We believe in a future where Additive Manufacturing is a core element in digitalizing manufacturing and relevant to every manufacturing process.
In times of change AM enables to accelerate adoption. But success requires a wholistic implementation strategy. Based on it’s expertise the AM I Navigator initiative has created a maturity and reference model for AM. On the next sections we have provided wholistic implementation blue prints segmented by 6 application families in 10 dimensions. It also provides transformation insights to advance implementation maturity.
There is no formal AM strategy or governance in place. Inititives are mostly standalone initiatives and lack coordination.
An initial AM strategy is being explored, with sporadic efforts to adopt some AM capabilities. There’s limited coordination and understanding of the potential impacts.
AM is considered o support strategic goals and implemented in selected application families. Dedicated team is set-up to oversee projects and allocate resources effectively.
A clear AM strategy is set with defined goals, timelines, and resources. An additive department is established. AM strategy is set to drive the organizations success.
Additive manufacturing is established as regular production process. AM activities are integrated across departments.Benefits from Additive Manufacturing as speed, freedom of design, resilience are systematically leveraged to create competitive advantage and strategic success.
The organization is a leader in AM and sets industry standards. It’s a key part of the company’s culture. Encouraging ongoing innovation and setting new trends.
Limited structured approach. Initial exploration of AM potential. Decisions based on intuition. No formalized processes for validation or standards.
Initial consideration of AM. Basic use of DfAM and rudimentary part adjustment. Basic validation and approaches towards standards.
Systematic approach to AM. Initial use of DfAM and efforts toward sustainability. Formalized validation and compliance with standards. Consistent documentation.
Extensive use of DfAM, incl. advanced optimization. Well-established sustainability practices. Full compliance with standards and seamless data exchange.
Company-wide integration of AM into PLM with advanced tool landscape. Holistic sustainability approach shaping best practices for industry compliance.
Fully established tool landscape, suitable to perform autonomous design adaptions, seamlessly embedded in IT landscape.
No utilization of AM and the specific materials.
Material selection for AM is based on expertise on traditional manufacturing. No strategic sourcing for materials in place to increase availability.
Sourcing of materials meeting internal standards. The selection of materials is now based on functional requirements of the application.
Ensuring a stable supply chain by suppliers by continuously providing certificates for each material batch. Selection of materials is also considering scaling requirements (e.g. industry certificates, availability).
The long term availability of materials are secured by 2nd sourcing and global supply. Selection of materials are application-centric and considering specifics for markets & business case.
Autonomous decision making for material selection based on requirements and connected to other Dimensions. Sourcing is automated and connected to other data-layers.
Conventional production engineering methodologies without AM considerations.
Beginning to evaluate AM technology and understand its implications. Focus on the printing process and use of standard properties and processes.
Incorporating AM-specific process chains (pre-print, print, post-print) with defined processes and understood influences on part and process quality with PDCA-cycles to develop specific process parameter sets and formalize knowledge. Isolated scope on each process step.
Defining standardized guidelines for the entire value chain and process specifications (steps, parameters, design, materials) to ensure high reproducibility through expert knowledge. Use independent tools for various process engineering stages (Part Check, Parameter Setting, Nesting), with a focus on interdependent process steps.
In-depth understanding of holistic process chain and influences on the process and part quality to reach reproducibility across different lines and locations. Usage of simulations and models to support production engineering with a digital process twin.
Autonomous production engineering for the complete process chain based on digital process twin considering part specifications (automatic, intelligent and connected to updated data streams and capabilities).
None
Majority manual processes. Partly basic automation with semi-automated processes and manual handling processes. Operator is taking decision based on own expertise. Planning without structured approach.
Advanced automation with single disconnected automated process steps. Manual work for handling needed. Operator receives support through local HMI/digital tools. Defined value stream mapping and optimization.
Integrated automation scenario with isolated automation islands covering the majority of process steps. Semi-automated handling throughout process chain. Operator is guided digitally in his work. Holistic production system for AM defined.
Modular full automation with scaled production lines and production cells covering entire process chain from material to product. Adaptive Production System with high standardization and proposed actions for operators. Dynamic optimisation of production system.
Flexible full automation for multiple scenarios connecting production cells. Adaptive Production System with high standardization and automated decision-making based on data. Autonomous optimisation of production system.
Informal, not defined.
Manual work order creation. Defined value stream map. Manual build job creation and scheduling for single process steps. Execution without support of dedicated system tool.
Professional MOM as a standalone system. Mainly manual creation of BOM & BOP per build job. Integrated non-conformance, standardized defect declaration, and certification. No integration between ERP and MOM systems.
Templates of BOM & BOP creation and mapping based on work orders. Vertical integration (OT/IT) including data acquisition and monitoring. Comprehensive tracking and tracing of parts, assets, and workforce. Integration of PLM into tool landscape and defined interfaces.
Mainly automated process and asset creation with flexible allocation and execution based on real time data from shop floor, in a comprehensive and interoperable system. Tracking and integration of 2nd level production streams (i.e. powder management, build plate refurbishment).
Autonomous process and asset creation with intelligent allocation and execution, in a fully vertical and horizontal interoperable E2E system.
None AM quality processes in place.
No structured quality control. Quality outside of industry and application certification requirements.
Part quality in single metrics is assessed and documented after completing production steps. Compliance with required standards and certifications assessed. Repeatability of each process step is assessed.
Product and Process quality planned and inspected for manufacturing process compliant with industry standards.
Proactive analytics for reproducibility across multiple production lines and locations; ensured reproducibility under changing conditions. Quality is directly linked to tracked process and material data.
Preventive actions based on tracked quality, process, and material data are applied autonomously with counter- measures to ensure needed quality and reproducibility. Reproducibility under dynamically changing conditions can be continuously ensured.
None
Maintenance & Service as needed, and event triggered.
Regularly performed maintenance to secure operational deficiency. Enablement of own staff for basic quick fixes.
Maintenance includes calibration and quality testing, with a fixed service framework to ensure high uptime. Data is leveraged to reduce downtime, and trained staff perform extended internal maintenance.
Maintenance based on KPIs for process quality. Continuous calibration procedures ensure high process and output quality. Guaranteed uptime through service with early detection systems for downtime.
Maintenance & Service actions are initiated autonomously based on various KPIs (Quality, Machine Health, Capacity). Automatically scheduled and performed with in-depth digital assessment upfront.
No structured AM activities in place; no efforts towards sustainability.
Exploring AM with initial initiatives; minimal regulatory compliance for sustainability; no strategic significance.
Implemented resource efficiency measures; basic recycling and minimal energy reduction efforts; initial lifecycle assessments.
Comprehensive resource optimization strategies; significant reduction in material, energy, and water usage; advanced recycling practices.
Fully integrated sustainability practices; continuous monitoring and improvement; real-time energy tracking and adaptive material use strategies.
Autonomous systems optimizing production for minimal environmental impact; closed-loop systems ensuring zero waste; full lifecycle management.
Employees have limited awareness and understanding of AM concepts. Minimal training and no structured efforts to develop skills.
Relevant employees are introduced to the fundamentals of AM with basic training programs.
Competence development becomes more targeted with specialized training in specific dimensions of AM.
Employees become proficient in using AM, with internal training programs becoming more valuable.
AM process chain is integrated part of organizations‘ training programs, all relevant employees are trained right-time in a structured way. It lives a culture of self-driven learning and embrazes to challenge existing processes and organizational setup.
Organization and employees are known as thought leaders in AM, contributing to innovations and advancements in the field, lifting their company to higher levels of business dynamic and success.