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Manufacturing Simulation Game - "LEGO"

ERP Data Management
Part 3 of 3


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STATIC AND DYNAMIC DATA
During the conversion to a new system, one of the most important tasks is to identify as early as possible what portion of the data can be considered "static" versus what data should be considered "dynamic."
Static data can be described as "stable data that does not experience a high frequency of change." The data elements that fall into this group­ing will vary by business and the processes that they employ, but would include such data elements as part number, description, bill of mate­rial, customer data, and supplier data together with many of the part codes. The significance of this static data is that this data can be con­verted before the final conversion takes place. This will significantly reduce the conversion risk and time table.
Dynamic data is the "data that can change on a daily basis and hence experiences a high degree of change." The dynamic data will be con­verted during the final stages of the conversion process. Dynamic data includes such information as inventory balances, open order data, and materials in transit. This information will typically be frozen just be­fore the final conversion takes place, and it may be necessary to put in place some processes to handle such situations as emergency shipments that must occur while the business is without a computer system. Any transactions that are processed in this manner would be added to the new system immediately following the final conversion and just be­fore the system is turned on for the users.
ONGOING DATA MANAGEMENT PROGRAM
the quality of the system data improves, control limits should be low­ered to reflect the improving quality of the system data.
LESSONS LEARNED
Having been involved in several major ERP system implementations, there are a number of lessons that I have learned, and these are summa­rized below:
• Start data cleanup task early. It always takes longer than one had originally thought.
• There are occasions when the use of an external consultant can be beneficial. Don't be proud—use a consultant if it is appropriate (executive level, software application level, or as a subject matter expert).
• As data cleanup teams get smarter, they will want additional re­ports to help with their cleanup tasks. Be very responsive to these demands as it shows cleanup teams are taking ownership of task.
• If one can clean up any data electronically, do it. Check conversion multiple times before actual conversion cut over to ensure that ev­erything is converting correctly.
• Clean up "planner codes" first and then produce all the exception reports sorted this way.
• Identify an owner for each key data element.
• Establish cleanup metrics and goals with data owner.
• Notify functional vice president how data cleanup effort is going.
• Do several mock conversions (two or three) before real conversion.
• Do not forget about exception conditions.
• Put in place an ongoing data management program.
IN CONCLUSION
The success of any system depends upon the accuracy of the data that it uses to perform its various calculations. To have a truly successful implementation, not only must the systems be running correctly, but the data must also have been successfully cleaned and converted. It is also imperative that the accuracy of the data be monitored, via the use of system health metrics, on an ongoing basis. This monitoring is best achieved through the use of a data management program that is con­tinuously monitored to ensure that it truly reflects the needs of the business users. With this type of data management program in place, one can ensure that a high level of data accuracy is maintained so that one can achieve the benefits from the newly installed system (i.e., "find the money"). With a high level of data accuracy, one can now position

Having now successfully converted to the new system, one should put in place an ongoing data management program. This will ensure that the data accuracy is main­tained on an ongoing basis. Figure 3 shows the major steps in this process.
The data management program would make regular (weekly preferred, monthly as a minimum) sweeps of the data files looking for exception condi­tions. If exception conditions are found, these would be reported back to the data owner for resolution. One would maintain "health metrics" that show the quality of the data over time. In addi­tion to examining the key data files, one may elect to run reconciliation reports between data files if the same informa­tion is held in any of the legacy sys­tems and the new system.
Having established a set of system health metrics, it is important to real­ize that these will change over time. As
Figure 3. Ongoing data management program
Feedback
Legacy Systems integrated with New System
Exception Conditions
Functional Users
New system can/will have better data integrity checks than old systems
May also need to include reconciliation checks between data bases
May need to change "System Health Metrics" overtime to keep data management program current the business to make more effective use of newly emerging technolo­gies such as sales force automation, E-commerce, synchronized manu­facturing, and other processes that will give the business a true com­petitive advantage.


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