Digital continuity across the value chain: the right information, where and when needed
Xlifecycle Ltd provides digital continuity and business transformation consulting services, helping organizations make the most of their digital strategies in order to:
ADAPT to change
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
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
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 minimise time-to-value realization
Digital transformation is not just about throwing technology at the problem; successful implementations often (always?) relate to business change and alignment across multiple perspectives: from people to data, processes, tools and technologies.
Unless there is a change and an adaptation blended across all pillars, there is no or little transformation. Cultural alignment is vital for digital transformation to work, so getting people on board should be the first concern.
Prioritizing digital transformation initiatives requires continuous capability development and digital roadmaps, coupled with a robust understanding of how much change the business can deal with, in a given period of time.
Digital transformation—towards smart everything: products, engineering, operations and Industry 4.0
Lifecycle of things
The rapid pace of technological change brings both opportunities and challenges for businesses, across domains and industries. As a matter of fact, every organizations increasingly rely on technology to differentiate their products, services, user experiences and operations, seeking for effectiveness and efficiency across their “X" lifecycle, where X = product, application, asset performance, talent, technology, data, process, material intelligence, etc.
Each of these things follow their respective lifecycle maturity change stages, as they coexist concurrently and evolve in iterative and interlinked loops.
A digital transformation occurs when a business uses digital technology to change the way it operates, particularly around customer interactions and the way value is created by employees and its wider supply chain; it implies business transformation, rather than IT transformation.
Digital transformations require challenging the status quo and experimenting with new ways of working, new operating models and new technologies. They are contextual to each and every organization; this is mainly due to cultural and contextual factors, but also to industry and organizational maturity.
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?
A common definition of digital maturity which applies to every context can be hard to pinpoint, hence it can be difficult to benchmark one organization with another. It is critical to communicate extensively about the digital strategy internally—especially in the language that the current leadership team can relate the most.
Successful transformations require more than executive awareness (necessary but not sufficient requirement); it is enabled by a combination of active business engagement, fostering continuous attention to deliver impactful and relevant messages at all levels, including C-level and B-level decision makers.
Regardless of the industry, organizations manage common pressures from profit margins, competitive advantage, customer expectations, sustainable performance, compliance, customer differentiation and technology requirements which drive the need for digital transformations.
The proliferation of new technologies has opened the door to new business opportunities, enabling organizations to align and create new business models to improve their operations, from their value chain to their value system. It is important to understand why companies seek to transform digitally, both from a content perspective and in what context, as well as how they formulate and implement their business transformation strategies.
The value chain includes the various activities within the organization's boundaries, from inbound logistics to services, and also consists of supporting activities to facilitate the end-to-end production process. The value system, on the other hand, extends to suppliers upstream and also to channels and end-customers downstream.
Provided as an example, frameworks like the above can help define new operating model enabling organizations to move through transformation roadmaps while accounting for a period of significant change, both internally and externally.
Digital Thread: a path to adopt innovative and transformative capabilities—connecting talents and Digital Twins
Following the product lifecycle across the extended enterprise, connecting data across multiple functional capabilities, including product innovation, enterprise execution and asset performance value chains: Bills of Materials are used for several purposes across the value chains; yet, they combine a lot of common information cascading from upstream activities.
The Digital Thread encompasses all meaningful relationship connections across business functions, between all product’s digital assets—and their maturity over their respective lifecycle.
Delivering value from Digital Twins, as well as any digital assets for that matter, requires a combination of business change, lean operations, pragmatic delivery and continuous improvement upon service transition from a strategic initiative into business-as-usual maintenance and support.
Robust Business Change
Robust change business case leadership and ongoing maintenance throughout benefit realization
Lean and effective implementation governance, data and people focused deployment; stakeholder management
Technical delivery management; vendor and third party performance coordination; operations and PMO
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.
Digital twin technology: why and how it matters?
Digital twins are live digital representations of physical assets (which exist or are yet to be created in the real world). In other words, they are virtual models of the real thing, and can be either related to a product or process. Multiple, yet different, virtual / digital twins are used throughout the product lifecycle to simulate, predict, and optimize the product and production system before investing in physical prototypes and assets.
Digital Twins are multi-purpose representations of the real; they coexist across to product maturity development stages; all Digital Twins do not serve the same purpose, they range from product concept to industrialization and operations. They include CAD, CAE and other CAx models, but also non-geometrical data and mathematical models. Digital twins also include predictive twins which are to model future state and behavior of a device, product or service.
Their scope moved beyond manufacturing and into the merging worlds of the Internet of Things, Artificial Intelligence and data analytics. As these things become more complex, they begin to be used in largely interconnected predictive models. In addition, digital platforms allow operational performance optimization, enhanced collaboration, scenario planning and effective simulations.
Aligning initial virtual twins to carry-over product strategies: concepts are architected based on product and platform-based virtual models, 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.
Gearing development digital twins towards seamless delivery: products are developed and industrialized while the enterprise is concurrently getting ready for start of production; aligning product and platform requirements to achieve economies of scale and scope across the extended enterprise and including design and manufacturing partners.
Providing in-field operational digital twins for continuous service: from connected products, asset optimization and shelf life extension, on-air embedded software monitoring and updates, asset performance optimization using machine learning, leveraging IOT, big data and AI to extend the product lifecycle.
Each function, business or technical domain requires different types of models, whose virtual twins are digital representation of the real, tailored for specific context and boundary condition validation. In the concept and industrialization stages, digital twins aim to simulate the physical product or, to be more precise, represent a subset of its behaviour.
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.