Enterprises are increasingly leveraging PLM processes and systems to improve product innovation, product profitability, and ensure regulatory compliance.
Product Lifecycle Management (PLM) was pioneered by the AMC/Jeep company in the mid-1980s. Seeking a way to compete more effectively against its larger, better-capitalized rivals, the company reasoned that making better use of its product data across the data's "lifecycle" would allow it to be nimbler and drive faster, more compelling innovations to market. AMC's innovation played a key role in the successful introduction of the Jeep Cherokee series - an iconic product that reshaped the global auto industry.
Product Lifecycle Management (PLM) 101 – The Product Lifecycle
To understand the product lifecycle, consider the lifecycle of a human being. We are conceived by our parents, then we grow and are born into this world. After we are born, we continue to grow and learn - we go to school, become an adult, get a job, and contribute to our world and society. Perhaps, we have children of our own and eventually, we retire from our work. We grow old - hopefully with grace and dignity. And then, we pass away, leaving behind our legacy. This is the lifecycle of a person.
A Product has a lifecycle as well. Inventors, scientists, and marketers envision and conceive it. It grows as engineers perfect and test it. It moves through technology transfer to manufacturing. There’s a ramp-up period associated with manufacturing it. The product is “born” as it is introduced into the market where it may sell for several years. It continues to grow as its creators may introduce new versions, variations, or flavors over its lifespan. Eventually, its useful life ends, and the product is retired.
The PLM process governs products from inception, through design and manufacture, to the retirement and withdraw of obsolete products. It spans people, processes, technology, and most importantly, data. It provides enterprises with a product data foundation for R&D, manufacturing, sales & marketing, and regulatory compliance.
The Ascendance of PLM
Product data in large enterprises is highly complex in terms of volume, velocity, veracity, and variety. And this complexity is increasing rapidly to meet the requirements of escalating regulatory complexity, customer demands to differentiate and innovate to smaller and smaller segments, and increasing pressure to optimize profitability. Product Data Management and Governance typically involves multiple global R&D operations, multi-tiered global supply chains, and a complex web of distribution, enabled by a wide array of both internal and external suppliers as well as governmental authorities
PLM capabilities are necessary to manage the structural interrelationships of the hundreds of thousands of individual components and subassemblies needed to manufacture a car or a piece of heavy equipment, to track approved designs, engineering drawings, and suppliers for medical implants, to manage multiple supply chains and testing methods in the pharmaceutical industry, to ensure ingredient and labelling compliance with local markets in packaged foods, and to ensure that regulatory compliance documents are updated and synchronized globally for many industries, just to provide a few examples.
PLM processes, rich data, organizational capabilities, and dedicated software applications are becoming more and more critical to the Enterprise. Just as in prior generations of process streamlining, capability enablement, and IT modernization, ERP was a key focal point. Most large enterprises are beginning - or continuing - to make major investments in PLM.
And businesses are seeing key benefits because of these investments: improved innovation cycle time, optimization of portfolios for profitability, and eased effort for compliance with the complex web of global and market level regulations.
Where are we heading with PLM?
Effective governance is essential to Product Lifecycle Management. In order to execute a PLM program, firms generally need to migrate a significant volume of highly complex data to modern business suites; however, data sources for PLM are more varied and more complex than “traditional” data migration projects. PLM data is not just "rows and columns" that firms can easily locate in existing legacy systems. Core PLM data - ranging from product specs to packaging details, artwork, and labels - often exists only in non-traditional or "unstructured" sources such as PDFs, Word documents, or Spreadsheets. And they are often stored only in Microsoft Sharepoint sites, network drives, and other lightly governed, fragmented locations that are spread across the globe at internal manufacturing plants, field offices, research organizations, CROs, outsourced plants, and co-packers, etc.
A well-run PLM program prepares this data for migration within a central location. To ensure simplified and streamlined operations, and enable new capabilities and new analyses in the face of this complex data, advanced data migration approaches to PLM using AI and Big Data are essential.
For large enterprises in Life Sciences, CPG, Aerospace, Industrials, and other verticals, advanced PLM capabilities are rapidly becoming table stakes. Firms that go the extra mile to take advantage of migrating their unstructured product data are reaping a wealth of additional information to win competitively.
> To learn more about PLM, view our webinar PLM and Great Data Make Product Portfolios Profitable HERE.
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