What is product lifecycle management (PLM)?
PLM deals with every stage a product goes through, from initial concept to retirement.
Product lifecycle management overview
Product lifecycle management (PLM) deals with every stage across a product’s lifetime. From the creative thinking of the product designer, to the valuable data being sent and received by your IoT network, and finally, to the frank and timely feedback you get from your customers – every piece of information across a product’s lifecycle is part of a bigger story.
And with today’s circular economies and more transparent supply chains, that story extends back to the raw materials used in production, and forward to the end-of-life stages of that product such as recycling and repurposing. The trick to a good PLM strategy is to listen to all those stories — and be able to act on them. This means integrating and analysing them so that you can make meaningful improvements and innovations, quickly.
What is PLM software?
As a technology, PLM software helps organisations to develop new products and bring them to market in far more efficient, collaborative, and sustainable ways. It integrates processes for each stage of a product's lifecycle across globalized supply chains, making it easier to track and share data along the product value chain – from initial design and engineering through manufacturing, and supply chain management. PLM solutions can help teams collaborate and work together, no matter where they are, using a common record of enterprise product data, such as parts and material requirements, engineering changes, workflows, and regulations. And when smart technologies like AI and the IoT come into the mix, modern PLM solutions can provide real-time insights into product performance, customer feedback, and market trends.
PLM meaning in business
In an age where innovation is key to business survival and success, PLM plays a critical role in helping manufacturers develop the next generation of products, at a lower cost, and with a faster time to market. While PLM can also be interpreted as a business strategy, three fundamentals impact the way teams work and the ability for organisations to grow and thrive:
Universal, secure, managed access and use of product definition information
Maintenance of the integrity of that product definition and related information throughout the life of the product
Management and maintenance of the business processes used to create, manage, disseminate, share, and use the information
The five phases of product development
There are many different ways to describe the phases of product development and no one industry standard. However, the phases below represent a typical development cycle.
- Concept and design: The ideation phase, where a product’s requirements are defined based on factors including competitor analysis, gaps in the market, or customer needs.
- Develop: The detailed design of the product will be created, along with any necessary tool designs. This phase includes validation and analysis of the planned product, as well as prototype development and piloting in the field. This generates vital feedback on how the product is used and what further refinements are needed.
- Production and launch: Feedback from the pilot is used to adjust the design and other components to produce a market-ready version. The production of the new product is scaled – followed by launch and distribution to the market.
- Service and support: Following the launch of the new product, the period of time when service and support is offered.
- Retirement: At the end of the product’s lifecycle, its withdrawal from the market must be managed – along with any retrials or absorption into new concept ideas.
How does a PLM system work?
A PLM system gives designers and engineers access to the critical data they need in real time. The system streamlines project management by linking CAD (computer-aided design) data with a bill of materials and other enterprise data sources, such as integration with an ERP system, and manages this product data through all stages of the product development lifecycle.
PLM also prevents designers and engineers from operating in a disconnected vacuum, giving them insight into external sources of information like customer and analyst feedback on current products, performance data on products in the field, and visibility into the limitations of downstream processes like manufacturing.
A PLM system also benefits teams beyond design and engineering. It can provide ‘single source of truth’ visibility to business stakeholders and/or suppliers for easy delivery of feedback early in the product development process.
Evolution of product lifecycle management
In the 1980s, American Motors Corporation (AMC) was a small player in the automotive industry. The company lacked the big budgets of larger players in the market, which hampered its ability to compete effectively. AMC leadership had the idea of tracking products from inception to end-of-life in order to improve processes and compete more efficiently – the first iteration of product lifecycle management.
The data gathered was used to inform better decisions from ideation through to procurement and the production process. AMC grew its market share and the company was later bought by Chrysler and became the auto industry’s lowest-cost producer by the mid-1990s.
Today, PLM has been adopted across manufacturing to foster collaboration, boost innovation, and efficiently support growth through designing to customer demand and product individualisation.
And in a time of digital transformation and accelerated change – Forbes predicts that due to COVID-19, manufacturing will experience five years of innovation in the next 18 months – PLM plays a critical role in helping companies get products to market faster.
70
%
of customers expect more customised or personalised products than they received three years ago
Oxford Economics
68
%
of customers expect the same quality products and services as they received three years ago, but more quickly
Oxford Economics
Five benefits of PLM
The following are five key reasons why companies choose to invest in PLM solutions.
- Improvements to development, engineering efficiency, and effectiveness: Results from the Industry Week survey found that silos are the biggest challenge to engineering team performance. PLM enables the bi-directional flow of real-time data to support better knowledge-sharing and collaboration.
- Elimination of errors during the engineering release process: It’s far simpler – and cheaper – to rectify product issues that are identified earlier on. PLM helps to reduce cost and offers the additional environmental benefit of reducing manufacturing waste.
- Reduced time to market: Offering a single source of truth with up-to-date information at every phase of the product lifecycle, PLM empowers project managers to control overlapping timelines and get products to market faster.
- Improved project delivery: A cross-enterprise, digital PLM solution supports advanced workflow management. In this use case, PLM allows a team to precisely calculate product costs and more effectively manage the handover to manufacturing new designs.
- Higher quality designs: PLM offers designers and engineers a deeper level of insight into product requirements. Ingesting data from many different internal and external sources, a PLM system with integrated machine learning can turn performance data and customer feedback into new feature suggestions.
Overcoming PLM challenges
Currently, fewer than half of R&D executives say they have visibility into the end-to-end, design-to-delivery process. This highlights that, for many organisations, PLM has not yet reached its potential as a single source of product truth.
In addition, the growing adoption of Industry 4.0 practices within manufacturing has led to an exponential increase in the amount of product and customer data available, providing greater visibility across product lifecycles. Data sharing among PLM entities could streamline product lifecycle management, but only if the data is correctly captured, analysed, and disseminated safely – bringing into focus the need for integrated AI, machine learning, and data encryption.
Finally, many PLM advocates struggle to communicate the relevance of the software beyond engineering. In all of the above cases, investing in a solution that integrates with existing enterprise systems – and offers built-in artificial intelligence – will increase the use and value that the wider organisation can gain.
Future of PLM technology
The demands of beating competitors to market, attracting top talent, and producing the highest quality product possible using sustainable practices will only continue to increase over time. PLM can help to meet these demands with shorter, more conscientious design and product engineering cycles, but only if organisations invest in the technology required to get there.
More objects are coming online with the Internet of Things (IoT) and designers and engineers stand to gain far greater visibility into products in the field, as well as the ability to update products that are already in the hands of consumers. As McKinsey highlights, this enables manufacturers to continue creating new customer value throughout the product lifecycle.
As sustainability continues to emerge as a topic of importance, businesses will look to modernise their product development processes through green product design, manufacturing, and logistics with the goal of achieving full supply chain sustainability.
As with other types of enterprise software, PLM systems are increasingly being offered in the cloud as software as a service (SaaS). This will make PLM more accessible to smaller companies and will continue to drive the collaboration that effective product development teams need while workforces become more distributed.
Digital twins are virtual models of a product that are connected to their physical ‘twin’ via IoT and are managed within PLM systems. The concept is still new, but digital twins are predicted to drive significant cost savings for manufacturers in the years ahead. IDC states that, “By 2023, 65% of global manufacturers will have realised savings of 10% in operational expenses through process digital twins driven by IoT and machine learning.”
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