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A Dynamic Model for Maximizing Financial Returns from Quality Improvement

This paper presents a dynamic model for maximizing financial returns from quality improvement in modern industries. It includes a conceptual model, mathematical model, numeric results, and conclusions. The model helps manufacturers identify critical quality attributes, define the effect of quality level on profits, and determine the optimal investment rate across the product life cycle.

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A Dynamic Model for Maximizing Financial Returns from Quality Improvement

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  1. 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

  2. Mile Stones • Motivation- Conceptual Model • Mathematical Model • Numeric Results • Conclusions • References

  3. 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

  4. Managerial challenge: maximize profits over planning horizon Identify a critical to quality attribute Define the effect of quality level on the profits From Managerial Challenge to Operational Activities Determine the optimal investment rate across the PLC • PLC- Product Life Cycle

  5. Motivation for Dynamic Optimization Sales Potential Sales/ Profits profits time Losses/ Investment PLC-Product Life Cycle

  6. The Mathematical Model Investment Knowledge Quality Knowledge : • Increased by investment • Deteriorates over time • Stimulates sales • Reduces losses Poor Quality Costs Investment Knowledge Sales Profit

  7. The Mathematical Model

  8. The Mathematical Model Maximization of the manufacturer’s net-earnings by determining the optimal quality- investment rate Cumulative Quality Knowledge

  9. Model parameters T=24[months] :Planning horizon- product life cycle Sales rate potential Customers’ sensitivity to quality Sales rate: Loss per unit : Taguchi’s quality loss coefficient Initial critical attribute variance Knowledge effectiveness on variance decrease

  10. 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

  11. Costs and Sales E(L(K(t)))-Poor Quality Cost [$/unit] S(K(t))- Sales Rate[units] Decrease Costs and increase Sales the faster the better

  12. Total Profit across the Planning Horizon Total Profit [$] Shadow Price[$/K’s unit] Sacrificing the present for the future

  13. 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

  14. Further Research Motivation • Add a discount rate (r) and capitalize the Profit • Find the optimal time for technology transition • Integrate investment in quality of design

  15. 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.

  16. Any Questions ?

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