3 reasons why PLM cannot drain product data swamp

August 13, 2015


The amount of data around us is growing. The same applies to engineering and manufacturing companies. Our ability to collect data is astonishing. But we are failing to bring the right data in the right form and at the right time to people. Product lifecycle management software is often recognized as a glue that should bring information about product and its lifecycle to everyone in a company.

Here is the thing – legacy data import is one of the most painful projects in PLM implementations. Few years ago, I came with a blog post – Who will take on legacy data in PLM? Guess what? Not many candidates in the list… Typical PLM implementation project is trying to allocate time to import existing data, but usually it is done as customization project by service organization and always requires additional resources.

Zero Wait State blog – The PLM State: Drain Your Data Swamp speaks exactly about the problem with existing data in organization. I like "swamp" metaphor, because it is really looks like a swamp. Think about legacy databases, tons of Excel files, existing PDM and PLM systems. What else? Emails, SharePoint website, wikis and many others…

The following passage from the article is a great value proposition for creating a system that will make existing data usable.

Data that isn’t used is data that isn’t profitable. All data should add value, whether by contributing to strategic decisions, marketing, process improvements, or regulations (data kept due to regulatory retention policies adds value by allowing your business to operate in a regulated environment). You paid for, and are paying for it already, so why wouldn’t you put that data to use?

But how do you drain a data swamp? The first step is to identify what data is likely to have been swamped. Identifying misplaced or forgotten data would seem like an insurmountable task, but look to those processes in your organization that generate data. I’m not talking about just test data or formal analyses, but all the data. That RoHS-compliant capacitor? That’s a data point. Device master records (DMR) and device history records (DHR) are obvious datasets. That prototype from CAD cost a lot to develop, so include that data where it can be leveraged for other designs rather than being ignored after its initial use.

Enterprise resource planning (ERP), project lifecycle management (PLM), laboratory information management system (LIMS), document management system (DMS) software and others can play a role in draining your data swamp, associating and linking disparate datasets and providing a means for their use.

Unfortunately, I see a very little progress with a process of drying data swamps by PLM systems. It made me think about reasons why is it hard to do. Here are 3 reasons why existing PLM platforms cannot do so.

1- Limited data modeling. Although all PLM systems have some kind of "flexible data modeling" capabilities, it is a lot of work to get data modeling done for all legacy data. Just think about converting all existing data structures into some sort of data models in existing PLM systems. It can be lifelong manual project.

2- Limited flexibility of relational databases. Majority of PLM architectures are built on top of relational databases, which provides almost no ways to bring unstructured or semi-structured data such as emails, PDF content, Excel spreadsheets, etc.

3- Absence of data transformation and classification tools. Existing PLM platforms have no tools that can allow you re-structure or re-model data after it is already imported into the system. Think about importing data and "massaging" it afterwards.

What is my conclusion? Companies are failing to bring existing data into PLM system because it is hard. Although, PLM vision is to provide a glue between variety of product information sources, in practice, it is very complex and expensive to achieve. Typical company is operating with number of siloed applications developed by different people and implemented to solve specific situational problems. It leaves holistic approach in product data management outside of scope in most of PLM implementations. Just my thoughts…

Best, Oleg

Image courtesy of jscreationzs at FreeDigitalPhotos.net

IoT, Industry 4.0 and PLM technological challenges

May 1, 2015


Connectivity and information technologies are changing our lives. Think about your everyday experience – news, driving, communication, banking. It is so different from we had 10 years ago. One of the main drivers behind the change is our ability to connect to different sources of information.

Now think about connectivity in a broader sense can change your business. I’m sure you’ve heard about Internet of Things (IoT) and about Industry 4.0, which is a broader vision of digital value chain and smart factory. Here is Wikipedia version of Industry 4.0 definition.

Industry 4.0 is a collective term for technologies and concepts of value chain organization.[1] Based on the technological concepts of cyber-physical systems, the Internet of Things[2] and the Internet of Services,[3] it facilitates the vision of the Smart Factory. Within the modular structured Smart Factories of Industry 4.0, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the Internet of Things, Cyber-physical systems communicate and cooperate with each other and humans in real time. Via the Internet of Services, both internal and cross-organizational services are offered and utilized by participants of the value chain.[1]

I’m preparing for my keynote presentation at ProSTEP iViP Symposium 2015 next week in Stuttgart. You can take a look on the program here. The theme of symposium – Smart Engineering clearly associated in my mind with the usage of information. PLM vendors are looking how to explore a potential of information usage for business. As an example, navigate to Harvard Business Review made by PTC CEO Jim Heppelman and Prof Michael Porter of Harvard Business School – How Smart Connected Products are Transforming Competition. Here is an interesting passage to think about:

…connected products are shifting competition in many industries, especially manufacturing. Smart, connected products enable four new categories of capabilities that create breakthroughs in differentiation and operational effectiveness, improve customer experience, and enable new revenue streams. To capitalize, manufacturing firms must rethink nearly everything they do—from how products are designed, and sourced, to how they are manufactured, sold and serviced, to putting in place a whole new kind of IT infrastructure.

Data management is one of the key elements in the success of connected technologies. The same happened in consumer products (think about scale of data technologies behind global connected product Google, Waze, Facebook, etc.)

Forbes article – Industry 4.0 — The Dollars In The Data brings an interesting perspective of data usage related to connected products. It speaks about self optimized assets and predictive maintenance. This topic is incredible complex and it goes much beyond a silly reminder on your car dashboard saying that your maintenance is in 21 days. It is about the ability to make an analysis of gigantic sets of information coming from connected products, mixing it with product data (eg. Bill of Materials) and creating predictive maintenance plans. Here is my favorite passage.

One of the most common use cases for high-value assets is predictive maintenance. Let’s use the example of a gas turbine. Almost ubiquitous internet connectivity means sensors from the turbines can transmit condition data (e.g. voltages, vibrations), usage (e.g. RPM), data, environmental data (e.g. temperature), and other parameters, almost in real-time.

The information sent by these sensors, combined with other information, such as the Bill of Materials, maintenance and engineering data, allows the utility company to retrospectively analyse the behaviour of a turbine. This yields a list of situations that could take the turbine out of service. Which will, in itself, be of great interest and use to the utility.

But rather than stop there, the utility carries on with their line of questioning. Now that they know “What happened?” and “Why did it happen?” it is natural to want to understand “When will it happen again?”, and also “What can we do to stop it from happening?”

These use cases made me think about technological challenges behind product data management capable to recombine data sets coming from senses and stored in PLM systems. In this specific example, we talk about as built serialized bill of materials for a specific gas turbine. The complexity will be growing as we move to different types of industrial equipment and consumer products. Just think about scale of data for this purpose.

What is my conclusion? The real life data management challenges are coming to engineering and manufacturing software providers. Existing technologies might be not up to the scale to support connected product strategies. I can see manufacturing companies looking for new technologies for connected digital world. I wonder if current PLM databases will be up to the job. The gigantic flow of data management and analysis will require a completely different approach in managing data. Just my thoughts…

Best, Oleg

Image courtesy of Stuart Miles at FreeDigitalPhotos.net


COFES 2015: Product Lifecycle, Supply Chain and Data Networks

April 17, 2015


I had a chance to share my thoughts about complexity of product lifecycle in supply chain at COFES 2015 Design and Sustainability symposium. Manufacturing companies and software vendors are facing new enterprise reality these days – distributed environment, connected work and cloud software. On the other side we have skyrocketing complexity of products. Each product is a system these days. Think about simple activity tracking device. It is a combination of hardware, mobile application, cloud data services, big data analytics and API to work with partners. The complexity of modern luxury car is 100M line of software code. Think about product information changes in the system which is combined from engineering, customer, field support and connected devices working together.

Product data complexity is introducing new level of challenge in front of software vendors. I think it is a time for software vendors to think how to break limits of existing PLM architecture to support a level of complexity demanded by manufacturing environment and complexity of products.

So, what to do if a single database approach is dead? Federated architecture was one of the approaches PLM vendors used in the past (Actually, I think, this is probably the only one that works in production for very large enterprises). But this approach is expensive in implementation and requires too much “data pumping” between silos. Opposite to that, an experience of some companies with network based data architectures shows some promising results.

COFES 2015: Product lifecycle, supply chain and data networks from Oleg Shilovitsky

What is my conclusion? The growing complexity of manufacturing environment and products creates the demand for new product lifecycle architectures. These architectures will be able to support management of multidisciplinary product data (mechanical, electronic, software) and will operate as a global distributed data network. Just my thoughts…

Best, Oleg

3D printing of electronics can change product data management

February 5, 2015


3D printing is changing the way we can manufacturing products. Which potentially means changes in how companies are going to manage product development processes. While it is still unclear how it may happen, I wonder if 3D printing can also change the way we manage data about product.

Forget about 3D printing as a way to make plastic covers for mobile devices and furniture for dolls. Medium article – 3D Printed Electronics Have Arrived speaks about very interesting innovation in 3D printing – Voxel8 printer capable to produce a complete electronic device as a single piece. Here is a high level explanation about printing process:

The printer uses a modular design to print both circuitry and plastic parts. One printer head extrudes PLA plastic, building the bulk of the object, while another head prints out circuitry using a very conductive ink. As the printing process goes along, the printer automatically pauses (thanks to some nifty software from Autodesk) to allow the designer to insert electrical components like motors and resistors into the print. Once the component is placed, the printer automagically resumes printing where it left off.

What future scenario you can think about? The following passage is proposing “printing phones in store” as an option:

This printer is important because this is your future. Eventually the price for circuit-printing printers will come down, and we will see electronics shops that print phones in the store, rather than buying them from a 3rd world sweat shop. I expect that within a decade average users may even be able to customize the shape and color of their phone to their liking.


The story made me think about how a new 3D printing approach can influence the way we are managing data about products. Currently, the design is done separately for electronics and mechanical parts. Think about PCB design, electrical components and plastic body. You have data managed separate in these systems. Then you have to bring all elements of product together to create an engineering and manufacturing bill of materials. The new approach can change some fundamental principles companies are managing data today. It is hard to say how it will work, but my hunch that composed bill of material should be available at much earlier stage. It might influence the integration of design and assembly tools.

What is my conclusion? Changing paradigms. This is probably the easiest way to describe a potential change that devices like Voxel8 can bring. It can change product data management fundamentals by requiring to manage product structure differently. It can potentially change processes between engineering and manufacturing as well. Just my thoughts…

Best, Oleg

A CT scan of a 3D Printed drone (courtesy: Voxel8)

IoT data will blow up traditional PLM databases

September 23, 2014


IoT is one of the recent popular buzzwords in business and consumer world. I’ve been touching it in few of my previous posts – 3 Things PLM can do with IoT tomorrow and PLM and IoT common trajectories . Things changed from 2009 when I first time touched PLM and Internet of Things. The opportunity to connect huge amount of devices to the internet looks like a reality we are going to face tomorrow. Gartner’s Hype Cycle Special Report for 2014 report put Internet of Things on the top of the curve indicating – Peak of Inflated Expectations.


So, what can we do about that? My attention was caught by Joe Barkai’s article – The Emergence of Application-Specific IoT. Read the article and draw your opinion. My favorite passage from the article was about "Content and IoT" – a tremendous value of content coming from connected devices.

The business potential is not in the conduit, or the “plumbing” of the IoT; it is in the content. However, the data streams of disparate devices cannot be simply connected. Nor can the portfolio of dissimilar applications communicating with these devices be simply daisy-chained as depicted by those futuristic scenarios. Data interoperability is critical to harvest the potential value of the IoT’s content and to enable new meaningful business models that utilize its data. That means not only compatible and interoperable data protocols, but also, more critically, data models and common semantics, so that disparate devices and services can be linked, aggregated and harmonized to form an IoT solution.

The last thing about linking, aggregating and harmonizing information coming from connected devices contains huge potential for design and product lifecycle management. We can certainly dream about how to bring all information into PLM applications. However, here is the thing… Will existing PLM data architecture is capable to handle such data scale? Remember my "Will PLM data reach yottabytes?" article.


I want to come back to my thoughts about future of data management and PLM. Existing data architectures were developed back 10-20 years ago. It looks like vendors will have to re-tool PLM platforms with new technologies and capabilities coming from a modern PLM data management landscape.

What is my conclusion? IoT is going to change the scale of data that modern product development and manufacturing companies are consuming. It is not about managing of documents or even bill of materials. The tremendous amount of data will be collected from connected devices and transformed into consumable information assets. I don’t think somebody can predict where this process can take us today. However, it is coming, for sure. It is an alarm for PLM architects, data scientists and IT managers in manufacturing companies. Just my thoughts…

Best, Oleg

Will GE give a birth to a new PLM company?

July 9, 2014


Navigate back into histories of CAD and PLM companies. You can find significant involvement of large aerospace, automotive and industrial companies. Here are few examples – Dassault Systemes with Dassault Aviation, SDRC with US Steel, UGS with McDonnell Douglas. In addition to that, involvement of large corporation as strategic customers, made significant impact on development of many CAD/PLM systems for the past two decades. Do you think we can see something similar in the future?

Inc. article GE’s Grand Plan: Build the Next Generation of Data Startups made me think about some potential strategic involvement of large industrial companies in PLM software business. The following passage can give you an idea of how startups will be organized.

A team from GE Software and GE Ventures has launched an incubator program in partnership with venture capital firm Frost Data Capital to build 30 in-house startups during the next three years that will advance the "Industrial Internet," a term GE coined. The companies will be housed in Frost’s incubator facility in Southern California.

By nurturing startups that build analytical software for machines from jet engines to wind turbines, the program, called Frost I3, aims to dramatically improve the performance of industrial products in sectors from aviation to healthcare to oil and gas. Unlike most incubator programs, GE and Frost Data are creating the companies from scratch, providing funding and access to GE’s network of 5,000 research assistants and 8,000 software professionals. The program has already launched five startups in the past 60 days.

This story connects very well to GE vision and strategy for so called Industrial Internet. The following picture can provide you some explanations of what is the vision of GE industrial cloud.


What is my conclusion? Industrial companies are looking for new solutions and probably ready to invest into ideas and innovative development. Money is not a problem for these companies, but time is very important. Startups is a good way to accelerate development and come with fresh ideas of new PLM systems. Strategic partnership with large company can provide resources and data to make it happen. Just my thoughts…

Best, Oleg

Picture credit of GE report.

What PLM Architects and Developers Need to Know about NoSQL?

July 7, 2014


People keep asking me questions about NoSQL. The buzzword "NoSQL" isn’t new. However, I found it still confusing, especially for developers mostly focusing on enterprise and business applications. For the last decade, database technology went from single decision to much higher level of diversity. Back in 1990s, the decision of PDM/PLM developers was more or less like following – "If something looks like document, use Excel and Office. Otherwise, use RDBMS". Not anymore. My quick summary of NoSQL was here – What PLM vendors need to know about NoSQL databases. You can go more deep in my presentation – PLM and Data Management in 21st century. If you feel more "geeky", and considering maybe summer development projects, I can recommend you the following book – 7 Database in 7 weeks.

John De Goes blog post The Rise (and Fall?) of NoSQL made me think how to explain the need of NoSQL for PLM implementers, architects and developers. In a nutshell, here is the way I’d explain that – NoSQL databases allow you to save variety of specific data in a much simple way, compared to SQL structured information. So, use right tool for the right job – key/value; document; graph, etc.

So, NoSQL is accelerating development of cloud and mobile apps. It became much faster since some specific NoSQL databases tuned for particular type of non-structured data:

With NoSQL: (1) Developers can stuff any kind of data into their database, not just flat, uniform, tabular data. When building apps, most developers actually use objects, which have nesting and allow non-uniform structure, and which can be stored natively in NoSQL databases. NoSQL databases fit the data model that developers already use to build applications. (2) Developers don’t have to spend months building a rigid data model that has to be carefully thought through, revised at massive cost, and deployed and maintained by a separate database team within ops.

However, everything comes with price. The important insight of the article is to point on how data can be reused for reporting and other purposes. The following passage summarizes the most visible part of what is missing in NoSQL:

It’s quite simple: analytics tooling for NoSQL databases is almost non-existent. Apps stuff a lot of data into these databases, but legacy analytics tooling based on relational technology can’t make any sense of it (because it’s not uniform, tabular data). So what usually happens is that companies extract, transform, normalize, and flatten their NoSQL data into an RDBMS, where they can slice and dice data and build reports.

PDM and PLM products are evolving these days from early stage of handling "records of metadata" about files towards something much more complicated – large amount of data, unstructured information, video, media, processes, mobile platforms, analytics. CAD/PLM vendors are pushing towards even more complicated cloud deployment. The last one is even more interesting. The need to rely on customer RDBMS and IT alignment is getting lest restrictive. So, the opportunity to choose right database technology (aka the right tool for a job) is getting more interesting.

What is my conclusion? Database technologies universe is much more complicated compared to what we had 10-15 years ago. You need to dig inside into data management needs, choose right technology or tool to be efficient. One size doesn’t fit all. If you want to develop an efficient application, you will find yourself using multiple data management technologies to handle data efficiently. Just my thoughts…

Best, Oleg


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