Posts Tagged ‘DATAForge’
Friday, January 8th, 2010
With the ever increasing emphasis on finding ways to reduce cost, one of the clear targets is IT and more specifically data management systems. On the surface it can seem like there is real fat to trim, and many times this is true. But it is easy to become lost in the details and eliminate or negate some of the potential savings. Some of these ideas may seem obvious but are often forgotten. The evidence is clear with missed timing and over budget issues seen.
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:
- Document the people on the project and their responsibilities. Let there be no question as to who does what.
- Everyone who has a job to do needs to understand what they need to do and have the documentation to reference.
- Keep the language simple. Focus on getting the point across. If it takes a rocket scientist to understand it you’ve failed.
- 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.
- 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.
Tags: Agile, automotive, BPO, Business Intelligence, data, Data Cleansing, data management, data quality, DATAForge, dataquality, development, eOTD, ERP, linkedin, maintenance, manufacturing, masterdata, Maximo, mdm, MRO, project management, SaaS, Software as a Service, spare parts, system implementation, Technology
Posted by Carl Hamlett | Post a Comment! »
Friday, October 23rd, 2009
With all the discussion focusing on Master Data Management and Data Quality, I always come back to these questions: How is the data structured and how is the accuracy and content completeness measured? In our business of managing the coding and verification of items and spare part information needed to keep manufacturing plants running, a structured schema of naming conventions (class), descriptive attribute standardization (properties) and verification at the sources of manufacture (coding @ source) is “key” to quality and completeness measurement. We are managing the ECCMA eOTD for the Automotive Industry Content Standards Council (AICSC) focusing on MRO naming definitions which is the foundation to a spare part description, just as a table of contents is the foundation of a text book.
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.
Tags: automotive, BPO, Business Intelligence, data, Data Cleansing, data quality, DATAForge, dataquality, eOTD, linkedin, maintenance, masterdata, Maximo, mdm, MRO, spare parts
Posted by Jackie Roberts | 1 Comment to View »
Tuesday, October 6th, 2009
The difference between a Decision and a decision is simple. A Decision spelled with an uppercase “D” is one based on data, information and real-life experience. A decision spelled with a lowercase “d” is one made without data, information or a real-life experience. All too often I have seen decisions based on an individual’s feelings or opinions of a given situation. It makes me shake my head. In some cases, a business will use an algorithm or spreadsheet with embedded formulas to “choose” the best decision based on a set of desired requirements and associated weights applied to each requirement. For certain decisions, this might be the most appropriate way to assess the situation. For me, working to develop web-based software applications, it just doesn’t work.
The most common method of decision making during a development cycle I have come in contact with is the “committee” driven requirements analysis. During this process a group of usually high level managers (far from end users) sit down and work their way through a spreadsheet of requirements to decide which ones should be included in the next six-month iteration of development. In my experience, the only information included in the requirement column is the perceived expected behavior or outcome of the given feature or change. The end of this type of development cycle is usually followed by hundreds of hours of testing and arguing about how each feature should work, how it actually works and how we ‘thought” it would work. As well as two strokes, three heart attacks and a combined three square inches of newly exposed scalp for the male members of the team…
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.
Tags: Agile, data, DATAForge, dataquality, development, linkedin, masterdata, national security, scrum, Technology
Posted by Chris Roberts | Post a Comment! »
Thursday, October 1st, 2009
I am all about the data, location management (to location and equipment), data quality, and methods to improve auto-processing, enhancing data, providing data reports and results that support our customer’s data requirements in their day to day activities.
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?
Tags: BPO, Business Intelligence, data, Data Cleansing, data quality, DATAForge, eOTD, linkedin, maintenance, manufacturing, masterdata, Maximo, mdm, Software as a Service, spare parts, Technology
Posted by Jackie Roberts | Post a Comment! »
Thursday, October 1st, 2009
My company’s fiscal year is based on the calendar year as many others are. So, customarily we start the budget planning process in October. It is a detailed process that all of my managers and business units participate in. We usually do a few iterations before it is finalized in mid December. Sound familiar? So here’s the question, after 2009, how do you plan for 2010? Everything we knew and could usually predict with some certainty in recent years went out the window in 2009. Where do you start to plan for the next year? Is it too early to plan for growth, if not, at what pace? What certainty can we count on when developing our plans? The simple fact is, for most of us, we don’t know enough at this stage in the recovery to forecast with certainty where our businesses will be, at least, through mid next year.
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….
Tags: BPO, Business Intelligence, data, Data Cleansing, DATAForge, dataquality, linkedin, maintenance, manufacturing, masterdata, Maximo, mdm, MRO, Software as a Service, spare parts, Technology
Posted by Art Healan | Post a Comment! »
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.
Tags: Business Intelligence, data, Data Cleansing, data quality, DATAForge, dataquality, linkedin, manufacturing, masterdata
Posted by Art Healan | 1 Comment to View »
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.
Tags: BPO, Business Intelligence, data, data quality, DATAForge, dataquality, eOTD, ERP, linkedin, maintenance, manufacturing, masterdata, Maximo, MRO, Software as a Service, spare parts, Technology
Posted by Jackie Roberts | Post a Comment! »
Friday, September 11th, 2009
October 2009 is going to be a busy and exciting month for DATAForge. We are scheduled to present at two events and hope to see you all at both
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.
Tags: Data Cleansing, DATAForge, dataquality, linkedin, maintenance, masterdata, Maximo, Purdue, Software as a Service, spare parts, XML
Posted by Industry News | Post a Comment! »
Wednesday, September 9th, 2009
At DATAForge we pride ourselves on designing simple, elegant, easy to use, web based software for a manufacturing demographic that has been flooded with overly complicated software, abound with options and restrictions, screens to control those options,restrictions and configurations. I’m tired of it. I don’t want you to get me wrong, there is certainly a time, place, and need for software that is configurable in every conceviable way. For example when a multi-state and international corporation is required by law to comply with one of the most complicated tax codes in the recorded history of Earth, then you get a pass for making an application complicated. In this case complication can and has saved many organizations millions or hundreds of millions of dollars, issues like The Sarbanes-Oxley Act of 2002 are not to be taken lightly.
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.
Tags: DATAForge, maintenance, manufacturing, masterdata, MRO, Software as a Service, Technology
Posted by Chris Roberts | 1 Comment to View »
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.
Tags: BPO, Business Intelligence, data, Data Cleansing, data quality, DATAForge, dataquality, eOTD, ERP, linkedin, maintenance, manufacturing, masterdata, mdm, MRO, SaaS, Software as a Service, spare parts, Technology
Posted by Jackie Roberts | Post a Comment! »