a dynamic model for maximizing financial returns from quality improvement
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A Dynamic Model for Maximizing Financial Returns from Quality Improvement. Ben-Gurion University of the Negev Department of Industrial Engineering and management. Assaf Yoshi & Prof. Gad Rabinowitz. Mile Stones. Motivation- Conceptual Model Mathematical Model Numeric Results - PowerPoint PPT PresentationTRANSCRIPT
A Dynamic Model for Maximizing Financial Returns from Quality Improvement
Ben-Gurion University of the NegevDepartment of Industrial Engineering and management
Assaf Yoshi & Prof. Gad Rabinowitz
Mile Stones
• Motivation- Conceptual Model
• Mathematical Model
• Numeric Results
• Conclusions
• References
The Conceptual Model
Manufacturers in modern industries are intensively
competing on increasing:
• Yield
• Profits across a the product life cycle
We find over a finite planning horizon:• Optimal investment in appraisal and prevention activities• Optimal level of conformance quality knowledge
Motivation The Model Numeric Results Conclusions
From Managerial Challenge to Operational Activities
Managerial challenge: maximize profits over planning horizon Managerial challenge: maximize profits over planning horizon
Identify a critical to quality attribute Identify a critical to quality attribute
Define the effect of quality level on the profitsDefine the effect of quality level on the profits
Motivation The Model Numeric Results Conclusions
Determine the optimal investment rate across the PLCDetermine the optimal investment rate across the PLC
• PLC- Product Life Cycle
timetime
profits
LossesLosses//InvestmentInvestment
SalesSales//ProfitsProfits
Motivation for Dynamic Optimization
Sales Sales PotentialPotential
PLC-Product Life Cycle
The Mathematical Model
Quality Knowledge :• Increased by investment• Deteriorates over time• Stimulates sales• Reduces losses
Motivation The Model Numeric Results Conclusions
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Investment Knowledge Sales Poor Quality Costs Profit
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Investment Knowledge
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The Mathematical Model
Motivation The Model Numeric Results Conclusions
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Maximization of the manufacturer’s net-earnings by determining the
optimal quality- investment rate
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The Mathematical Model
Motivation The Model Numeric Results Conclusions
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Cumulative Quality Knowledge
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dttCoststIncomeshorizonplanningtheacrossearningNet )]()([
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T=24[months] :Planning horizon- product life cycle
Model parameters
Sales rate potential
Customers’ sensitivity to quality
Loss per unit :
Sales rate:
Motivation The Model Numeric Results Conclusions
Taguchi’s quality loss coefficient
Initial critical attribute variance
Knowledge effectiveness on variance decrease
)(tK
)(tK
Optimal Quality Investment & Knowledge
u(t) -Investment Rate [$/Month] K(t)-Accumulated Knowledge
Decreased marginal contribution of knowledge results in exponential decrease of investment rate
Motivation The Model Numeric Results Conclusions
Costs and SalesE(L(K(t)))-Poor Quality Cost [$/unit] S(K(t))- Sales Rate[units]
Decrease Costs and increase Sales the faster the better
Motivation The Model Numeric Results Conclusions
Total Profit across the Planning Horizon
Total Profit [$] Shadow Price[$/K’s unit]
Sacrificing the present for the future
Motivation The Model Numeric Results Conclusions
Conclusions
Motivation The Model Numeric Results Conclusions
Optimal investment in conformance quality :
• Can be determined via Taguchi’s Loss function
• Increase Sales and reduces costs
• Generates positive revenue across a finite planning horizon
• ROI~470% , While investment is returned within 3 months
Further Research Motivation
Motivation The Model Numeric Results Conclusions
• Add a discount rate (r) and capitalize the Profit
• Find the optimal time for technology transition
• Integrate investment in quality of design
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References – [1] Banuelas, A., 2005. An application of Six Sigma to reduce waste.
Quality and Reliability Engineering International, Vol.21, 553-570.
– [2] Teng, J.T., Thompson, G.L., 1995. Optimal strategies for general price-quality decision models of new products with learning production costs. European Journal of Operational Research 93, 476-489.
– [3] Vorus, J., 2006. The dynamics of price, quality and productivity improvement decisions. European Journal of Operational Research 170, 809 – 823
– [4] Green, S.G., Welsh, M.A., Dehler, G.E., 1996.Transferring Technology into R&D: a comparison of acquired and in- house product development projects. Engineering and Technology Management 13, 125-144.
– [5] Worrell, E., 2001. Technology transfer of energy efficient technologies in industry: a review of trends and policy issues. Energy Policy 29, 29-43.
– [6] Bennett, D.,1997. Transferring manufacturing technology to China: supplier perceptions and acquirer expectations. Integrated Manufacturing Systems 8/5, 283-291.
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