I spend a lot of time analyzing the product data quality, missing data elements, system accessibility because the data elements are impossible to pull out of the system or not collected to support our clients’ enterprise requirements for purchasing, engineering and maintenance decisions. I have to admit, I am always astonished by what I see (or don’t see) and the time and cost to pull data from a system. The reality is the data entered in these systems and the systems themselves are considered a support function (indirect or non-product activity) and not the core revenue generating stream for the business however the data is the life support of BI, accurate and available data is critical for smart and efficient business decisions. The missing gap in most business intelligence programs is a foundational flaw, referred to as data integrity and data quality or the lack thereof.

A business has two options, augment their BI decisions with a data quality scoring model, IDHS, a simple example “I am confident that our inventory budget should be 1 million dollars this year, based on the IDHS (+/- 30%) the actual budget could range from 700,000 to 1.3 million.”  The easiest reality is to budget the 1.3 million, with the plan to come in under budget, .3 million provides a safe cushion. This also alleviates the over budget spending and the tedious tasks of re-budgeting or canceling other important initiatives mid quarter / year.

The other option is to incorporate a structured and standardized Master Data Management process with Data Governance to collect, manage, cleanse (legacy / new data), enrich and disseminate information to the various systems. The goal is to create one master record set to ensure that decisions are based on accurate and complete data sets to implement meaningful BI reporting and analytics.

The results of data quality improvements are because of the requirements and processes of MDM. My definition is “An MDM program includes the Data Governance to define data requirements (structure, format and content), and the data processes to manage data activities such as collecting (extraction of BOM data or the data request web form), evaluating, matching (auto and mismatch), structuring, verifying and enriching to minimum data requirements, tracking history of change and data use, quality-assurance, reporting and distributing data (MAXIMO, ORACLE, SAP or another client’s systems) throughout an enterprise to ensure consistency and control. The MDM program will also include an on-going data maintenance process to manage data updates for this information.”

The following elements of data quality should be part of the governance program for your master data. This is critical to support a global enterprise. The discussions and metrics should include:

Accuracy: We intellectually understand the meaning of accuracy. An email address is either right or wrong, however in the product information world it can be a little more complex, this is where data governance is instrumental. The same spare part can be purchase from the manufacturer (one part number) or maybe a supplier (another part number)? A part number can be many different versions; for instance, a master org record is setup with a part number to purchase safety gloves, except one part number can’t buy you safety gloves; you must include the size as a description element in order to purchase. The result of an inaccurate glove record is you may receive all small gloves, but you really wanted large or you may not receive any gloves. Different manufacturers and suppliers have different ordering and purchasing rules.

Standardization: Is absolutely critical to BI reporting. Standardization is the map to how data is entered, referenced and stored to support ease of data access. The data elements should include classification naming, attributes, part numbers including formats, unit of measures, manufacturer and supplier names, addresses, web urls, relationships to parent companies and so forth.

Structured to support multiple uses: If you have one master organization and are only concerned with purchasing systems then structure may not be a concern, but to a global enterprise with multi-systems, the structure of use is extremely important as the data is disseminated to maintenance or inventory systems. In a purchasing system a ’Bearing, Ball’, part number ‘12345’ should only be set up once but in an “end use” structured environment, that ’Bearing, Ball’ is referenced to many pieces of equipment located  and used on other equipment and in other plants, it is also listed in engineering drawings, etc. If the multiple use structure is set up correct you can report “where used” for inventory sharing, internal purchasing programs supporting reduction in inventory.

Completeness: Having all data elements entered into the system required for the safe and efficient use of each item. If your data set has some missing prices and a report is provided the value of the inventory, obviously the report is inaccurate. The governance requirements include minimum required data elements. In the world of product data, the process may require a special speedy set up for a critical item that is urgent, however the MDM processes includes going back to acquire the missing information.

Accessibility: The ability to pull information from a system is the foundation of reporting. This is a continual struggle when I am working with a new client. I often ask the questions, “Is the expertise available to be able to query and pull data as needed from existing systems?”, “Is the data stored parametrically or as concatenated text fields?”, “is the table structure extremely complicated?” Accessing the businesses information and providing the ability to slice / dice the information critical to BI.

In this fast moving, big data intense world of collecting and storing information for businesses, the reporting and analytics to enable meaningful decision making is critical, so I ask the question “What does data have to do with business intelligence? EVERYTHING”

In the perfect MDM world, naturally not only does the data (product, services, spare parts) adhere to governance, structure of classification, quality and content but also a data structure of location of use. An example of structure could incorporate naming standards for location of use, for example plant or office. Within the plant, the use could be referenced to a department, referenced to a piece of equipment and to a specific location within the department. This type of structure is preset in a MDM plan and will benefit the maintenance data structure. The MDM data plan and structure meets the requirements of the complete enterprise, the purchasing department may only require 5 or 6 data elements but the maintenance department will require 10 or more. This is why Master Data Management requires a complete view of all data concepts and use.

Think of how powerful the analytics are if the enterprise is set up with established standards through governance for plant / facilities location structure, location names, equipment location structure and equipment naming standards. The benefits include the ability to view equipment and spare parts enterprise wide enabling the initiation of common spare parts strategies, spare parts sharing programs supporting inventory planning and reduction.

This type of MDM planning also supports equipment moves or disposals with the view of spare parts associated to the equipment. The spare parts can be packaged and moved or disposed of at the time of the disposition of the equipment. I can’t count the number of times that I have been told that I am not even sure if we still have this piece of equipment that these inventoried spare parts are used on.

Now the beauty, yes I said beauty, is that the required data structure can be set up with templates, written into requirements and contracts to equipment suppliers and when the bill of material data deliverables are sent to the engineering department of the enterprise (entry point) ensuring the data location governance structure is audited and at that point accepted to start the data cleansing and purchasing setup or rejected to fix the data structure errors. Other key data elements are classification, verification, enrichment and translation before the data is setup in any of the enterprise systems.

The by-product of the well executed MDM governance plan is that once the spare parts data is processed, the cleansed record is then propagated into the purchasing system, engineering library and maintenance system. The maintenance system is fully loaded with spare parts information associated to equipment and locations of use ready for the maintenance staff to set up their tasks for the equipment maintenance and planning strategies.

A flawed philosophy is to migrate the legacy data, meet the deadline, call the project green and successful while the users must figure out to how to correct and update the data. These ERP systems are not designed to handle the volume of change to data, provide a simple method to track change, obsolete a record with history view and archive functionality is non-existent.  Another reason this is a flawed philosophy is that purchasing contracts are set up based on the “bad” data, a unit of measure, part number or manufacturer change will void a contract resulting in wasted time of valuable resources and at the end of the day an inability to source the item, this could set in motion a critical manufacturing line shut down. Let face it, an ERP system is designed store a product or service record providing the business a method to transact, not to cleanse a record to a single master version of an accurate classified, verified and technically described Master Record. Therefore the activity of migrating data to a new system is not Master Data Management.

Master Data Management needs to be independently structured and separately managed in the organization not through IT. It is critical that within the Master Data Management organization to properly represent the business assets of the data (engineering, purchasing, customer, etc). The data is the core information used as the foundation to run the operations, sometimes referred to as the BI for the analytics of sound decision making processes. If the data is incorrect in the new systems, how is the BI improved? How is the business case ever calculated and successfully achieved? I can’t even imagine trying to tally up the potential “cost savings” when bad data is migrated to a new system.

Establishing a MDM program will need to have clear and well defined ownership, stake in the end user organizations and representation in the design and schedule of the software roll outs with full participation in all the projects with data involved. They should also participate in the project design strategy for systematically cleansing, classifying and migration of the data to the new system. Strategy should include an audit of data in the legacy system, let’s face it there maybe 20 year old records with no transactional history or balance on hand in inventory. Should this data be moved to the new system? The answer is NO.

An MDM data strategy to support the IT team can encompass a number of options. A simple option is the publishing of a long term schedule establishing adequate time for the data group to meet the data cleansing and classification requirements. This is not always possible, so what about a phased strategy? Some of the possible steps should include

• Evaluation of data to review transactional use 
• Evaluation of the stock on the shelf and confirm that none of the inventory should be obsolete and disposed of.
• Review of data related to the equipment but is not inventoried
• Identify data that should not be moved to the new system
• Establish data priorities for cleansing starting with high transaction use and stock items classified and cleansed first.

An ongoing maintenance and new set up process is imperative to be established with an easy method to request an urgent record during the data migration to support the day to day operations of the business.

We need to get out of the mindset that MDM is simply a data migration to a new system. MDM is a business process to establish the single version of accurate information which is then propagated throughout the organization, part of which is the proper migration of data from legacy systems.

 A key component for Master Data Management (MDM) is data cleansing which has multiple disciplines such as address cleansing or PIM (product information management). My expertise is in PIM, therefore my meetings have been focusing on data in the ERP and Inventory system.

My latest meeting was with an informed Material Manager, he understood the concepts of master data management, after the introduction meeting, he stated that “We had a data cleansing project and it did not work, I ended up going back and correcting the data.” Through the discussion, I came to believe that the data cleansing company, extracted the data and attempted to auto classify a half million records. As a purchaser of these types of services, I asked what was the process for mapping and quality checks?

The business issue is the buying team’s inability to utilize spend analytics and the solution is that the data needs to be referenced to the UNSPSC^® (The United Nations Standard Products and Services Code^®). The scope of the project is mapping the purchasing data to the UNSPSC^®. In my experience, I have identified four general levels of PIM data cleansing, 1) auto mapping 2) auto mapping with a manual review 3) verification and 4) enrichment. The cold hard facts are “buyer beware”.

The detail of the levels are:

1. Auto mapping: if you have a large collection of data, automation is a requirement however there are some issues. First, auto mapping incorrect, incomplete and inconsistent data will result in a system that will still have incorrect, incomplete and inconsistent data. The quality of the auto mapping is dependent on the structure of the data. If the data is structured to a noun or class, the auto mapping process will have high quality rate. If the data is set up as “free text”, the results will be dismal. This method will not address duplication or data quality in your system.
2. Auto mapping with a manual review: this process will take the results of the auto-mapping process and add a step of a manual review of the data. The question of the review, will all records be audited in the review, or is the process to review just the records that when the auto mapping just failed? How will consistency of the audit be managed? Again there are still the inherent issues as described in the auto mapping process.
3. Verification: In order to improve data quality, the data cleansing process requires verification with the manufacturer (service or product). The verification process assures that the purchasing record is set up to the correct manufacturer (referenced to the supplier via the contract), part number for restock ordering, UOM (Purchasing Unit of Measure), description with correctly classified i.e. BEARING, TAPER and the UNSPSC^®. Our process is to request the manufacturer to provide the UNSPSC^®. If the manufacturer cannot provide the UNSPSC^®, the item is correctly classified; the auto map to the UNSPSC^® will be successful. The verification process positions the data to identify duplication, manufacturer obsolescence and inaccurate data requiring additional information from the business to reconcile.
   
4. Enrichment: The fourth level of data cleansing quality, in addition to verifying, the data is enriched, this can be obtaining a price, warranty with the terms, additional description attributes, ECCN (Export Control Classification Number), recommended repair spare part information, eCl@ss, NSN (National Stock Number) or any other data element your business requires.

The conclusion is asking the right questions of how my data cleansing project will be implemented and managed are essential to making it a successful data cleansing project.

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.

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?

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 have a scenario to build a data flow task for Data Cleansing.

Logic 1 to be build:
Source data would be like 1050 and I should convert it to 1.050
Source data would be like 085 and I should convert it to 0.85

Profiling, structuring or normalizing data without any referential information risks errors in business use, especially if the data is use for purchasing or maintenance. If the goal is to automate the data normalization, the data needs to be referenced to metadata, 1050 could be a part number? Or a quantity? It could be an attribute representing a measurement such as length or diameter. Is it an inch or foot or meter?

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

The requirements for our clients business requires the complete documentation of spare part record change history including: Spare parts list submitted by, equipment used on, location of equipment, verification and data enrichment including who verified, change in information, when, etc. Why is this information important?

1. Spare Parts List – The supplier submitted spare parts list should be made a mandatory requirement for equipment design and build. In order to support a maintenance organization all suppliers should submit a full bill of material with recommended spare parts identified for the equipment they plan to deliver. The supplier requirement should include the original manufacturer for each spare part. Additional information tracked should include who submitted, file name, equipment name, equipment warranty, terms of warranty, when submitted and all contact information.

2. Use on Equipment – each spare parts list should include equipment part or model number, standardized name and a category of equipment. The standardized naming conventions are extremely beneficial for multi-facility maintenance use and will support common tasking procedures.

3. Location of Equipment – this information is essential for the export to a CMS maintenance system enabling spare parts to be set up for maintenance, work orders created and tracked and asset management.

4. Verification – is essential for accuracy of data quality. The verification process of a spare part is sometimes a true investigation. We receive data with suppliers listed as the manufacturer, partial part numbers, conflicting descriptions, incomplete descriptions, etc. Each data element change should be documented with when changed, who revised, what was changed and why.

5. Data Enrichment – What does the full enterprise (purchasing, engineering or maintenance) need to support the business activity? A spare part record should be touched 1 time and all information required should be included at the time the record is set up. Data Enrichment will include a reference to a class (category), required attributes to describe the part supporting the technical long description, estimated price, ECCN (Export Compliance Classification Number), UNSPSC® (United Nations Standard Products and Services Code®), lead time, warranty, terms of warranty, tasking information, etc.

In order to implement an accountable data governance program and useable data structure, a well planned data mapping should be documented for legacy systems of the enterprise. A complete data governance program will enable new efficiencies for data processing and the management of improved business processes such as parts sharing, identifying critical spares, strategic spare parts purchasing, and warehousing.

If we’re talking about a large company then inevitably with this new system comes the monolith project with whole organizations of people and processes, projects and documentation. The compulsion is to be sure that everyone, everywhere who has any relationship to it has their input and their needs accounted for. Along the way, the cost of implementation and other peripheral indirect costs have likely negated a great deal of at least any short term savings. Not to mention the potential increase in continuous maintenance costs and loss in performance. These are a few things I’ve learned from experience and I welcome yours.

Always have a specific objective when planning for development or evaluating software to purchase that overrides all others. Start with something like a mission statement, “We need this new system for….”

Determine the Real Needs. Try to separate the “must haves” from the “nice to haves”. Bells and whistles are great but there needs to be a true benefit. Seek a balance between development time, software performance, hardware performance and user experience. I always try to put special emphasis on the user group which stands to benefit the most. Having many users who can do their job faster and more efficiently can add up to real savings versus the few users who have a special need which bogs down the project and performance.

Change is inevitable. If some requests for additional features come along, evaluate them against the mission objective. There is nothing wrong with listening and investigating ideas for project add-ons as long as the benefits outweigh the costs in time and money, but there needs to be a limit or you’ll never complete the project. Good ideas can always be implemented later if it makes sense then you’ll have the benefit of the research already done, but be quick with the research. Evaluate the impact for doing it now or waiting. Here are some good questions to start with: 1) How much more money?  2) Would this be faster/cheaper for programming to do it now versus waiting and doing a more complicated enhancement?  3) Is the impact to the users great enough to warrant it?

Know the roles. Good ideas can come from anyone. Every project must have a project champion who makes the final decisions (and live with them) and also eliminate roadblocks. You need a user advocate who has done the job and knows what it takes. Have programmers who possess both talent and vision, not just code crunchers, and listen to them.

Have good documentation, and “Good” is subject to interpretation. This is another area where the KISS principle is very often not utilized. If you have to hire ten people to sit in meetings just to maintain your documentation you’ve probably overcomplicated it and certainly increased your project cost. I try to start with these principles:

1. Document the people on the project and their responsibilities. Let there be no question as to who does what.
2. Everyone who has a job to do needs to understand what they need to do and have the documentation to reference.
3. Keep the language simple. Focus on getting the point across. If it takes a rocket scientist to understand it you’ve failed.
4. Of course, document the issues, decisions made, by whom etc. but be sensible. Document enough to cover for the “he said/she said” but content is most important. No bonus points for flash.
5. Know who is supposed to have what done and when. Another obvious one here but I see too often where target dates are determined top down with little or no thought to cost or the tasks. Don’t let the tail wag the dog. Pushing hard to get the job done is fine but be realistic. Listen to the people who know before making bold predictions.

The first step is to develop the Identification Guide (IG) in order to baseline the properties needed to best describe the class. For example, let’s take the class of SCREW, SHOULDER and the properties TYPE, MATERIAL, FINISH, THREAD SIZE, DRIVE SIZE, SHOULDER DIAMETER, SHOULDER LENGTH, THREAD LENGTH, HEAD DIAMETER, HEAD HEIGHT, SHOULDER LENGTH TOLERANCE, MINIMUM TENSILE STRENGTH, CLASS, HARDNESS RATING and PACKAGE QUANTITY. The IG also provides the information needed for our analysts to acquire properties and our applications to sequence the properties within the short and long descriptions that are built:

SCREW,SHOULDER – | TYPE: HEX HEAD | MATERIAL: 18-8 STAINLESS STEEL | FINISH: PLAIN | HEAD STYLE: HEX | THREAD SIZE: 3/8-16 INCHES | DRIVE SIZE: 3/4 INCHES | SHOULDER DIAMETER: 1/2 INCHES | SHOULDER LENGTH: 2-1/2 INCHES | THREAD LENGTH: 3/4 INCHES | HEAD DIAMETER: 3/4 INCHES | HEAD HEIGHT: 1/4 INCHES | SHOULDER LENGTH TOLERANCE: ±0.005 INCHES | MINIMUM TENSILE STRENGTH: 80.000 POUND-FORCE PER SQUARE INCH | CLASS: 2A | HARDNESS RATING: B85 TO B95 ROCKWELL A | PACKAGE QUANTITY: 2

Each time an item is submitted for coding or processing the item is imported into a master database. Through intervention by our data analysts, the item navigates its way through a number of checkpoints including an auto-suggest to propose a class. The class and properties via the IG are the requirements our coding analysts use to verify the accuracy of the information submitted, to verify the completeness and to acquire the additional information needed to enhance and build an item or spare part description for our clients to base real business decisions.

The implementation of the eOTD is a two process scenario when working with our clients. First, the legacy data is mapped to the class, the item data is profiled, cleansed and enhanced to meet the requirements of eOTD IG, ensuring the client’s data quality goals are met. The updated item information needs to be applied to existing client item data. It is critical that all changes to data be tracked and logged. A properly planned and executed update to legacy ERP and CMMS systems should be initiated to incorporate the enhanced and corrected item information into the user facing systems. This is an extremely critical step as the downstream information flow will affect systems and uses such as inventory re-distribution, purchasing and contract management, engineering bills of materials and maintenance schedules. A thorough and complete mapping of data through the enterprise should be used to understand data flow across all business units. The mapping should include data entry points and data use points through all departments which set up all of the cost saving pay points as the data processing is streamlined.

The second process is an on-going data maintenance plan for new items that are introduced into the organization. This process should start at the introduction of item information into the system. All items and spare part information should be verified with the manufacturer and classified to the eOTD before setup or use in any system. The length of time the coding process requires is a critical element as the item or spare part information should be as complete as possible while at the same time be ready and waiting for the buyer to put the item on a contact or a maintenance employee to setup the tasking information in the CMMS for a piece of equipment. The only requirement for the employees who use the information after its initial entry into the system is to perform the actual requirement of their job and not to decipher a cryptic unstructured description.

If the items are pre-processed using the eOTD and the associated ISO standards, every item and spare part will be structured and standardized. The engineering, purchasing and maintenance departments will focus on the core of their day to day specialized responsibilities instead of searching for parts or dealing with trying to purchase items that a supplier does not recognize or have to acquire the missing information.

We all agree on some of the basic benefits both in process and cost such as reducing inventory with the identification of duplicate items, facilitation of inventory sharing and internal purchasing programs, reduced employee time searching for parts, common spare part usage strategies, reduced downtime in manufacturing equipment due to lack of information availability and ability to manage using a just in-time inventory model. The eOTD and its Identification guides are the building blocks and the roadmap to achieving structured and accurate data that can be reliably used to base real world decisions.

For more information on the eOTD please visit www.eccma.org.

Every day I make decisions. Some turn out to be the right ones. Some turn out to be horribly wrong. Since I accepted the role of product manager rather than simply a project team member, I have put a great deal of thought around decision making. I ask myself questions like: “What information do I need to make a decision the right one?,” “At what point do I have enough information to make the decision?,” “Does the outcome of each decision I make effect the remainder of the product launch in a positive way?” I still can not answer all the questions I have about decision making. However, I have used the following principles to aid in making the right decision most of the time:

1.) Make lots and lots of small Decisions. When you send your developers off on a mission to complete a large section of code or forge an entire revision to an app in one shot, there are inevitably a lot of decisions that have to be made along the way. If your development team has to make these decisions on the fly, against an imaginary timeline, there is a large chance the decisions will be made without all pertinent information. It’s more likely each small Decision will be the right one if you use all the information available from the complete team at each point a decision is required.
2.) Keep the communication channels open between developer and subject matter experts. I recommend daily touch points of less than 15 minutes. Meetings are expensive, time consuming and often attendees are never prepared. I prefer discussions to take place at the programmer’s workstation while he or she is working on changes. This allows demonstrations of current and expected behavior to be shown immediately. Real information and real code turns into a visual aid. It is important all team members understand the purpose is not to meet the schedule. The real purpose is to launch an application people love to use. I might go as far as saying it should be expected that as you move from design to development, you’ll need to make lots of little changes along the way. I question any development cycle where there is little difference between the original designs and the product at launch.
3.) Test, Test, Test. After each Decision there should be some time spent to test it. Testing does not have to be a project in itself. Testing should be performed by both developer and the team member who is in the best position to interpret what will and will not benefit the end user. Testing at each available opportunity is essential to minimizing the amount of change required after a bad decision is made. For example, if a change is made and not tested, each change implemented from that point forward could require revisiting if it’s found the original change was in error.

These principles also contribute to a pleasure filled work environment by allowing each team member to work on what they love. Development does not have to sit in endless conceptual meetings, nor does the product management team need to wait months or weeks to debut new features. The three principles I illustrated above can and should be applied to any development scenario. Using these principles to govern product testing and design reduces our development cost and gets our product to users faster. And in the end, that’s what it’s all about.

Here is the million dollar question, this is one scenario: Over a million records in a year, legacy and new records submitted for processing from 2,500 different users and two different business processes (single submit and BOM extract). What technology would be required to intelligently automate the processing of these records to a Master Data Quality Standard?

Remember this is an on-going maintenance process, not a one time migration of non-cleansed data to a new ERP or maintenance system, nor am I referring to parsing the records into different fields of the new ERP system but ensuring that the records are verified, structured, properly attributed with full descriptions and additional information to support the business needs.

First, let’s look at the Wikipedia definition of Product Information Management (PIM) “PIM systems generally need to support multiple geographic locations, multi-lingual data, and maintenance and modification of product information within a centralized catalog to provide consistently accurate information to multiple channels in a cost-effective manner.”

Future PIM software purchasers, what evaluation methods are you using to ensure that your PIM software purchase will support the continuous update and flow of data for your entire enterprise system? Here are some items to take into consideration during your evaluation, these are all items that I ask about and would recommend that you request the answers in writing:

1. How is the change history of the data stored in the system and how easily can it be retrieved?
2. Has the performance of all modules of the software been tested and what is the base line?
3. Request references (at least three) for each module of the software.
4. What is the software product work flow and how is the data processing assigned to employees?
5. Ask to review the documentation and take the time to review; this should be a window into the complexity of the system.
6. Request the design process model and how the software company incorporates customer feedback?
7. What is the bug fix process? What is the quality system to implement a bug fix?
8. What is the software company’s philosophy on customizations at your cost?
9. How is language handled? Translations referenced to a master record?
10. If the software solution is multi module system, how are the master records referenced through
the entire solution?
11. What are the long term design strategies or road maps for each module of the software solution? Ask for the earlier road maps and the software release note to evaluate the how well the software company plans and implement updates to the systems.

And I can go on and on, the licensing; customizing and implementing software in your environment can be extremely costly and time consuming, does Caveat emptor “Let the buyer beware” work in the business world or is there a “Lemon Law” when purchasing software?

So what can be done to insure profitability, or least stability, until growth returns? Control and further reduce costs. Already been there, done that? You have cut staff, benefits, wages, renegotiated prices and terms with suppliers, cut services, slowed production, cut inventories, everything you can think of. Are you sure? How well do you manage your Enterprise wide Master Data Indirect Materials / Commodities spend? What? Everything you buy that supports your facilities and the build of your products. Most large manufactures manage direct material precisely but don’t have an organized approach to their full advantage throughout the Enterprise to strategically manage indirect materials. A solution, fully implemented, provides a number of benefits:

1. “Cleansed” data, eliminating duplication of the same item coded to several different part numbers.

2. Consistent pricing for each and every part / component verified to the OEM level with lead time and warranty information. Minimizing your need to buy spare parts / commodities from distributors or your build sources.

3. Enterprise-wide material management to the department level in every Manufacturing Operation.

4. A reuse or repurposing of excess inventory in Manufacturing Engineering.

5. Able to search inventory with standardized part naming conventions and in multiple languages.

Bottom-line, an aggressive Enterprise wide well executed strategy can and will save your company significant dollars in the first 12 months of implementation. That’s 2010 folks….

As I have been working with indirect or non-production spare parts and commodities, I am equally surprised at how little thought of organization goes into the activities that supports the product build or even the facilities. Usually, I find that this whole issue is not dealt with in an organized fashion and is somewhat left to chance. All of the same thought that goes into product development should go into the manufacturing of the product. Why isn’t a Master Database of all indirect materials / commodities required for the Enterprise so the information can be commonly shared? With lead time, common pricing, warranty information, vendor or vendors, etc? First, no one individual owns the enterprise information across the different functional teams. Secondly, it is a decentralized task. Each individual manufacturing facility handles its own needs to get product out the door. In the meantime corporate purchasing is trying to support or at least get its arms around what the Enterprise needs.

By managing this spend consistently throughout the Enterprise, corporations can help ensure product gets out the door 24/7 and reduce their manufacturing cost substantially.

Here are a few data management tips:

1. Data needs a simple way to be imported into the system. Data comes from a number of sources so a dynamic mapping and import procedure to an internal processing area is useful for data analysis.
2. Yes, there needs to be an area to work on data before it is promoted to a Master Data Status. Software developers need to understand that data is never in a pristine state ready to be entered as a Master Data Record. Never!
3. Data processing requires a managed work flow through the system. Imagine the issue to have thousands of records for analyzing and many employees trying to manage who has what records outside the system. Just not functional work scenario.
4. Never copy data from one software module or grid to another, always reference. Cost per record to manage the data is increased every time a person needs to manually update an aspect of a record more than once.
5. Performance of the software is imperative. To really capitalize on software and technology reporting and analysis need to be done on thousands of records at a time. Time is money.
6. Provenance tracking is extremely imperative especially when “Cataloging @ Source” is the foundation to the quality of the record. Data should be identified with history: where the data originated, contact information, data and time, a revision level, file name, all associated records on the file, etc. MDM system developers, can you start to see the importance of this information?
7. Data needs to be cleansed and profiled; it is important that the software processing tools understand all aspects of the data. For instance search rules should not be so rigid that it takes an analyst manual actions to find a duplicate record because of an extra space or a slash. A worse case scenario is to take the data out of the system to work the data in excel, I am not going to even comment any more on that scenario except that it is totally unacceptable to remove data from a system to try to normalize it. Remember there is a lot of data brought into the business and the cost to manage the data is not core to the primary business, it is an indirect cost. The solution is not outsourcing to a “low cost, low skilled” worker in another country when much of the preprocessing can be done at the expense of CPU time.
8. Data changes, if you have a number of different modules in your software package what is the strategy to support aggregation of the changes to the different business units using the data? Does your software only update in one module and the other modules are in an out of sync situation? Again remember software should be designed to simplify the processes to support the business needs.
9. We live in a global economy language translation and localization of data is more important now than ever. What are the methods translate and maintain localized data?
10. Reporting and exporting of information is critical. It is a requirement to export a segment data set to send to a business customer or run a report of the activities of the work. A MDM system must be able audit data activities through the complete process of import through promotion to a master record.

I am a firm believer that software should not dictate a business process but should be designed to streamline and add efficiency to lower the cost the activity. If you are designing MDM systems, your team should include experts in data management, data quality and business process expertise with applicable experience. Businesses should not be paying for customizations to your software to be support basic 101 management of data.

DATAForge will be presenting at the FMMUG 2009 Best Practices to be held October 11th and 12th. This years event is to be hosted by Purdue University. The mission of the Facilities Management Maximo User Group (FMMUG) is to provide a forum for Maximo users in the facilities management industry to exchange information, methods and experiences. This exchange of information is designed to optimize the use of Maximo’s capabilities. For more information visit http://www.fmmug.org.

DATAForge will also be presenting on behalf of the Automotive Industry Content Standardization council at the 10th Annual ECCMA ISO 8000 Data Quality Conference on October 27th, 28th and 29th. This years event will be held at the historic Hotel Bethlehem in Bethlehem, Pennsylvania. Whether you are new to data quality or a seasoned professional, this conference will provide you with a unique opportunity to discuss the latest trends and check out the latest technology. If you have an interest in improving the accuracy of your vendor, material, service or asset masters, improving the descriptions in your ERP software or buy-side or sell-side catalogs or if you are looking for solutions to data integration challenges, the ECCMA conference is the place to be! Please visit the ECCMA website for more information.

The same logic of presenting every imaginable, option, configuration, button, screen, step, radio button, piece of information has been applied to many software packages. You would think in a large organization, simplicity would be king…not so…I am currently consulting with a large multi-national organization to help in the deploymentof a centralized system to house all product information for their MRO or Maintenance, Repair and Operations. Which, in practical terms, means that they are centralizing their databases of information required to order, maintain, and use any item that can potentially be purchased but does not go into their final product.

Not a small task by any measuring stick. Master Data Management, data cleansing, data normalization, intra-organization de-duplication are on the radar of most if not all large businesses. The most important part of the process is to choose application(s) that are the best fit for your organization, not the one that is made or owned by the largest company, and not the one who has the most clever marketing, not the one that appears in the latest report by the best marketed research firm (think about the ratings agencies who rated toxic subrime mortgage backed securities AA or AAA)

The software that was chosen xxxxxx (contractually obligated not to say the name) has one main screen for entering most of the data related to any given item, this screen contains no less than 50 possible fields in tabular form. There are also 3 additional screen each with less than 50 fields for data entry, these subsequent screens are used to associate ansillary information such as pictures to an item. The screens that DATAForge uses – one screen with 25 or less (depending on the type of data). The remainder of the information is gathered organically and seamlessly based on the way the application is used and who is using it.

When we design a solution the question on each team members mind is “How can I make this easier and faster to do for the end user?”

When evaluating an application force the vendor to show you how it will be used (not tell you), make them show you their solution is faster and more efficient. Lots of options, inputs, and fields are not always the users friend.

What is your Life Cycle Data Management Strategy?

1) Drawing Libraries – The items in the library need to be cleansed and profiled to a classification schema. The schema requires standard naming conventions and technical descriptions. The schema can be designed within your company, priority purchased from another vendor or you can opt for using an open classification dictionary for public use such as the ECCMA eOTD.

2) Common Component Listing – provides a listing of preferred components that support the inventory management strategies for your organization. All equipment designers and builder are required to use the common components identified. Note: common components are set up in the drawing libraries.

3) Spare Part Acquisition – Place the components on purchasing contacts at the beginning of design, this will facilitate the ease of spare parts planning and purchasing. An item on contract provides purchasing the data needed to run analytical algorithms in order to better negotiate pricing organization wide. If the item is set up accurately to a standardized classification dictionary with technical descriptions only one time the whole organization can realize the benefits of the Life Cycle Data Management Strategy.

4) Inventory – supports optimal inventory management by promoting the ability to plan stocking levels and strategies with nearby facilities. Think about the implementation of spare parts sharing or an internal purchase first program. The most important requirement is the standardization or normalization of the data; the part needs to be classified only one-way and should be shown in every system the same way.

5) Maintenance –The use of standardized components coupled with a data management strategy allows the organization to streamline the number of different components used to serve the same function on different equipment. Also reducing the number of parts in inventory and maintenance management tasks.

Life Cycle Data Management Plans starts with component standardization and cleansing the data in your equipment drawing libraries and all downward systems including maintenance. This strategy avoids duplicate inventory items and at the same time promotes an internal purchase philosophy that puts a priority on inventory sharing before issuing supplier purchase orders. Standardizing inventory with information elements such as predefined stocking levels, identification of critical inventory, functionally equivalent item identification and purchasing analytics as well as enhanced vendor management are all necessary steps for a manufacturing business to remain competitive in today’s world of lean low overhead manufacturing.

To realize cost savings from new process efficiencies, these separate legal entities need to integrate the information flow and internal groups within each entity such as purchasing, manufacturing engineering, plant maintenance, facilities management, warehousing, commodity management, and asset recovery. Each area must share the mission-critical master data related to the spare parts. Truly integrating the information flow within the conceivably 50-plus business units that indirectly work together across the automotive supply chain to deliver just one item to an OEM sounds literally impossible and cost prohibitive. However, your opinion may change when you read the next couple sentences.

It is estimated that process failures and bad information cost business $1.5 trillion or more in the U.S. alone (Larry English, 2007). A study of large companies, a majority of which have revenues of more than $1 billion, found that 31 percent believe that their costs for incorrect data are $1 million or more per year (Dave Waddington, 2008). The most common element needed by (and from) all involved in the supply chain of the spare parts that keep our lines running is data quality and content as information is transmitted from one organization to another.

Figure 1. Typical Supply Chain Spare Part Data and Information Flow

There is a lot of activity and even more information available around Master Data Management (MDM). MDM and data quality initiatives have become an industry trend these days. To champion a successful MDM effort, formal strategies regarding data standardization in content and structure, as well as import, storage, display, and transmission from your enterprise resource planning (ERP) systems to industry partners are mandatory.

Every supplier, OEM, and manufacturer is using a unique set of data standards to attempt to achieve true “quality” for their data. But how powerful, efficient and beneficial to the automotive industry can the use of silo developed standards be? If all partners were using the same data standards, naming conventions, and requirements to describe spare parts, we can greatly streamline the process needed to exchange the information and at the same time reduce the number of physical and business process failures resulting from the low-quality descriptions contained in our legacy systems, and in most cases, new state of the art ERP systems.

The elements required to achieve a symbiotic information flow for the automotive industry are the same:

A common understanding of what data is needed for a particular class or type of item;
A common method to store the data;
A common method to display the data; and
A common method to transmit the data to those entities that do business together.
The answer is to simplify and standardize the methods used for the exchange of structured, accurate, and efficient data-sharing in an automated fashion, rather than manually sharing as it has traditionally been done. The Electronic Commerce Code Management Association (ECCMA) and DATAForge LLC have formed the Automotive Industry Content Standardization Council (AICSC). The purpose of the council is to facilitate the addition of automotive industry specific terminology to the electronic Open Technical Dictionary (eOTD), create identification guides for quality descriptions, or data requirement statements for individuals, organizations, locations, goods and services.

This also helps develop an automotive supply chain specific spend analysis classification. The dictionary being maintained by ECCMA and the AICSC is ISO standard and public domain; any organization can benefit from its use. ECCMA and the AICSC work with automotive-centric businesses to standardize the way data and information is stored, viewed, and exchanged.

Figure 2. Quality Description:

ECCMA has brought together thousands of experts from around the world and provides them a means of working together in the fair, open, and extremely fast environment of the Internet to build and maintain the global, open-standard dictionaries that are used to unambiguously label information. ISO 22745 spare parts data is capable of being used in any ISO 8000 computer application (neutral exchange), is easily translated, and must stand the test of time (long-term data retention) by using a public domain concept identifier.

Jacqueline Roberts is vice president of DATAForge LLC. For more information about ECCMA, visit the ECCMA Web site.

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