers are complicated, messy, and can be really problematic when you don't have all the ingredients you need at the right time. How do we adapt when we're missing an ingredient and still make forward progress BUT not limit ourselves or cause more harm than good, like forcing rework as an example?
Manufacturing Operations Management (MOM) alone do not capture all knowledge as a holistic "thing". They typically are collections of discrete and unconnected elements between ERP data, MES Data, NCM/Quality system data, etc.. Without connecting across these systems with a "graph" or "network" based knowledge base, you're still working with a giant pile of data that humans will always have to translate/synthesize/update to make it actionable across the cross functional teams involved in an A&D production system
This is like creating the basis for your production systems unique "chatGPT" or AI Chat Bot that can help you organize, synthesize, analyze, and report out the best actionable information for total system level outcomes like throughput, cashflow, inventory optimization, and more.
The problem right now is that typical A&D knowledge bases are composed of a tightly knit group of experts that have worked sometimes in the same production environment for decades but otherwise have worked within the same companies for decades. This creates a "social network" if you will that props up the system's ability to produce a complicated product in a complicated environment.
There is also a workforce related element. Due to the retirement waves over the past 10+ years, COVID related workforce changes, and recent labor market limitations and turnover, as well as specifically related to skilled manufacturing technicians, this "undocumented social knowledge base" has unraveled. Other major dynamics causing huge disruptions include the knock on COVID supply chain disruptions as well as geopolitical instabilities resulting from the war in Ukraine. So now we're seeing a perpetual struggle to "get back to the baseline of yesteryear". The struggle, however, will continue without capturing the knowledge in something durable like a Production Digital Twin, you'll be forever chasing a chimera. This perpetual catch up, rework of the knowledge base, increasing quality issues and restarting of the learning curve is costing incredible amounts of money, time, effort and emotional impact to these companies, employees, and customer base. They directly impact the bottom line.
So how do you solve these issues?
Rapid collection and digitization of the production knowledge base in holistic, multi-faceted, analyzable, and actionable digital twins consisting of Dependency Structure Models (DSMs), Intelligent Scheduling Precedence Networks, Network Visualizations and Analytics, and Graph Databases at the core. Along the way we find valuable and actionable insights at this knowledge base and provide laser focused recommendations for improvements in the production system and product architecture. The final outcome is a trained team to maintain and update these digital models as you use them to design, build, maintain and improve your production systems.
In the big picture Collinear Group will support your strategic goals by integrating this digital twin system into your overall business system strategy and architecture, integrating disparate data streams causing gaps in the flow of actionable knowledge, surrounding these with customized applications for your unique environment. Our cross domain expertise, trusted ecosystem of providers, and digital enablement capabilities are key ingredients to accelerate building, adopting, and harvesting value from these digital twin solutions. By partnering together, we'll create an empowered and aligned workforce working together towards your goals and vision for your company.
For more information please contact email@example.com.
ABOUT THE AUTHORS
Kevin Ettwein is a Collinear Group Executive Consultant and for over 25 years served as a senior executive at Boeing, Spirit AeroSystems and Lockheed Martin leading production and quality organizations. Kevin helps aerospace OEM and Tier 1 companies assess and optimize their production system designs and gain a competitive edge.
David Prestin is a Collinear Group senior consultant and for over 20 years has served in industrial and systems engineering leadership positions at aerospace OEMs. David helps aerospace OEM and Tier 1 companies leverage today’s cutting-edge network analytics and technology to streamline complexity out of design, supply chain and manufacturing processes to cut costs and utilize a dynamic workforce more efficiently.