Product Lifecycle Management (PLM) architecture refers to an organized, methodical collection of technology and policies that help to manage product data across the enterprise. The wide range of interconnected applications and processes throughout the product lifecycle form a complex technology stack that requires clear procedures and governance. A robust system architecture – one that connects product data from multiple sources and ties it to the relevant processes – can help organizations take their digital strategy to the next level. Defining system roles and accountability – and ultimately, enabling information to flow smoothly, is essential to unlock the full potential of our data and products.
Before we get into the ins and outs of PLM applications, we need to discuss some key concepts about the overall PLM information system’s landscape.
First of all, let’s talk about clients and servers. Client/server computing divides an application into three basic components – a client, a server, and a network that connects the client to the server. Clients are typically the end users. The computer you’re using is probably a client. And every time you open a CAD program or print a drawing on your office’s printer network, your client computer is asking a server to do something to complete those tasks. A server is a computer that does things for other computers. When a server gets a request, it processes that request, finds whatever data is needed, and composes a reply. And often, these requests involve databases.
You can think of a database as a library and the server as a librarian that provides you with the books you need instantly.
A database is a collection of information that is organized so it can be easily accessed, managed and updated. Databases can be NoSQL or relational. For now, we will focus on traditional relational databases. They organize information into rows and columns in tables. A row is a single record in a table. In following example, you can see a customer table, containing 4 columns (ID, Name, Age and Email) and 4 records (or rows).
Our Enterprise PLM technology also includes all the tools we use to manage, retrieve and deliver data from those databases.
RELATIONAL DATABASE MANAGEMENT SYSTEMS and SQL
A relational database management system (RDBMS) is a program to create, update, and administer a relational database. The most popular RDBMS are Microsoft SQL Server, IBM’s DB2, Oracle and MySQL. SQL is a language used to command the database management system, to create new tables, to insert and update data, and retrieve information from across multiple tables. SQL has been around for a long time and has a very standardized set of structures and syntax. However, the various database vendors typically add additional capabilities into their specific implementation of the language, creating unique dialects for their own products.
In this example, the client (end user) request is to open a product structure from a CAD application. The application server collects the required information from the database and file server to put the model together, then presents it to the client.
UNDERSTANDING THE PLM ARCHITECTURE
We can divide the main components of a PLM architecture stack into three main categories: PLM applications, core systems and business intelligence platforms. Core systems consolidate and enrich the data that PLM applications create. PLM applications are usually discipline- specific and involve 3D modelling and simulations. Business intelligence platforms are used to present data and extract insights.
This categorization is useful for grouping and structuring the key PLM components. However, it needs to be customized for each company, depending on the firm’s existing system landscape and business models. (For example, companies offering only software products will not incorporate manufacturing into their core systems.)
Also known as authoring applications, PLM applications create most of the data that defines the product. Once they contained just mechanical parts, but today’s products are becoming more and more complex. Sensors, electrification and software have become an integral part of modern products. Some examples of groups of PLM applications are CAD (Computer Aided Design), CAM (Computer Aided Manufacturing) or Finite Element Analysis (FEA). In his book “Product Lifecycle Management(Volume 2)”, John Stark provides a comprehensive description of enterprise PLM applications.
Enterprise data inevitably spans many systems. Nowadays, effectively consolidating product information is essential to staying competitive, and that’s what core systems do. They collect and put together key information coming from PLM applications and provide the required access levels and views to the data.
Let’s start by breaking down the core systems of today’s PLM stack. We’ll focus on six major core systems and provide a short description of what they do and how they are connected.
Customer Relationship Management (CRM) systems are often regarded as the single source of truth when it comes to customer data. In CRM, we can track our sales leads and prospects. We can capture information about the conversations we have with our customers, where and when we met them, or what products or services they’re interested in. If they become a customer, we can track their purchasing history and all the interactions we have with them. We can visualize our sales pipeline and the value of the opportunities, and the sales we are working towards. Salesforce, Microsoft Dynamics, Oracle, SAP or Sugar are some of the main CRM software providers.
Product Data Management (PDM) systems focus on product definition, design and use. Through the product’s lifecycle, a vast amount of data is required to develop, manufacture, deliver, support and service the product. Tracking product requirements, making 3D models and drawings available to non-designers, distributing product specifications, releasing BOMs or tracking part changes are examples of things we can do in PDM systems. Siemens, Dassault, Autodesk, Aras or PTC are among the main PDM software providers.
ERP stands for Enterprise Resource Management. ERP systems address logistical and financial processes and handle information related to time, money, people and machines. ERP tracks customer transactions and material purchases so that we know what was bought, by whom, from which company, and how much we paid. HR processes are handled in ERP as well – from new hires to salary and position within the organization, key employee personal information is captured and managed in ERP. SAP, FIS or Oracle are among the most popular solution providers for ERP.
Very closely connected to ERP is Supply Chain Management (SCM). In order to obtain the raw materials and resources required to bring products to market, companies need to interact with numerous suppliers and partners. SCM systems monitor, supervise and integrate all of a company’s key business activities to ensure that its supply chain is efficient and cost-effective.
MES (Manufacturing Execution Systems) control all the activities occurring on the shop floor. They monitor machines, analyse production and control manufacturing operations. A good MES system offers real-time information and provides online notifications and alerts to operators and machines so they can take required actions to solve problems or improve performance.
MRO stands for Maintenance, Repair, & Operation. An MRO system proactively manages inventory to keep operations running, addresses downtime issues, schedules maintenance routines and workflows and helps you manage risks and improve asset availability. Also known as Enterprise Asset Management (EAM), it concentrates on managing physical assets and is particularly relevant for industries with complex products or long product lifecycles.
Business intelligence platforms
An analytic platform is a solution for managing data and generating business analytics from that data. Reports, interactive dashboards and data visualizations can communicate the stories that live within the data and provide business insights in real time. Today’s attribution modelling platforms can ingest and analyse vast amounts of information to provide measurable insights to support decision-making and guide the development of business strategy.
These platforms provide advanced statistical analysis and modelling capabilities that help you analyse and measure your product’s performance, consumer demand, trends, market competitiveness, or direct competition. Although traditionally only the largest enterprises could afford an analytics program, nowadays the technology is no longer a cost barrier.
Putting product data to good use and creating real value starts with a well-thought PLM architecture. Connecting and consolidating data from different sources, enabling smooth information flow and ensuring that the different systems talk to each other is the foundation of making Product Lifecycle Management your firm’s competitive advantage.
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