Concept Generation and Selection Methods

Understanding Concept Generation in Engineering Design

In the competitive landscape of product development, the ability to generate innovative concepts and systematically select the most promising solutions separates successful engineering projects from costly failures. For manufacturers and engineering firms across Atlantic Canada, mastering concept generation and selection methods is essential for bringing products to market that meet both technical requirements and commercial objectives.

Concept generation represents the creative phase of the engineering design process where teams explore the solution space, generating multiple potential approaches to address identified customer needs and engineering specifications. This phase typically occurs after problem definition and before detailed design, serving as a critical bridge that transforms abstract requirements into tangible design directions.

Research indicates that decisions made during the concept phase determine approximately 70-80% of a product's final cost, despite representing only 5-7% of total development expenditure. For Nova Scotia's growing manufacturing sector, which contributed over $2.5 billion to the provincial economy in 2022, optimising this early-stage decision-making process offers significant competitive advantages.

Systematic Approaches to Concept Generation

Effective concept generation requires structured methodologies that balance creative exploration with engineering discipline. The most successful engineering teams employ multiple complementary techniques to ensure comprehensive coverage of the solution space.

Functional Decomposition

Functional decomposition involves breaking down complex design problems into smaller, more manageable sub-functions. This approach allows engineering teams to address each function independently before integrating solutions into complete concepts. A typical product might decompose into 15-30 sub-functions, each requiring its own solution exploration.

For example, when developing a new marine equipment housing for the Maritime fishing industry, the overall function "protect electronics in harsh marine environment" might decompose into sub-functions including:

• Seal against water ingress (IP67 or IP68 rating requirements)

• Dissipate internal heat (managing 50-150 watts of thermal load)

• Resist corrosion from salt spray exposure

• Absorb mechanical shock and vibration

• Provide electromagnetic interference shielding

• Enable field serviceability in remote locations

Morphological Analysis

Morphological analysis, developed by Swiss astronomer Fritz Zwicky, creates a structured matrix combining solutions for each sub-function identified through functional decomposition. This technique systematically generates concept variants by combining different solution elements across functional categories.

A morphological chart for a typical product development project might contain 6-10 functions with 3-5 solution options each, theoretically yielding thousands of possible combinations. Engineering teams then apply technical judgement to identify the 10-20 most promising combinations for further evaluation.

Brainstorming and Creative Techniques

While structured methods provide systematic coverage, creative techniques remain valuable for generating breakthrough concepts. Effective brainstorming sessions typically follow established protocols:

• Groups of 4-8 participants with diverse technical backgrounds

• Sessions lasting 45-90 minutes with clear time boundaries

• Deferral of judgement during idea generation phases

• Target of 50-100 ideas per session before filtering

• Building upon and combining ideas from other participants

Additional creative techniques include SCAMPER (Substitute, Combine, Adapt, Modify, Put to other uses, Eliminate, Reverse), biomimicry approaches drawing inspiration from natural systems, and analogical reasoning from solutions in unrelated industries.

Concept Selection Methodologies

Once a portfolio of concepts has been generated, engineering teams must systematically evaluate and select the most promising options for further development. Effective selection methods balance quantitative analysis with engineering judgement while managing the inherent uncertainties of early-stage concepts.

Pugh Concept Selection Matrix

The Pugh matrix, developed by Stuart Pugh at the University of Strathclyde, remains one of the most widely used concept screening tools in engineering practice. This method evaluates concepts against selection criteria using a reference concept (datum) as the baseline for comparison.

Implementation involves the following steps:

• List all concepts as columns in a matrix format

• Define 8-15 selection criteria based on customer needs and technical requirements

• Select a reference concept (often an existing solution or competitor product)

• Rate each concept against each criterion as better (+), same (S), or worse (-) than the datum

• Sum positive and negative scores to identify leading concepts

• Iterate by using the top concept as a new datum or combining strong elements

The Pugh matrix's strength lies in its ability to facilitate structured discussion among cross-functional teams while identifying opportunities to combine the best features of multiple concepts.

Weighted Decision Matrices

For more refined selection, weighted decision matrices assign numerical importance ratings to each criterion and score concepts on a quantitative scale. This approach provides greater discrimination between closely-ranked concepts and creates documented rationale for selection decisions.

Typical implementation uses a 1-5 or 1-10 scoring scale for both criteria weights and concept ratings. The weighted score for each concept equals the sum of (criterion weight × concept rating) across all criteria. Selection matrices with 10-15 criteria and 5-8 concepts generate sufficient analytical depth while remaining manageable for team discussion.

Engineering teams should validate weighted matrix results through sensitivity analysis, examining how selection outcomes change when criterion weights vary by ±20-30%. Robust selections remain stable across reasonable weight variations, while sensitive results indicate need for additional information or criteria refinement.

Analytical Hierarchy Process

The Analytical Hierarchy Process (AHP), developed by Thomas Saaty, provides a mathematically rigorous framework for multi-criteria decision making. AHP uses pairwise comparisons to establish both criteria weights and concept ratings, reducing the cognitive burden of simultaneously comparing multiple options.

AHP requires (n × (n-1))/2 pairwise comparisons for n elements, meaning 10 criteria require 45 comparisons for weight determination alone. While computationally intensive, AHP includes consistency ratio calculations that identify illogical comparison patterns, improving decision quality for high-stakes selections.

Integrating Technical and Economic Evaluation

Concept selection must balance technical performance with economic viability. For product development projects in Nova Scotia's diverse industrial base—spanning ocean technology, aerospace components, and agricultural equipment—this integration is particularly critical given the capital constraints typical of Maritime manufacturers.

Technical Feasibility Assessment

Technical feasibility evaluation examines whether concepts can achieve required performance specifications using available or readily developable technologies. Key assessment dimensions include:

• Technology Readiness Level (TRL): Concepts requiring technologies below TRL 4-5 carry significant development risk

• Manufacturing complexity: Evaluation of required processes, tolerances, and quality control capabilities

• Material availability: Assessment of supply chain accessibility, particularly important for Atlantic Canadian firms

• Regulatory compliance: Early identification of certification requirements (CSA, Transport Canada, Health Canada)

• Integration risk: Complexity of interfaces with existing systems or infrastructure

Preliminary Economic Analysis

Early-stage economic analysis necessarily involves significant uncertainty, with cost estimates typically accurate only within -30% to +50% at the concept stage. Despite this uncertainty, relative economic comparison between concepts provides valuable selection input.

Useful early-stage economic metrics include:

• Estimated bill of materials cost based on analogous products or parametric models

• Manufacturing investment requirements for tooling, equipment, and facilities

• Development timeline and associated engineering labour costs

• Projected unit volumes and price points to establish margin potential

• Lifecycle costs including warranty, service, and end-of-life considerations

Managing Uncertainty in Concept Selection

All concept selection involves decision-making under uncertainty. Effective engineering teams acknowledge this uncertainty explicitly and employ strategies to manage associated risks.

Set-Based Concurrent Engineering

Rather than selecting a single concept prematurely, set-based concurrent engineering maintains multiple promising concepts through early development phases. This approach, pioneered by Toyota, delays convergence until sufficient information exists to make confident selections.

Practical implementation involves carrying 2-3 leading concepts through preliminary design, with planned decision gates where accumulated test data and analysis results enable informed convergence. While this approach increases early-phase investment by 30-50%, it significantly reduces late-stage design changes that can cost 10-100 times more to implement.

Prototyping and Testing

Rapid prototyping technologies have dramatically reduced the cost and time required to generate physical concept representations. For Maritime engineering firms, accessible prototyping options include:

• 3D printing (FDM, SLA, SLS) for form and fit verification

• CNC machining for functional metal prototypes

• Laser cutting and forming for sheet metal concepts

• Bench-scale testing for performance validation

Targeted prototype testing can resolve key uncertainties identified during concept selection, transforming subjective ratings into objective performance data. A well-designed prototype test programme costing $10,000-50,000 can prevent selection errors that would cost hundreds of thousands in late-stage redesign.

Team Dynamics and Decision Governance

Concept selection quality depends significantly on team composition and decision-making processes. Engineering organisations must establish clear governance structures that leverage diverse expertise while avoiding common decision-making pitfalls.

Cross-Functional Team Composition

Effective concept selection teams include representatives from:

• Design engineering (mechanical, electrical, software as applicable)

• Manufacturing engineering and production operations

• Quality assurance and regulatory affairs

• Procurement and supply chain management

• Marketing and product management

• Field service and customer support

Research consistently demonstrates that cross-functional teams generate better concept selections than homogeneous engineering groups, despite requiring more coordination effort. The diverse perspectives identify potential issues earlier and ensure concepts align with downstream constraints.

Avoiding Decision Biases

Engineering teams must guard against cognitive biases that compromise selection quality. Common biases include anchoring on the first concept presented, confirmation bias favouring concepts aligned with preconceptions, and groupthink suppressing dissenting technical opinions.

Mitigation strategies include anonymous initial concept ratings, devil's advocate assignments to critique leading concepts, and structured red team reviews before final selection decisions. Documenting selection rationale creates accountability and enables post-project learning about decision quality.

Implementing Best Practices in Your Organisation

Successfully implementing concept generation and selection methods requires organisational commitment to structured product development processes. Engineering leaders should consider the following implementation priorities:

• Training and capability building: Ensure team members understand and can apply multiple concept generation and selection techniques

• Process standardisation: Develop organisation-specific templates and procedures adapted to your industry and product complexity

• Tool integration: Incorporate selection matrices and decision documentation into existing product development workflows

• Knowledge management: Archive concept selection documentation for future reference and organisational learning

• Continuous improvement: Conduct post-project reviews to refine methods based on selection outcome quality

For engineering firms and manufacturers throughout Nova Scotia and Atlantic Canada, mastering concept generation and selection methods provides a sustainable competitive advantage. These systematic approaches reduce development risk, accelerate time-to-market, and improve the probability of commercial success for new products and systems.

Sangster Engineering Ltd. brings extensive experience in product development and engineering design to organisations across the Maritime provinces. Our team in Amherst, Nova Scotia, helps clients implement effective concept generation and selection processes tailored to their specific industry requirements and organisational capabilities. Whether you are developing new products, improving existing designs, or establishing robust engineering processes, contact Sangster Engineering Ltd. to discuss how our expertise can support your innovation objectives.

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