Description
Presentation of Budget provision and analysis of under utilization of funds, remidial action through Taguchi loss function.
BUDGET ITEMS -IEOT
• • • • INDIGENOUS LINE ITEMS -78 IMPORTED LINE ITEMS -14 DRE LINE ITEMS - 94 TOTAL LINE ITEMS -186 Rs 11.16 Crs Rs 16.32 Crs
• RE 02-03 APPROVED • BE 03-04 APPROVED
Budget Utilization RE 02-03
Particular Actual REup to Actual 2002-03 Mar '03 Utilisation (1) (2) % 99.00 21.00 123.00 627.00 247.00 30.14 10.59 108.71 568.84 219.58 30.44 50.43 88.38 90.72 88.89 _ 83.96 Surplus/ Shortfalls (2-1) -68.86 -10.41 -14.29 -58.16 -27.42 -179.14
Capital Stores & Spares Contractual Payment Man Power Other Non-Plan Total
1117.00 937.86
Budget Analysis Six Sigma Benchmarking for Budget Performance
• Excellent utilization, but an average budget Process capability - A paradox.
• % utilization or yield/system efficiency: 937.86/1117*100= 83.96% • Defect Rate = 1-Yield =1-0.8396= 0.1604 • Defect Rate/CTQ = 0.1604/7 = 0.0229142 • DPMO= 0.0229142*1000000=22,914 • ? level = 3.5 • Inference “Just an average process”
CTQ In A Budget Process
1. Delay in approved RE. 2. FA allotted but unutilized. 3. Re-appropriation not accounted/incorrectly accounted. 4. Incorrect reporting for cash expenditure, DRE items 5. Store Procedure not inter twined with financials for in tangible items like soft ware 6. Inappropriate forecasting. 7. Delays due centralized procurement.
How To Improve Budget Process Capability
1. 2. Apply rigorous ZBB. Use appropriate forecast tools with proper linkages to physical target. 3. Translate user’s need to financials. 4. Issue alerts for non-movement of resources. 5. Configure SAPs UFSO to account for re-appropriation cases. 6. Modify SAP budget A/C procedures to account for use of fund by other entity to the entity of source of fund. 7. Extract automatic budget utilization from SAP based on actual cash expenditure/invoice accounting system. 8. Adopt suitable policies for intangible material accounting. 9. Make provision for historical data, past expenditure accounting data. 10. Generate Pareto’s ABC analysis for fund utilization and monitoring.
Robust Design Considerations
• Inadequacy of conformance to specs-based measures like defect rates parts per million has led to Robust design. • Robust design seeks to minimize the effect of noise factors on the product performance. • Variation in the operating environment such as temperature and humidity (external noise factors). • Variation due to wear-out imperfections in inputs and the production process (internal noise factors) • Select design parameter so that the mean is on the target (no bias) and variability is minimized.
•
Robustness Strategy Variation reduction is universally recognized as a key to reliability
and productivity improvement. There are many approaches to reducing the variability, each one having its place in the product development cycle. The Six Sigma approach has made tremendous gains in cost reduction by finding problems that occur in manufacturing or whitecollar operations and fixing the immediate causes. The robustness strategy is to prevent problems through optimizing product designs and manufacturing process designs. The Robustness Strategy uses five primary tools: P-Diagram is used to classify the variables associated with the product into noise, control, signal (input), and response (output) factors. Ideal Function is used to mathematically specify the ideal form of the signal-response relationship as embodied by the design concept for making the higher-level system work perfectly. Quadratic Loss Function (also known as Quality Loss Function) is used to quantify the loss incurred by the user due to deviation from target performance, capturing the degradation of product performance due to variation. Signal-to-Noise Ratio is used for predicting the field quality through laboratory experiments. Orthogonal Arrays are used for gathering dependable information about control factors (design parameters) with a small number of experiments.
• • • • • •
• •
Is Dr Taguchi’s Loss function alternative to 6? strategy ? Robust Quality Vs Zero Defect
• Zero variability achievement is practically impossible, therefore establish target value as well as acceptable specification limits about the target. Parts within this limits is equally good and falling outside is completely bad. • Cost Vs Variability curve is either step curve or a parabolic curve, which generally defines gradation of acceptability. Seek to eliminate bias and reduce variability. • Loss Function Model: Taguchi defined quality in negative sense as “the loss to the society from the time the product is shipped due to bad quality of the product the customer may be dissatisfied and may switch over to other manufacturer. • Loss functions aims at capturing the degradation of product performance due to variation. • Expectation taken over the noise factors. Need to identify design parameters (product design characteristics whose nominal setting can be specified). • Indirect loss- good will and market share. • Direct loss- Increased cost of scrap, warranty, after sales service, increased marketing efforts etc.
Dr Taguchi’s Loss function •
Expected loss : Consequence of bad quality measured by expected value of monetary losses and arbitrary user of the product is likely to suffer at an arbitrary time during the product life span due to performance variation. Three types of loss functions: Target is Best. Small is Better Large is Better.
• 1. 2. 3.
From Experiment To Performance Statistics
• • • • • • • Experiment : Design/Noise [D/N] Performance Characteristics : Y[N/D] Performance Characteristics : Z[D] Additive Model : Y[N/D]= [(D)+?(D)] Minimize Expected Loss ? E[Y(N/D)-? ] ² ?(D)²: non-adjustable component = VAR[Y(N/D)] B(D): adjustable component. Step 1: That is to say find parameters that effect the variance & determine setting that minimize variance . Step2: That is to say find other parameters that affect the mean and determine setting that minimize bias.
Dr Taguchi’s Loss Function Model
• The smaller the variation, better is the quality while larger the deviation from the target the larger is the loss to the society. The term society indicates both producers as well as consumers at large. • L(Y) = K(Y-t)² parabolic curve where • L(Y)=loss to the society when a product is produced at a value ‘Y’ • T=target value assume L(Y)=0 at ‘t’ and • K= a constant such that K=c/d ² where c= the loss when a produced at a specification limit (producer’s loss in monetary units) and d= deviation from the target (tolerance of the consumer) for n units of products the average loss per unit is ? L(Y)=k[?² + (Y-t) ²] where Y= process average, ? = process standard deviation and t= target value.
Taguchi’s Loss function parabolic curve
L(Y)
Customers tolerance
t-d
t
t+d
Y
Budget Analysis-Cost of under utilization of budget Six Sigma Analysis Supplemented by Taguchi?s Loss Function. (Slide 1 fixing target : Budget KPI)
Budget Head Target in % Approved Budget in Lakhs 99 Target budget in Abs Value 79.2
Capital
? 80% ? 80%
%Exp Target Utilization =956.3/1117 =85.61
St & Spa
21
16.8
Manpower
? 90% ? 80%
627
564.3
? =target value =0.8561
Contractual
123
98.4
Others
? 80%
247
197.6
Total
1117
956.3
Budget Analysis-Cost of under utilization of budget Six Sigma Analysis Supplemented by Taguchi?s Loss • Basic idea is to relate sigma level to cost of poor quality. How ? Through Taguchi Loss function • Mean ? = Actual overall budget utilization = 83.96%= 0.8396 • Target ? = 0.8561 (KPI) • Deviation from mean • ? = ?(0.3044-08396)²+(0.5043-0.8396)²+(0.88380.8396)²+(0.9072-0.8396)²+(0.8889-0.8396)²/5 = 0.2856 • Loss function L=K[?² + (?-?)²] • K=c/d² where c= loss when produced at a specification = 179.14 (unutilized budget) • d= deviation from target = (UCL-LCL)/2 where UCL = ?+3? and LCL = ?-3? • Here UCL = 1.6964, LCL= -0.0172 and d= 0.8568
Taguchi loss function –Budget Process Capability Analysis
• ?= 0.8396 ? = 0.2856
Strategy: 1. Shift mean to target
2. Reduce variation 3. Combine both
LS=0.0172
US=1.6964 t=0.85615
Budget Analysis-Cost of under utilization of budget Six Sigma Analysis Supplemented by Taguchi?s Loss • Loss function L=K[?² + (?-?)²] =179.14/(0.8568)²[(0.2856)²+(0.8561-0.8396)] = 19.97 • Case 1: Shift mean to target # change ? from 0.8396 to 0.85. Recalculate „L? • Effect L=19.91 Reduced marginally • Case 2 : Reduce Variation ? from 0.2856 to 0.2 Recalculate „L? • Effect L=9.827 Reduced substantially • Case 3 : (Combination case) Shift mean to target and also reduce the variation with ? =0.85 and ? = 0.2, Recalculate „L? • Effect L=9.76 Reduced even further
Budget Analysis-Cost of under utilization of budget Six Sigma Analysis Supplemented by Taguchi?s Loss Summary Of The Effects of Loss Function
• Savings :
• Case 1 : Shift mean to target L-L1= 19.97-19.91=0.06 • Case 2 : Reduce variation L-L2 = 19.97-9.827= 10.143 • Case 3: Combine both effects L-L3= 19.97-9.76=10.21
doc_258072535.ppt
Presentation of Budget provision and analysis of under utilization of funds, remidial action through Taguchi loss function.
BUDGET ITEMS -IEOT
• • • • INDIGENOUS LINE ITEMS -78 IMPORTED LINE ITEMS -14 DRE LINE ITEMS - 94 TOTAL LINE ITEMS -186 Rs 11.16 Crs Rs 16.32 Crs
• RE 02-03 APPROVED • BE 03-04 APPROVED
Budget Utilization RE 02-03
Particular Actual REup to Actual 2002-03 Mar '03 Utilisation (1) (2) % 99.00 21.00 123.00 627.00 247.00 30.14 10.59 108.71 568.84 219.58 30.44 50.43 88.38 90.72 88.89 _ 83.96 Surplus/ Shortfalls (2-1) -68.86 -10.41 -14.29 -58.16 -27.42 -179.14
Capital Stores & Spares Contractual Payment Man Power Other Non-Plan Total
1117.00 937.86
Budget Analysis Six Sigma Benchmarking for Budget Performance
• Excellent utilization, but an average budget Process capability - A paradox.
• % utilization or yield/system efficiency: 937.86/1117*100= 83.96% • Defect Rate = 1-Yield =1-0.8396= 0.1604 • Defect Rate/CTQ = 0.1604/7 = 0.0229142 • DPMO= 0.0229142*1000000=22,914 • ? level = 3.5 • Inference “Just an average process”
CTQ In A Budget Process
1. Delay in approved RE. 2. FA allotted but unutilized. 3. Re-appropriation not accounted/incorrectly accounted. 4. Incorrect reporting for cash expenditure, DRE items 5. Store Procedure not inter twined with financials for in tangible items like soft ware 6. Inappropriate forecasting. 7. Delays due centralized procurement.
How To Improve Budget Process Capability
1. 2. Apply rigorous ZBB. Use appropriate forecast tools with proper linkages to physical target. 3. Translate user’s need to financials. 4. Issue alerts for non-movement of resources. 5. Configure SAPs UFSO to account for re-appropriation cases. 6. Modify SAP budget A/C procedures to account for use of fund by other entity to the entity of source of fund. 7. Extract automatic budget utilization from SAP based on actual cash expenditure/invoice accounting system. 8. Adopt suitable policies for intangible material accounting. 9. Make provision for historical data, past expenditure accounting data. 10. Generate Pareto’s ABC analysis for fund utilization and monitoring.
Robust Design Considerations
• Inadequacy of conformance to specs-based measures like defect rates parts per million has led to Robust design. • Robust design seeks to minimize the effect of noise factors on the product performance. • Variation in the operating environment such as temperature and humidity (external noise factors). • Variation due to wear-out imperfections in inputs and the production process (internal noise factors) • Select design parameter so that the mean is on the target (no bias) and variability is minimized.
•
Robustness Strategy Variation reduction is universally recognized as a key to reliability
and productivity improvement. There are many approaches to reducing the variability, each one having its place in the product development cycle. The Six Sigma approach has made tremendous gains in cost reduction by finding problems that occur in manufacturing or whitecollar operations and fixing the immediate causes. The robustness strategy is to prevent problems through optimizing product designs and manufacturing process designs. The Robustness Strategy uses five primary tools: P-Diagram is used to classify the variables associated with the product into noise, control, signal (input), and response (output) factors. Ideal Function is used to mathematically specify the ideal form of the signal-response relationship as embodied by the design concept for making the higher-level system work perfectly. Quadratic Loss Function (also known as Quality Loss Function) is used to quantify the loss incurred by the user due to deviation from target performance, capturing the degradation of product performance due to variation. Signal-to-Noise Ratio is used for predicting the field quality through laboratory experiments. Orthogonal Arrays are used for gathering dependable information about control factors (design parameters) with a small number of experiments.
• • • • • •
• •
Is Dr Taguchi’s Loss function alternative to 6? strategy ? Robust Quality Vs Zero Defect
• Zero variability achievement is practically impossible, therefore establish target value as well as acceptable specification limits about the target. Parts within this limits is equally good and falling outside is completely bad. • Cost Vs Variability curve is either step curve or a parabolic curve, which generally defines gradation of acceptability. Seek to eliminate bias and reduce variability. • Loss Function Model: Taguchi defined quality in negative sense as “the loss to the society from the time the product is shipped due to bad quality of the product the customer may be dissatisfied and may switch over to other manufacturer. • Loss functions aims at capturing the degradation of product performance due to variation. • Expectation taken over the noise factors. Need to identify design parameters (product design characteristics whose nominal setting can be specified). • Indirect loss- good will and market share. • Direct loss- Increased cost of scrap, warranty, after sales service, increased marketing efforts etc.
Dr Taguchi’s Loss function •
Expected loss : Consequence of bad quality measured by expected value of monetary losses and arbitrary user of the product is likely to suffer at an arbitrary time during the product life span due to performance variation. Three types of loss functions: Target is Best. Small is Better Large is Better.
• 1. 2. 3.
From Experiment To Performance Statistics
• • • • • • • Experiment : Design/Noise [D/N] Performance Characteristics : Y[N/D] Performance Characteristics : Z[D] Additive Model : Y[N/D]= [(D)+?(D)] Minimize Expected Loss ? E[Y(N/D)-? ] ² ?(D)²: non-adjustable component = VAR[Y(N/D)] B(D): adjustable component. Step 1: That is to say find parameters that effect the variance & determine setting that minimize variance . Step2: That is to say find other parameters that affect the mean and determine setting that minimize bias.
Dr Taguchi’s Loss Function Model
• The smaller the variation, better is the quality while larger the deviation from the target the larger is the loss to the society. The term society indicates both producers as well as consumers at large. • L(Y) = K(Y-t)² parabolic curve where • L(Y)=loss to the society when a product is produced at a value ‘Y’ • T=target value assume L(Y)=0 at ‘t’ and • K= a constant such that K=c/d ² where c= the loss when a produced at a specification limit (producer’s loss in monetary units) and d= deviation from the target (tolerance of the consumer) for n units of products the average loss per unit is ? L(Y)=k[?² + (Y-t) ²] where Y= process average, ? = process standard deviation and t= target value.
Taguchi’s Loss function parabolic curve
L(Y)
Customers tolerance
t-d
t
t+d
Y
Budget Analysis-Cost of under utilization of budget Six Sigma Analysis Supplemented by Taguchi?s Loss Function. (Slide 1 fixing target : Budget KPI)
Budget Head Target in % Approved Budget in Lakhs 99 Target budget in Abs Value 79.2
Capital
? 80% ? 80%
%Exp Target Utilization =956.3/1117 =85.61
St & Spa
21
16.8
Manpower
? 90% ? 80%
627
564.3
? =target value =0.8561
Contractual
123
98.4
Others
? 80%
247
197.6
Total
1117
956.3
Budget Analysis-Cost of under utilization of budget Six Sigma Analysis Supplemented by Taguchi?s Loss • Basic idea is to relate sigma level to cost of poor quality. How ? Through Taguchi Loss function • Mean ? = Actual overall budget utilization = 83.96%= 0.8396 • Target ? = 0.8561 (KPI) • Deviation from mean • ? = ?(0.3044-08396)²+(0.5043-0.8396)²+(0.88380.8396)²+(0.9072-0.8396)²+(0.8889-0.8396)²/5 = 0.2856 • Loss function L=K[?² + (?-?)²] • K=c/d² where c= loss when produced at a specification = 179.14 (unutilized budget) • d= deviation from target = (UCL-LCL)/2 where UCL = ?+3? and LCL = ?-3? • Here UCL = 1.6964, LCL= -0.0172 and d= 0.8568
Taguchi loss function –Budget Process Capability Analysis
• ?= 0.8396 ? = 0.2856
Strategy: 1. Shift mean to target
2. Reduce variation 3. Combine both
LS=0.0172
US=1.6964 t=0.85615
Budget Analysis-Cost of under utilization of budget Six Sigma Analysis Supplemented by Taguchi?s Loss • Loss function L=K[?² + (?-?)²] =179.14/(0.8568)²[(0.2856)²+(0.8561-0.8396)] = 19.97 • Case 1: Shift mean to target # change ? from 0.8396 to 0.85. Recalculate „L? • Effect L=19.91 Reduced marginally • Case 2 : Reduce Variation ? from 0.2856 to 0.2 Recalculate „L? • Effect L=9.827 Reduced substantially • Case 3 : (Combination case) Shift mean to target and also reduce the variation with ? =0.85 and ? = 0.2, Recalculate „L? • Effect L=9.76 Reduced even further
Budget Analysis-Cost of under utilization of budget Six Sigma Analysis Supplemented by Taguchi?s Loss Summary Of The Effects of Loss Function
• Savings :
• Case 1 : Shift mean to target L-L1= 19.97-19.91=0.06 • Case 2 : Reduce variation L-L2 = 19.97-9.827= 10.143 • Case 3: Combine both effects L-L3= 19.97-9.76=10.21
doc_258072535.ppt