Data continuity across the value chain: the right information, where and when needed

It's really hard to design products by focus groups. A lot of times, people don't know what they want until you show it to them.
Steve Jobs
Smart everything

Developing appealing new products and successfully launching them to market relies on the expertise of specialists who use the appropriate development tools and processes. New product introduction (NPI) necessitates a blend of innovation, efficient business operations, and technical and financial change management traceability.

Enabling new product development (NPD) involves disciplined and goal-oriented planning that aims to converge towards commercialization and predictable revenue expectations. This includes increasing certainty, analysis, and documentation as the product release date approaches.

Xlifecycle Ltd offers digital continuity and business transformation consulting services that help organizations optimize their digital strategies in order to:

  • ADAPT to change

    By discovering and aligning requirements from new technologies, products and market attributes, customer expectations, to sustain competitive advantage while continuously managing total cost of ownership.

  • ADOPT digital continuity solutions

    By aligning people, data, process and technology across the business change roadmap, managing complexity and cross-functional stakeholders, defining critical success factors, from RFQ definition to project service transition, including vendor & partner delivery and relationship management.

  • TRANSFORM operating models

    By defining fit-for-purpose operating practices and new organizational structures to maximize value from digitization and IT modernization, aligning skills and talents to education requirements to enable effective deployment and operational transition to minimize time-to-value realization.

Successful digital transformation requires more than just implementing technology; it involves aligning the business across multiple perspectives, from people to data, processes, tools, and technologies. Unless there is a cohesive approach across all pillars, little or no transformation will occur.

Cultural alignment is vital for digital transformation to succeed, so the first concern should be getting people on board. Prioritizing business transformation initiatives requires continuous capability development, digital roadmaps, and a robust understanding of how much change the business can handle within a given period of time.

Towards smart everything: from product engineering to operations and Industry 4.0

The fast-moving nature of technological advancements presents both opportunities and challenges for businesses operating in various sectors. Today, companies are relying more and more on technology to differentiate their products, services, user experiences, and operations, in order to achieve efficiency and effectiveness across their entire lifecycle, whether it be for a product, application, asset performance, talent, technology, data, process, material intelligence, or other domains.

These elements go through different lifecycle maturity stages and exist concurrently, with their evolution occurring in iterative and interlinked loops.

Lifecycle of things

A digital transformation happens when a business leverages digital technology to alter its operations, particularly in how it interacts with customers and how value is created by employees and the broader supply chain. It involves business transformation, rather than solely IT transformation.

To achieve a successful digital transformation, a company must challenge the current state of affairs and try out novel methods of working, new operating models, and new technologies. Such transformations are specific to each organization, depending on cultural and contextual factors, as well as industry and organizational maturity.

A universal definition of digital maturity that applies to all contexts can be difficult to establish. It's challenging to compare one organization's level of digital maturity with another's because it's heavily influenced by various factors, such as culture, context, industry, and organizational maturity. Thus, companies need to define their own digital maturity levels based on their unique characteristics and circumstances.


    Is this a technical upgrade, a business transformation, or both?


    Are the teams engaged and committed to the change?


    Who will benefit from the change and how will value be realized?


    Are priorities aligned across the business and support functions?


    How to track and measure progress with business owners and change leads?


    Is the organization ready to operate effectively once the change is implemented?


    What are the next steps in the transformation roadmap?


    What are the lessons learned to feed into next improvement steps?


    How is the business run, what is the operational governance, is it effective?


    How to transition business changes to the new operating model?

To do this, it's crucial to communicate the digital strategy effectively and extensively within the organization, especially to the leadership team. The language used in the communication should be relatable and understandable to the current leadership team. This is important because the leadership team's buy-in and support are critical in driving the digital transformation efforts forward.

Without their understanding and endorsement, it will be difficult to obtain the necessary resources and commitment to make significant progress in the digital journey. Therefore, the leadership team should be part of the digital transformation process and not just spectators. They need to be involved in shaping the strategy, setting the direction, and guiding the organization towards the desired digital future.

In today's business landscape, organizations across industries face similar pressures such as maintaining profit margins, staying ahead of the competition, meeting customer expectations, ensuring sustainable performance, adhering to compliance regulations, and keeping up with ever-evolving technological requirements. These pressures drive the need for digital transformations.

With the proliferation of new technologies, organizations now have the opportunity to create and align new business models that improve their operations, from the value chain within their boundaries to the value system that extends beyond their boundaries. However, it is crucial to understand why companies seek digital transformation, the context in which they operate, and how they formulate and implement their transformation strategies.

The value chain encompasses various activities within the organization's boundaries, including inbound logistics, operations, outbound logistics, marketing and sales, and services. In contrast, the value system extends to suppliers upstream and to channels and end-customers downstream. By considering both the value chain and the value system, organizations can identify areas where they can leverage digital technologies to create value for their customers and drive innovation within their industry.


Is this a technical upgrade, new capability or business transformation, or both?


How is change embraced? What is the training and communication culture? What are the anticipated hindrances? What mitigations are required?


Are the relevant teams and sponsors engaged and committed to the change?


How will the change be implemented? Who will drive what? Who are the supporters and detractors? How to influence and manage expectations?


Who will be affected by the change, how will value be realized and when?


How will continuous benefit realization be tracked? How can value be maximized across the enterprise? What is the time to value creation and delivery velocity?


How are priorities aligned across the core business and support functions?


How is the scope controlled across technical and business dependencies? How well is the deployment plan and benefit realization managed?


How to track and measure progress with business owners and change leads?


What are the CSFs and KPIs to define and track progress towards successful change? Who are the business owners, data custodians, and champions?


Is the organization ready to operate effectively once the change is rolled out?


Who are the early adopters? How will the change agents operate? What decisions will be required when? How will production impacts be handled?


What are the next steps in the change and digitalization combined roadmap?


Does the deployment strategy require refinement? Have business priorities changed? Which capability and / or business function will be deployed next?


What are the lessons learned to feed into next change or improvement steps?


What worked well and not so well? Was the organization ready and able to adapt? What is likely to happened next time, and are risks mitigated and how?


How is the business run, what is the operational governance, is it effective?


Once new solution elements are implemented, how are business benefits adjusted? How will subsequent continuous improvement actions be tracked?


How to effectively drive business changes per the new operating model?


How to integrate the new platform with existing infrastructure and processes? How users be trained and equipped to use the new solution effectively?

One way to enable organizations to successfully navigate the complex process of digital transformation is through the use of frameworks like the one described above. These frameworks can provide a structured approach to defining a new operating model that takes into account the significant changes that come with transformation, both within the organization and in the broader external environment.

By following a clear roadmap and set of guidelines, organizations can work towards their digital transformation goals with greater confidence and efficiency. This can include implementing new technologies, streamlining processes, and building new capabilities that enable them to better compete in their industry and meet the evolving needs of their customers.


Digital Thread: a path to innovation, connecting talents and Digital Twins

Bills of Materials (BOMs) play an important role in connecting data across the product lifecycle and the extended enterprise, spanning various functional capabilities such as product innovation, enterprise execution, and asset performance value chains. While BOMs serve multiple purposes across the value chains, they often contain a lot of common information that flows from upstream activities. 


By following the product data lifecycle and leveraging BOMs, organizations can better connect data and streamline their operations across various functional areas.To achieve end-to-end visibility and control over the product lifecycle, organizations need to establish a Digital Thread that links all relevant information and data across different business functions, products, and their respective lifecycles.

The Digital Thread involves connecting data across functional silos and enabling business collaboration through various enterprise platforms, including Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), Material Resource Planning (MRP), Manufacturing Execution System (MES), Customer Relationship Management (CRM), Supply Chain Management (SCM), Asset Lifecycle Management (ALM), and other IT/OT systems.

By connecting all these systems, organizations can gain a holistic view of their product lifecycle, from concept to retirement, and ensure that all relevant stakeholders have access to the same accurate and up-to-date information. This enables them to make informed decisions, improve collaboration, reduce errors and delays, and ultimately, improve their overall business performance.

To successfully deliver value from Digital Twins and other digital assets, a holistic approach is necessary that includes business change, lean operations, pragmatic delivery, and continuous improvement. This approach should be implemented throughout the entire lifecycle of the digital asset, from the strategic initiative stage to business-as-usual maintenance and support.

Robust Business Change

Robust change business case leadership and ongoing maintenance throughout benefit realization

Lean Operations

Lean and effective implementation governance, data and people focused deployment; stakeholder management

Pragmatic Delivery

Technical delivery management; vendor and third party performance coordination; operations and PMO

Continuous Improvement

Continuous client requirement alignment with leadership of solution architecture and technical deployment

A digital twin is a conceptual model representing an equivalent digital replica of a physical object or future asset, driving innovation, streamlined developments and operations throughout their lifecycle.
Adapted from Dr Michael Grieves (2011)

Digital twin technology: why and how it matters?

The transformation towards digitalization can bring significant benefits to organizations, but requires a shift in mindset, processes, and technology. This change must be carried out in a lean and pragmatic manner, with a focus on delivering value to the customer while minimizing waste and maximizing efficiency.

In addition, it is essential to continuously improve upon the digital asset, including the Digital Twin, through regular maintenance, support, and upgrades. This approach ensures that the digital asset remains relevant and continues to provide value to the organization over its entire lifecycle.


Digital Twins are virtual representations of physical objects or systems that coexist throughout the product development and operation phases. These models can take many forms, from CAD and CAE to non-geometric data and mathematical models. Additionally, they can be used for a variety of purposes, such as product concept, industrialization, and operations, and can even include predictive twins that model future states and behaviors.

As the technology evolves, Digital Twins are becoming increasingly important in the context of the Internet of Things, Artificial Intelligence, and data analytics. By incorporating data from multiple sources, digital platforms can optimize operational performance, facilitate collaboration, support scenario planning, and enable effective simulations.

However, realizing the full potential of Digital Twins requires a combination of business change, lean operations, pragmatic delivery, and continuous improvement. This means transitioning from a strategic initiative to business-as-usual maintenance and support, and continually refining and enhancing the models over time.

Effective data


In order to align initial virtual twins with carry-over product strategies, concepts are designed using virtual models based on product and platform, such as cost models, market and technical attributes, CAD carry-over, make or buy strategy, technology benchmarking, virtual product definition, cost base, and virtual build requirements.


The alignment of initial virtual twins with carry-over product strategies involves designing concepts with virtual models based on product and platform, including cost models, concept BOM, market and technical attributes, CAD carry-over, make or buy strategy, technology benchmarking, virtual product definition, cost base, and virtual build requirements.


To provide continuous service through in-field operational digital twins, connected products are optimized for shelf life extension, embedded software is monitored and updated on-air, asset performance is optimized using machine learning, and IOT, big data, and AI are leveraged to extend the product lifecycle.

During the operations phase, digital twins leverage the Internet of Things (IoT), product and infrastructure connectivity, support and enhance the way products are used and maintained, continuously embedding new requirements while minimizing disruption and improving customer experience. During the concept and industrialization stages of a product's lifecycle, digital twins aim to simulate the physical product, or more precisely, represent a subset of its behavior.

 By doing so, they can provide valuable insights into how the product will perform in the real world, and help identify areas for optimization and improvement before it is actually built. However, it's important to remember that the scope and complexity of digital twins can vary widely depending on the specific domain and stage of the product lifecycle.