1. Inequity aversion in screening contracts: experimental evidence and model analysis IFORS, 15 th July 2014, Guido Voigt

2. G. Voigt –Magdeburg, Agenda Motivation Model Supply chain performance Outlook and discussion 2

3. G. Voigt –Magdeburg, Asymmetric information in supply chains 3 Asymmetric information (may) deteriorate supply chain performance …. …. evolve in many supply chain interactions – Joint lot-sizing – Joint lot-sizing (holding costs- and/or fixed costs) – Pricing – Pricing decisions (production costs, end-customer demand) – Capacity – Capacity decision (backorder costs, investment costs) Solution concept: Screening contracts

4. G. Voigt –Magdeburg, Screening contracts: Basic Idea Supplier (uninformed) Low cost buyer High cost buyer ? Contract “low” Contract “high” Buyers: Profit maximizer Empirical: Buyers are insensitive to small payoff differences Research questions: Impact on Supply Chain Performance ? Behavioral robust contract design ? 4

5. G. Voigt –Magdeburg, Agenda Motivation Model Supply chain performance Outlook and discussion 5

6. G. Voigt –Magdeburg, Model B asks S for smaller lot-sizes q – B : lower inventory holding costs ( h /2 * q ) – S : higher fixed costs ( f * d / q ) B : may switch to alternative supplier (costs: R) – S does not know the buyer‘s actual benefit (h) – … but has to lower wholesale price ( w ) to induce higher order sizes Supplier ( S )Buyer ( B ) 6

7. G. Voigt –Magdeburg, Classical Screening Model Probability distribution p i w.r.t. holding costs h i, i=1,…,n ; h 1 <h 2 <…<h n Screening idea Screening idea: One contract A i = for each possible holding cost realization h i 7

8. G. Voigt –Magdeburg, Example for 3 holding cost levels - low (l), med (m), high (h) 8 ql=ql=400wl=wl=9,83 qm=qm=189wm=wm=10,89 qh=qh=129wh=wh=11,78 Buyer's costq l, w l q m, w m q h, w h Alternative hlhl 1183 12431500 hmhm 15831371 1500 h 198315601500 Supplier‘s profit7836665580

9. G. Voigt –Magdeburg, Experimental results – Classical screening contracts 9 Inderfurth et. al (2012): Profit maximum ≈ 70% Near profit maximum (10 cent difference) ≈ 26% – Huge cost effect on supply chain performance Other ≈ 4% q l, w l q m, w m q h, w h

10. G. Voigt –Magdeburg, Inequity aversion Buyer h i not inequity averse with probability α i  Buyer h i is inequity averse with probability (1-α i )  „Empirical“ objective function: 10 Supplier does not exhibit inequity aversion if it is too costly Frequency α i depends (linearly) on payoff difference t i between alternatives Slack

11. G. Voigt –Magdeburg, Behaviorally robust Principal-Agent model (OR) Challenge: Objective function not anymore concave,… but uni-modular for linear α(t i ) Note: „Bunching“, i.e., q i =q i+1, occurs more frequently – derivation of optimal contract much more complex 11

12. G. Voigt –Magdeburg, Buyer's costq l, w l q m, w m q h, w h Alternative hlhl 1072110611381500 hmhm 14721289 1500 h 1872147114401500 Supplier‘s profit6725785320 Behaviorally robust contract 12 ql=ql=400wl=wl=8,72tl=tl=0,35 qm=qm=183wm=wm=10,15tm=tm=0 qh=qh=151wh=wh=10,62th=th=0,6 ql=ql=400wl=wl=9,83 qm=qm=189wm=wm=10,89 qh=qh=129wh=wh=11,78 „Classical“ contract parameters

13. G. Voigt –Magdeburg, Agenda Motivation Model Supply chain performance Outlook and discussion 13

14. G. Voigt –Magdeburg, Supply chain performance Fraction of strictly profit maximizing buyers Classical screening benchmark 14

15. G. Voigt –Magdeburg, Supply chain performance Fraction of strictly profit maximizing buyers Classical screening benchmark Classical contract parameters 15

16. G. Voigt –Magdeburg, Supply chain performance Fraction of strictly profit maximizing buyers Classical screening benchmark Classical contract parameters Behavioral robust contract parameters 16

17. G. Voigt –Magdeburg, Summary, conclusion, and Outlook Critical assumption in screening theory: strict profit maximization Neglecting behavioral irregularities leads to high performance losses Main contribution – Derivation (and solution procedure) for the optimal „behavioral robust“ contract Main limitation: linear dependency of α i and t i Outlook: Experimental testing 17

18. G. Voigt –Magdeburg, Thanks! guido.voigt@ovgu.de 18

19. G. Voigt –Magdeburg, Numerical example: 3 buyer types w i irrelevant for supply chain performance Behavioral robust parameters: Order size – q 2 is lower (less likely) – q 3 is higher (more likely) 19 + -

20. G. Voigt –Magdeburg, Relevant Literature Probablistic choice models* and type dependent decision errors**: – The higher the profit of an alternative, the higher the probability that the alternative is chosen (not supported by experiments) – ↔ (not supported by experiment) Increase pay-off differences between alternatives – Closest to the underlying work – Only for the case of 2 types (many aspects not addressed) – Restrictive assumption on „decision error“ (monotone hazard rate) * Basov and Danilkina (2006) and Basov (2009), ** Basov and Mirrless (2009) *** Laffont and Martimort, 2002 (Textbook) 20

21. G. Voigt –Magdeburg, Parameters numerical example 21

22. G. Voigt –Magdeburg, Complete set of profit/cost impact – Behavioral robust contract 22

23. G. Voigt –Magdeburg, Impact of bunching (1/2) 23

24. G. Voigt –Magdeburg, Impact of bunching (2/2) 24

25. G. Voigt –Magdeburg, References Inderfurth, K.; Sadrieh, A. and Voigt, G. (2012) The Impact of Information Sharing on Supply Chain Performance in Case of Asymmetric Information. Forthcoming in Production & Operations Management Basov, S. (2009) Monopolistic Screening with Boundedly Rational Consumers, The Economic Record, 85, pp. S29-S33 Basov, S. and Danilkina, S. (2006). Quality and product variety in a monopolistic screening model with nearly rational consumers, Proceedings of the 35th Australian Conference of Economists, pp. 1- 21, http://www.business.curtin.edu.au/files/Basov_Danilkina.pdf Laffont, J.-J. and Martimort, D. (2002) The theory of incentives: The Principal-Agent Model. Princeton University Press, Princeton/New Jersey 25