All companies that make and sell products have to make this decision very early on. Some companies might not have the maturity or business processes in place when this decision needs to be made. Changes to policies done at a later time could result in additional complications so most companies chose to remain on their current policy and process.
Intelligent part numbers are used to clearly identify the type of part, its commodity or sometimes even the location of use in the overall product. Typically companies develop a matrix mapping commodity to specific sequences of part numbers. For example 12-???? Could represent sheet metal, 13-??? could represent PCBs and so on.
Unintelligent part numbers on the other hand are based on ERP/PLM system’s ability to automatically generate the next higher number.
Companies that develop intelligent part numbers can clearly distinguish between top level products and lower level assemblies easily, in addition be able to develop logic to drive procurement, review and approval cycles based on part number sequences.
Developing part number sequences can be costly, they require manual setup, customization of applications (ERP, PLM etc.) and require due diligence on the part of engineers to follow a defined process. Depending upon the rules, the business groups must pay attention to number of possible parts for a given commodity (by projecting in to the future) and also plan on adding new commodities as the need arises. Typically this could require an additional head count to manage the process and tools. In my experience, a lot of engineers would rather focus on innovation and turn out their designs and go quickly from concept to prototype to production release and not be bogged down by having to pull a new number and update their documentation and slow things down.
Unintelligent part numbers provide engineers with the ability to conceptualize their design and generate new numbers easily with minimal data in the beginning and quickly release their designs and then provide additional data. Often engineers might not know what the right commodity / material needs to be when they are working on a concept. This lack of knowledge typically results in non value added work in recreating parts with the right material and commodity if they had made a mistake. Unintelligent part numbers do have a drawback which is that it doesn’t provide any information on the part type or any other data.
As ERP & PLM systems have matured, most have introduced a classification scheme / module with which parts can be classified. Typically classification systems capture information like whether the part is OEM or not, commodity, material, assembly or not, compliant or not (for RoHS, WEE, Reach etc.), Critical part or not, in addition the description can be broken down to clearly identify the parts. For e.g., socket head cap screw could be classified into a class of screws with a sub group of socket head or not and so on.
So if we can get so granular and capture all the information we need, we could use the classification system to drive activities like procurement based on commodity, ABC coding by commodity / part class and conditional workflows for ECO cycles based on part type and whether a full review is required or not. In addition, there are other uses like knowledge management and capturing the right questions when quality issues occur based on type of part/product.
New part creation could be streamlined by checking against classification schema and existing parts to see existing parts can be re-used. This re-use has a lot of benefits. I have seen/heard of benchmarks done by a number of companies where they have found that the cost of a part through its lifecycle (concept to obsolescence) is around $3000 to $5000.
Implementing a classification system is more complex than implementing intelligent part numbers. If you chose to do this mid stream, you will need to launch a data quality / clean up program to ensure data integrity and adherence to rules of classification and then launch this activity.
In summary, there is no easy answer for the debate on intelligent vs. unintelligent part numbers. Classification systems provide a lot of merit which outweigh the effort required clean up existing data and setup a new system.
Intelligent part numbers are used to clearly identify the type of part, its commodity or sometimes even the location of use in the overall product. Typically companies develop a matrix mapping commodity to specific sequences of part numbers. For example 12-???? Could represent sheet metal, 13-??? could represent PCBs and so on.
Unintelligent part numbers on the other hand are based on ERP/PLM system’s ability to automatically generate the next higher number.
Companies that develop intelligent part numbers can clearly distinguish between top level products and lower level assemblies easily, in addition be able to develop logic to drive procurement, review and approval cycles based on part number sequences.
Developing part number sequences can be costly, they require manual setup, customization of applications (ERP, PLM etc.) and require due diligence on the part of engineers to follow a defined process. Depending upon the rules, the business groups must pay attention to number of possible parts for a given commodity (by projecting in to the future) and also plan on adding new commodities as the need arises. Typically this could require an additional head count to manage the process and tools. In my experience, a lot of engineers would rather focus on innovation and turn out their designs and go quickly from concept to prototype to production release and not be bogged down by having to pull a new number and update their documentation and slow things down.
Unintelligent part numbers provide engineers with the ability to conceptualize their design and generate new numbers easily with minimal data in the beginning and quickly release their designs and then provide additional data. Often engineers might not know what the right commodity / material needs to be when they are working on a concept. This lack of knowledge typically results in non value added work in recreating parts with the right material and commodity if they had made a mistake. Unintelligent part numbers do have a drawback which is that it doesn’t provide any information on the part type or any other data.
As ERP & PLM systems have matured, most have introduced a classification scheme / module with which parts can be classified. Typically classification systems capture information like whether the part is OEM or not, commodity, material, assembly or not, compliant or not (for RoHS, WEE, Reach etc.), Critical part or not, in addition the description can be broken down to clearly identify the parts. For e.g., socket head cap screw could be classified into a class of screws with a sub group of socket head or not and so on.
So if we can get so granular and capture all the information we need, we could use the classification system to drive activities like procurement based on commodity, ABC coding by commodity / part class and conditional workflows for ECO cycles based on part type and whether a full review is required or not. In addition, there are other uses like knowledge management and capturing the right questions when quality issues occur based on type of part/product.
New part creation could be streamlined by checking against classification schema and existing parts to see existing parts can be re-used. This re-use has a lot of benefits. I have seen/heard of benchmarks done by a number of companies where they have found that the cost of a part through its lifecycle (concept to obsolescence) is around $3000 to $5000.
Implementing a classification system is more complex than implementing intelligent part numbers. If you chose to do this mid stream, you will need to launch a data quality / clean up program to ensure data integrity and adherence to rules of classification and then launch this activity.
In summary, there is no easy answer for the debate on intelligent vs. unintelligent part numbers. Classification systems provide a lot of merit which outweigh the effort required clean up existing data and setup a new system.
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