Digital continuity across the value chain: the right information, where needed, when needed, for as long as needed
Xlifecycle Ltd provides digital continuity and business transformation consulting services, helping organizations make the most of their digital strategies. Simply put, our purpose is to enable our clients 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. It relies predominantly on 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 change is vital for digital transformation to work, so getting people on board should be the first concern.
Successful transformations contribute to realizing significant business benefits (with potential associated delivery risks which require mitigation). Non-transformational initiatives also carry risks as they tend to be deprioritized, under-resourced, or simply cancelled if perceived as little value added (poor return on investment or / and limited learning). 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 threats 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; it implies business transformation, rather than IT transformation. Hence, digital transformation means challenging the status quo and experimenting with new ways of working, new operating models and new technologies. However, digital transformation will look differently and mean different things for every organization; this is mainly due to cultural and contextual factors, but also industry and organizational maturity.
7-block framework with key considerations before embarking on a new digital transformation journey:
Is this a technical upgrade or a business transformation?
Are you really bought in, and is your team?
Are you prepared to share value creation with your customers?
Have you put walls around your digital team?
Do you know how to measure the value you intend to create?
Are you ready to make the tough calls about your team?
Will you be ready to spin off your digital lifecycle?
A common definition of digital maturity which applies to all can be hard to pinpoint. In any cases, 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 transformation requires 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. It is also concerned with anticipating, planning, mitigating and reacting to challenges during deployment of integrated technologies, coupled with appropriate workforce enablement through fit-for-purpose education and re-skilling curriculum, as well as potential organization redesign.
Regardless of the industry, organizations manage common pressures from profit margins, competitive advantage, employee 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.
where X = product, application, asset performance, talent, technology, data, process, material intelligence, etc.
The 7-block framework helps define the operating model enabling organizations to move through transformation roadmaps while accounting for a period of significant change, both internally and externally. A dedicated cross-functional team is to be created to lead culture stream throughout the change.
Digital thread: a path to adopt innovative and transformative capabilities – connecting talents and digital twins
Following the digital thread across the extended enterprise, connecting data across multiple functional capabilities, including product innovation, enterprise execution and asset performance value chains.
The digital thread encompasses all meaningful relationship connections across business functions, between all product’s digital assets – and their maturity over their respective lifecycle – including (albeit not limited to) version iterations of Bills of Materials (BOM), mechanical materials, electrical materials and electronics, software, CAx models, requirements, product and process simulations, service manuals, technical publications, variant effectivities, product variants, colour attributes, weight attributes, cost and budget models, asset performance, etc.
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 requirement alignment and technical solution architecture principle leadership
A digital twin is a virtual representation of a physical product or process, used to understand and predict the physical counterpart’s performance characteristics.
Digital twin technology: why and how it matters?
Simply put, digital twins are live digital representations of physical assets (which exist or are yet to be created in the real world). They are virtual models of the real. 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.
Digital twin technology moved beyond manufacturing and into the merging worlds of the Internet of Things, Artificial Intelligence and data analytics. As more complex "things" become inter-connected, predictive models, avdanced digital hubs and platforms provide business analysts, data scientists and other professionals with the ability to optimize operations throughput efficient collaboration, scenario planning and simulations.
Aligning start-up virtual twins to carry-over strategy: 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.