Product Lifecycle Management - The Increasing Value for Successful Digital Innovation and Engineering Transformation
As global organizations embark on much talked about Digital Transformation programs, PLM emerges as the backbone of digitization for all processes and functions that participate in the product lifecycle.
Product Lifecycle Management or PLM is established as the enterprise system that helps to manage the engineering data during product development. The research firm CIMdata defines PLM “as a business approach to solving the problem of managing the complete set of product definition information – creating that information, managing it through its life, and disseminating and using it throughout the lifecycle of the product”.
Use of digital technologies is not new in engineering and product development. What is new, however, is that product development organizations need to increasingly strategize and execute their product development, manufacturing and product launches from an all-digital perspective leveraging smarter technologies, more useful data and better insights. Digital tools are already playing a significant role in the complete lifecycle of any product, right from the product conception to retirement. However the varying maturity of digital technologies adopted in each phase and the varying instances in time when a particular phase in the product lifecycle is digitized has led significant inefficiencies in the process or rather significant opportunities to make a material improvement in the engineering and R&D processes through adoption of integrated enterprise PLM. It is the time to look at PLM as a critical business system.
Increasing Product Complexity and the Value from PLM
It is clear that PLM is key to the engineering and R&D efficiency of a global organization. Product innovation and investments in R&D continue to recover and grow from the lows of a few years back. With more R&D activity and investments the value from PLM will only grow. However there is an increasing complexity in product development posing new challenges for PLM. In the current times of IoT and Industry 4.0, software lead differentiation for mechanical or physical products emerges as strategic for product success. The increasing content of electronics and software in products has significantly added to the product complexity. This brings challenges for global manufacturers and product engineers to collaborate for concurrent, multi-disciplinary engineering while designing the right and safe products.
In a report SAE International has highlighted how General Motors restructured its Global Vehicle Engineering organization to improve cross-system integration. The Harvard Business Review estimated that a typical car contains approximately 2,000 functional components, 30,000 parts, and 10 million lines of software code. Add to this the complexity that arises from each industry’s own product characteristics, a space shuttle has over a million unique parts, over 5 years of cycle time, 100+ partners and suppliers from all over the world; capital intensive industries have to maintain the accuracy as the engineering data changes from eBOM to mBOM to Installed BOM to Service BOM. With mass customization coming to fore, there is great value from PLM to manage the product configurations for manufacturing agility.
We are looking at business changes across industries where highly competitive companies decide to collaborate with each other for specific products or technologies and need to share their designs, research, technology, infrastructure or suppliers and yet compete in the market place. True global engineering with multiple R&D centres and multiple manufacturing plants contributing towards the same product and variants make the management of the product development process really complex.
Engineering Product Analytics and the Value from PLM
During product development design engineers mostly design for form-fit-function requirements when they should consider parameters like cost of the product, manufacturability, regulatory requirements, standards, supplier capability, serviceability and more. This happens because the digital thread is broken and it is difficult to keep track of and make these requirements available to the design engineers. A PLM backbone further enables the ability to build in analytics and rules that can be made available upfront to the design engineers.
The high cost of engineering changes – effort cost, process cost, tooling cost, tremendously high cost if a product is recalled – warranty and loss of goodwill have put the new age PLM practices on the agenda of the CFO and CEO in addition to the CTO. Product delays, cost overruns and quality issues not only affect the organization but also customer confidence.
With Industry 4.0 and IoT there is even more product data available, data from machines and not just form IT tools and applications. There is no better alternative than PLM to correlate this data to the engineering data within the organization. And then put in place engineering analytics that helps to build products that meet the user requirement even more closely, do not create service problems, and even support new revenue models.
Leading global companies have discussed with us about PLM as a competitive product strategy - leveraging digital assets consistently from concept to service. By extending the digital coverage, manufacturers will become fully integrated digital businesses and, therefore, well positioned to be more competitive. However to realize the benefits of the order of magnitude promised, it is necessary to have a coherent vision for PLM adoption amongst the stake-holders across the product lifecycle.