PLM: The Backbone of the Fashion Industry

backbone plm

When we mention the fashion industry—who automatically springs to mind? Fast fashion vertical retailers such as H&M, ZARA or C&A? Although these are giants in the industry, they aren’t the only ones in the market. There are many other smaller companies, such as S. Oliver or Tom Tailor, who are trying to compete. Others are also entering into niche markets in an attempt to differentiate themselves because not everyone needs such levels of product freshness. In this post we’re going to take a look at the “backbone” of this industry: PLM.

During recent years collection cycles have been getting shorter due to increased demand, there are commonly 12 collections a year, or even more—if we consider the vertical retailers.

Product Development Lifecycle (PLM), The Backbone in the Fashion Industry

But did you ever stop to think about how this complete fashion cycle works? How is it possible that you can buy the latest fashion at a reasonable price in just 6 to 8 weeks from it appearing on the runways?

This complete process is based on Information Technology and would not be possible without Excel files—so here’s where PLM comes into the fashion game! This article is written to give you a holistic understanding and visualization about the backbone of a fashion company and its product lifecycle combined with the importance of the PLM system.

Getting started with the product development

The creation of the new collection reports of top selling items are pulled out of the PLM system from the previous month/season.

In addition, the product manager will provide the briefing of the new trends and themes from the latest runways in the so called “kick-off meeting.”

According to the target group, the collection will be developed.

The structural set-up of one department will be ideally divided by their product group.

One team consists of at least 3 people: the designer, product developer and buyer. Then the designer starts with the scribbling and sketching. For every item, a new article is generated in the PDM. This way, the data will be collected throughout the development cycle.

The product definition in the fashion industry

In the fashion industry a product will be defined by these four main attributes:

  • Size Range
  • Colour
  • Fabric
  • Product Group

These key attributes broadly define the article. However, many companies add additional information to the article key to complete the product definition—such as seasongender or department.

Managing the product data through the collection’s lifecycle

Most of the product development process data is managed in the PDM system. It’s an agile design process, and the product description in the PDM gives designers, developers and buyers a common understanding of what the article looks like.

Images are usually created in a Computer Aided Design (CAD) program and published to the PDM system. This way, everyone has access to the designs, and there’s less room for confusion. The collaboration is in real time, and everyone on the team can work at the same time—which helps save time!

Based on the designs, the product developer defines the technical specification of the product. Specifying a product involves modifying the basic pattern, grading and fitting it for different sizes. Apart from the insurance of the wash care instructions, the product developer—also called the technician—is in charge of the complete sampling process: until the item is ready for production and its follow up.

Using the information in the PDM system the buyer purchases the fabric, dyes, and orders accessories like zippers, labels etc. The buyer is also involved in planning and coordinating the process, from sampling to production.

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Keeping track of the product development

There are several milestones and meetings where the collection will be reviewed. Everyone on the team—and of course, the product manager—must approve the final production. The development process and part of the buying process are managed within the PDM system. Most companies use a different system to track all of the collection financials: the Enterprise Resource Planning system (ERP).

In the fashion industry, PDM and ERP are the two main systems to manage the complete product lifecycle. There is a constant information exchange between the two. Mainly PDM is for the design and development, whereas ERP covers the planning, purchasing, inventory, sales, marketing, finance and human resources.

The buyer plans the inventory and based on the calculations, purchases the fabric (or other accessories necessary for the assembling of the product) using ERP. The buyer is also in charge of ordering the fabric from the fabric mills to the final production using the tracking (SCM) and planning tool.

Two more systems are often used in the fashion industry: SCM and CRM.

Managing the supply chain through the lifecycle

SCM stands for Supply Chain Management. It’s used to manage the flow of goods and services, the movement and storage of raw materials, work-in-process inventory, and finished goods from point-of-origin to point-of-consumption.

That’s how the buyer can follow up on the cycle, from manufacturing to product delivery at the point-of-sale. This tracking system makes the workflow transparent and provides the possibility to act just in time if a delivery of the goods is delayed. The sales information will be later reported into the ERP system.

Keeping close to the customer to develop better products

CRM stands for Customer Relationship Management. CRM puts the customer at the heart, closing the product development cycle with the product feedback, giving the final customer a voice. This information is mainly collected from social media and e-commerce platforms in the fashion industry, where the customer can provide their opinion. CRM connects the product to the customer, providing feedback that’s key to planning new collections, listing the best sellers or making complaints visible.

Closing the loop, the PLM system gives the opportunity to gather all insights of a product, to make the setup and forecast for the new collection. Once again we are at the beginning of a new product lifecycle.

In future posts, we will discuss the threats and how future technologies are shaping the fashion industry.

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Business Information Systems in PLM

business information systems

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 business 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 the 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.


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.

business information systems


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.)

PLM Applications

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.

Core Systems

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.

business information systems

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 information and 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.

Making sense of your product data.

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Product Data Management (PDM): Making Sense of Your Product Data



Data is everywhere, and the things you can do with it are amazing. With product data management (PDM) you can reinvent your business, improve efficiency and help your employees do their jobs better.
Data tells you who your customers are, what engages them and how they use your company’s products. It allows you to make more informed decisions and identify the products, services and processes where your business isn’t delivering the best possible results.


We use data to describe our products through their whole lifecycle. Large amounts of information are created and used to develop, sell, deliver and service the product. From bills of materials (BOMs) to product configuration rules or analytic models, a vast variety of data is relevant to the lifecycle. This data is managed in different systems: computer-aided design, ERP and manufacturing execution systems, all the way down to automation systems on the factory floor.

Each of these systems is designed to support different actors and phases of the product lifecycle. We can’t just dump all our product data into one system and expect that it will work in every part of the product lifecycle. The goal is to tie it all together to make the most of it, which is where PDM comes in. But each business is unique, and usually no “off-the-shelf” solution is available.


“Good data” is reliable data under control. It’s quality data that’s easy to find and available when needed. But data silos are still a common problem in most organizations. These silos kill the ability to share, collaborate, and embrace data as a competitive advantage.

And data is not cheap. Challenges related to data silos, skill sets and poor technology come to the fore when it comes to converting data into actionable insights.

Organizing product information and providing access to clean and trustworthy data are major challenges in PLM. If product data isn’t managed properly with PDM, we’re likely to encounter quality and efficiency issues, and we won’t be able to embrace the full power of advanced analytics.

Making sense of your product data.

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Before we move forward, let’s standardize some of the vocabulary we’ll use as we talk about data.

Structured data

Data in traditional row-column databases is considered structured data. Structured data can be transactional or master data.

  • Transactional data

Transactional data describes business events—for example, buying products from suppliers or selling products to customers are both business events.

  • Master data

Master data is data that remains unchanged over time. It describes key business entities such as products, suppliers or customers.

Wait, give me an example!

Imagine that you’re going to purchase a computer. The master data describing the computer includes, among other data, the manufacturer’s name, the model series and the monitor size. The master data that describes you as a customer includes your name, email and billing address. The transactional data involved here would be the computer’s price, the current discount or the method of payment.


Unstructured data

Unstructured data is data that isn’t specifically structured to make it easy for machines to understand. It doesn’t conform to a specific, pre-defined data model. Unstructured data can be e-mail messages, documents, videos, photos, audio files, presentations, etc.
After buying your new computer, you call your friend to tell him about your purchase. The audio file of your conversation is one example of unstructured data.

Semistructured data

Some experts believe that the term “unstructured data” is misleading, because the files usually contain metadata produced by the software they were created with. However, what is internal to the document is truly unstructured. Chuck Densinger and Mark Gonzales, in ”There’s No Such Thing as Unstructured Data” discuss this topic in detail.

For example, if you take a picture of your new computer, your camera will probably store the date and maybe the location where you took the picture. You might also upload the picture to social media and tag it with the words #computer or #newtoy. In some sense, you’re categorizing the file and making it a bit easier for machines to extract insights from your data.

To get business value out of the data, organizations must combine unstructured and structured data wisely.


Let’s use the knowledge buried within our data to make better decisions and improve performance. That’s the universal goal—but the reality is far different. The fact is, the Information Revolution hasn’t lived up to its promise for most companies. Isolated islands of unconnected data and a lack of skills and talent are among the major challenges.

What can you do to unlock your information? The recipe for success begins with a well-considered architectural plan. Information must be consolidated in a meaningful way, and architecture is the glue that holds it all together.

A good approach is to start with the right questions. What information will help your employees do their job better? Where could data provide a tangible benefit so that you can sell more and offer better services? You need to identify what data your business requires, where you can get it from, and how you’re going to use it.

These 7 steps provide a simple checklist to help you get your data out of the silos and put your information in action.

  1. Set clear goals for your data initiative

How do you eat an elephant? One bite at a time. We all know the saying, but we often fail to apply this lesson when it comes to PDM. We usually bite off more than we can chew, and get choked up. Then we get lost, overwhelmed, and frustrated with our messy data. And in the end, we fail. Our budget runs out and we fail to achieve any tangible results. Everybody is disappointed.
Sound familiar?
First thing you need to do is review and clarify the results you want from your data initiative.

What outcomes are you trying to achieve? Do you want to improve data quality? Extract insights from your customer data to improve your products? Are you willing to provide clear instructions to improve the efficiency of your organization?

Having a clear picture of what success looks like, what your main stakeholders expect of your initiative, and the main benefits your organization will obtain will help you to focus and start slicing the elephant.

  1. Break down your data into key dimensions

Break your data down into dimensions. Key dimensions can be data about customers, products, employees, inventory, suppliers, locations, etc.

  1. Identify and classify what data is being used, where, and why

Analyse which of the dimensions you identified is strategically more important. Take that dimension, and learn what data is used where, by whom, when and why. It’s crucial to analyse the complete process, as the same data might be created or used by different stakeholders at different phases in the product lifecycle. Find out which attributes describe those dimensions in each lifecycle stage and system. You’ll probably discover that the same data is named differently in different systems.

Document the information flow “as-is,” while identifying inconsistencies, pain points and possible improvements.

  1. Start creating a corporate attribute dictionary

An attribute dictionary is a set of common attributes used by multiple systems and stakeholders. The attribute dictionary manages the attributes’ ID, name, definition, data type and other relevant metadata for the corporation (as, for example, its units of measure).

This is a powerful concept, as it standardizes attributes and attribute definitions that are common across multiple systems. It eases integration and empowers interoperability.

The following table, extracted from the POSC Caesar Association’s data services, shows one example of an attribute definition:

  1. Map existing attributes to the corporate attribute dictionary

This step involves going through all the attributes you have identified for one dimension and mapping them to the common dictionary attributes. Often, the same thing will be named differently in each system. Even within a single system, multiple attributes are often used to define an identical property in different lifecycle stages or processes.

  1. Catalogued attributes set and define their ownership

Once you have a common definition for all the attributes that refer to one dimension, draft the information flow again using the common dictionary attributes. Try to group attributes logically, based on process, activity or lifecycle stage.

As soon as you have a clean information flow defined, ask yourself: Who are the data producers and data consumers at each lifecycle stage? The main goal is to define which system attribute sets are mastered, and by whom. It’s also important to identify who consumes that information and to which applications the attribute sets must be published.

In the following example, the product developer masters the material attribute set in PDM through research and design. This information is published to CRM, ERP and CPQ. Then the Manufacturing manager takes the material attribute set and masters it in ERP, publishing it to CRM, MES and PDM.

  1. Plan the implementation, estimate the costs, and define the business case

Since you now have a clear idea of how the information should flow, it’s time to plan the implementation. The attributes need to be adjusted to the dictionary, and interfaces between systems must be redesigned. Sketch a plan, and define a business case. Again, you need to break the implementation into pieces and prioritize strategically. Which areas will bring quicker wins? Once your implementation project gets the green light, it comes down to the dirty details, which matter most: tuning the systems and making sure the information flows smoothly.

  1. Document and communicate the changes

Finally, document the information flow, new data models, and instructions. Communicate the changes, and make sure all stakeholders understand how they need to work with the data.


Becoming a data-driven organization is at the top of the agenda for leading companies aiming to outperform their peers. Information drives your organization forward and differentiates you from your competitors. But for this to happen, data must be treated as a true corporate asset. The company must invest and secure the required resources to leverage new business models that exploit data. Effective information management and PDM requires a big commitment from the top.

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