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Manufacturing Simulation Game - "LEGO"

ERP Data Management
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Figure 1 shows the two major processes needed to get accurate data for our new system. During any new system implementation prepara­tion activities, a data cleanup plan needs to be developed to examine the critical data in the legacy systems. This will identify which data elements need to be corrected before the legacy systems are converted. It should be pointed out that some data can be corrected electronically during the system conversion process, but time must be allowed dur­ing the actual conversion process to have the data owners check to ensure that this final conversion has been successfully executed.
The second essential ingredient in maintaining accurate data is an ongoing data management program that occurs after the system con­version when the new system is being used. A data management pro­gram enables one to be continuously monitoring the quality of the sys­tem data.
When developing the new system implementation plan, data cleanup must be a key part of this plan with its own full-time team. The data cleanup team is a vital part of any new system implementation and should be organized in the same manner as the requirements defini­tion, detailed design, application programming, system/stress testing, and implementation project teams. One of the first activities that they have to perform is to identify any mandatory system data that may be required in the new system. In addition, they then need to identify if there is any company mandatory data that may be an elective data field in the new system.
The next questions that one needs to answer are
• Does this data currently exist, either in the legacy system electroni­cally or manually in some hard-copy folders?
• How accurate is this data?
• How are we going to capture any missing data?
An ideal first step is to identify the data element that shows who is responsible for a part, often "planner code" or "buyer code," and start by cleaning up this data element. This data el­ement can then be used to distribute the vari­ous exception reports that need to be cleaned up to the individual responsible for the part. The cleanup teams then start looking at the data in the legacy systems and develop the various exception conditions that need to be reviewed and corrected. The use of a 4GL or any other report writer program is a quick and easy way to extract data from the current legacy systems. By way of an example, the sort of "part mas­ter" and "bill of material" data that one would look at includes the following:
Define data elements for review and conditions for clean-up
• Part Master
- Do all the part numbers have a planner code?
- Are the current part numbers too long?
- Do the current part numbers contain any special formatting, and is this to be car­ried over into the new system?
- Do the required part number codes ex­ist, such as commodity code, ABC code, part code, unit of measure, etc.?- Do all purchased parts have buyer codes?
- Do all parts have a lead time value? • Bill of Material
- Do all top-level assemblies have a bill of material?
- Are there any purchased parts that have bills of material (there may be cases when this is correct, but these should be reviewed)?
- Do all manufactured parts have a bill of material?
- Have the units of measure been checked? This includes counts to identify any unusual UoMs that perhaps should be converted to a more commonly used UoM and also errors such as a part having a UoM of "EA" and the quantity field shows the usage as "1.32".
- What are the number of levels in the bills of material?
- Are there any effectivity conflicts?
The questions above reflect the type of information that would be examined by the functional users as they review the data in the legacy systems. This same type of information needs to be developed for all the major databases that will be converted to the new system, such as work center data, process plans, open shop orders, open purchase orders, cus­tomer data, and supplier data together with all the financial data. Once the users start this cleanup process, they will probably be making addi­tional report requests to help analyze additional data conditions. One should support these report requests immediately as it is a clear indica­tion that the users are taking this data cleanup task seriously.
To help ensure that the data cleanup task is proceeding on schedule, goals and metrics should be set up. A sweep of the legacy systems should be done to size the extent of the cleanup effort. These reports should then be reviewed with the functional users and a "get well" plan developed. Once this has been agreed upon with the functional users, metrics should be developed that highlight whether the plan is being achieved. If the plan is not being achieved, then the functional user team can use the plan to help quantify the additional resources that are needed to get the cleanup task completed on schedule.
It is important to realize that the corrected data should be available to support system testing. This helps ensure that when reviewing the re­sults of system testing, any incorrect results are not caused by bad data.
In several cases where I have been involved in data cleanup projects, we summarized by functional vice president how the data cleanup ef­fort was proceeding in their area. This information was then presented at the executive steering committee meetings when reviewing the imple­mentation status.
Due to the size of the data cleanup task, it may be necessary to priori­tize the sequence in which the cleanup activities are undertaken. An addi­tional consideration is that quality data will be needed to support the sys­tem testing activity, and so this will be a consideration in establishing the cleanup completion date. It is also important to realize that some cleanup goals may not be zero but rather at some level above this based on the business process involved. In these cases the goal chart would include an "upper control limit," and if a count occurs above this limit, the functional users must initiate a cleanup plan to get back under control.
The manner in which this process works is summarized in figure 2. It is important to remember that while one is performing this task, the data in the legacy systems is getting better, so one does achieve some immediate benefit.

To Be Continued


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