A response to CIMdata’s PLM Road Map & PDT North America 2023.
These days, every PLM conference refers to digital thread connectivity across digital twins as an essential enabler to digital transformations. However, most analyst and marketing messages address challenges in implementing these connections. Connecting the dots across data, processes and systems implies effective cross-functional stakeholder engagement. When it comes to digital transformation, it starts with aligning the organization towards realistic, yet ambitious, vision and goals.
Case in point, Peter Bilello, president and CEO of CIMdata, recently reported on digital twin lessons for engineers from the 2023 PLM Road Map & PDT North America event. Based on Bilello’s summarization of the event, I have come up with the following five forecasts:
- MBSE and AI will drive future digital thread investments, though the transition to “model-based” is not straightforward as OEMs are not always able to engineer the required models due to product data complexity.
- Poor technical interoperability, the lack of openness and limited standards alignment between solution editors are recurring challenges.
- Digital thread and data integration decisions must also be value-driven; hence the importance to define end-to-end key performance indicators (KPIs).
- Digital simplification and clean data orchestration are pre-requisites to building effective digital threads.
- Developing and maintaining a local digital-capable workforce is critical to support reshoring ambitions.
In this post, I discuss these points in the context of driving value from digital threads.
Where to Start When Building Digital Threads?
Managing the product lifecycle means developing and introducing compliant and sustainable products to market and driving customer adoption and satisfaction, in a cost-effective way. Building the digital thread is part of the lingo, often used when referring to data integration, in a value-driven way.
“The value of the digital thread lies in the myriad of data links that feed and validate decision-making. This includes hundreds, thousands or perhaps millions of information nodes and data repositories that involve numerous systems and the processes they enable,” wrote Bilello.
In my opinion, however, describing digital threads as complex data interdependencies is not enough. Beyond the technical architecture of such data interfaces and integration layers, the idea of building the right digital threads must also be justified—based on business benefit, ROI, supportability and more. It is not just about connecting everything to everything.
Defining a minimum viable product (MVP) scope for integration is an integral part of strategic digital transformation. It is about minimizing process integration complexity to enable master data alignment between business functions, across authoring and consuming platforms. Driving value from digital threads implies a combination of four elements:
- Data-driven architecture: is there a clearly identified enterprise ecosystem of solutions to manage the product lifecycle as well as author and consume data sources?
- End-to-end data maturity and metrics: how do data linkages support the product lifecycle, and how to best link information to make it actionable?
- Intelligent system engineering: beyond data integration, how are product development feedback loops implemented to drive continuous improvement?
- Effective collaboration: how to combine data analytics at source and cross-functional data consumption through enterprise data lakes to drive effective decision-making?
Now, let’s connect this to the five perspectives extracted from Bilello’s article.
1. MBSE and AI
Model-based system engineering (MBSE) assumes the definition and refinement of relevant models to drive business value and ongoing change management traceability. The same can be assumed from what might one day be called model-based digital threads, when looking at connecting or integrating multiple data sources, and therefore multiple models. The role of AI and ML is certainly expected to grow, and is already very relevant in some industries such as consumer goods or pharmaceutical sectors. AI is especially useful in identifying new data patterns, assessing new consumer experiences or building new simulation models.
AI also has the potential to bring automation, process and data mining to the next level, from removing barriers to entry for organizations, to creating new business models and managing or reducing complexity.
2. Technical Interoperability Standards
From a technical standpoint, achieving interoperability between digital platforms can be complex, as there may not be straightforward mappings between default datasets and processes. Technology standards play a vital role in establishing digital threads, although they are only a facilitator. Modern enterprise software editors offer APIs, connectors and integration solutions using low code or no code approaches to incorporate relevant business intelligence and custom applications across various data sources.
There are several integration platforms available to bring together these data linkages and custom business rules, allowing enterprise applications to work seamlessly—including iPaaS and similar solutions. These applications provide significant business value by supporting cross-functional use cases and ensuring smooth technical implementation. They contribute to building trusted data analytics, while promoting user adoption and adherence to established processes.
3. Value-driven Threads
Broadly speaking, business value comes from data consistency and the ability to adjust, reuse and scale. Beyond trusted data analytics, this includes trusted data at the source where it is authored and consumed. The concept revolves around integrating various digital assets, such as data, software applications and analytics, in a cohesive manner to create a seamless flow of information and insights.
By leveraging value-driven digital threads, organizations can achieve several benefits, including improved operational efficiency, enhanced product quality, reduced time-to-market, better customer experiences and increased competitive advantage. The focus is on utilizing digital technologies and data analytics to extract valuable insights, enable informed decision-making and create measurable business impact.
Practically speaking, this ranges across numerous use cases which reflect data-driven decision-making and closed feedback loops, for example:
- End-to-end product development iterations. By integrating PLM data from product design, manufacturing simulation and customer feedback, organizations can improve collaboration, optimize product iterations, identify quality issues early, provide proactive corrective actions and effective compliance reporting.
- Supply chain optimization. By integrating data from suppliers, logistics partners and internal systems, organizations can optimize inventory management, streamline logistics and enhance demand forecasting, leading to improved operational efficiency, reduced costs and minimized disruptions.
- Customer experience enhancement. By integrating customer data from various channels, such as e-commerce platforms, social media and customer relationship management (CRM) systems, businesses can gain a holistic view of their customers through targeted marketing campaigns, tailored product recommendations and improved customer support, ultimately enhancing customer satisfaction and loyalty.
- Predictive maintenance and asset management. By connecting equipment sensors, maintenance records and analytics platforms, organizations can use real-time data analysis and predictive models enable proactive maintenance scheduling, minimizing unplanned downtime and reducing maintenance costs.
4. Digital Simplification
Effective digital threads involve the strategic integration of enterprise platforms like PLM, ERP, CRM, MES and others to simplify and optimize business operations. It entails leveraging digital technologies across the ecosystem to streamline processes, eliminate complexity and enable seamless data flow across different functional areas.
It is not about bringing all data in a single platform, but rather defining clear separation of concern and modular integration—for example, plug and play interfaces based on business use cases. Digital simplification might mean different things across the enterprise, or perhaps be implemented in different ways based on business scope. For example:
- Integrating PLM with ERP provides real-time visibility into product development costs, inventory requirements and procurement needs, ensuring accurate planning and resource allocation.
- Integrating MES with ERP links production data with resource planning, inventory management and financial systems. This streamlines production planning, material tracking and cost management, ensuring efficient operations and accurate financial reporting.
- Integrating CRM with ERP allows for seamless order processing, inventory management and financial tracking, ensuring smooth customer experiences from purchase to delivery.
5. Digital-capable Talents
Re-shoring can be challenging as investing in new technologies and building new skills takes time. Talent shortages can also be amplified with emerging technologies as it takes time to align with academia. Beyond technical knowledge, there are also challenges in building business expertise to translate functional use cases in technical terms. Operational- and digital-capable talents always come together when driving digital transformation.
Arguably, low code/no code solutions are meant to bridge the technical talent gaps by empowering more and more business SMEs to become “citizen developers” and similar. With the rise of MBSE and AI, new skills and competencies will be required. Getting digital-capable talents is therefore a continuous quest for organizations and policymakers seeking to promote competitive sustainability.
