My first question is “how you would get information when it’s not in your system”? Does your supplier manage inventory for all of your plants and facilities resulting in a global view of spend? Does your supplier manage your data to the OEM or to suppliers so you have duplicate inventory costs?

Just considering the MRO items, the information could come from engineering or the integrated supplier. Logically, the integrated supplier would have been provided the part information from your company in order to setup and purchase the items in the first place. It is likely that they have the records as they were given them and they are linked to item setup in the purchasing system. The top level source would have been engineering who would have either had the equipment constructed or been responsible for the equipment purchase and the parts along with them. If after or during the purchasing activity the “key” item record is setup in the purchasing system using the part supplier information versus the OEM information, this will lead to item duplication. Duplication then will create overstock, variant pricing, variant lead times and other inconsistencies that add unnecessary cost.

Based on what you are saying it sounds like items in your system are based on either the part supplier data or specifically identified by the integrated supplier (their item number). The best scenario is when the OEM part is what is setup as the key item, having the purchase action to the OEM directly (OEM setup as a supplier) removing the “middle man” cost. Second after that is having the OEM part as the item, linked to the specific supplier(s) for purchase. Local purchase suppliers are still linked to the same item also. Having the same item record used across the enterprise is optimum.

I would also add that there should be a means to discover OEM part information as a reactive purchase need comes from maintenance. Parts are typically identified physically with OEM information. For example an Allen Bradley/Rockwell module with have the Allen Bradley part number physically stenciled on it. If a part breaks and maintenance needs one, there must be a way to find out if that part is in stock and a way to buy it if is not.  We believe that enterprise wide viewable, verified and standardized OEM part information will reduce the cost for maintenance by eliminating the time consuming discovery of part information in your systems and the correct parts are stocked. This approach also enables part sharing between facilities that is limited without common data. Part sharing in turn reduces overall cost through reduction of inventory.  With plants here in the U.S. and worldwide, this type of advanced planning is where the true brunt of the savings come through.

Obviously, much depends on the specific agreements with your integrated supplier. But consider the following questions. If the data stored in your system is not the OEM information then it’s logical to assume that it is data created by the integrated supplier from the OEM data.

    1) How does your company know that the information is accurate? Are there any checks between the data given to the integrated supplier and what you have in your system?

    2) How does your company know if they have the correct parts setup in the system and stocked appropriately? It seems that there is an opportunity for the integrated supplier to setup and stock items which aren’t necessary and would only be discovered through data transparency.

    3) How does your company know that you are getting the best price on parts? Even if there is a cost savings agreement with the integrated supplier, if there are duplicates the opportunity for piece cost reduction is lost when the true usage is not known because of part duplication. 

My second question in this. It seems from your response that everything is running quite smoothly. But is that true in Manufacturing? Do they ever experience loss of production because a vital part could not be found or was out of stock? How about Maintenance? Inventory management? Engineering? These are the departments that should be surveyed because there is a benefit for them too.

 Physical protection of our nation’s infrastructure happens at many levels, namely, National which includes all the United States armed forces protecting our land, sea and airspace borders. At the state level which includes state police forces, state highway patrols, and the National Guard forces. The local protection is handled by each and every local police force in addition local community civilian efforts to monitor suspicious activity around neighborhoods more commonly known as neighborhood watches.

 These amounts to hundreds of thousands of human resources that need to be analyzed managed and deployed properly to ensure the nation’s entire critical “brick and mortar” infrastructure is protected adequately and to ensure those individuals are given the proper tools to execute their assigned tasks. The question becomes how do we effectively inventory all of the nation’s human and physical resources in order to facilitate real-time assessments of our ability to protect our critical infrastructure?

 That question presents several seemingly insurmountable problems… Potentially tens of thousands of local level sources of data, thousands of sources for state level data, thousands of sources of federal data in addition to thousands of data sources contained within the firewalls and control of private and publicly owned companies as is the case with major power distribution and communication corporations. To make matters worse there could be hundreds of different software vendors that support the collection, storage, maintenance, and reporting of this information. Each software package has a unique and often proprietary data model and most likely a unique and proprietary meta data schema used to tag the meaning of each database field and values within those fields. Without consistent and easily discernable database schema from each data source, integrating the mass data becomes an impossible task to do in a way that the resulting information can be trusted to make decisions in what amounts to life or death situations.

 One potential solution to this problem is a combination approach which would include a National Master Data Management initiative with the ultimate goal of achieving a level of data quality sufficient enough to accurately derive infrastructure intelligence information to be used to assess the vulnerability any given infrastructure asset has to attack and the potential damage and casualty fallout if an attack were to occur at a location such as any bridges that carry large amount of cargo across the Mississippi. Not just any data will do when allocating resources to secure and protect such important national resources; the data needs to be quality data, data which consists of profiled and standardized vocabulary, the syntax or format of the data, the provenance or source of the data as well as the accuracy and completeness of data. Data that does not meet minimum requirements for the metrics I listed previous can not be used to make decisions that are supported by accurate data.

 My proposal of a National Master Data Management Program would in its most basic description include the creation of a national mashup of these thousands of data sources into one Infrastructure Security Data Warehouse that would be used to govern, analyze and report the readiness of the nation’s infrastructure if an attack were to occur.       

 A mashup is a relatively new term used to describe a database application that pulls information from tens to thousands of data sources and integrates the data together so it can be analyzed over and over again. At the same time would not require the replacement of the thousands of legacy systems the local, state, federal, and private entities use to run their day to day operations.

 I have oversimplified the problem and solution in this explanation, the actual solution is very technical and requires professionals who have managed data integration, data cleansing, data governance and master data management programs in the past. An initiative like we are proposing is not a short term project with a defined beginning and end date, in reality the project began more than 30 years ago with the collection of electronic data. A master data management program for something as critical as the United States infrastructure is not simply a program; it is a complete change in the thinking and the way we interact with the data we spend so much time, money and resources archiving and cataloging. It is a cultural change.

 More technically speaking the idea is to create a classified open technical dictionary which will contain all the terms and definitions needed to describe, at their most atomic level, every single data field we require to generate the information needed to assess and prioritize potential infrastructure targets. We will then tag or associate one of the classified numbers and terms to every single piece of asset data (Master Data) determined necessary to implement the infrastructure protection strategy. We will use a combination of publicly available meta data and newly created meta data to tag all the data elements. This allows us to store and report on them quickly and accurately from a central location with a centralized team, resulting in a true Infrastructure Intelligence Master Data Program. Once all the target data is in a standard format we can further national security goals of increasing the level of quality related to information and reporting, increase the level of interaction between local state and federal authorities and provide threat advisories to citizens and the law enforcement community. As well as providing data that can be used in a variety of training exercises and computer simulations of potential attacks.

 Core to the proposed data quality/master data management program are the business processes used to carry out the data cleansing and enrichment processes. The methods used NEED to be vetted and thoroughly tested with large datasets and algorithms comparable to the complexity of the algorithms that will need to be developed to predict things such as; How many people will be effected if a particular power substation is physically or cyber attacked?, Which railroad infrastructure is key to keeping the flow of military supplies to ports for distribution to our forces worldwide?, Once a threat is identified how long will it take to mobilize enough manpower to properly defend any given location?, Which power generation facilities are large enough that if damaged would results in blackout for 5% or more of the population?, Which open air water sources, if poisoned will cause the most death?. The processes and methods used to execute the project in its initial phase and on an ongoing basis are just as if not MORE important than the quality of the initial data sets, they are mutually independent on each other.

 The implementation of an ongoing and permanent national Infrastructure Master Data Management initiative would be of great benefit and could be essential to the long term growth and protection of the United States of America.

The first fundamental rule is that the customer business doesn’t stop. For all you data purists out there that believe that one day a switch to turn on the cleansed database is in the near future, please include me, I would like to see it. Most master data management projects included years and years of legacy data; therefore there is an acceptance to draw a line in the database by last used date. When I design a data cleansing project, I will have a new item setup process referenced to legacy items, this way the client business continues and as the new items are analyzed and setup, we can reference and update the legacy item information. Independently, we will always have the legacy data cleansing parallel the new set up process.

As the data cleansing project is designed, let’s start to explore the data elements and classifications. Every client will have their material types and material status set up but generally during the data / systems assessment there should be a thorough review of industry standards vs. company processes. I find that our clients appreciate the opportunity to bench mark their processes and data structure elements such as material types and status.  We will start with material type and material status.

Material Type

Material types can be as simple as goods and services or as complicated as service, critical spare, spare part, commodity, generic, blueprint, etc. The material type is a critical element to classify which template is used for setup in the downstream legacy systems with an inventory stocking strategy applied.

Obviously a service can be standardized by the class type to describe the service where a cost for the service can be standardized. The definition of the service is described by the properties, for instance a service class of CLEANING, OFFICE can be set up with descriptive elements such as 10,000 square feet, light cleansing (dusting / vacuuming), etc. From a purchasing perspective, the buyer can run the reports globally to determine how much is spent for office cleaning then evaluate the costs and utilize best practice sourcing strategies and other global supply chain processes to lower costs. The purpose of the standard naming conventions of classes and property are to provide enough standardize information to provide the ability to compare and cost services or products.

If a critical spare is being set up for sourcing and inventory, then the part has been evaluated by maintenance or engineering and determined that the spare is critical for production uptime. An inventory plan is developed for stocking the critical spare including an initial buy quantity, plan for stores (inventory) setup of item’s unit of measure (each, assembly, package, etc.), min / max, reorder quality, stocking location, etc.

Material Status

In addition to applying a “material type” to the item records, due to the longevity of materials used in the manufacturing operation, a material status should be utilized as a long term data maintenance process. In dealing with component manufacturers and suppliers, a component may be active from a plant use perspective; however the component manufacturer no longer manufactures the item. How is that possible? A piece of equipment can have a 10 year or a 50 year life span, to maintain a piece of equipment, a list of recommended spare parts is identified and set up for equipment maintenance. If the spare part component is obsolete by the manufacturer but the piece of equipment is still in use on the production line, the material status would be “obsolete active”. A different buy / stock strategy would be implemented, such as purchase all available stock from the manufacturer or another alternative is to source with unconventional methods such as through eBay or maybe contract the item to be built by a local shop.

Typical material statuses that I have experienced are active, inactive item referenced to an active item, obsolete active, obsolete inactive (typically the status to start the disposal process) and archive. The archive status is a classification used by the analysts to allow the viewing of the item information but is not visible to the client or the item record is not exported to the client systems.

I would appreciate any input or better yet a discussion of the different material types and material status used in Product Information Management (PIM) or Master Data Management (MDM). As an industry we inherited material types and material status used in a purchasing system or maintenance systems designed to meet business function but not from the data quality or master data management perspective. What are the proper data requirements for a material type or material status? The MDM or PIM software companies and data quality consultants need to provide input from the data management perspective to provide long term data management functionality.

Speaker submission guidelines can be found here: Online Proposal Form.

All questions regarding speaking may be directed to Wilshire Conferences at maya@wilshireconferences.com. The deadline for submitting your proposal is June 4, 2010, and we anticipate being able to notify accepted speakers by June 14, 2010.

Thanks and we look forward to hearing from you!

Wikipedia’s definition for “Garbage In, Garbage Out, is a phrase in the field of computer science or information and communication technology. It is used primarily to call attention to the fact that computers will unquestioningly process the most nonsensical of input data (Garbage in) and produce nonsensical output (Garbage out).”

If you enter “garbage in” to a computer system, having the data passed through some very expensive ERP or CMMS software, isn’t going to change the data quality, the business results are equivalent to “garbage out”, which will be apparent in the day to day business activities and subsequent reporting used to determine the health of your business. Is it obvious that data should just not be moved from one system to a new system without a MDM program?

Let us now explore the concept of data migration. Wikipedia’s definition for Data Migration is the process of transferring data between storage types, formats, or computer systems. Data migration is usually performed programmatically to achieve an automated migration, freeing up human resources from tedious tasks. It is required when organizations or individuals change computer systems or upgrade to new systems, or when systems merge.

If an MDM program is not in process when implementing a new software or upgrading an existing software, the project should include an evaluation of the data and/or an evaluation of the additional functionality of the “to be” model of the new software identifying the new data required for improved business processes, reporting and the plan for legacy data clean up. A data migration project needs to be more than moving data from a legacy system to the new system.

I asked the question to one user of a maintenance software implemented a number of years earlier as I had the opportunity during a site visit at a plant. The software had awesome abilities to create and manage the relationships between equipment and spare parts, supplier contacts as well as the potential to improve processes, reporting and streamlining the information required for a maintenance organization. The company invested in the software / hardware, understood the ROI but lack the understanding of the data needs or management. The software was implemented however the majority of the functionality was not used, therefore the ROI was never achieved. When I asked why, I was told “no data and we don’t have time to add the data.”

Another scenario I came across, purchasing moved data from a legacy system to a new ERP system. The data wasn’t set up to a data governance or MDM procedure, legacy data riddled with duplication, obsolete information, unstructured descriptions and so forth. Different system, same legacy data quality and the ROI was never achieved.

I have one simple question, why invest in a software product if the data is not going to be treated as an asset? The results of a successful implementation are that the business processes are streamlined; simplified and reporting capabilities are enhanced through enabling both Master Data Management and Software functionality.

Garbage In, Garbage Out or Garbage Extracted, Garbage Migrated as we are moving to the next generation of technology. Are we relying on a skewed nonsensical output based on low quality data to make our critical business decisions?

PROGRAM OF EVENTS  

Tuesday October 12, 2010

• Pre-conference ISO 8000-110:2009 Master Data Quality Certification Workshop 
• Welcome Reception (includes open bar and hors d’ oeuvres)

Wednesday October 13, 2010

• Opening Address
  Overview: The critical need to maintain the quality of master data.
• Panel PresentationsFundamental updates on the progress of the practical application of the eOTD (ECCMA Open Technical Dictionary), ISO 22745 and ISO 8000 for the collection, validation, and distribution of master data in support of the procurement of goods and services as well as inventory and asset management initiatives. The panels will address the importance to using the standards to define and manage data requirements as well as the latest trends in spend analysis, cataloguing at source and data cleansing and rendering.
• Exhibition
  A unique opportunity to see the latest offerings from leading data service and software application providers.
• Annual Awards Dinner
  Celebrate and share achievements with colleagues and friends.

Thursday October 14, 2010

• Workshops

Workshops will cover new technology and practical examples of vendor specific data quality application software and data cleaning services.

*Content subject to change.

Collaboration and co-opetition are at the base of this impressive growth. “We know that on a going forward basis, our interdependence as an industry will have a significant impact on our ability to manage the recovery and sustain profitable growth,” remarked J. Scot Sharland, executive director.

AIAG’s membership actively engages in numerous initiatives, to facilitate industry consensus and resolve issues via the adoption of common business practices (e.g. engineering, logistics, packaging, quality, environmental health and safety, health care, etc.) and interoperable business systems counting application-to-application (A2A), plant-to-business (P2B) and business-to-business (B2B).

By collaborating with competitors in a neutral environment, members are able to identify inefficiencies in business processes. Initiatives are then developed and implemented at AIAG, by member companies, to save the industry millions of dollars and drive rework, error and scrap out of the global automotive supply chain.

New member companies joining the AIAG family in 2009 include:
Alta Mfg. Co.
AmeriPlate Inc.
Anderson Cook
Autodesk Inc.
Bellwright Industries, LLC
Bianchi Public Relations
Borg Indak, Inc
BridgeSpeak
Burlington Technologies Inc.
California Manufacturing Technology Consulting
CHEP USA Inc
Circuit Works Corporation
Colonial Diversified Polymer Products, LLC
Corporation for International Business
D & R Technology, LLC
DATAForge, LLC
Detroit Products International, LLC
Ditech, Inc.
Durapart Industries AS
Edicom Corporation
Epic Technologies
Fontaine International, Inc
Foster and Associates, Inc.
Francis Tuttle Technology Center
GZA GeoEnvironmental, Inc.
Huntington Quality Associates, Inc
I.D. Systems, Inc.
INA Industria Nacional De Autopartes, A.C.
International Rectifier Corp.
International TechneGroup, Inc.
Johnson Controls, Inc.
KPA, LLC
M.K. Chambers Company
Magni-Power Company
Metaldyne
Methode Electronics, Inc.
Michigan State University
Microsoft Corporation
Monbat PLC
Morbern Inc.
Mueller Impacts Company
Neuman Aluminum
Nissan Motor Manufacturing Corporation USA
North Carolina State University Industrial Extension Service
Oracle Corporation
ORBIS Corporation
Orick Tool & Die, Inc.
Panasonic Automotive Systems of America
Paramount Group
Plexus Corporation
Polymer Inc
Q&A Chemical Co., Ltd.
Qdos Flexcircuits BDN. BHD
Quality House, S.C.
Radar Industries, Inc.
Resource International LLC
RF-IDI, LLC
RSJ Technical Consulting
SEEBURGER, Inc.
Sinclair Community College
Symbolic Systems, Inc.
System Seals, Inc.
Tecnologico De Monterrey
TFT Global Inc. – Woodstock
THRU-U.COM INC
Tieco International (Aust) P/L
Trademerit Corp.
Unicell Limited
Universidad Iberoamericana, A.C.
Vertare, LLC
Vitec LLC
Vogelsang Corporation
Watlow
Williams Controls, Inc.

Links:
http://www.aiag.org

Data vs. Information definition: to simplify the concept, data is managed by a combination of a team of analysts and software to achieve the goal of a cleansed record or useable information. Data is imported and profiled, classified, structured, verified, enriched, translated and reports generated; we create useable information from low quality data for use in decision making related to engineering, purchasing, maintenance, marketing, sales, etc. The data that is exported into client systems is information that will meet a predetermined set of data governance rules and information quality requirements.

Data Quality Experts, let have a discussion on the definitions of data quality, does an address or a product detail meet the requirement if only classified? Or should verification at source (contact for address or manufacturer / supplier for product) be required at initial setup of the data in the system or maintenance scheduled as part of the data governance program? Is the data incomplete? Does the MDM process include a question / answer scenario to complete the data?

MDM software designers and developers can we also have a discussion on the software’s ease of use to manage the stages of data cleansing to support a MDM philosophy and using advanced techniques to automate the management, add intelligence in processing data imports, workflows and data cleansing stages of classifying, profiling, matching, translation, data audit analytics, exception reports and status reporting of a data record?

I believe these are great discussion points and will serve as great blog topics.

I am a very detail oriented person; I have to be as one of my largest accounts requires me to participate in the day to day deployment of global MDM processes for one the largest automotive manufacturers! I am very interested to learn how businesses in other industries manage their data. I would hope that sharing of information and best practices among industry partners will be a win-win situation. At a minimum the discussion will be refreshing; the sharing of innovative information the will spawn the creative improvement needed to create truly efficient knowledge driven business processes, data classifications, metadata and definitions and translation. . . is anyone interested in discussing the logistics of managing translation as part of Master Data Management?

Is anyone interested in discussing my struggles and sharing yours trying to find standard global translations for ISO UOM (Unit of Measures)?

Is anyone interested in discussing what fields should be included in a MDM Data Governance Program for MRO data; UNSPSC, warranty, term of warranty, lead time, estimated price, ECCN, etc.

What Schema or classification structures are you using for spare parts and maintenance items? What about a discussion on using a public vs. priority classification system?

What are some best practices for migrating, profiling, structuring, mismatching and re-verifying legacy system data?

We have a nifty data mismatch process for manufacturer contact information; will this be easily implemented for a CRM data project? What about patient contact information in the healthcare industry?

There are a few bloggers out there that continually add content to their writings but it is starting to appear to be a small group, anyone out there interested in achieving data quality want to discuss “real” life best practices, lesson learned or discuss HOW of MDM, data quality or data cleansing.

Thank you for your comments in “Something beyond MDM is coming your way – would MDM 2.0 fly?” and starting the discussion to expand the definition of MDM to include data integrity, data quality, entity resolution, matching, data integration, governance, metrics and analysis. The topics discussed should also include work flow (management of data and analysts), translation management, data structuring, data profiling, duplication removal, data change management, verification contact management, etc.

The MDM and PIM software industry needs to take a step back to understand actual day to day business requirements of data management to achieve Master Data Quality. Lesson one is that data is created and supplied by many sources in many different formats at various quality levels. Data is created by engineering, submitted by integrators, manufacturers and suppliers. To add to the complexity of the information flow, data is introduced into businesses systems in different departments (engineering or purchasing or maybe plant from maintenance) with different data requirements to meet the needs of that job function. Now the next dynamic is mashing new data to existing legacy data in a number of systems to ensure no duplicates are created, managing obsolete / recommended use and functional equivalents. The old philosophies of a PIM or MDM software to “hold, provide search functionality and maybe a shopping cart” isn’t going to meet the true requirements of the new definitions of Master Data Management.

To meet the new definitions the MDM or PIM software needs to provide horse power to electronically and intelligently processing data to identify exceptions for manual intervention by an analyst. Data should be processed one time to ensure that the data record will be enriched to meet the requirements of the enterprise and then the record is moved to a maintenance program (managed also by the MDM or PIM software). The processing of data needs to be efficient and cost effective, from my perspective the cost of data management should be covered by the cost saving achieved by MDM management.

I look forward to the discussions as the definition of MDM is expanded to include data quality, data governance, data provenience as the software industry provides the intelligence, functionality and business processes to cleanse, enrich and management data for my client to ensure their ability to make confident business decisions based on data integrity and accuracy.

Here is to the future of PIM and MDM!

Jackie Roberts