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.

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.

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

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.

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.

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.

The data quality issues that industries around the world are experiencing have occurred as a result of many years of manual inventory and purchasing record maintenance, through mergers and acquisitions of companies and business units as well as data migrations from various legacy systems into new fangled ERP black holes. There are a number of reasons why.

A common data trap frequently fallen into is assuming that just because you are implementing a new ERP system your organization will now have quality data. Remember the old computer motto – “Garbage In, Garbage Out”. Let me tell you based on first hand experience that there is nothing “sexy” about bad data when the production line is down or any other time.

Data Cleansing and Data Profiling is a very tedious and detailed oriented service. There are a number of key rules to follow whether the profiling and cleansing work is done internally or outsourced to someone who specializes in data cleansing. Here are some rules to consider before a project is started:

1) Conduct a detailed and comprehensive data mapping through all internal systems including engineering, purchasing, asset management, plant inventory management, etc. The goal is to standardize and document all data sources within the enterprise one time and ensure that each department is accounted for and determines what data elements are required to complete their business required tasks.

2) Build a central data cleansing database and make sure all locations using each item are referenced. This ensures that updated information will be passed back to the various legacy systems. You will need old information and updated information for this stage of the process.

3) The data cleansing database should include a balance of electronic scripting for data corrections and manual auditing. A solid process for answering questions needs to be set up. My preference is that the system should use a web utility that tracks data change history and other data related information such as contact information, issue resolution status, classification, questions and answers, etc.

4) The data needs to be referenced to a classification schema and a standard implemented for descriptions and properties. 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.

5) Free text is not our friend in the data standardization world. If all possible use a system that has built in data rules and ensure anyone entering data into the system understands the standards and the importance of quality data in addition to the high cost to businesses using bad data.

6) Data Cleansing and Profiling the proper way is not “cheap”, but the cost of cleaning the bad data is always less than the expenditures incurred by cleansing your data multiple times or continuing to operate your organization based on erroneous information generated from one or multiple dirty databases.

Cleansed data permits the removal of duplicated inventory items, an internal purchase philosophy that puts a priority on inventory sharing before issuing supplier purchase orders, standardizing inventory with predefined stocking levels, identifying critical pieces of inventory, identifying functionally equivalent items, use of engineering component standardization libraries and facilitates purchasing analytics as well as enhanced vendor management.

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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Typically, maintenance or inventory information is not given any due diligence until it is needed. For instance, a bill of material (BOM) is received, all the parts are set up in your ERP system, and the item record sits untouched until you need to place an order or set the item up in your maintenance system. Then you find that the part number is inaccurate and the supplier doesn’t recognize it, or there is an essential piece of information missing from the description needed to complete the order and bring the line back up. There is a solution: ISO-22745.

The ISO-22745 standard provides the framework needed for any organization to conduct business with internationally recognized data quality. Its most basic purpose is to provide a means to realize the benefits of ISO-8000, which is the ability to specify syntax, semantic encoding, and specification of data requirements for messages containing master data that is exchanged between organizations in the supply chain. Once an organization begins to standardize the descriptions it uses to describe materials, the organization can also begin to see cost savings and cost avoidance by implementing business intelligence algorithms to identify conditions such as duplicate items in inventory, purchase price disparities between facilities, vendor reductions, and identification of functional equivalent items.

ISO-22745’s primary facilitator is the open technical dictionary (OTD), a database of concept IDs and associated descriptive words used to “tag” individual data elements. Once each element is tagged with the concept ID from the OTD, the descriptive elements can be stored, sent, received, and displayed by different organizations without losing any meaning. This is done for multiple languages at once, with no need to translate into multiple languages independently.

ISO-22745 also includes guidelines for the use of identification guides (IG). An identification guide is a statement of requirements describing what data is needed about an item. If all elements are included in the description, this IG facilitates the machine-aided analysis of data quality because we have a clear understanding of what data is required without a person having to review the data.

ISO-22745 describes XML formats that can be used to automate the exchange of ISO-8000 master data.

i-xml is used to specify the data requirements or IG.
q-xml is used to query another organization for the data elements specified in the IG.
r-xml is used to reply to requests for specific data elements.
Together, these formats allow for the machine aided exchange of master data.

The Electronic Commerce Code Management Association (ECCMA) provides a very mature OTD, known as eOTD, which contains more than 440,000 terms that can be used to generate descriptions. ECCMA and DATAForge have also formed the Automotive Industry Content Standardization Council (AICSC). The AICSC is here to help organizations move from proprietary methods of managing descriptions to an ISO method that includes working together as an industry to meet the common goal of lowering operating overhead related to catalog maintenance.

Chris Roberts is an associate product manager at DATAForge™ LLC

For more information on AIAG’s activities and initiatives in electronic commerce, visit the AIAG Web Site or contact Mohammad Abidi.

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To realize cost saving from new process efficiencies, these separate legal entities need to “integrate” the information flow to manufacturers and within each manufacturer to internal groups such as purchasing, manufacturing engineering, plant maintenance, facilities management, warehousing, commodity management, and asset sharing / recovery need to share the mission critical master data related to the spare parts. A truly integrated information flow could conceivably touch a number of business units that indirectly work together across the supply chain to deliver just one item to a manufacturer. The most common element needed by (and from) all involved in the supply chain of the spare parts that keeps the equipment running is data standardization, data quality and an electronic method of transmittal. A study of large companies, a majority of which have revenues of more than $1 billion, found that 31% believe that their costs for incorrect data are $1 million or more per year.1

Data standardization and data cleansing cost should be covered with cost saving initiatives. In addition to the initial data cleanup; strong data governance processes should be implemented for on-going data setups.

1Dave Waddington, “Growing Adoption of Master Data Management by Business?” citing an Information Difference survey of 112 companies, 65% of which had revenues of more than $1 billion, IT-Director.com, IT Analysis Communications Ltd., June 23, 2008.

DATAForge LLC is scheduled to present our best practice “Electronic Structured Spare Parts Data Population of Maximo” at the Facilities Management Maximo Users Group (FMMUG) http://www.fmmug.org/ hosted by Purdue University on October 11^th and 12^th. Setting up spare parts and tasking in Maximo starts at the beginning of equipment design with the bill of materials parts list, the equipment asset number and plant location. Our best practice, provides a complete and automatic electronic transfer of the Bill of Material for a piece of equipment that mashes up to an equipment listing with location. The data is imported with the item records referenced to a category key of perishable spare / non spare. The perishable spares are imported to a data verification tool where analysts process and cleanse the spare part records. Once the equipment with plant location and all associated spare parts are complete we use a simple to use interface for transfer of data to Maximo, thus giving users the power to move thousands of records at a time creating Equipment, Items, Companies, Spare Parts, etc. with all of the correct fields related, to take advantage of the Maximo hyper-linking ability. The results are a fully accurate data enabled Maximo without manual part verification or data entry of equipment, items, spare parts or companies. The documented time savings for one program is two skilled trade persons for two years. Look for our best practice case study in October.

What about the other costs? What does it cost a global manufacturer the lack of visibility of the “spend” or the inability to manage vendors selling like or equivalent products?

It’s estimated that process failures and bad information cost $1.5 trillion or more in the U.S. alone.[i]