Better Resource Utilization - Using Business Applications?

In an earlier post, I had outlined an idea to improve the usability of enterprise systems by creating a unified task dashboard. By having one dashboard for all activities, which could span multiple applications, users/resources can get a holistic view.

In this post, I want to extend this idea and would like to propose to the software companies/product manager’s work on expanding the capabilities of their tasks/work flows and start looking into unified resource utilization!

The first step would be to capture business process execution with accurate tasks within workflows. The second step would be to accurately estimate the time required to perform the tasks.

If and when we can track all tasks across all applications, we should be able to generate data, reports and metrics on resource utilization and be able to estimate current and future work loads accurately and be able to assign the right resources to the right problem and thus improve effectiveness and efficiency of the organization.

"Disclaimer: The views and opinions expressed here are my own only and in no way represent the views, positions or opinions - expressed or implied - of my employer (present and past) "
"Please post your comments - Swati Ranganathan"

Profile data of migration leader

What skills do you need to become a data migration leader?

If you have gone through my posts on this subject, you will be able to guess!

1. Strategic thinker with excellent analytical skills, able to balance strategy with tactical execution
2. Excellent understanding of business process (flow of information, physical parts/products and finance) and
3. Excellent understanding of technology (Databases (SQL, Oracle), Business applications (ERP, PLM, CRM etc.), programming,
4. Project management whiz

This might be a tall order but start working on each of these skills. Practice makes perfect.

"Disclaimer: The views and opinions expressed here are my own only and in no way represent the views, positions or opinions - expressed or implied - of my employer (present and past) "
"Please post your comments - Swati Ranganathan"

Data Migration: A summary of my posts!

Over the last 3+ months, I have outlined my thoughts on data migration. In order to be successful with large scale implementations of business systems like (ERP, PLM, CRM, BPM etc.), data migration is a key element.

Data migration is often ignored and not enough attention is paid to this portion of the overall project.

The methodology I have outlined in these posts can be applied to a number of projects including data consolidation, server consolidation, migration from one application to another and the list goes on.

The key is to pay attention to the business needs and to make them successful by taking care of the technology and project management issues!

Good Luck.


1. Data Migration: Challenges & Joy!
http://improveprocess.blogspot.com/2009/07/data-migration-challenges-joy.html

2. Data Migration: Challenges & Joy!
http://improveprocess.blogspot.com/2009/07/data-migration-challenges-joy-part-2.html

3. Rules For Successful Data Migration
http://improveprocess.blogspot.com/2009/07/rules-for-successful-data-migration.html

4. Phases of Data migration
http://improveprocess.blogspot.com/2009/07/phases-of-data-migration.html

5. Phases of Data migration
http://improveprocess.blogspot.com/2009/07/phases-of-data-migration.html

6. Phases of Data migration: Analysis
http://improveprocess.blogspot.com/2009/07/phases-of-data-migration-analysis.html

7. Phases of Data migration: Design
http://improveprocess.blogspot.com/2009/07/phases-of-data-migration-design.html

8. Phases of Data migration: Test
http://improveprocess.blogspot.com/2009/08/phases-of-data-migration-test.html

9. Phases of Data migration: Validation
http://improveprocess.blogspot.com/2009/09/phases-of-data-migration-validation.html

10. Data migration: Risks
http://improveprocess.blogspot.com/2009/09/data-migration-risks.html

11. Tips for Successful Data Migration.
http://improveprocess.blogspot.com/2009/10/tips-for-successful-dat-migration.html

"Disclaimer: The views and opinions expressed here are my own only and in no way represent the views, positions or opinions - expressed or implied - of my employer (present and past) "
"Please post your comments - Swati Ranganathan"

Tips for Successful Data Migration.

  1. Maintain your sense of humor.
  2. Expect delays and/or road blocks.
  3. Run the data migration using traditional project principles.
  4. Secure alignment and approval from steering committee and stakeholders as changes occur.
  5. Appreciate the inter-dependencies.
  6. Understand your business process, data, system and application landscape. (Devil is in the details)
  7. Get the right software tools.
  8. Use the right resources.
  9. Plan for down time.
  10. Perform at least two dry runs (Wash Rinse Repeat)
  11. Develop risk mitigation plan.
  12. Communicate your plan early and socialize with all impacted users.


"Disclaimer: The views and opinions expressed here are my own only and in no way represent the views, positions or opinions - expressed or implied - of my employer (present and past) "
"Please post your comments - Swati Ranganathan"

Intelligent part numbers! Why and Why Not, Can PLM / ERP systems help?

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.

"Disclaimer: The views and opinions expressed here are my own only and in no way represent the views, positions or opinions - expressed or implied - of my employer (present and past) "
"Please post your comments - Swati Ranganathan"

Unified Task Dashboard! Utopia?

In an earlier post, I had listed a number of emerging or new TLA's (three letter acronyms) in the enterprise application space like ERP, PLM, PDM, CRM, SCM, SRM, BPM etc...As the usage of these of applications and technologies mature within different organizations, users will soon have a set of task dashboards which outline the tasks they have been assigned within each of these applications and when it is due.

this begs the question, if we can integrate applications and have strategies like data integration / master data integration why cant we integrate the applications and create a unified task dashboard?

Most of the integrated software vendors could provide this capability but companies which have chosen best of breed applications will struggle with this unless they learn to federate and build services which can kick off / complete tasks and seamlessly integrate the applications and provide their users with one interface.

This could impact user adoption and greatly increase speed to proficiency of users and is rarely considered during software selection, planning and implementation!

"Disclaimer: The views and opinions expressed here are my own only and in no way represent the views, positions or opinions - expressed or implied - of my employer (present and past) "
"Please post your comments - Swati Ranganathan"