As industries race to innovate faster and smarter, product complexity has emerged as a defining challenge. From electric vehicles to connected medical devices, today’s products integrate mechanical, electronic and software components, driven by customer expectations for personalization, sustainability and digital connectivity. Managing this multidimensional complexity while maintaining profitability is now a core differentiator for enterprises.

The surge in product complexity is fueled by rapid technological advancements, global supply chains and stringent regulatory and sustainability mandates. Add rising challenges around data privacy, cybersecurity and continuous software updates, and it becomes clear that traditional product development approaches are no longer sufficient.

Product Lifecycle Management (PLM) systems have become the digital backbone for modern enterprises, enabling centralized data management, cross-functional collaboration and regulatory compliance. They connect every stage of the product journey — from conception to retirement — while ensuring visibility, traceability and governance. The global PLM market is projected to reach $13.12 billion by 2030, driven by growing demands for collaborative product design, improved operational efficiency and accelerated digital transformation. PLM systems are foundational across manufacturing, engineering, automotive, aerospace, medical devices, telecom, semiconductor and consumer product industries.

However, despite significant investments, many organizations struggle to fully realize the business value of their PLM systems. Common challenges include fragmented processes, difficulty integrating partners and siloed development cycles, leading to missed requirements and poor collaboration. The absence of a single source of truth further complicates decision-making.

While out-of-the-box (OOTB) PLM functionality exists, it often requires enhancement to meet specific business needs. Low user adoption, data quality issues and poor system integration diminish effectiveness, while misalignment with business goals and inadequate change management reduce the return on PLM initiatives. High implementation and customization costs, along with ongoing maintenance and licensing fees, add financial strain. Inefficient lifecycle processes further reduce operational agility.

Research indicates that most PLM implementations fail to meet their original objectives due to budget overruns, delays or failure to realize anticipated value. Studies indicate that 67% of PLM programs exceed planned budgets and timelines, with an average cost overrun of 32% and delays of approximately four and a half months. This creates a growing “value gap” between PLM investments and the actual returns.

Bridging the PLM value gap

To close this gap, enterprises must reframe PLM from an engineering system to a strategic business platform – one that simultaneously drives efficiency, cost control and innovation enablement.

Below are five strategic levers to maximize value and minimize cost:

1. Streamlining processes for faster time-to-market: PLM process optimization focuses on simplifying workflows, automating repetitive tasks and reusing design knowledge through standardized templates. With KPI-driven governance and test automation frameworks, organizations can accelerate time-to-market while improving quality and collaboration. We partnered with a leading Japanese automotive OEM to optimize processes using value stream mapping, achieving a 10–15% productivity improvement and a 20% reduction in redundant applications.

2. Driving cost efficiency through license optimization: Enterprises often overspend on unused or underutilized PLM licenses. Usage audits, role-based access control and usage-based licensing models can drive significant cost reductions. For a European automotive major, our PLM license optimization initiative resulted in a 40% annual cost reduction by aligning license allocations with actual usage.

3. Resource optimization: As PLM workloads evolve, efficient data and infrastructure management become crucial. Archiving obsolete data, tiered storage based on access frequency and cloud readiness assessments reduce costs while improving performance. For a leading European retail enterprise, we facilitated PLM migration from a vendor-hosted, customer-managed environment to AWS, reducing infrastructure and maintenance costs by up to 30%.

4. PLM integrations and data transfer: Seamless integration across PLM, ERP, MES and CRM eliminates data silos and enhances collaboration. Event-driven architectures and API-based frameworks ensure faster data transfer, greater transparency and reduced manual effort. 

5. Leveraging advanced technologies for future readiness: AI/ML, digital twins and digital threads are transforming the PLM value chain. These capabilities enable predictive insights, automated validations and real-time traceability across the lifecycle. According to Gartner, by 2028, 80% of digital threads will originate from PLM.

A structured framework for PLM optimization

A successful PLM optimization journey requires a structured, phased roadmap to ensure measurable impact across operations, costs and innovation.

At HCLTech, we recommend a three-phase framework:

Phase 1: Assessment

• Current state analysis: Evaluate existing infrastructure and processes
• Total cost of ownership (TCO) recalculation: Map all direct and indirect costs
• Value stream mapping: Identify bottlenecks and redundancies in existing PLM-driven processes
• Strategic alignment: Define clear business objectives, such as 10% reduction in time-to-market or a 15% reduction in operational costs

Phase 2: Implementation

• Infrastructure and performance tuning: Manage hardware, databases, virtualization and FinOps for cloud-based PLM
• Lean process integration and automation: Automate repetitive tasks across the PLM workflow
• Vendor management and licensing: Conduct usage audits, implement role-based access controls and explore usage-based licensing models to reduce costs
• Enterprise software integration: PLM integration with ERP, MES and other critical systems to eliminate data silos and manual data transfers
• Data classification and governance: Define policies for data classification (public, restricted) to determine on-prem versus cloud storage based on compliance and performance needs

Phase 3: Continuous improvement

• Key performance indicators (KPIs): Define and continuously monitor KPIs related to PLM performance, such as design cycle time, ECO lead time, user adoption and various cost heads, including cost per user, infrastructure cost per product line
• Feedback mechanisms: Establish regular loops with users, IT and leadership to identify new optimization opportunities
• Leverage emerging technologies: Continuously evaluate how new technologies like AI/ML/GenAI, IoT, AR/VR can be leveraged within the existing systems

By adopting this strategic framework, organizations can navigate the complexities of PLM optimization, ensuring their existing systems continue to deliver maximum business value while maintaining cost efficiency in an ever-evolving technological landscape.

Reimagining PLM as a strategic growth enabler

Our PLM practice helps clients maximize ROI from existing systems through its Discover–Define–Build–Operate methodology, integrating program management, governance and organizational change management. Proprietary accelerators, AI/GenAI-enabled frameworks and automation toolkits bridge the gap between technology and execution.

By aligning PLM with business strategy, enterprises can shift from system-centric optimization to outcome-driven transformation, delivering measurable gains in productivity, cost efficiency and innovation velocity.

As products become smarter and expectations rise, PLM must evolve from an engineering tool into a strategic business enabler. Enterprises that modernize and optimize PLM systems today will achieve operational excellence and unlock new revenue opportunities through faster innovation and sustainable growth. 

Learn more about HCLTech’s engineering services and solutions.