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 concerns 'things', data or systems which mature over time, require to be managed, are experienced by people, and contribute to creating value as part of sustainable closed-loops.
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.
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.
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.
Value chain design – balanced scorecard – budgeting process – strategy map
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
Value chain alignment
Make or buy strategy adjustment – outsourcing remix – ENVA improvement – NVA reduction – new capability introduction
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 (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:
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?
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.