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CAUSES

I have chosen the above data elements and examples because I believe they reflect the two main recurring causes:

•    First, a significant number of system problems result from inatten­
tion to detail:

-      Data is recorded, transcribed, or keyed without scrupulous at­
tention to accuracy. The wrong value gets entered.

-      Data to be converted is not mapped to the new fields with ad­
equate regard for how the system uses the field compared to
how the "legacy" system used it.

-      Conversion programs are not thoroughly "scrubbed" and tested
before they are applied to the database.

-      The existing database is not scrubbed for errors before conver­
sion, so erroneous data is converted, with sometimes truly strange
results.

-      Naming and numbering schemes that satisfy engineering's per­
ceived need for precision may be totally unrealistic for actual
system end-users.

•    The second major cause is lack of understanding:

-      Without some education in the theory and concepts of require­
ments planning, people do not understand what the software is
trying to do, or why it is trying to do it.

-      Without adequate training in the software, people do not always
comprehend the system-generated messages. Software program­
mers have some limited space in which to compose the text. We
know what the words mean, but what is the concept that is being
communicated?

PREVENTION

Given the two causes above, it should come as no surprise that educa­tion and training are two keys to prevention. A third key is preemption.
 

Time after time, I have seen that the fewer people in a company who are APICS-certified, the more difficult the implementation is. A college degree is not necessarily an adequate substitute for certifica­tion, unless the degree course material covered the same body of knowl­edge as the APICS program. People, at least on the implementation team, have to understand how and where the various pieces fit in the big picture.

The implementation team also must be well trained in how the sys­tem works because they will be developing the (hopefully written) policies and procedures that the end users will follow to perform their job tasks. The end users must then be trained to follow the approved and tested policies and procedures.

To illustrate this point, we can return to the example of the disap­pearing item record, although the policy I will describe has more uni­versal data entry application. Simply put, whenever someone keys in data from a source document, record it. If the system returns an ID number, record number, batch number, etc., record it on the source document. Now there is a cross-reference in case the computer record later "disappears."

 

If the system does not provide such a return, then the next best thing is a two-part option. First, validate the key data field for the record (like part number) against the source document, character by charac­ter, and checkmark the data element on the source document. Then, immediately after you press "enter" and the screen clears, inquire against that field and verify that the system can return the record.

 

Some people, in fact, have adopted the habit of both recording the return ID on the source document and inquiring to ensure record ac­cessibility.

 

Now let us consider preemption as it applies to data conversion. To help prevent data conversion errors, it is very useful, early on, if pos­sible even before the implementation team has access to the new sys­tem, to map the existing database. Build a matrix or spreadsheet data­base. For each data field, record its ID, common name, current usage (yes or no {if "no" stop here, data field will not be converted}) field length, data type (alpha, numeric, alphanumeric), number of decimal places, list of valid codes, list of who (functionally) uses/needs the information in the data field, brief narrative of how it is used in system, where it is used (list of programs, screens, reports that use, call, or display the data element), how populated/updated (manually, by pro­gram # ??), cross dependencies (if field xyz has value q then this field must be value s or t), etc. Leave blank a final column called "map to."

 

This exercise will help crystallize your thoughts about how you do your jobs, what data you really need to convert, and what that data should look like. It will also indicate what needs to be done to clean up the database and assure its integrity before conversion. Finally, the worst part of mapping the existing database to the new database is now done. Your system consultants, who know the new system, can very effi­ciently provide a first draft "fill-in" of the final column.

SUMMARY

Let us quickly review what we have covered. Common errors in the item master data set came be mitigated if

1.        Item numbers are kept relatively short, block numbering schemes
are avoided, and significance is held to an absolute bare minimum.

2.        Item-naming conventions are descriptive, with a consistent set of
"rules" for establishing new item names/descriptions, and search
text conventions reliably provide a viable "short list" of selections.

3.        Units of measure are selected for minimization of decimals and
ease of use and conversion. Metric-based systems are becoming
more popular.

4.        Start with a clean database, and thoroughly test the conversion pro­
grams. Minimize reliance on "defaults." (For relatively small data
bases, it may be quicker, cleaner, and less costly in the long run to
manually enter the data using temporary hire data entry clerks).

5.        Thoroughly understand what "instructions" the material planning
codes convey to the system software, how the system tries to imple­
ment the basic material planning theory, and how the various codes
interact with each other.

6.        Train the end users to use tested and approved local policies and
procedures to minimize the chances for erroneously accessing or
maintaining the database.

7.        Pay attention to detail.

Follow these guidelines and avoid a lot of the headaches that ac­company a system implementation. Good Luck.

For balance of this article, click on the below link:

Lean Manufacturing Articles and click on Series 11


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