Need for data interpretation and traceability
The Industrial Internet of Things (IIoT) is the use of Internet of Things (IoT) technologies in manufacturing. It incorporates machine learning and big data technology, harnessing ‘real-time’ sensor data, machine-to-machine (M2M) communication and automation technologies that have existed in industrial settings for years. By accessing a wide range of data repositories across the organization, IIoT can enable companies to pick up on inefficiencies and problems sooner, saving time and money and supporting business intelligence efforts, and create competitive advantage with enhanced products and services.
In manufacturing specifically, IIoT holds great potential for quality control, sustainable and green practices, supply chain traceability and efficiency.
New product features and the rise of embedded electronics, hardware and software, sensor and electrical technologies put a great deal of importance onto how ECAD and telematics data is managed – especially in collaboration with the more traditional mechanical and enterprise data which resides in Product Lifecycle Management (PLM). This translates by a number of new technical and business requirements to enable manufacturers to create MORE competitive products, FASTER and with BETTER quality and features:
- The integration of software or Application Lifecycle Management (ALM) with the enterprise Bill of Materials (BoM), ECAD and MCAD data lifecycle is critical for full product configuration, from design to service and maintenance.
- The rising number of mobile and wireless features, such as embedded software upgrades, implies the need for greater end-to-end data traceability, security, regulation compliance and safety; for example, to support ‘real-time’ external data feeds to products that are already in-use by end customers.
ALM vs PLM
The software world is much more complex than the mechanical world; it requires a lot more data integration, speed of processing, data volumes, cross-functional technical collaboration, as well as service to manufacturing and design cross-loop integration.
By design, PLM simply cannot manage such large amount of data changes which propagate up to and until the end of service of products. PLM is an ecosystem that focuses on engineering changes pre-production and manufacturing changes during production or assembly. It assumes less and less product design changes as products mature, while ALM is to support continuous product changes even after it is released or in-service (after-sales).
ALM provides an integrated environment for software development, ensuring complete traceability across the entire application lifecycle, including QA and test management, testing automation, demand management, configuration and change management, DevOps, and linking tests to requirements.
ALM covers the entire software development lifecycle (SDLC) and much more across governance, development and operations.
Traditionally, PLM assumes that product attributes are defined and managed across its lifecycle, up to a maturity level that allows for its design to be frozen – i.e. under change management from the time that designers put their “pencil down”. PLM provides a platform for BOM management, xCAD collaboration, change and document management, compliance, system engineering and development.
Broadly speaking, software developers don’t put their “pencil down” at the same time that other engineers do (…). This implies that the sheer amount of change that manufacturing organizations are facing is continuing to increase, perhaps not so in the mechanical space, but rapidly in the software space and associated disciplines (hardware integration, electronics, sensors, etc.).
ALM-PLM Integration: the “xLM ecosystem”
Both ALM and PLM enable software development teams to effectively manage project requirements and provide a means to carry out security testing transparently and with complete traceability. Integrating them as part of an “xLM ecosystem” enables teams to collaborate effectively while managing complexity, product quality and flawless deployment, considering that:
- Robust data security and compliance are important success factors when implementing IIoT solutions.
- Development and continuous alignment of these requirements is also a critical success factor.
Traditionally, ALM and PLM used to be isolated disciplines, operating within the boundaries of their own “silos”. Innovative products such as connected or autonomous products rely on IIoT and IoT technologies and create a need to break these boundaries as part of an “xLM ecosystem”, fundamentally reshaping the lifecycle management software industry. Integrating ALM and PLM provides opportunities for architecture and process alignment, software and hardware requirement integration, covering also electrical, mechanical, manufacturing and service BOM alignment, consistent defect and change management, as well as test strategy and result integration, traceability for compliance and service operations.
What are your thoughts?