Product Data: Finding It - Using It by Deploying a Search Engine Solution (2), The Easy Way to Benefit from Benchmarking


2PLM Newsletter

John Stark Associates                                                                                                                                               July 2, 2012 - Vol15 #7

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Product Data: Finding It - Using It by Deploying a Search Engine Solution (2)
by Dick Bourke
Part 2: The Power of Enhanced Search Engines

When search is mentioned, many readers will think first of Google or Bing. However, most general-purpose search engines are not suitable – as is – for engineering and manufacturing search applications. Nonetheless, some can be enhanced to find product data scattered throughout an enterprise in various applications, repositories and files.

In this column, I'll describe the power of a search engine solution (SES) as it may be applied to meet the needs of engineering and manufacturing enterprises. Be aware, though, that software choices, listed in Part 1, make use of a wide range of semantic-based technologies. The following, therefore, is a generalized description of SES capabilities.

Enhanced Search Engine Solutions
The Search Engine
A popular general-purpose search engine is Lucene/Solr, an open source platform. It powers the search and navigation features on many company sites and in many software vendor products, including some of those previously identified. Solr is described as "blazing fast": A hi-tech manufacturing company asserts that search results display in less than two seconds when accessing 16 million items and metadata from within 24 million documents, drawings and images.

Additionally, to boost Solr's other capabilities, service firms provide numerous enhancements, e.g., more powerful content classification, for one. Some enhancements are optional – some are not: A purpose-designed user interface is an essential enhancement. It must unify access to multiple product data repositories and formats through a single user sign-on.

Search engines operate with an index – an optimized file format that supports rapid data accessing and displaying of search results. It is defined as:

     "A systematically arranged list; in computerized systems it is a representation
     of content to speed retrieval by the governing algorithms."


The usual approach to SES indexing is based on text values. This is in stark contrast to relational databases, for example, SQL (Structured Query Language); it stores data in tables, records, attributes and values. What's more, an index eliminates any need for an intermediate relational database to help queries.

In search engines, utility programs (sometimes called a "spider" or "crawler") mine the current contents in the enterprise's IT infrastructure to create the index. It is separate from – and does not change – the existing data attributes. Searching can begin once indexing has been completed during implementation of the SES. As changes in content occur, the utility updates the index to assure changes are available for searching.


The Searching Process
Here is a simple frame of reference for searching – within security guidelines:
  • Wide Search – to access all forms of product data stored throughout the enterprise in multiple repositories by employing an array of connectors (to be discussed in a later column)
  • Deep Search – to drive past metadata into file and document contents by taking advantage of "full text" capability

With full text capability: "The search engine examines all words/objects in every stored document as it tries to match the search criteria entered by the user. This distinguishes it from searches based on metadata or parts of the original texts represented in databases, such as titles or selected sections." (Source: Wikipedia).

In other words, full text does not rely on the enterprise's metadata being consistent. (A real "gotcha" with a relational database query is that it does rely on consistent metadata – a questionable assumption.) Users, therefore, need full text capabilities to drive through various file formats to access all relevant product data crucial for decision-making purposes.

The significance of full text search becomes more apparent in view of the two basic types of data: structured and unstructured. Structured data means defined field formats, for example, a product number stored in PLM and ERP systems.

Unstructured text, however, deserves the most attention because of its overwhelming presence – more than 80% of a company's data, according to surveys. It exists in CAD drawings, MS Word, PDF and emails, to cite a few.

After a user enters a specific search criterion, an initial results list is presented for further analysis and action. These results can be refined to narrow the list to the most truly relevant data; additional detail about this subject will be covered in future columns.

The Value of Full Text Searching
In essence, full text searching gains significant benefits for the enterprise – among them:

  • Achieving higher-quality decisions
  • Negating the need for users to remember where the needed data is stored
  • Avoiding costly reorganizing and migrating data to satisfy relational database retrievals

What's Next?
Please send me your questions; I'll respond to them in the next column. Later, I'll address more detail about how an SES works.


You can contact Dick Bourke at

The Easy Way to Benefit from Benchmarking
by Roger Tempest
The PLM Governance Benchmark (see 2PLM Apr 23) may be a complex collaborative programme, but it has been structured for easy participation.

Quotations about doing things the easy way go back as far as the Chinese philosopher Lao Tzu, who said: "Do the difficult things while they are easy and do the great things while they are small."

The full Governance Benchmark will transform our detailed understanding of PLM. PLM Teams from some of the most advanced implementations in the world will be able to apply structured, neutral comparisons to produce quantified results across the whole scope of PLM and PDM. From the start, the exchange of ideas and best practices will generate a new body of shared knowledge. Into the future, as the Benchmark runs from year to year, it will produce a growing library of factual data that will become a global Reference Model for PLM performance.


Now is the time to do the great thing while it is small, (and to add value to your PLM implementation at the same time). The easy and simple way to take part in the Benchmark is to generate your organisation's 'Navigation Model' - a standardised representation of your PLM drivers, capabilities and processes that gives a clear and neutral picture of the direction of your PLM. This is an outstanding tool to give visibility for PLM within your company, and your organisation's 'Navigation Model' can be compared to that of any other organisation in the world.

Creating your Navigation Model is easy, and involves only the 'Level 0' module (the full Benchmark progresses to 3 more levels). Details can be found in the Benchmark Brochure or via It is a simple, inexpensive cross-reference for your PLM strategy and management, and puts you on the path towards more detailed metrics in the future.

Roger Tempest is co-founder of the PLMIG. Membership of the PLMIG is available via


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