Get the most of your lifecycle

What defines lifecycle

Everything has a lifecycle: simply put, things (as well as people) initiate, mature, and ultimately become obsolete and get recycled. All lifecycles exist within a specific and dynamic context between system development (maturity increase from concept to industrialization) and system stability (mature operations, performance continuous improvement and maintenance).

A definition of lifecycle refers to the series of changes that a product, process, activity, etc. goes through during existence or usage. In an industry, commercial product or service context, this concens 'things' or systems which evolve over time, require to be managed, are experienced by people, and contribute to creating value as part of sustainable closed-loops. 

Products are developed from concept to start of production, following defined carry-over and platform strategies; their lifecycle continues to mature as product issues are addressed throughout production and assembly, in collaboration with multiple design partners, engineering-to-order and manufacturing-to-order suppliers.

Lifecycle of things

Product-service-experience creation is the de facto set of disciplines which covers concept-to-cradle activities, across multiple domains and stakeholders, in which products are created according to principles of a circular economy (focusing on positive holistic society-wide benefits rather than the traditional make-use-dispose cycle).

Lifecycle management extends to all things, from engineering, manufacturing, quality, sales and marketing, enterprise collaboration and management, maintenance and service, as well as all operations supporting the full enterprise digital chain. 

Degining and integrating lifecycles across multiple 'things' and across enterprise operations requires a holistic business transformation and organizational change approach. This implies an alignment of talent, process, data and technology for a specific scope of work: product, application, asset performance, material intelligence, etc. and their respective lifecycle following the digital thread across the enterprise.

Digital thread

This also includes technologies, processes and data embedded into physical products, applications (software), asset performance (execution machines, in-filed products). Concurrently, talents (people, skills, experience), technologies, data and processes (internal and external services) follows their respective lifecycle.

In every industry, data leads to knowledge, and ultimately to intellectual property and competitive advantageThere are plenty of opportunities to leverage data and convert it into value with the proliferation of connected devices, Big Data, inexpensive storage solutions, data mining and analytics tools to visualise and navigate (or 'drill into') it to the required level of detail.

The race to value generation in engineering research and development (upstream) is yet to catchup with the manufacturing domain (downstream) in terms of data-driven improvement opportunities. As a matter of fact, Industry 4.0, industrial Internet of Things (IoT) and connected machines are things that relate to manufacturing activities, mostly downstream of new product introduction activities.

Broadly speaking, data helps driving effective decisions:

  • Data consistency

    How is data used in the engineering and product development space (upstream) vs in the manufacturing space (downstream)?

  • Data traceability

    How does data quality and trust inform the decision making process, both upstream and downstream of the product development cycle?

  • Data model accessibility

    Are data models, types and structures similar or different and, if different, how are decisions made in the respective areas?

  • Decision making process

    What decisions are made upstream and downstream, and are they more data-driven or intuitive in one area and the other?

  • Decision model modularity

    Considering that a lot of information flows downstream from engineering and product development to manufacturing, how much from the cascaded data is used downstream in the decision making process? In other words, do the decisions made downstream rely on data created (or authored / mastered) upstream?

Effective data drives product and process improvement opportunities

Understanding the business lifecycle and how people, products and services interact or interface is critical to improve the decision-making process and user experience, considering the relevant decisions required throughout successive lifecycle stages.

Digital continuity