Will Google Set Future PLM Information Standards?

December 5, 2013


One of the core capabilities of Product Lifecycle Management is the ability to define and manage a variety of information about product – requirements, design, engineering, manufacturing, support, supply, etc. In order to do so, PLM vendors developed data management technologies and flexible frameworks that can handle product data. You may find some of my previous posts about PLM data modeling – PLM and Data Model Pyramid and What is the right data model for PLM?

Nowadays, Data Management is going through a cambrian explosion of database options. I touched it a bit in my writeup PLM and Data Modeling Flux. If you have few free minutes over the weekend, take a look on the presentation – PLM and Data Management in 21st century I shared on TechSoft TechTalk few weeks ago in Boston.

The biggest problem in handling product data and information is related to a growing complexity of data and dependencies. In addition to original complexity of multidisciplinary data representations (design, engineering, manufacturing), we can see a growing needs to manage customer data, supply chain, social networks, etc. I’ve been pointing on Google Knowledge Graph as one of the interesting technologies to maintain a complex set of interlinked information. Read my post – Why PLM need to learn about Google Knowledge Graph to learn more. Google is not the only vendor interested in how to maintain product data. My post – 3 things PLM can learn from UCO (Used Car Ontology) speaks about variety of technologies used to model car product information.

The following article caught my attention this morning (thanks to one of my blog readers) – Google adds car facts, prices to its Knowledge Graph. I found it very interesting and connected to my previous thoughts about how product information can be managed in the future. Here is a passage from the article:

Starting today, users can search the make, model, and year of a car to find out a variety of information, directly from the Google search page. For instance, if you search “Tesla Model S”, the Knowledge Graph will now show up and present you with the MSRP, horsepower, miles-per-gallon, make, and available trims. Different cars show a different set information, as well. Should you search “Ford Focus”, you will be presented with the MSRP, MPG, and horsepower, as well as the engine size, body styles, and other years.

I made few searches and captured following screenshots. You can see how Google Knowledge Graph semantically recognizes right information and snippets of data about vehicle.


The following set of information about Mazda6 also presents the fact GKS keeps the information about multiple models and model years (revisions?).



What is my conclusion? Most of CAD/PLM companies are very protective about data models and the ways they store, manage and share data. It can provide a potential problem in the future, which will probably will become more open and transparent. We clearly need to think what standards can support future product information modeling. Here is the thing – consumerization can come to this space exactly in the same way it came to some other domains. The future product information management standards might be developed by web giants and other companies outside of CAD/PLM space. Data architects and technologies must take a note. Just my thoughts…

Best, Oleg

*picture is courtesy of http://9to5google.com blog.

Why PLM needs to learn Open World Assumption?

December 6, 2012

Have you heard about OWA (Open World Assumption)? If you completed your Math 101 and Mathematical Logic time ago, refresh your memories by navigating to the following Wikipedia article. Here is the definition:

In formal logic, the open world assumption is the assumption that the truth-value of a statement is independent of whether or not it is known by any single observer or agent to be true. It is the opposite of the closed world assumption, which holds that any statement that is not known to be true is false. The open world assumption (OWA) is used in knowledge representation to codify the informal notion that in general no single agent or observer has complete knowledge, and therefore cannot make the closed world assumption. The OWA limits the kinds of inference and deductions an agent can make to those that follow from statements that are known to the agent to be true. In contrast, the closed world assumption allows an agent to infer, from its lack of knowledge of a statement being true, anything that follows from that statement being false.

The OWA approach is opposite to CWS (Closed World Assumption) used by programming languages and databases.

The closed world assumption typically applies when a system has complete control over information; this is the case with many database applications where the database transaction system acts as a central broker and arbiter of concurrent requests by multiple independent clients (e.g., airline booking agents). There are however many databases with incomplete information: one cannot assume that because there is no mention on a patient’s history of a particular allergy, that the patient does not suffer from that allergy.

PDM and PLM, as a typical database-driven applications, are following CWA approach. In many situations it makes a lot of sense. When you releasing BOM to production you want to be sure all line items in this Bill of Material are secured and released. However, it made me think that CWA might provide some limitation to PLM application development today and even more in the future. I’ve been reading Semanticweb.com blog – Introduction to Open World Assumption. Navigate to the link to read more. The article provide a good explanation about systems with complete and incomplete information. Here is the snippet of this definition.

The CWA applies when a system has complete information. This is the case for many database applications. For example, consider a database application for airline reservations. If you are looking for a direct flight between Austin and Madrid, and it doesn’t exist in the database, then the result is “There is no direct flight between Austin and Madrid.” For this type of application, this is the expected and correct answer. On the other hand, OWA applies when a system has incomplete information. This is the case when we want to represent knowledge (a.k.a Ontologies) and want to discover new information. For example, consider a patient’s clinical history system. If the patient’s clinical history does not include a particular allergy, it would be incorrect to state that the patient does not suffer from that allergy. It is unknown if the patient suffers from that allergy, unless more information is given to disprove the assumption.

Lifecycle and Incomplete information

I came to conclusion that incomplete information modeling approach (supported by OWA) can provide some advantages to the systems intensively focusing on product lifecycle modeling and lifecycle information modeling. Think about lifecycle as a information discovery. Modern PLM business problems are facing situation of information incompleteness almost every day. New regulations, changed business requirements, new product configurations, etc. All these situations require to apply changes to existing PLM systems. Flexibility is one of the key requirements. OWA approach can improve the ability of PLM system to support a change and to decrease the cost of this change.

What is my conclusion? Flexibility and cost of change are two major requirements to PLM systems today. The time when PLM development was focused on the OTB (Out of the box) approach is over. The ability to apply changes or to connect a new source of information without modification of the system code can be an interesting opportunity. PLM developers can check how to apply OWA principles and to make PLM system more robust and reliable. Just my thoughts…

Best, Oleg

Picture credit semanticweb.com article.

What is the right data model for PLM?

August 17, 2012

I think the agreement about importance of the data model among all implementers of PDM / PLM is almost absolute. Data drives everything PDM / PLM system is doing. Therefore, to define the data model is the first step in many implementations. It sounds as something simple. However, there is implied complexity. In most cases, you will be limited by the data model capabilities of PLM system you have. This is a time, I want to take you back in history.

Spreadsheet Data Model

Historically, it became the most commonly used data model. And the reason is not only because Excel is available to everybody. In my view, it happened also, because tables (aka spreadsheets) is a simple way to think about your data. You can think about table of drawings, parts, ECOs. Since almost everything in engineering starts from Bill of Material, to think about BOM table is also very simple. The key reason why in many cases spreadsheet model became so wide-accepted are simplicity and absolute flexibility. Engineers love flexibility, and this data model became widely popular.

Relational Data Model

This data model was developed by Edgar Codd back more than 50 years ago. Database software runs on top of this model, and we got what known today as RDBMS. Until second half of the last decade, it was the solution all PDM /PLM developers were relying. First PDM systems were developed based on RDBMS. However, they had low flexibility. Because of rigorous rules of this model, making changes was considered as not a simple task. One of the innovations of late 1990s was to develop a flexible data model as an abstraction on top of RDBS. Almost all PDM/PLM systems in production today are using object abstractions developed on top of the relational data model.

The challenges of Spreadsheets and Relational Databases

Despite these technologies are proven and used by many mainstream applications, it is far from perfection. One of the product development demands is flexibility. Spreadsheet model can deliver that, but gets very costly within the time. Relational data model can combine flexibility and support manageability of data. However, it becomes to make a change in these models is costly. Identification, openness and expandability is problematic in relational data models opposite to some other web-based solutions.

Future data models – NoSQL, RDF, etc.

Thinking about what comes in the future, I want to spell to buzzwords – NoSQL and Semantic Web. I can see a growing amount of solutions trying to adopt a variety of new data platforms. NoSQL comes to the place as an alternative solution to Relational Database. If this is a first time you’re hearing this buzzword, navigate to the following Wikipedia link. NoSQL is not all the same. It combined the whole group of solutions such a key-value stores, object databases, graph databases, triple store. Semantic web is collaborative movement led by W3C. The collection of Semantic Web technologies (RDF, OWL, SKOS, SPARQL, etc.) provides an environment where application can query that data, draw inferences using vocabularies, etc. Part of these standards something called Linked Data – a collection of data set in open formats (RDF) that shared on the web.

What is my conclusion? Many of the technologies used by PLM companies these days are outdated and came from the past 20-25 years. There is nothing wrong in these technologies. They are proven and successfully used for many applications. However, in order to achieve the next level of efficiency and embrace future of PLM, new horizons need to be explored. Data flexibility, openness and interoperability – these elements are absolutely important in the future of PLM. Options to use future data models coming from past 10 years of web experience need to be explored. Important. Just my thoughts…

Best, Oleg

Image: FreeDigitalPhotos.net

PLM, RDBMS and Future Data Management Challenges

January 5, 2012

It is not unusual to hear about problems with PLM systems. It is costly, complicated, hard to implement and non-intuitive. However, I want to raise a voice and speak about data management (yes, data management). Most of PDM/PLM software is running on top of data-management technologies developed and invented 30-40 years ago. The RDBM history is going back to the invention made by Edgar Codd at IBM back in 1970.

I was reading Design News article – Top automotive trends to watch in 2012. Have a read and make your opinion. One of trends was about growing complexity of electrical control units. Here is the quote:

As consumers demand more features and engineers comply, automakers face a dilemma: The number of electronic control units is reaching the point of unmanageability. Vehicles now employ 35 to 80 microcontrollers and 45 to 70 pounds of onboard wiring. And there’s more on the horizon as cameras, vision sensors, radar systems, lanekeeping, and collision avoidance systems creep into the vehicle.

It made me think about potential alternatives. Even if I cannot see any technology these days that can compete on the level of cost, maturity and availability with RDBMS, in my view, now it is a right time to think about future challenges and possible options.

Key-Value Store

These types of stores became popular over the past few years. Navigate to the following article by Read Write Enterprise –Is the Relational Database Doomed? Have a read. The article (even if it a bit dated) provides a good review of key-value stores as a technological alternative to RDBMS. It obviously includes pros and cons. One of the biggest "pro" to use key-value store is scalability. Obvious bad is an absence of a good integrity control.

NoSQL (Graph databases)

Another interesting example of RDBMS alternative is so-called noSQL databases. The definition and classification of noSQL databases is not stable. Before jumping into noSQL bandwagon, analyze the potential impact of immaturity, complexity and absence of standards. However, over the last 1-2 year, I can see a growing interest into this type of technology. Neo4j is a good example you can experiment with in case you are interested.

Semantic Web

Semantic web (or web of data) is not a database technology. Opposite to RDBMS, Key-value stores and graph databases, semantic web is more about how to provide a logical and scalable way to represent data (I wanted to say in "semantic way", but understand the potential of tautology :)). Semantic web relies on a set of W3C standard and combines set of specification describing ways to represent and model data such as RDF and OWL. You can read more by navigating to the following link.

What is my conclusion? I think, the weak point of existing RDBMS technologies in the context of PLM is a growing complexity of data – both from structural and unstructured aspects. The amount of data will raise lots of questions in front of enterprise IT in manufacturing companies and PLM vendors. Just my thoughts…

Best, Oleg

PLM and The Power Of Pull

June 23, 2010

I had chance to have a talk with David Siegel, entrepreneur, speaker and the author of a new book “Pull“. We met during the Semantic Technology Conference in San-Francisco yesterday. The sub title of this book state – The Power Of Semantic Web To Transform Your Business. You can take a look on David’s web site – The Power Of Pull. My first question to David was about the idea of name “Pull”. His answer was very interesting. The working name of the book was Business 3.0. However, people didn’t get this name. So, he tried to get to the bottom line of what his ideas are about and came to the definition of “Pull”. In the nutshell of Pull, we are going to move to the world where information will be available at the time we need it. I found it interesting, since it solves, in my view, one of the most important problems we have today with enterprise software in general and PLM specifically- how to make systems more intuitive?

Pull Concept: Product Lifecycle Management

Here is my simple explanation about how to shift to Pull in PLM. On the very basic level PLM is about how to track Products and Information related to products from the early beginning (actually interaction with potential customer about what product they want) through all phases of product design, engineering, manufacturing and disposal. The interesting thing – I can see this chain does exist today. However, when we want to touch it, get information about it, analyze it, we need to push a lot of stuff around us. It related to all aspects of a product life cycle. We need to work on so many sources of information to get actually what we need. Existing systems are trying to organize everything around products. The so called “single point of truth”. So, it puts “the product” is in the middle. The shift to Pull may happen when we will put people in the middle of the circle. Then all information about a product will become findable and available for pull.

How to Pull Product Lifecycle Data

Product Lifecycle is represented by data. There are lots of data around us representing ideas, information about potential customers, requirements, product design, manufacturing, supply chain, retail, physical products. The most interesting question for me is how we can make this data available and retrievable at the time we need it? The naive answer – just search across all web sites, intranet and enterprise software products to find all what I need. However, here is the problem – I don’t know what I need to get. I’d prefer somebody will take care to organize information around me. This is “aha moment” to think about “pull” as a different concept. Product Lifecycle Data need to have an ability to be organized to become available. I can see initial shifts into this direction across multiple spaces. One of them is personalization of search and organizing of social and real-time information. I think, we will see more products going into this direction.

PLM, Pull  and Openness

Openness will play a significant role in PLM shift to Pull. Today, PLM is the kingdom of proprietary information. Software vendors take a significant care about how to make data created in their software products available only in their own products. There is a simple and obvious explanation to that- business. They are making money by selling their software products. Just think about, what if tomorrow they will make money from making data created using their software product available? This is a major shift to “Pull” concept. It will shift industry from a closed and proprietary world we live today into the open world of tomorrow.

What is my short take on this? Important. Take your time to read this book. It contains lots of ideas in diverse set of business fields. Find your problems. Focus on low hanging fruits. Get it done.

Best, Oleg



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