A Dynamic Model for Maximizing Financial Returns from Quality Improvement

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 ?