FIELD OF THE IMVENTIOH
The invention relates to a computer-implemented process for providing improved quality control (QC) of deliverable designs and/or products and/or services in service industries.
BACKGROUND AMD THE PRIOR ART
Instruments and methods for measuring quality and assuring quality control have been developed and used in the service industries for a long time. The nature of services, being intangible, makes service quality control very challenging. The increasing importance and growth of the services industry in our economy has generated the need for an improved service quality control method.
FMEA (Failure mode effects analysis) is a qualitative evaluation tool used for improvement of designs and designing processes. The FMEA is a technique for finding the importance of a defect on the overall functionality of a design. FMEA is a qualitative evaluation method that is based on analysis of the evaluator, hence greatly depends upon the experience and knowledge of the evaluator. Therefore, the FMEA has a problem that analysis without an individual is difficult. It is a direct factor estimation method based on past phenomena. But FMEA is usually carried out before the final stage of designing based upon failure of the final product. Hence any defect incorporated in the initial stages of design remains undetected until the final

stage. If FMEA shall be carried out at the drawing board stage, then the unnecessary loss of resource and time can be checked at an earlier stage.
US patent 7050935 discloses fault rates in designs are estimated by subjective expert opinion. This method used statistical methods viz. Poisson distribution to estimate fault rates. But this covers only the reliability analysis of the system that comes into picture after the part is designed.
In the US patent US5586252 a method of getting FMEA data in a tabular form through brainstorming is disclosed. This patent deals with an automated LAN based system for getting FMEA table. Here each member simultaneously enters failure modes for an apparatus or process into their respective workstations. The information is displayed anonymously to the other members. The correctness of input is verified by consensus including voting with automatic anonymous tallying, if required. A final list of failure modes is prepared, verified for correctness and completeness, and entered into the database. But this prior document covers performing FMEA on a network and is web based where the stage of implementing FMEA is in the final stage of designing but not at the drawing board stage.
The US patent 6063133 discloses a method for determining
reliability characteristics for a technical installation
using single FMEA table for all the configurations of
technical installation separately. This covers performing
FMEA on a technical system for reliability study and

analysis at a stage before implementation of the actual design.
According to US patent 6253115 the FMEA tool allows the user to perform a failure mode and effects analysis to identify major problems and risks associated with a given design. In one embodiment, each application also includes an automatic experimentation capability, which allows the user to enter data in either a manual mode or an automatic mode. The automatic mode allows for easy integration of various engineering tools and also allows the user to automate repetitive processes and tasks. But this deals with implementing a Six Sigma DFSS process by isolating one of the sub processes.
US patent 52 69014 provide an automatic programming system, which automates the trading-off operation of producing program structure, failure mode and effects analysis (FMEA) and design reviewing operation at the time of the program design. This invention particularly deals with CflD design at a step backward to product design. But this document deals with the customization of the programming to generate specific task based software and the FMEA is not implemented at the drawing board stage.
US patent 6973358 deals with design system including both two-dimensional and three-dimensional CAD system where at a stage before manufacture at product design stage or manufacturing process design stage the influence of defect occurrence in a component or process on other components and processes is estimated, evaluated with previously stored database containing failure rates of components and

such process helps in quality control and design improvement. Though this particular prior art deals with a step backwards to product design stage it does not use FMEA as a tool at the drawing board stage and considers all the aspects of the designed parts ready for manufacture.
The present invention uses a methodology which helps in the "concept to design" phases by tackling the process improvement by the way of error reduction in an objective way instead of by chance. It is a robust design methodology, which gives the engineers more capability to address the probable failures at the drawing board stage itself by providing scope for mistake proofing. This methodology also aids in the process improvement by helping the designer to focus on the improvement areas through a more objectively based "End Effect" rating scale.
This gives us a better objective understanding and direction as to how to go about further analyzing the data and taking corrective action. This can give rise to a 6 Sigma DMAIC project and in-turn justifies continuous improvement towards achieving CMMI L5.
OBJECTS OF THE INVENTION
Accordingly, one object of the present invention is to overcome the disadvantages of the prior art.
Another object of the present invention is to provide a computer-implemented process for improved quality control

of deliverable designs and/or products and/or services in service industries.
Yet another object of the present invention is to address probable failures at the drawing board stage itself by providing scope for mistake proofing.
SUMMARY OF THE INVENTION
According to one aspect of the present invention there is
provided a computer-implemented process for providing
improved quality control of product designs, said process
comprising the steps of:
Determining types of errors of the design;
Categorizing number of errors based upon nature of errors;
Assigning a first numerical value being defined as Severity
Number to each error category based upon possible severity
of failure;
Assigning a second numerical value being defined as Failure
Effect number to each error category based upon weightage
given to effect of failure of end product;
Determining a final score for each error category as a
simple product of Severity Number and Failure Effect
number; said final score being defined as End Effect
Number;
Identifying said error categories with high End Effect
Number as focus areas; and
Providing corrective actions to focus areas at the level of
concept design for continuous quality control.

DETAILED DESCRIPTION OF THE INVENTION
The present invention relates to a computer-implemented process for quality control of deliverable services. First using a Microsoft Excel or any such means the errors are identified and captured. Then these errors are categorized depending upon their nature and effect by using appropriate processing means. These errors are classified into specific Error Categories in a tabular form using appropriate categorization module.
Each of the error categories are considered separately and a numerical value assigned by using means to each of these categories based upon the severity of failure of an error category. Here the severity means the worst potential end effect of the failure of an error irrespective of actual failure of the product. This numerical value is called as Severity Number (SN), which varies from 1 to 4. Table 2 is a common FMECA table used in the industry and this table is used as reference for developing custom made and different SN tables that can categorize errors and their degree of effect of failure on the specific deliverable services. This SN table for the particular deliverable can be modified using appropriate means based upon need and requirement of that particular field.

Table-2: Severity Number Table

Severity Category Failure Effect SN
Catastrophic * Loss of life, life threatening or resulting in permanent injury or occupational illness
* Loss of launch site facilities
'Loss of system
* Long term detrimental environmental effects 4
Critical * Temporary disabling or occupational illness but not life threatening
* Loss or major damage to systems or facilities
* Loss or major damage to public or private property
* Short term detrimental effects 3
Major * Mission or system degradation 2
Negligible * Any other effect 1
SN table only indicates the severity of the situation but another numerical value is assigned using means for each error category based upon the weightage that is given to the effect of failure of errors in the end product in case it fails. This numerical value is called as the Failure Effect Number (FE). Table 3 is a FMECA table used in the industry which can be modified to generate specific Failure effect Tables by putting the FE number corresponding to the same error categories. But FE is assigned corresponding to the effect of failure of the end product in case it actually fails. A specific FE table for the particular deliverable can be constructed using appropriate means based upon consensus obtained by a web based process that may use appropriate software in a conventional way.

Table- 3: Failure Effect Niamber Table

Severity Category Failure Effect FE
Catastropiiic * Loss of life, life threatening or resulting in permanent injury or oxupational illness
* Loss of launch site facilities
* Loss of system
* Long term detrimental environmental effects 4
Critical * Temporary disabling or occupational illness but not life threatening
* Loss or major damage to systems or facilities
* Loss or major damage to public or private property
* Short term detrimental effects 3
Major 'Mission or system degradation 2
Negligible * Any other effect 1
These SN & FE numbers are not based on probability of failure but on factual information of design for manufacturing (DFM) stored in a database. The logic in assigning SN S FE numbers is purely based on the domain knowledge generated over time and stored using appropriate means in the computer database. Only based on this logic of assignment it is possible to objectively evaluate the errors to take corrective improvement actions using appropriate means. So it is the FE number, which is more critical which forms the basis for the distinction between SN & FE numbers.
Now a final score is obtained by multiplying the Severity Number and Failure Effect Number for each error category, which is called as the End Effect Number (EF). Theseend

effect numbers are used for objective evaluation of the error categories. The error categories with high EF number are identified using appropriate means as focus area where corrective measures are implemented at the drawing board stage. This quality control method approach is applied in different stages of designing, which helps in implementation of corrective measure at a very early stage of designing.
The different stages of a design are;
Stepl. Start
Step 2. Receiving Models or Drawings as Input
Step 3. Downloading Drawing or inputs From Server
Step 4. Input Drawing Checking Activity
Step 5. Training and Knowledge sharing
Step 6. Capturing requirements
Step 7. Clarification Needed from Customer
Step 8. Drawings Sent to Customer for clarification.
Step 9. Feedback From Customer
Step 10. Given to Engineer for Modeling, final Assembly &
self QC
Step 11. Peer QC
Step 12. Final QC
Step 13. Upload on server
Step 14. Download by Customer
Step 15, Review
Step 16. Stop
The steps from 1 to 16 are the generic steps involved in routine activities in a services industry. Steps 10-13 are the region where in the errors could creep in and passed to

the customer. These errors may be disastrous in case the design is accepted and the part manufactured by oversight. Steps 14-15 represent the design review stage at the customers end and subseguent quality control at the level of engineers when the design is returned in the feedback loop. The methodology being claimed in the present invention implemented in steps 10-13, which strengthens the guality of deliverables.
Extending this methodology to the concept to design stage, it would definitely take care of the design aspects as well, and would lead to a toned down FMEA, which would be conducted later on. This shows a definite robustness built into the design, as this would take care some of the FMEA's design aspects before hand. A typical application is described in the example provided below.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRflWINGS
Figure 1 shows a Pareto chart with a random plot of error categories.
Figure 2 shows a bar graph with plot of error categories with respect to End Effect Numbers.
Figure 3 shows a generic flow chart where the typical application of the methodology for CAD modeling and drawing is illustrated.

DETAILED DESCRIPTION OF THE ACCOMPANYING DRAWINGS
Figure 1 and figure 2 shows a Pareto chart with a random plot of error categories and a bar graph with plot of error categories with respect to End Effect Numbers respectively. And from their comparison the effectiveness of the invention can be enumerated.
Figure 3 is a generic flow chart with the following steps:
Step 1.Start of operation
Step 2.Receiving CAD Models or PDF Drawings as Input
Step 3.Downloading CAD Models or inputs From Server
Step 4.Capturing requirements in an excel sheet
Step 5.If Clarification is needed from Customer, then
drawings are sent to Customer for clarification Step 6.If Clarification is Not Needed from Customer; it is
given to the Engineer for Modeling, final Assembly S
self-quality check Step 7. After Getting Feedback From Customer the design is
given to the Engineer for Modeling, final Assembly S
self-quality check Step 11. Peer quality check of Model or Drawing Step 12. Final quality check of Model or Drawing Step 13. Upload of Final Model or Drawing on server Step 14. Download by Customer Step 15. Review Step 16. Operation Stops

Example-1:
A typical application being that of a service industry is provided who are into the delivery of CAD models S manufacturing drawings as the deliverables.
Table 1 shows the categories of error that are prevalent in CAD modeling as captured upon in the Excel sheet and classified with a unit frequency of occurrence. The acronyms for specific error categories are:
BOM= Bill of Materials
GD&T = Geometric Dimensioning & Tolerancing
Datum ref.= Datum reference
Typo. Error in notes= Typographical error in notes
Drg. format= Drawing format
Assy. Mismatch= Assembly mismatch
Table- 1: Category of Errors and their frequency

Category of Errors Number of errors
Dimensions
Typo, error in notes
Part number
BOM
Views not matching
Drg. format
Part naming
Angle of projection
Datum ref.
Missed out feature
Sketch constraints
GD&T
Assy, mismatch

But as given in the figure 1 the error categories are all represented without providing any scope for preferable application of corrective measure for a particular error category.
Table 4 and 5 represents a SN & FE table obtained for CAD models for all of the error categories classified in the Table 1. The End Effect Number is also shown in the tables.
Table- 4: The Error categories and their End Effect Numbers

Category of Errors Severity No. (SN) Failure Effect (FE) End Effect No. (EF = SN*FE)
Dimensions 4 4 16
Typo, error in notes 1 1 1
Part number 1 1 1
BOM 2 2 4
Views not matching 1 1 1
Drg. format 1 1 1
Part naming 1 1 1
Angle of projection 1 2 2
Datum ref. 3 4 12
Missed out feature 4 4 16
Sketch constraints 1 1 1
GD&T 3 4 12
Assy, mismatch 2 2 4

Table- 5: The Error categories ranks on the basis of End Effect Number

Category of Errors Severity No. (SN) Failure Effect (FE) End Effect No. (EF = SN-FE)
Missed out feature 4 4 16
Dimensions 4 4 16
GD&T 3 4 12
Datum ref. 3 4 12
BOM 2 2 4
Assy, mismatch 2 2 4
Angle of projection 2 2
Views not matching
Typo, error in notes
Sketch constraints
Part number
Part naming
Drfl. format
For example in the Table 4, Dimension error is mentioned as catastrophic on its severity effect and again the actual failure effect is also catastrophic for a CAD model. A dimension error is called catastrophic for its failure effect on the basis of Table 2 where it is seen that loss of life, loss of system, loss of functionalities and failure effect with detrimental environmental effect is categorized as catastrophic. Therefore, a SN and FE value of 4 is assigned to it.
Again according to accumulated information on errors common to CAD models that even though "GD5T" error is a critical error not catastrophic with respect to the worst potentials [among other errors in comparison) of failure but this error is catastrophic if actually this error occurs at product level.

Similarly for other error categories the SN and FE values are assigned. This assignment is made on the basis of the tables 2 and 3.
Figure 1 shows a Pareto chart with a random plot of error categories with count "one" for all opportunities and is of no help for any objective corrective action. Figure 2 is a bar graph in which the End Effect Number is plotted against the error categories. Now on comparing this figure with Figure 1, impact of this methodology is realizable. Here the process of the present invention helps in finding the focus areas conveniently for application of corrective measures.
Figure 3 is a generic flow chart showing the process of CAD modeling. It shows number of stages of quality control. At each of these stages and more particularly at the stage where the engineers implement a self-quality check, this improved quality control methodology is applied. In this figure the area of application of this methodology is pointed out as the focus area.

WE CIAIM
1. A computer-implemented process for providing improved
quality control of product designs said process
comprising the steps of:
Determining types of errors of the design;
Categorizing number of errors based upon types of
errors;
Assigning a first numerical value being defined as
Severity Number to each error category based upon
possible severity of failure;
Assigning a second numerical value being defined as
Failure Effect number to each error category based
upon weight age given to effect of failure of end
product;
Determining a final score for each error category as a
simple product of Severity Number and Failure Effect
number; said final score being defined as End Effect
Number;
Identifying said error categories with high End Effect
Number as focus areas; and
Providing corrective actions to focus areas at the
level of concept design for continuous quality
control.
2. Computer-implemented process as claimed in claim 1 further comprises of graphical representation of end effect numbers of error categories.
3. Computer-implemented process as claimed in claim 1 further comprising steps of logging and analyzing the final scores so as to improve the quality control of focus area.

4. Computer-implemented process age claimed in claims 1
and 2 wherein the End Effect number is represented as
a simple bar graph with categories of errors plotted
on X axis and End effect number on the Y axis.
5. Computer-implemented process as claimed in claims 1 to
4 comprises CAD models, PDF drawings and the like.
6. Computer-implemented process as claimed in any
preceding claim further comprising providing of
corrective actions to the focus areas at multiple
stages of quality controls.
7. Computer-implemented process as claimed in any
preceding claim being carried out at drawing board
stage of designing.