Sourcing Decisions under Uncertainty

Transcription

1 Sourcing Decisions under Uncertainty David A Wuttke 1 January 23, 2014 Decision-Making in a World of Incomplete and Evolving Knowledge, MPI for Mathematics in the Sciences 1 Institute for Supply Chain Management - Procurement and Logistics (ISCM), EBS University 1 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

2 Decision-Making in a World of Incomplete and Evolving Knowledge New types of heuristics Sourcing decisions under uncertainty Stochastic ordering policies Supply chain coordination Broadened scope my talk today! 2 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

3 Initiating and Sustaining Supplier Involvement in Development Projects: Behavioral Aspects in the Contract Design Joint work with Karen Donohue and Enno Siemsen (both from Carlson School of Management, University of Minnesota) 3 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

4 Suppliers need incentives to innovate Consider a decentralized supply chain Buyer: increased revenue from innovative products Supplier: research and development (R&D) cost uncertainty Research questions How should firms create a contractual environment to incentivize supplier innovation? What are the underlying behavioral factors that influence the suppliers decisions? 4 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

5 Innovation and incentives Innovations in supply chains Leverage different innovation capabilities (Cui et al. 2012, Gerwin & Ferris 2004, Billington & Davidson 2012) Potentially sub-optimal levels of research and development efforts (Gilbert & Cvsa 2003, Xiao & Xu 2012, Plambeck & Taylor 2007, Bhaskaran & Krishnan 2009, Wang & Shin 2012, 2013) Innovation incentive alignment Renegotiation of contracts where a buyer invests in innovation and a contract manufacturer invests in capacity (Plambeck & Taylor 2007) Alignment of incentives and costs among firms collaborating in R&D (Bhaskaran & Krishnan 2009) Royalty contract to share benefits (Xiao & Xu 2012) 5 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

6 Behavioral operations management Increasing importance of behavioral operations Behavioral operations has become an accepted sub-field of the discipline of operations management. (Croson et al. 2013) Real OM decisions often substantially biased making the study of behavior in OM important (Bendoly et al. 2006) Laboratory experiments can be used to build better operations management models (Katok 2011) Contracting Early BOM studies on inventory policies, e.g. newsvendor (Schweitzer & Cachon 2000) Increasing focus on contracting in supply chains (Katok & Wu 2009, Cui et al. 2007, Zhang et al. 2013) Our study attempts to add to this stream by focusing on innovation contracts 6 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

7 Innovation incentive game with breach penalty contract (w I, p) buyer accept? supplier do not invest invest c 1 ξ continue? no default version production costs: k buyer s revenue: r0 supplier s revenue: w0 default version production costs: k buyer s revenue: r0 supplier s revenue: w0 breach penalty: p R&D phase 1 costs: c1 R&D phase 1 supplier yes ζ R&D phase 2 innovative version production costs: buyer s revenue: supplier s revenue: R&D phase 1 costs: R&D phase 2 costs: k ri wi c1 ζ 7 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

8 Continuation decision based on observed signal (supplier) expected continuation profit expected stop profit E ζ [π s,cnt (w I, p) ξ] E ζ [π s,br (w I, p) ξ] E ζ [w I c 1 ζ k ξ] E ζ [w 0 c 1 p k ξ] w I c 1 k E ζ [ζ ξ] w 0 c 1 p k Proposition (Continuation Decision) w I E ζ [ζ ξ] w 0 p The optimal continuation strategy for a profit-maximizing supplier in phase 2 is a threshold policy where the innovation investment should continue if ξ ξ, where ξ := µ 1 (w I w 0 + p). 8 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

9 Acceptance decision (supplier) expected accept profit basic profit { }] E ξ [max E ζ [π s,cnt (w I, p) ξ], E ζ [π s,br (w I, p) ξ] π s,rej ξ (w I w 0 )F (ξ ) p(1 F (ξ )) + c 1 + µ(z)f (z)dz + expected additional revenue expected breach penalty + R&D costs phase one expected R&D costs phase two Proposition (Acceptance Decision) The optimal acceptance decision for a profit-maximizing supplier is to choose the innovation contract over the default option if (w I w 0 )F 1 (ξ ) p(1 F 1 (ξ )) + c 1 + c 2 (ξ ) F 1 (ξ ). 9 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

10 Optimal offer (buyer) max w I,p (r I w I ) F 1 (ξ ) + (r 0 w 0 + p) (1 F 1 (ξ )) s.t. (r I r 0 )F 1 (ξ ) p(1 F 1 (ξ )) c 2 (ξ ) F 1 (ξ ) c 1 = 0 ξ = µ 1 (w I w 0 + p) Proposition (Optimal offer) A profit maximizing buyer will set contract parameters as follows: ξ wi =w 0 + c 1 + µ(z)f 1 (z)dz + (r I r 0 )(1 F 1 (ξ )) p =(r I r 0 )F 1 (ξ ) c 1 ξ =µ 1 (r I r 0 ) ξ µ(z)f 1 (z)dz, with and 10 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

11 Innovation reward contract contract (wi 1, wi 2) buyer accept & invest? supplier do not invest invest basic version production costs: buyer s revenue: supplier s revenue: basic version production costs: buyer s revenue: k r0 w0 k r0 c1 ξ continue? no supplier s revenue: wi 1 R&D phase 1 costs: c1 R&D phase 1 supplier yes ζ R&D phase 2 innovative version production costs: k buyer s revenue: ri supplier s revenue wi 1 + wi 2 R&D phase 1 costs: c1 R&D phase 2 costs: ζ Proposition (Equivalence of contracts) Both contracts are equivalent. A profit maximizing buyer will set contract parameters as follows: wi 1 =w 0 p and wi 2 =p + wi w 0 11 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

12 Model summary we know when the supplier would continue with R&D.... whether the supplier would accept a contract.... which contract a buyer should propose.... that penalty and reward contracts are equivalent. We don t know how boundedly rational suppliers deviate.... whether suppliers would accept such offers at all. 12 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

13 Framing: penalty versus reward Mental accounting, a set of cognitive operations to organize, evaluate, and keep track of financial activities (Thaler 1999) Social exchange theory, willingness to engage in exchange (Blau 1964, Homans 1958, Emerson 1976) Social exchange theory mental accounting Penalty contract cost emphasis uncompensated loss Reward contract reward emphasis investment Hypothesis (H 1 : Framing) The acceptance rate of the reward contract is higher than the acceptance rate of the penalty contract. 13 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

14 Real options value innovation value (v) =: core value (v c ) + real options value (v r ) People understand the concept of options value in general. (Arrow & Fisher 1974, Rauchs & Willinger 1996) But they often misevaluate it. (Delquié 2008, Schoemaker 1989, Kremer et al. 2013) Specific in our setting: even after observing the signal the uncertainty remains increased value entirely shifted to buyer Hypothesis (H 2 : Real options value) Real suppliers are more likely to accept an offer, ceteris paribus, if the real options value marks a small fraction of the overall innovation value. 14 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

15 Escalation of commitment: the continuation decision Consistency: Avoidance of cognitive dissonance (Festinger 1957) and voluntary compliance (Freedman & Fraser 1966, Cialdini et al. 1978)... because I said yes before. Economic significance of sunk costs: Higher sunk costs should evoke a stronger sunk cost effect (Kahneman & Tversky 1979, Thaler 1980, Staw 1976)... because the loss would be high. Hypothesis (H 3 : Escalation of commitment) (a) Individuals are more likely to escalate commitment to R&D if they also made the initial decision. (b) The continuation rate will increase with the magnitude of sunk cost. 15 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

16 Treatment Design Investment and continuation decision (20 rounds) baseline high real options value penalty contract reward contract Only continuation decision (20 rounds) high real high baseline options value sunk costs penalty contract reward contract 16 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

17 Virtual decision environment: acceptance decision 17 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

18 Virtual decision environment: continuation decision 18 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

19 Sample overview Pre-tests with 10 managers, mostly supply chain and procurement sample 110 students (reduced sample of 99), incentive compatible payoffs (average $14.40) 58% undergraduate students Average acceptance rates base high rov penalty frame 61% 52% reward frame 69% 64% 19 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

20 Hypothesis 1: Framing ANOVA results: base high rov penalty frame 61% 52% reward frame 69% 64% Probit results: Average marginal effect of the rewards frame about 8% (p < 0.05) Hypothesis 2: Real options value ANOVA results: base high rov penalty frame 61% 52% reward frame 69% 64% Probit results: Average marginal accept propensity under high real options value about 8% lower (p < 0.05) 20 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

21 Hypothesis 3a: Consistency effect more deescalation errors if only continuation (µ = 0.41, p < 0.01) more escalation error if both (µ = 0.6, p < 0.01) likelihood to continue increases by 3 percentage points if both decisions are made (p < 0.05) Hypothesis 3b: Economic significance average sunk costs: 4% versus 55% continuation propensity similar (p = 0.156). error types very similar potential explanation: high transparency in our experiments (Heath 1995) 21 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

22 Conclusion Extension of previous literature on innovation incentives in supply chains (Gilbert & Cvsa 2003, Xiao & Xu 2012, Plambeck & Taylor 2007, Bhaskaran & Krishnan 2009, Gupta & Loulou 1998, Wang & Shin 2012, 2013) Two equivalent incentive contracts studied For the buyer (!), the reward contract performs better centralized decision maker (individual responsibility) decentralized decision maker (diffused responsibility) incremental innovation lower reward higher reward (low v r ) predicted guarantee lower guarantee radical innovation lower reward higher reward (high v r ) higher guarantee higher guarantee 22 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

23 Heuristics (a bit of speculation) Pro-innovation bias: New products are better Framing: Avoid penalties which restrict your action space Real options value: Anchor on information-less expected value Continuation: If I said yes in the beginning, that was optimal Thank you very much for your attention! 23 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

24 Bibliography I Arrow, K. J. & Fisher, A. C. (1974), Environmental preservation, uncertainty, and irreversibility, The Quarterly Journal of Economics 88(2), Bendoly, E., Donohue, K. & Schultz, K. L. (2006), Behavior in operations management: Assessing recent findings and revisiting old assumptions, Journal of Operations Management 24(6), Bhaskaran, S. & Krishnan, V. (2009), Effort, revenue, and cost sharing mechanisms for collaborative new product development, Management science 55(7), Billington, C. & Davidson, R. (2012), Leveraging open innovation using intermediary networks, Production and Operations Management. Blau, P. M. (1964), Exchange and power in social life, New York: Wiley. Cialdini, R. B., Cacioppo, J. T., Bassett, R. & Miller, J. A. (1978), Low-ball procedure for producing compliance: Commitment then cost., Journal of personality and Social Psychology 36(5), Croson, R., Schultz, K., Siemsen, E. & Yeo, M. (2013), Behavioral operations: The state of the field, Journal of Operations Management 31(1-2), 1 5. Cui, T., Raju, J. & Zhang, Z. (2007), Fairness and channel coordination, Management Science 53(8), Cui, Z., Loch, C., Grossmann, B. & He, R. (2012), How provider selection and management contribute to successful innovation outsourcing: an empirical study at siemens, Production and Operations Management 21(1), Delquié, P. (2008), Valuing information and options: an experimental study, Journal of Behavioral Decision Making 21(1), Emerson, R. M. (1976), Social exchange theory, Annual review of sociology 2, Festinger, L. (1957), A theory of cognitive dissonance, Vol. 1, Stanford university press, Stanford. 24 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

25 Bibliography II Freedman, J. L. & Fraser, S. C. (1966), Compliance without pressure: the foot-in-the-door technique., Journal of personality and social psychology 4(2), Gerwin, D. & Ferris, J. S. (2004), Organizing new product development projects in strategic alliances, Organization Science 15(1), Gilbert, S. M. & Cvsa, V. (2003), Strategic commitment to price to stimulate downstream innovation in a supply chain, European Journal of Operational Research 150(3), Gupta, S. & Loulou, R. (1998), Process innovation, product differentiation, and channel structure: Strategic incentives in a duopoly, Marketing Science 17(4), Heath, C. (1995), Escalation and de-escalation of commitment in response to sunk costs: The role of budgeting in mental accounting, Organizational behavior and human decision processes 62(1), Homans, G. C. (1958), Social behavior as exchange, American journal of sociology pp Kahneman, D. & Tversky, A. (1979), Prospect theory: An analysis of decision under risk, Econometrica 47, Katok, E. (2011), Using laboratory experiments to build better operations management models, Foundations and Trends in Technology, Information and Operations Management 5(1), Katok, E. & Wu, D. (2009), Contracting in supply chains: A laboratory investigation, Management Science 55(12), Kremer, M., Minner, S. & Wassenhove, L. N. (2013), On the preference to avoid ex-post inventory errors, Production and Operations Management. Plambeck, E. L. & Taylor, T. A. (2007), Implications of breach remedy and renegotiation design for innovation and capacity, Management Science 53(12), Jan 23, 2014, Leipzig Supplier Innovation David Wuttke

26 Bibliography III Rauchs, A. & Willinger, M. (1996), Experimental evidence on the irreversebility effect, Theory and decision 40(1), Schoemaker, P. J. (1989), Preferences for information on probabilities versus prizes: The role of risk-taking attitudes, Journal of Risk and Uncertainty 2(1), Schweitzer, M. E. & Cachon, G. P. (2000), Decision bias in the newsvendor problem with a known demand distribution: experimental evidence, Management Science 46(3), 404. Staw, B. (1976), Knee-deep in the big muddy: A study of escalating commitment to a chosen course of action, Organizational Behavior and Human Performance 16(1), Thaler, R. (1980), Toward a positive theory of consumer choice, Journal of Economic Behavior & Organization 1(1), Thaler, R. H. (1999), Mental accounting matters, Journal of Behavioral Decision Making 12(3), Wang, J. & Shin, H. (2012), The impact of contracts on upstream innovation incentives in a supply chain, Working paper, available at SSRN Wang, J. & Shin, H. (2013), The optimal innovation decision for an innovative supplier in a supply chain, Working paper. Xiao, W. & Xu, Y. (2012), The impact of royalty contract revision in a multistage strategic r&d alliance, Management Science 58(12), Zhang, Y., Donohue, K. & Cui, H. (2013), Contract preferences and performance for the loss averse supplier: Buyback versus revenue sharing. working paper/ under revision. 26 Jan 23, 2014, Leipzig Supplier Innovation David Wuttke