Everything has a lifecycle
All things initiate, mature, and ultimately become obsolete and get recycled or up-issued into new versions. 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 lifecycle refers to series of maturity states that a product, process, data set or activity goes through during its existence or usage: from concept to work-in-progress, under review, frozen and released (for illustration, in the context of product development).
Maturity states are defined based on what is being developed, the ideation, collaboration and review process; they change forward or backward based on promotion and demotion decisions. Concept (new) or WIP mark initial states, whereas released and obsolete are final states.
In industry, product or service context, lifecycle states concern how data mature or change over time and contribute to creating value as part of sustainable closed-loop systems.
Defining and understand appropriate maturity gates helps with using tested and approved information or standards, reduce data duplication and increase collaboration and re-use across projects and teams.
Managing the '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. For example, systems and sub-systems typically go through new product introduction or development cycles (NPI or NPD processes): from requirements and new input / technological or product attributes to design, develop, build, operate / maintain gates.
Designing 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.
Lifecycle management concerns all things: 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.
Value chain design / balanced scorecard / budgeting process / strategy map for internal and external operations
Organizational structure / skill and talent management / learning & development / sales & marketing / delivery and supportive functions
Revenue enhancement / cost reduction / profitability improvement / asset reduction / return on capital employed improvement / service delivery improvement
Leadership alignment / business case / benefit realization management / stakeholder management / change management
Reskilling / customer value proposition improvement / education curriculum / talent acquisition / new functional teams
Value chain alignment
Make or buy strategy adjustment / outsourcing remix / ENVA improvement / NVA reduction / new capability introduction / RACI change
In every industry, data leads to knowledge, and ultimately to intellectual property and competitive advantage. There 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 to the required level of information.
Broadly speaking, data helps driving effective decisions:
How is data used in the engineering and product development space (upstream) vs in the manufacturing space (downstream)?
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?
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 (IIoT) and connected machines are things that relate to manufacturing and assembly activities, mostly downstream of new product introduction activities—but also upstream as part of improvement research activities.
At Xlifecycle Ltd., we pride ourself in being direct and straightforward when defining and leading business change and data continuity improvement roadmaps. This is not only about mapping what does not work or what does not exist today, but about brainstorming and recommending what could-should be the way-forward to enhance business value.
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.
Customer feedback is great for telling you what you did wrong. It's terrible at telling you what you should do next.