PLM: from sync to link

October 17, 2014

plm-data-link-sync

Data has an important place in our life. Shopping lists, calendars, emails, websites, family photos, trip videos, documents, etc. We want our data to be well organized and easy to find. Marketing folks like to use the term – data at your fingertips. However, the reality is just opposite. Data is messy. We store it in multiple places, we forget names of documents and we can hardly control it.

Everything I said above applies to manufacturing companies too. But, it gets even more complicated. Departments, contractors, suppliers, multiple locations and multiple systems. So, data lives in silos – databases, network drives, databases, multiple enterprise systems. In my article – PLM One Big Silo, I’ve been talking about organizational and application silos. The data landscape in every manufacturing company is very complex. Software vendors are trying to crush silos by introducing large platforms that can help to integrate and connect information. It takes time and huge cost to implement such system in a real world organization. Which makes it almost a dream for many companies.

In my view, openness will play a key role in the ability of system to integrate and interconnect. It will help to get access to information across the silos and it leads to one of the key problem of data sharing and identity. To manage data in silos is a complex tasks. It takes time to organize data, to figure out how to interconnect data, organize data reporting and to support data consistency. I covered it more in my PLM implementations: nuts and bolts of data silos article.

Joe Barkai’s article Design Reuse: Reusing vs. Cloning and Owning speaks about the problem of data re-use. In my view, data reuse problem is real and connected directly to the issue of data silos. I liked the following passage from Joe’s article:

If commonly used and shared parts and subsystems carry separate identities, then the ability to share lifecycle information across products and with suppliers is highly diminished, especially when products are in different phases of their lifecycle. In fact, the value of knowledge sharing can be greater when it’s done out of sync with lifecycle phase. Imagine, for example, the value of knowing the manufacturing ramp up experience of a subsystem and the engineering change orders (ECOs) that have been implemented to correct them before a new design is frozen. In an organization that practices “cloning and owning”, it’s highly likely that this kind of knowledge is common knowledge and is available outside that product line.

An effective design reuse strategy must be built upon a centralized repository of reusable objects. Each object—a part, a design, a best practice—should be associated with its lifecycle experience: quality reports, ECOs, supplier incoming inspections, reliability, warranty claims, and all other representations of organizational knowledge that is conducive and critical to making better design, manufacturing and service related decisions.

Unfortunately, the way most of companies and software vendors are solving this problem today is just data sync. Yes, data is syncing between multiple systems. Brutally. Without thinking multiple times. In the race to control information, software vendors and implementing companies are batch-syncing data between multiple databases and applications. Parts, bill of materials, documents, specifications, etc. Data is moving from engineering applications to manufacturing databases back and forth. Specifications and design information is syncing between OEM controlled databases and suppliers’ systems. This data synchronization is leading to lot of inefficiency and complexity.

It must be a better way to handle information. To allow efficient data reuse, we need to think more about how to link data together and not synchronize it between applications and databases. This is not a simple task. Industry that years was taking "sync" as a universal way to solve problem of data integration cannot shift overnight and work differently. But here is a good news. For the last two decades, web companies accumulated lot of experience related to management of huge volumes of interconnected data. The move towards cloud services is creating an opportunity to work with data differently. It will provide new technologies of data integration and data management. It also can open new ways to access data across silos. As a system that manage product data, PLM can introduce a new way of linking information and help to reuse data between applications.

What is my conclusion? There is an opportunity to move from sync to link of data. It will allow to simplify data management and will help to reuse data. It requires conceptual rethink of how problems of data integrations are solved between vendors. By providing "link to data" instead of actually "syncing data", we can help company to streamline processes and improve quality of products. Just my thoughts…

Best, Oleg


IoT data will blow up traditional PLM databases

September 23, 2014

plm-iot-data-explosion

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.

Hypercycle_Gartner_2014-2

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.

bigdatasizing-1024x746

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


How to visualize future PLM data?

August 12, 2014

collective experience of empathetic data systems

I have a special passion for data and data visualization. We do it every day in our life. Simple data, complex data, fast data, contextual data… These days, we are surrounded by data as never before. Think about typical engineer 50-60 years ago. Blueprints, some physical models… Not much information. Nowadays the situation is completely different. Multiple design and engineering data, historical data about product use, history of design revisions, social information, data about how product is performing coming in real time from sensors, etc. Our ability to discover and use data becomes very important.

The ways we present data for decision making can influence a lot and change our ability to design in context of right data. To present data for engineers and designers these days can become as important as presenting right information to airplane pilots before. Five years ago, I posted about Visual Search Engines on 3D perspective blog. I found the article is still alive. Navigate your browser here to have a read. What I liked in the idea of visual search is to present information in the way people can easy understand.

Few days ago, my attention was caught by TechCrunch article about Collective Experience of Empathetic Data Systems (CEEDS) project developed in Europe.

[The project ]… involves a consortium of 16 different research partners across nine European countries: Finland, France, Germany, Greece, Hungary, Italy, Spain, the Netherlands and the UK. The “immersive multi-modal environment” where the data sets are displayed, as pictured above — called an eXperience Induction Machine (XIM) — is located at Pompeu Fabra University, Barcelona.

Read the article, watch video and draw your conclusion. It made me think about the potential of data visualization for design. Here is my favorite passage from the article explaining the approach:

“We are integrating virtual reality and mixed reality platforms to allow us to screen information in an immersive way. We also have systems to help us extract information from these platforms. We use tracking systems to understand how a person moves within a given space. We also have various physiological sensors (heart rate, breathing etc.) that capture signals produced by the user – both conscious and subconscious. Our main challenge is how to integrate all this information coherently.”

Here is the thing. The challenge is how to integrated all the information coherently. Different data can be presented differently – 3D geometry, 2D schema, 2D drawings, graphics, tables, graphs, lists. In many situations we can get this information presented separately using different design and visualization tools. However, the efficiency is questionable. Many data can be lost during visualization. However, what I learned from CEEDS project materials, data can be also lost during the process of understanding. Blindspotting. Our brain will miss the data even we (think) that we present it in a best way.

What is my conclusion? Visualization of data for better understanding will play an increased role in the future. We just in the beginning of the process of data collection. We understand the power of data and therefore collect an increased amount of data every day. However, to process of data and visualizing for better design can be an interesting topic to work for coming years. Just my thoughts…

Best, Oleg


Will public clouds help enterprises to crunch engineering data?

August 6, 2014

google-data-center-crunches-engineering-data

The scale and complexity of the data is growing tremendously these days. If you go back 20 years, the challenge for PDM / PLM companies was how to manage revisions CAD files. Now we have much more data coming into engineering department. Data about simulations and analysis, information about supply chain, online catalog parts and lot of other things. Product requirements are transformed from simple word file into complex data with information about customers and their needs. Companies are starting to capture information about how customers are using products. Sensors and other monitoring systems are everywhere. The ability to monitor products in real life creates additional opportunities – how to fix problems and optimize design and manufacturing.

Here is the problem… Despite strong trend towards cheaper computing resources, when it comes to the need to apply brute computing force, it still doesn’t come for free. Services like Amazon S3 are relatively cheap. However, if we you want to crunch and make analysis and/or processing of large sets of data, you will need to pay. Another aspect is related to performance. People are expecting software to work at a speed of user thinking process. Imagine, you want to produce design alternatives for your future product. In many situations, to wait few hours won’t be acceptable. It will be destructing users and they won’t use such system after all.

Manufacturing leadership article Google’s Big Data IoT Play For Manufacturing speaks exactly about that. What if the power of web giants like Google can be used to process engineering and manufacturing data. I found explanation provided by Tom Howe, Google’s senior enterprise consultant for manufacturing quite interesting. Here is the passage explaining Google’s approach.

Google’s approach, said Howe, is to focus on three key enabling platforms for the future: 1/ Cloud networks that are global, scalable and pervasive; 2/ Analytics and collection tools that allow companies to get answers to big data questions in 10 minutes, not 10 days; 3/ And a team of experts that understands what questions to ask and how to extract meaningful results from a deluge of data. At Google, he explained, there are analytics teams assigned to every functional area of the company. “There’s no such thing as a gut decision at Google,” said Howe.

It sounds to me like viable approach. However, it made me think about what will make Google and similar computing power holders to sell it to enterprise companies. Google ‘s biggest value is not to selling computing resources. Google business is selling ads… based on data. My hunch there are two potential reasons for Google to support manufacturing data inititatives – potential to develop Google platform for manufacturing apps and value of data. The first one is straightforward – Google wants more companies in their eco-system. I found the second one more interesting. What if manufacturing companies and Google will find a way to get an insight from engineering data useful for their business? Or even more – improving their core business.

What is my conclusion? I’m sure in the future data will become the next oil. The value of getting access to the data can be huge. The challenge to get that access is significant. Companies won’t allow Google as well as PLM companies simply use the data. Companies are very concerned about IP protection and security. To balance between accessing data, providing value proposition and gleaning insight and additional information from data can be an interesting play. For all parties involved… Just my thoughts..

Best, Oleg

Photo courtesy of Google Inc.


The end of single PLM database architecture is coming

August 5, 2014

PLM-distributed-cloud-database-architecture

The complexity of PLM implementations is growing. We have more data to manage. We need to process information faster. In addition to that, cloud solutions are changing the underlining technological landscape. PLM vendors are not building software to be distributed on CD-ROMs and installed by IT on corporate servers anymore. Vendors are moving towards different types of cloud (private and public) and selling subscriptions (not perpetual licenses). For vendors it means operating data centers, optimize data flow, cost and maintenance.

How to implement future cloud architecture? This question is coming to the focus and, obviously, raising lots of debates. Infoworld cloud computing article The right cloud for the job: multi-cloud database processing speaks about how cloud computing is influencing what is the core of every PDM and PLM system – database technology. Main message is to move towards distributed database architecture. What does it mean? I’m sure you are familiar with MapReduce approach. So, simply put, the opportunity of cloud infrastructure to bring multiple servers and run parallel queries is real these days. The following passage speaks about the idea of how to optimize data processing workload by leveraging cloud infrastructure:

In the emerging multicloud approach, the data-processing workloads run on the cloud services that best match the needs of the workload. That current push toward multicloud architectures provides the ability to place workloads on the public or private cloud services that best fit the needs of the workloads. This also provides the ability to run the workload on the cloud service that is most cost-efficient.

For example, when processing a query, the client that launches the database query may reside on a managed service provider. However, it may make the request to many server instances on the Amazon Web Services public cloud service. It could also manage a transactional database on the Microsoft Azure cloud. Moreover, it could store the results of the database request on a local OpenStack private cloud. You get the idea.

However, not so fast and not so simple. What works for web giants might not work for enterprise data management solutions. The absolute majority of PLM systems are leveraging single RDBMS architecture. This is fundamental underlining architectural approach. Most of these solutions are using "scale up" architecture to achieve data capacity and performance level. Horizontal scale of PLM solutions today is mostly limited to leverage database replication tech. PLM implementations are mission critical for many companies. To change that would be not so simple.

So, why PLM vendors might consider to make a change and to think about new database architectures? I can see few reasons – the amount of data is growing; companies are getting even more distributed; design anywhere, build anywhere philosophy comes into real life. The cost of infrastructure and data services becomes very important. In the same time for all companies performance is an absolute imperative – slow enterprise data management solutions is a thing in the past. To optimize workload and data processing is an opportunity for large PLM vendors as well as small startups.

What is my conclusion? Today, large PLM implementations are signaling about reaching technological and product limits. It means existing platforms are achieving a possible peak of complexity, scale and cost. To make the next leap, PLM vendors will have to re-think underlining architecture, to manage data differently and optimize cost of infrastructure. Data management architecture is the first to be considered. Which means end of existing "single database" architectures. Just my thoughts…

Best, Oleg


Importance of data curation for PLM implementations

August 4, 2014

curate-data-mess

The speed of data creation is amazing these days. According to the last IBM research, 90% of the data in the world today has been created in the last two years alone. I’m not sure if IBM counting all enterprise data, but it doesn’t change much- we have lots of data. In manufacturing company data is created inside of the company as well as outside. Design information, catalogs, manufacturing data, business process data, information from supply chain – this is only beginning. Nowadays we speak about information made by customers as well as machined (so called Internet of Things).

One of the critical problems for product lifecycle management was always how to feed PLM system with the right data. To have right data is important – this is a fundamental thing when you implement any enterprise system. In the past I’ve been posted about PLM legacy data and importance of data cleanup.

I’ve been reading The PLM State: Getting PLM Fit article over the weekend. The following passage caught my special attention since it speaks exactly about the problem of getting right data in PLM system.

[…] if your data is bad there is not much you can do to fix your software. The author suggested focusing on fixing the data first and then worrying about the configurations of the PLM. […] today’s world viewing the PLM as a substitute for a filing cabinet is a path to lost productivity. Linear process is no longer a competitive way to do business and in order to concurrently develop products, all information needs to be digital and it needs to be managed in PLM. […] Companies are no longer just collecting data and vaulting it. They are designing systems to get the right data. What this means on a practical level is that they are designing their PLM systems to enforce standards for data collection that ensure the right meta data is attached and that meaningful reports can be generated from this information.

PLM implementations are facing two critical problems: 1/ how to process large amount of structured and unstructured information prior to PLM implementation; 2/ how constantly curate data in PLM system to bring right data to people at the right time. So, it made me think about importance of data curation. Initially, data curation term was used mostly by librarian and researchers in the context of classification and organization of scientific data for future reuse. The growing amount and complexity of data in the enterprise, can raise the value of digital data curation for implementation and maintenance of enterprise information systems. PLM is a very good example here. Data must be curated before get into PLM system. In addition to that, data produced by PLM system must be curated for future re-use and decision making.

What is my conclusion? The complexity of PLM solutions is growing. Existing data is messy and requires special curation and aggregation in order to be used for decision and process management. The potential problem of PLM solution is to be focused on a very narrow scope of new information in design an engineering. Lots of historical record as well as additional information are either lost or disconnected from PLM solutions. In my view, solving these problems can change the quality of PLM implementations and bring additional value to customers. Just my thoughts…

Best, Oleg


PLM security: data and classification complexity

July 30, 2014

security-plm

Security. It is hard to underestimate the importance of the topic. Information is one of the biggest assets companies have. Data and information is a lifeblood of every engineering and manufacturing organization. This is a key element of company IP. Combined of 3D models, Bill of Materials, manufacturing instructions, suppliers quotes, regulatory data and zillions of other pieces of information.

My attention caught Forrester TechRadar™: Data Security, Q2 2014 publication. Navigate to the following link to download the publication. The number of data security points is huge and overwhelming. There are different aspects of security. One of the interesting facts I learned about security from the report is growing focus on data security. Data security budgets are 17% as for 2013 and Forester predicts the increase of 5% in 2014.

forrester-data-security-plm

The reports made me think about some specific characteristics of PLM solutions – data and information classification. The specific characteristic of every PLM system is high level of data complexity, data richness and dependencies. The information about product, materials, BOMs, suppliers, etc. is significantly intertwined. We can speak a lot of about PLM system security and data access layers. Simple put, it takes a lot of specifics of product, company, business process and vendor relationships. As company business is getting global, security mode and data access is getting very complicated. Here is an interesting passage from report related to data classification:

Data classification tools parse structured and unstructured data, looking for sensitive data that matches predened patterns or custom policies established by customers. Classiers generally look for data that can be matched deterministically, such as credit card numbers or social security numbers. Some data classiers also use fuzzy logic, syntactic analysis, and other techniques to classify less-structured information. Many data classification tools also support user-driven classification that users can add, change, or conrm classification based on their knowledge and the context of a given activity. Automated classication works well when you’re trying to classify specic content such as credit card numbers but becomes more challenging for other types of content.

In my view, PLM content is one of the best examples of data that can be hardly classified and secured. It takes long time to specify what pieces of information should be protected and how. Complex role-based security model, sensitive IP, regulation, business relations and many other factors are coming into play to provide classification model to secure PLM data.

What is my conclusion? I can see a growing concern to secure data access in complex IT solutions. PLM is one of them. To protect complex content is not simple – in many situations out of the box solutions won’t work. PLM architects and developers should consider how to provide easier ways to classify and secure product information and at the same time be compliant with multiple business and technical requirements. Important topic for coming years. Just my thoughts…

Best, Oleg


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