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KNOWLEDGE MANAGEMENT FOR PRODUCT AND PROCESS DESIGN

KNOWLEDGE MANAGEMENT FOR PRODUCT AND PROCESS DESIGN. Gülru F. Özkan, Cheryl Gaimon College of Management Georgia Institute of Technology 800 West Peachtree Street, GA Tech Atlanta, GA 30308-0520. MOTIVATION. Knowledge  source of competitive advantage

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KNOWLEDGE MANAGEMENT FOR PRODUCT AND PROCESS DESIGN

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  1. KNOWLEDGE MANAGEMENT FOR PRODUCT AND PROCESS DESIGN Gülru F. Özkan, Cheryl Gaimon College of Management Georgia Institute of Technology 800 West Peachtree Street, GA Tech Atlanta, GA 30308-0520

  2. MOTIVATION • Knowledge source of competitive advantage • Focus: Management of product and process design team knowledge • Dynamic knowledge management (Spender, 1996) Learning-by-doing Lapre and Van Wassenhove (2000, 2001), Ittner et al. (2001), Biskup and Simons (2004). Induced learning Mukherjee et al. (1998), Hatch and Mowery (1998), Lapre and Van Wassenhove (2001), Adler and Clark (1991), Goldstein (2003), Carrillo and Gaimon (2000), (2004). Knowledge transfer

  3. MOTIVATION • Internal Knowledge transfer: Argote & Ingram (2000), Darr, Argote and Epple (1995), Emery (2002), Szulanski (1996), Teece (1977), Zellmer-Bruhn (2003) • Knowledge deployment product attributes and process capabilities • Concurrent Engineering Chakravarty (2001), Ha and Porteus (2001), Krishnan et al. (1997), Smith and Eppinger (1997), Eppinger (2001), Maccormack and Iansiti (2001)

  4. RESEARCH QUESTIONS • What drives the manager’s strategies for knowledge transfer between the product and process design teams? • What drives the manager’s strategy to pursue induced learning for each team? • How are these strategies impacted by learning-by-doing? • When should the manager delay her pursuit of knowledge transfer and why? • How does time to market pressure affect the development of team knowledge?

  5. MODEL DYNAMICS Induced learning Induced learning Knowledge transfer Process Design M(t) Product Design D(t) Knowledge transfer Learning-by-doing Learning-by-doing

  6. MODEL DYNAMICS Process Design Product Design M(t) D(t) Learning-by-doing Learning-by-doing a[D(t)]1 a[M(t)]r1

  7. MODEL DYNAMICS Induced learning Induced learning d2g(t)[D(t)]4 m2g(t)[M(t)]r4 Process Design M(t) Product Design D(t)

  8. MODEL DYNAMICS m1b(t)[D(t)]r2[M(t)]r3 Knowledge transfer Process Design M(t) Product Design D(t) Knowledge transfer d1(t)[M(t)]2[D(t)]3

  9. CUMULATIVE KNOWLEDGE AND NET REVENUE The cumulative levels of knowledge embedded in the product over the development cycle by the product (X(T)) and process design (Y(T)) teams drive net revenue: V[X(T),Y(T),T] Where, X'(t) = δ1(t)D(t) Y'(t) = δ2(t)M(t) and T is the product release time (terminal time).

  10. MODEL OBJECTIVE V[X(T),Y(T),T] - {C1[(t)] + C2[b(t)] + C3[(t)] + C4[g(t)]}dt (1) (2) (3) (4) Net revenue earned at the product release – costs of pursuing (1) & (2) knowledge transfer and (3) & (4) induced learning.

  11. , b, , or g , b, , or g 0 0 time T T time tmax Knowledge Transfer and Induced Learning for Product and Process Design Teams

  12. CASE 1: Knowledge Transfer and Induced Learning for Product Design Team (t) (t) 0 time 0 time T t t T

  13. CASE 2: Knowledge Transfer and Induced Learning for Product Design Team (t) (t) 0 0 time T time T

  14. CASE 3&4: Knowledge Transfer and Induced Learning for Product Design Team (t) or (t) 0 T time (t) or (t) 0 time T t or t

  15. EXAMPLE: Semiconductor Industry (t) b(t) (t) 0 T time 0 0 time time T T g(t) 0 time T tb t

  16. Optimal Launch Time (T*) VX(T)1(T)D(T)+VY(T)2(T)M(T) = -VT RHS LHS T* time

  17. Optimal Launch Time (T*) RHS LHS T1* T* time

  18. Optimal Launch Time (T*) RHS LHS T* T2* time

  19. INSIGHTS ABOUT (t) and (t) • Optimal rate of knowledge transfer or induced learning for the product design team is larger if: • the team is more capable of learning-by-doing ( or 1) is large; • the rate of returns of knowledge transfer in either direction (2, 3, r2 and r3) are large; • the rate of returns of induced learning pursued for the product design team knowledge (4) is large; • Marginal value of an additional unit of process design team knowledge (2) is large (small).

  20. CONCLUSIONS We obtain insights on a manager’s dynamic strategy for induced learning and knowledge transfer in relation to: • team’s ability to benefit from learning-by-doing. • team’s ability to benefit from knowledge transfer or induced learning. • effect of disruption (cost) due to induced learning or knowledge transfer. • initial level of product (process) design knowledge. • extent of time-based competition (optimal launch). • relative importance of product versus process design team knowledge as a driver of net revenue.

  21. FUTURE DIRECTIONS • Consider forgetting, or knowledge depreciation. • Various net revenue functions at the product release times reflecting different industries. • Analysis of different representations of knowledge transfer effectiveness. • Numerical examples based on different industry characteristics. • Imperfect knowledge transfer processes.

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