Posts Tagged ‘data’

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“What’s the difference?”

Monday, September 21st, 2009

I have worked for many years supporting major manufacturing clients with operations throughout the world. Often times it has been centered around product engineering support and product documentation. Everything from initial development, prototyping, testing, production, parts (production and after-market), operator and service documentation – soup to nuts. I have always been impressed by the great lengths companies go to ensuring that when the product is ready for market nothing has been left to chance. They know every part that is needed, whether custom built or purchased (supported by engineering drawings), the best price, lead time, how much inventory is needed, sourcing risks to consistent part numbering schema. Virtually every detail that needs to be done to get product successfully out the door and supported has been thought through numerous times.

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.

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Who Represents the Data in your Master Data Management Software Systems Designs?

Thursday, September 17th, 2009

Those of us that are representatives of Master Data Management initiatives, data quality projects and the users working the processes developed by software makers have a difficult journey in front of us. It seems that for years software developers have designed cumbersome transactional data management systems that do not begin to understand real time data management and what effort it really takes to achieve an on-going Master Data Management program. I have two initial questions: Do these software companies toting one press release after another about Master Data Quality Management even understand the importance of on-going change management to a master data record? How does a business stay in front of the information flow if the software system does not dynamically adapt to the ebb and flow of data volumes and requirements? Software companies track updates and revisions to software code, data is of the same importance sometimes it is of greater importance; the number of data level updates can be monumental depending on the size of the company. Isn’t the end result of a multi-million dollar software system implementation supposed to drive efficiencies and streamline the activities to support their businesses? Cost saving and real time data management is the name of the game.

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.

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Life Cycle Data Management Strategy

Thursday, September 3rd, 2009

Life Cycle Management implies a single “cradle to grave” plan that integrates production support planning, acquisition and sustainment strategies. Think about the importance of data flow and the criticality of accurate data throughout the complete life cycle of a piece of equipment: design, build, install, spare part acquisition, inventory management, maintenance, spare parts sharing and finally, asset disposal. From a data perspective, remember the old computer motto: “Garbage In, Garbage Out”.

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.

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Why Data Cleansing?

Thursday, August 27th, 2009

The statistics around data cleansing are overwhelming and there are mountains of discussions, white papers and tweets available pertaining to Data Quality, Data Profiling and Master Data Management. I think we need to take a step back and try to understand how and why data cleansing has become such a hot topic. You may have realized that business data typically isn’t as streamlined and efficiently maintained as we thought it was. Your organization may have shipped purchased items back because they were not what you thought you had ordered. In some cases another department was found to have the item in inventory, even though we have the item on urgent delivery status from a supplier because the item is set up under a different number or description, you couldn’t have possibly known the item was actually available from existing inventory.

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.

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The Spare Parts World and What It Could Be

Thursday, August 20th, 2009

The conundrum of spare parts management at a high level is perceived and often approached as a process that should be simple. Looking at it from the perspective of the many different entities that form the supply chain and are required to work together symbiotically—component manufacturers, Tier One and Tier Two suppliers, and OEM manufacturers—the logistical expertise needed to coordinate the information flow is anything but simple.

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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ISO 22745 Standard Based Exchange of Product Data

Thursday, August 20th, 2009

When a spare parts list, bill of material, or other product information for your ERP or inventory system is received, what processes do you follow to make sure the data is accurate and complete?

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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The Spare Parts World

Tuesday, August 11th, 2009

Spare parts management at a high level is perceived and often approached as a process that should be simple. Looking at it from perspectives of the many different entities that form the supply chain and are required to work together – component manufacturers, tier 1 suppliers, tier 2 suppliers, and manufacturers, the logistical expertise needed to coordinate the information flow is anything but simple.

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.

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Outsourcing; how do I compete?

Tuesday, July 28th, 2009

I get it, you operate globally and the cost of labor in the states is 4 to 5 times higher than the wages in the countries that typically receive outsourced work. I have only one question; is the only factor taken into account when deciding to outsource from the US to a foreign country cost? When the RFP is evaluated does intellectual property protection and security, quality of work product, time zone communication issues, the geopolitical climate or increasing price trends enter into the decision making process?

I once spoke with a purchasing agent employed by a Fortune 500 company and this is how outsourcing was explained to me…”even if takes someone in a foreign low wage country 3 attempts to get the work correct, we are still are saving 25% over their competitors in the US.” Of course, I had a number of responses, including: Was the cost to manage and audit the work 3 times included in the cost saving analysis? Of course not, the cost savings estimate is only documented at the RFP phase.

Each day our company evaluates our internal and customer processes to build automation and intelligent software applications that increase throughput, improve accuracy without manual intervention and provide our customers with a continuous stream of process improvements. I believe long term our cost are competitive, the challenge is educating new customers to understand the unique and beneficial processes that allow them to capitalize long term implementing  our data quality solutions.

My hope is that I will never see another response to an RFP “Need more competitive pricing or to include “off shore” solution – This is required for more competitive proposal and for further consideration”

How long will it take US salaries to race to the bottom so work can be outsourced back to the states? I hope that this is not the answer, let’s discuss what US vendors need to do offer the long term value add processes that off shore options do not?

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What is the Cost of Bad Data?

Friday, July 10th, 2009

How does a company apply a “cost” to bad data when the costs are so fragmented across the organization? There are obvious costs such as a part not being in inventory, purchasing has tried to buy the part but the supplier didn’t recognize the part number, now production is down and everyone is scrambling to find the replacement part. In this case the cost of the bad data can be assigned.

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]

[i] Larry English, “Information Quality Tipping Point: Plain English about Information Quality,” DM Review, July 2007.

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Data Integrity – How is this really achieved?

Thursday, May 21st, 2009

Data integrity is the assurance that data is consistent and correct. Spare parts, sounds fairly simple?
What are the basic elements of a part record; name, part number, description? Data Integrity is used way too much but is a very vague concept. Let’s just look at the purchasing department; it is easy if the part records are only used by the purchasing department where the main objective is to purchase the item. This example is all the data that the buyer will need to purchase this switch.

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How would a buyer know that these are the same parts? Two different manufacturer names and two different part numbers; this scenario will cause duplication in a purchasing system. The result is the additional work of creating and maintaining two contracts but also cause downstream effects such as excess inventory with more than 1 stocking location, lack of a volume purchase or a global view.

 Question: Is the answer to always to confirm the actual manufacturer and set up supplier references?

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