PLM and Unknown Unknowns Use Cases

April 30, 2013

Recent tragic event in Boston, raised again the question about critical role of real time information integration. You may think, it is not something that related to engineering and manufacturing software. Until recent time, I’ve seen it exactly in the same way. However, with the latest trends in the developing of data and information systems, I can see how big data, data analytic and analyzes can be used by business enterprise software too. Getting back to events of 9/11, Donald Rumsfeld, US Secretary of State for Defence, stated at a briefing: ‘There are known knowns. There are things we know that we know. There are known unknowns. That is to say, there are things that we now know we don’t know. But there are also unknown unknowns. There are things we do not know we don’t know.’ Originally “unknown unknowns” statement was considered as a nonsense. However, if we think twice, the concept of unknown unknowns might be relevant to many companies in manufacturing.

One of the key roles of PLM these days is to help companies to innovate. There are some many definitions of “innovation”. You can think about innovating organization, innovative processes. Here is the thing. Most of companies these days are afraid about how not to get “surprised” by innovation coming from unknown innovators, competitors and other factors – new economic condition, financial impacts, new product segments, cross domain innovation, etc.

In my view, the key element of preventing “unknown unknowns” impact is to get better analyzes of the data in your company and outside. Companies owns a lot of data business data, stored in databases and mainframes behind the firewall. This is “known knowns” because in this area business decisions are generally made based on historical data. This is where PLM/PDM operates today. There are lots of data that mostly unstructured and resides in emails, blogs, internet, websites, etc. This is a place of “known unknowns”. Companies dealing with big data and some others are trying to solve today. The biggest danger is coming from unknown unknowns. We need a solution to fix it.

What is my conclusion? There are many things that can influence manufacturing organizations. We live in a very dynamic world. Market conditions are changing, new competitors are entering market in a very disruptive way, financial market influence, employees turnover. These are “unknown unknowns” of PLM and future innovative solutions software vendors can come with to market. Just my thoughts…

Best, Oleg

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PLM and Product Data Insight

February 8, 2013

Data is a trending topic these days. Big Data is even fascinating. It made me think about the meaning of power. In the past, oil was a meaning for power. These days it applies to data. Social data, corporate data, any data. To have the ability to dig into the data, discover facts, relationships and make decisions spins minds of companies, technologists and investors. All I mentioned above applies to manufacturing companies and the data that these companies holds on their servers, data centers and desktop computers.

I’ve been reading WIRED article Data-Visualization Firm’s New Software Autonomously Finds Abstract Connections. I wonder… is it a data analysis revolution or another "fancy graphic" of big data? Aysdi – the company behind the article and video is promising you to discover the data and connections you don’t know. Here is a brief description of what system is doing, including some tech ideas

Their new product is called the Iris Insight Discovery platform. It’s a type of machine learning that uses hundreds of algorithms and topological data analysis to mine huge datasets before presenting the results in a visually accessible way. Using algebraic topology, the system automatically hunts down data points close in nature and maps these out to reveal a network of patterns for a researcher to decipher — any closely related nodes of information will be connected and clustered together, like how a social network arranges its data according to relationship connections.

If you don’t have time to read the article, watch the video below. It will give you the idea of what is that about.

It is interesting that Aysdi is coming with some background of manufacturing from DARPA. For the moment, system provide some result in medicine. I wonder if data in manufacturing companies containing product, supply chain and many other aspects can be targeted using this tech.

What is my conclusion? Manufacturing companies are under stress about making an improvement in their decision management process. Decisions are complicated and can be driven by many factors. Product data insight. It can be interesting way to learn what impact product cost, supply chain, manufacturing processes and many other things. It might sounds like a magic. However, many of today’s technologies could potentially considered as a magic 10 years ago. Just my thoughts…

Best, Oleg

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Will PLM crunch untapped data in manufacturing organizations?

January 24, 2013

Do you remember the golden era of desktop searches? I remember first time I had a chance to run Google Desktop on my computer. The most inspiring moment was to see documents and emails that you completely forgot about. Today, desktop search solutions are not as popular as before. Our personal digital life moved to the cloud. Application search, such as Outlook search and others improved significantly (thanks to open search solutions reused by many vendors). The focus of "data crunch" moved from a single desktop solution to cloud and mobile devices. Despite a huge promise of enterprise search solutions, majority of them are experiencing difficulties to provide efficient, reliable and cost-effective solution that can help to organization to capture and search trough massive amount of digital data. Focused search solutions are more efficient and we can see them coming from enterprise software vendors.

However, it doesn’t solve the problem of huge amount of existing data in organizations. I’ve been reading Crowdshifter article Behold The Untapped Big Data Gap. It shows some data coming from IDC study. Here is an interesting quote:

…article reported that 23% of data within the digital universe of 2012 could be useful for big data collection and analysis purposes if tagged. However, there is a huge gap in the amount that has been tagged versus the amount that remains without semantic enrichment. Only 3% has been tagged and only .5% has been analyzed.

Source: IDC/EMC.

Manufacturing organizations are desperately looking how to improve their decision management process. To leverage the existing data in an organization can be an interesting approach. I can bring many examples from PLM space where data about change management history, maintenance, suppliers, etc. can help to make a better decisions. For the moment, the majority of the information stored in application silos and cannot be used in an easy way. This data can easy become digital garbage similar to last year papers on your desktop and similar to old documents and email on your desktop before desktop search era.

What is my conclusion? To analyze data is a tough job. It requires computing resources, time, investment and smart algorithms. Google laundry list of results won’t be helpful. The new methods of data crunching and data discovery need to be developed. With only .5% of data analyzed and 3% of data tagged, we have a huge potential to tap in. Just my thoughts…

Best, Oleg

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PLM and Big Data Industrial View

December 7, 2012

Last week, I followed Gilbane Conference Boston online. Navigate here to dig for more info. Gilbane conference focus is content, web and mobile. My primary interest was about content. Let me say differently – growing content in organization and online. This is not a surprising topic these days. You can see many charts these days online presenting a growing content online and in enterprise organizations. Another trending word is "big data". I’m sure you’ve heard this buzzword before. Nevertheless, here is Wikipedia definition from this article.

In information technology, big data[1][2] is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. The challenges include capture, curation, storage,[3] search, sharing, analysis,[4] and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to "spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions."[5][6][7]

One of Gilbane’s presentations about big data caught my attention – Big Data for Enterprise and Marketing Applications — Three Views. CMS article Big Data Explosion Offers Value provide a good write up of this presentation. What was interesting to me is to see how the value of Big Data presented beyond the point of Twitter data analyzes and other web-oriented application. The following paragraph focuses on Big Data and Industrial view by GE’s GM Brian Courteny:

Brian Courtney, GM of Industrial Data Intelligence for GE, discussed a critical but less-publicized aspect of Big Data — its role in automating the monitoring and analysis of industrial data. He said GE uses both batch processing, the offline analysis of “massive repositories of data for patterns and insights,” and stream processing, the real-time analysis of “web-scale data to identify trends and anomalies as or before they occur,” to determine data patterns that indicate likely failures in GE technology such as electricity-generating turbines and airplane engines and then monitor equipment for those patterns in real time.

Another article GE, Industrial Internet and radical efficiency is providing more examples about how GE is planning to leverage Big Data technology to improve their products. Here is my favorite passage:

Something important is going on here. GE’s new focus is about “the convergence of the global industrial system with the power of advanced computing, analytics, low-cost sensing and new levels of connectivity permitted by the Internet." It’s about how "the deeper meshing of the digital world with the world of machines holds the potential to bring about profound transformation to global industry, and in turn to many aspects of daily life, including the way many of us do our jobs.”

The examples above make a lot of sense to me in the connection to PLM, product development and manufacturing. Monitoring of products in a real life becomes an interesting and fascinating topic. It can provide significant impact of design improvements and help manufacturers to innovate. Sounds like a primary role for PLM these days – to boost innovation among manufacturing companies. Think about data trend analyzes that can prevent potential failure of systems in a car that can alert customer to approach server center. Dream? I’m not sure and think we will see it soon.

What is my conclusion? Monitoring products in a real life is a interesting topic. However, most of the limitations today are related to inability to analyze a massive amount of data produced during the monitoring. Relational databases used by majority of PLM platforms cannot scale. BigData technologies can change it. It is an interesting application of tech originally developed in a consumer space. Just my thoughts…

Best, Oleg

pic is courtesy GreenBiz article.

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PLM: from EGOsystem to ECOsystem

December 1, 2012

I just came back AU2012 in Las Vegas. Among many meetings, I had during AU, I attended Innovation Forum – The Reality of the cloud. The reality of events these days that you can attend actively by participating in social networking via Twitter. One of the tweets during the cloud presentation was Chad Jackson’s: – Think about data as an eco-system.

"Think about data as an ecosystem" from the #Cloud #Innovation forum at #AU2012 twitter.com/ChadKJackson/s…

— Chad Jackson (@ChadKJackson) November 29, 2012

It made me think about PLM as data eco-system. Watch Gerd Leonhard presentation- The future of the internet (SoLoMo) futuristic presentation with strange title – Big Data, Big Mobile, Big Social. I found it is interesting. Navigate here to take a look.

Few slides caught my special attention in the context of PLM and Data Ecosystem discussion. One of them is related to Paul Baran research back in 1960 (way before the internet and even early PLM systems). He was pioneering some of early work related to computer networks. Navigate to the following link to read more. Here is an interesting passage:

The pioneering research of Paul Baran in the 1960s, who envisioned a communications network that would survive a major enemy attacked. The sketch shows three different network topologies described in his RAND Memorandum. The distributed network structure offered the best survivability.

Another slide that sticks in my memory was the comparison of Egosystem and Ecosystem. That slide made me laugh. Especially when I put it next to one of my previous post about PLM Egoism. Think about PLM system transformation. A year ago, during AU2011, I was talking about transformation from Database to Networks. This slide is representing the way how ego-centric PLMs need to be transformed into reliable and modern PLM eco systems.

What is my conclusion? Today’s PLM EGOsystems are not sustainable. The centralized approach made PLM implementation weak and not able to survive long term lifecycle, evolution and business changes. The result is heavy PLM systems that require propriety maintenance. Change management of these systems is either expensive or impossible. It is a time to think about data networks and networked system models. Just my thoughts…

Best, Oleg

pictures courtesy of gleonhard presentation

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PLM Supply Chain – Go Big Data or Go Home

July 30, 2012

For many people PLM is associated with Engineering. At the same time, it is not true. Very often, major portion of product design, development and manufacturing is delivered by partners (suppliers). Value chain management, supplier integration during different phases of a product-development process is very important. These days with growing trends in globalization and interest of companies to optimize and product cost, it became even more important than usual.

Many companies are looking how to innovate in product development. I’d like to talk about an interesting trend in supply chain optimization related to Big Data. In information technology, big data is still very loosely defined term. The following report caught my attention few days ago – ‘Big Data – Go Big or Go Home’ by Lora Cecere of Supply Chain Insights. I downloaded Big Data report via this link. Report is talking about Big Data as a new concept in Supply Chain. I recommend you to read this report. I found it quite insightful. It made me think about many problems in a supply chain PLM systems are trying to solve.

I captured few interesting passages. One of them is about solving business problems. Very often, I can see PLM vendors are talking too much about technological pieces related to data exchange between OEM and suppliers. At the same time, companies are losing focus of business problems.

For the business leader, it is not about data. It is about solving the business problem. In fact, as supply chain leaders try to tackle new problems, most do not realize that they are entering into the world of Big Data, it just happens. The term is not in their vocabulary. They just want to do more, and solve new problems, with new forms of data. They are frustrated with current systems.

At the same time, we need to admit that data is growing exponentially in the enterprise and global value chain. Here is another interesting passage highlighting the scale of data growth:

Data is Growing in the Enterprise. Today, 8% of respondents have a Petabyte of data in a single database. It is growing. It is a concern of survey respondents. 47% of companies responding to the survey either have or expect to have a database with a petabyte of data in the future. It is higher for those currently having Big Data initiatives underway (68%). The petabyte is the new terabyte.

Until now, I can see how PLM vendors are mostly focusing on "transactional data". Nowadays, a lot of additional data sources are coming into play of product development and supply chain. Take a look on the picture I captured form the same report – transactional data is only small piece of information need to be used to optimize supply chain.

Another interesting aspect is the relation between supply chain and product data. Analyzing the ability of a company to use various data sources for supply chain, we can see "product data traceability" as one of the top 3 factors. It leads me directly to the data located in PLM and other engineering systems.

What is my conclusion? Big data is one of the big things PLM can use to optimize supply chain, in my view. PLM vendors need to switch gears from supply data exchange towards supply chain optimization. In order to do so, PLM vendors need to bring additional capabilities to analyze supply chain, related information. It is an important topic to for coming years. Just my thoughts…

Best, Oleg

Image: FreeDigitalPhotos.net

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