Data management is the usage, storage, curation, and consumption of collected information. In 2007, Cragin, M. H. et al., defined in their poster “An educational program on Data Curation” that data curation was: “the active and on-going management of data through its lifecycle of interest and usefulness to scholarship, science, and education; curation activities enable data discovery and retrieval, maintain quality, add value, and provide for re-use over time”.
However, this quality and value are of integral value to business and industry as well. In the current climate of information saturation and fast data flow, clear and concise information is paramount to the effective and efficient running of any organization. By closing the gap between physical and digital documentation, issues and changes within and without a product are transparent and easier, and quicker to respond to.
In the article “Cheat Sheet: What is Digital Twin?”, Armstrong, M. M. (2020) gives the example of a digital twin, a digital model of a physical product, whether it be a car, building, bridge, or jet engine. In this article, Armstrong emphasizes the efficacy of having an interactive digital resource to assist users in tackling complex issues such as: visualizing products in use, connecting disparate systems and promoting traceability, troubleshooting far away equipment, and managing complexities and linkage within systems-of-systems.
Data is at the center of a successful and organic workflow, unfortunately, many businesses even today have information stored and spread across the many different facets and wings of their company, partners, and suppliers. This data takes various forms, whether it be physical documents, emails, presentations, CAD files, and grading sheets, or just a PDF that only one of two people have; perhaps it does not seem like such a big concern, but what if a change needs to be made to a current product?
Suddenly you are faced with the problem of disseminating that information amongst the various departments, making sure the people that need to know what has changed know, and then how the various departments now communicate with each other how those changes affect their area of responsibility and if that will, in turn, affect any other sect. This process can become especially tedious in areas of vehicle or machinery manufacture, where a single product may have hundreds or thousands of individual pieces, all affected by the change in a single other parts.
This ripple effect exists even in comparatively simpler products, like television sets, where a small change to the screen size or image composition affects the entire item. But, if all this documentation was in one place, linked to its constituents and masters – succinct, accessible, and clear in its meaning; this ripple effect is eliminated and the tedium and time sink required to communicate all the changes required in other areas is non-existent.
When a digital model of the product and data exist in tandem, with the ability of users to interact and modify that data quickly and easily, representing all the components and functions involved in creating that product; a change that affects the product or process is easily charted and its effects are readily apparent. As a result, the vision of the product remains unaltered and the genealogy of the information persists.
CIMdata (n.d.) defines PLM as A strategic business approach that applies a consistent set of business solutions that support the collaborative creation, management, dissemination, and use of product definition information. Supporting the extended enterprise (customers, design and supply partners, etc.). Spanning from concept to end of life of a product or plant. Integrating people, processes, business systems, and information. Product lifecycle management (PLM) systems are the medium between product and operators; an interactive database that enables malleability and acts as a single source of truth. An online resource, a PLM is what gives a user the visibility to track and trace a product, its components, and the documentation required for every stage; providing an assurance of quality and trust at every stage, or highlighting problematic areas that require attention.
PLM systems have often been considered as useful libraries for a C-suite overview; as an up-to-date source of reliable data, a component, product or catalog can be analyzed on its performance and how often it is used – for example, does a product perform better in specific seasons, or is there a larger requirement for newness?
However, PLM systems are also crucial for normal users. With all the relevant product and design information in one place, a PLM system dispenses with the normal manual compiling of data from various sources (whether this is a specific person, company wing, or affiliated partners); allowing all queries, complex or mundane, to be made in a single place.
Doing away with the physical organizing of documents and traditional paper trails and assembling a virtual archive in its place, reduces the time spent on chasing after necessary documents (that are two weeks overdue) or the general need to remind individuals what is required of or from them.
As a result, a PLM reduces the burnout and stress of individuals that regularly and frequently encounter and deal with these situations, providing the much-needed catharsis of a centralized and monitored directory.
A dossier of current details, providing a cost and time-efficient display of impact, value, quality, issues, requirements, next steps, groups, individuals, accountability, performance, composition, and more – doing what would take weeks or months physically in only moments, whilst highlighting problematic areas in need of address that could have otherwise gone unnoticed.
If you would like to know more about product lifecycle and data management as a solution and how it could streamline your data- and workflow, contact Bombyx today.
Cragin, M. H., Heidorn, P. B., Palmer, C. L., & Smith, L. C. (2007). An Educational Program on Data Curation. IDEALS @ Illinois. Retrieved November 9, 2021, from https://www.ideals.illinois.edu/handle/2142/3493.
Armstrong, M. M. (2020, December 4). Cheat Sheet: What is Digital Twin? IBM Business Operations Blog. Retrieved November 9, 2021, from https://www.ibm.com/blogs/internet-of-things/iot-cheat-sheet-digital-twin/.
CIMdata. (n.d.). All About PLM. CIMdata. Retrieved November 10, 2021, from https://www.cimdata.com/en/resources/about-plm.