Supply Chain Network Structure and Risk Propagation

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Supply Chain Network Structure and Risk Propagation John R. Birge 1 1 University of Chicago Booth School of Business (joint work with Jing Wu, Chicago Booth) IESE Business School Birge (Chicago Booth) Supply Chain Structure July 8, 2014 1 / 20

Themes Supply chain relationships create direct and indirect effects on firms Correlation and reliability issues in particular create nonlinear effects on the value of supplier connections Including nonlinear effects leads to a wide variety of equilibrium network configurations New databases (e.g., Bloomberg SPLC) provide opportunities to investigate financial and operational supply chain network interactions Empirical results on firm returns show significant supplier and customer effects, lagged effects from supplier shocks, and differences in the second-order risk impact of centrality depending on the firm s chain level Birge (Chicago Booth) Supply Chain Structure July 8, 2014 2 / 20

Outline Basic network configurations Impact of nonlinearity on supply chain structure and risk propagation Empirical data set Analysis Birge (Chicago Booth) Supply Chain Structure July 8, 2014 3 / 20

Network Implications 1 Correlation among business interests and suppliers may create opportunities for increasing or decreasing risk Birge (Chicago Booth) Supply Chain Structure July 8, 2014 4 / 20

Network Implications 1 Correlation among business interests and suppliers may create opportunities for increasing or decreasing risk 1 A manufacturer may have an incentive to add an independent supplier to increase reliability Birge (Chicago Booth) Supply Chain Structure July 8, 2014 4 / 20

Network Implications 1 Correlation among business interests and suppliers may create opportunities for increasing or decreasing risk 1 A manufacturer may have an incentive to add an independent supplier to increase reliability 2 A distributor may have incentivest to add similar suppliers to build on existing capabilities Birge (Chicago Booth) Supply Chain Structure July 8, 2014 4 / 20

Network Implications 1 Correlation among business interests and suppliers may create opportunities for increasing or decreasing risk 1 A manufacturer may have an incentive to add an independent supplier to increase reliability 2 A distributor may have incentivest to add similar suppliers to build on existing capabilities 3 Result: Increasing connections may have different effects at different levels of the chain Birge (Chicago Booth) Supply Chain Structure July 8, 2014 4 / 20

Network Implications 1 Correlation among business interests and suppliers may create opportunities for increasing or decreasing risk 1 A manufacturer may have an incentive to add an independent supplier to increase reliability 2 A distributor may have incentivest to add similar suppliers to build on existing capabilities 3 Result: Increasing connections may have different effects at different levels of the chain 2 Effects from connections are then nonlinear (e.g., from interactions) Birge (Chicago Booth) Supply Chain Structure July 8, 2014 4 / 20

Network Implications 1 Correlation among business interests and suppliers may create opportunities for increasing or decreasing risk 1 A manufacturer may have an incentive to add an independent supplier to increase reliability 2 A distributor may have incentivest to add similar suppliers to build on existing capabilities 3 Result: Increasing connections may have different effects at different levels of the chain 2 Effects from connections are then nonlinear (e.g., from interactions) 3 Nonlinear (joint firm) effects may be critical in the formation of supply chain links Birge (Chicago Booth) Supply Chain Structure July 8, 2014 4 / 20

Network Model 1 Previous work: Jackson and Wolinsky (1996): Firm i maximizes utility u i by creating connections ij in graph G (distance d ij ) where u i = w ii + j i δ d ij w ij (ij) G c ij. Birge (Chicago Booth) Supply Chain Structure July 8, 2014 5 / 20

Network Model 1 Previous work: Jackson and Wolinsky (1996): Firm i maximizes utility u i by creating connections ij in graph G (distance d ij ) where u i = w ii + j i δ d ij w ij (ij) G 2 Results: For symmetric networks, either G is complete, a star graph, or linkless. c ij. Birge (Chicago Booth) Supply Chain Structure July 8, 2014 5 / 20

Network Model 1 Previous work: Jackson and Wolinsky (1996): Firm i maximizes utility u i by creating connections ij in graph G (distance d ij ) where u i = w ii + j i δ d ij w ij (ij) G 2 Results: For symmetric networks, either G is complete, a star graph, or linkless. 3 Innovation: allow nonlinear effects w i,jk x ij x ik where x ij = 1 for (ij) G c ij. Birge (Chicago Booth) Supply Chain Structure July 8, 2014 5 / 20

Network Model 1 Previous work: Jackson and Wolinsky (1996): Firm i maximizes utility u i by creating connections ij in graph G (distance d ij ) where u i = w ii + j i δ d ij w ij (ij) G 2 Results: For symmetric networks, either G is complete, a star graph, or linkless. 3 Innovation: allow nonlinear effects w i,jk x ij x ik where x ij = 1 for (ij) G c ij. w i,jk may be positive or negative depending on correlation of j and k interests and their reliability Birge (Chicago Booth) Supply Chain Structure July 8, 2014 5 / 20

Network Model 1 Previous work: Jackson and Wolinsky (1996): Firm i maximizes utility u i by creating connections ij in graph G (distance d ij ) where u i = w ii + j i δ d ij w ij (ij) G 2 Results: For symmetric networks, either G is complete, a star graph, or linkless. 3 Innovation: allow nonlinear effects w i,jk x ij x ik where x ij = 1 for (ij) G c ij. w i,jk may be positive or negative depending on correlation of j and k interests and their reliability Hypothesis: negative correlation yields negative w i,jk. Birge (Chicago Booth) Supply Chain Structure July 8, 2014 5 / 20

Implications for Supply Chain Structure Even symmetric networks with this cost structure may have widely varying equilibrium structure With a single parameter α for the correlation, a full range of degree distributions exist Examples: Birge (Chicago Booth) Supply Chain Structure July 8, 2014 6 / 20

Implications for Supply Chain Structure Even symmetric networks with this cost structure may have widely varying equilibrium structure With a single parameter α for the correlation, a full range of degree distributions exist Examples: Birge (Chicago Booth) Supply Chain Structure July 8, 2014 6 / 20

Implications for Supply Chain Structure Even symmetric networks with this cost structure may have widely varying equilibrium structure With a single parameter α for the correlation, a full range of degree distributions exist Examples: Birge (Chicago Booth) Supply Chain Structure July 8, 2014 6 / 20

Implications for Supply Chain Structure Even symmetric networks with this cost structure may have widely varying equilibrium structure With a single parameter α for the correlation, a full range of degree distributions exist Examples: Birge (Chicago Booth) Supply Chain Structure July 8, 2014 6 / 20

Implications for Supply Chain Structure Even symmetric networks with this cost structure may have widely varying equilibrium structure With a single parameter α for the correlation, a full range of degree distributions exist Examples: Birge (Chicago Booth) Supply Chain Structure July 8, 2014 6 / 20

Supply Chain Risk Propagation Example framework: Suppliers A, B; manufacturer M; distributor/wholesaler W A M B W Shocks: probability p of individual survival of each without considering the effects of these nodes Birge (Chicago Booth) Supply Chain Structure July 8, 2014 7 / 20

Risk Implications Systematic risk: volatility of survival probability conditional on not having an idiosyncratic bad shock. For each node in simple network: conditional survival probability p 3. Volatility =p 3 (1 p 3 ): decreases as p increases for p > 0.5. Birge (Chicago Booth) Supply Chain Structure July 8, 2014 8 / 20

Expanded Network Example framework: Suppliers A i, B i ; manufacturer M i ; distributor/wholesaler W i A 1 M 1 B 1 A 2 M 2 B 2 W 1 W 2 Birge (Chicago Booth) Supply Chain Structure July 8, 2014 9 / 20

Propogation in Expanded Network Manufacturer M 1 can survive if one of the wholesaler nodes is wiped out; Wholesaler W 1 cannot survive either manufacturer s loss (due to lower margin/market power). Implications: More-connected M 1 s conditional survival probability: (1 (1 p) 2 ) 3 = p 3 (2 p) 3 > p 3, (1) where p 3 is the conditional survival of the singly-connected M 2. WholesalerW 1 s conditional survival probability: W 2 s conditional survival probability: Also, because p(2 p) 2 < 1, (1 (1 p) 2 ) 2 p = p 3 (2 p) 2. (2) p 4 (2 p) 2 < p 3 (2 p) 2. (3) p 4 (2 p) 2 < p 3, conditional survivalin simple tree. (4) Result: Wholesaler/distributor with more connections faces higher risk while the opposite is true for manufacturers. Birge (Chicago Booth) Supply Chain Structure July 8, 2014 10 / 20

Verifying Hypotheses Empirically New database: Bloomberg SPLC, 25000 25000 supply chain connections With effort can be fully collected (so far, 8000 8000) Size distribution: Birge (Chicago Booth) Supply Chain Structure July 8, 2014 11 / 20

Verifying Hypotheses Empirically New database: Bloomberg SPLC, 25000 25000 supply chain connections With effort can be fully collected (so far, 8000 8000) Size distribution: Birge (Chicago Booth) Supply Chain Structure July 8, 2014 11 / 20

Verifying Hypotheses Empirically New database: Bloomberg SPLC, 25000 25000 supply chain connections With effort can be fully collected (so far, 8000 8000) Size distribution: Birge (Chicago Booth) Supply Chain Structure July 8, 2014 11 / 20

Verifying Hypotheses Empirically New database: Bloomberg SPLC, 25000 25000 supply chain connections With effort can be fully collected (so far, 8000 8000) Size distribution: Birge (Chicago Booth) Supply Chain Structure July 8, 2014 11 / 20

Verifying Hypotheses Empirically New database: Bloomberg SPLC, 25000 25000 supply chain connections With effort can be fully collected (so far, 8000 8000) Size distribution: Birge (Chicago Booth) Supply Chain Structure July 8, 2014 11 / 20

Degree distributions Date for 8000 8000 firms In- and out-degree follows exponential distributions Birge (Chicago Booth) Supply Chain Structure July 8, 2014 12 / 20

Degree distributions Date for 8000 8000 firms In- and out-degree follows exponential distributions Birge (Chicago Booth) Supply Chain Structure July 8, 2014 12 / 20

Degree distributions Date for 8000 8000 firms In- and out-degree follows exponential distributions Birge (Chicago Booth) Supply Chain Structure July 8, 2014 12 / 20

Degree distributions Date for 8000 8000 firms In- and out-degree follows exponential distributions Birge (Chicago Booth) Supply Chain Structure July 8, 2014 12 / 20

Degree distributions Date for 8000 8000 firms In- and out-degree follows exponential distributions Birge (Chicago Booth) Supply Chain Structure July 8, 2014 12 / 20

Supply Chain Relationship Hypotheses Performance metric: stock return (proxy for direct shock effects and systematic risk) First-order effects Suppliers and customers concurrent performance relates to the firm Supplier momentum (one-month lag) may be related to firm performance Customer momentum (following Cohen and Frazzini (2008) not related to firm performance Second-order (systematic risk) effects Centrality influences firm risk and return performance More central manufacturing firms have lower returns (lower risk) More central logistics (transportation, wholesale, retail) firms have higher returns (higher risk) Birge (Chicago Booth) Supply Chain Structure July 8, 2014 13 / 20

First-Order Effects Model: r i,t = α + β 1 r i,t 1 + β 2 +β 4 j w in j w in ij r j,t + β 5 ij r j,t 1 + β 3 j w out ij r j,t + ɛ i,t. j wij out r j,t 1 Coefficients α and β k, k = 1,..., 5 (estimated); j w ij inr j,t 1 - one-month supplier momentum, j w ij out r j,t 1 - one-month customer momentum, j w ij inr j,t - concurrent supplier return, and j w ij out r j,t - the concurrent customer return. Use US firms in SPLC. Monthly returns over 2010-2012. Include common risk factors (MKT, SMB, HML, MOM). Birge (Chicago Booth) Supply Chain Structure July 8, 2014 14 / 20

First-Order Results Table: Fama-Macbeth Regression of Concurrent Returns and Momentum. α r i,t 1 j win ij r j,t 1 j wout ij r j,t 1 j win ij r j,t j wout ij r j,t Ave. Coef -0.001-0.088*** 0.036** 0.024 0.399*** 0.755*** (T-Stat) (-0.96) (-11.06) (2.17) (0.95) (20.90) (3.12) Ave. Coef 0.009*** -0.090*** 0.057*** 0.004 (T-Stat) (10.38) (-9.08) (2.96) (0.09) Ave. Coef 0.009*** -0.047*** (T-Stat) (10.53) (-6.96) Ave. Coef 0.008*** 0.022** (T-Stat) (11.09) (1.83) Ave. Coef 0.008*** -0.040 (T-Stat) (10.92) (-0.66) Ave. Coef 0.003*** 0.619*** (T-Stat) (3.61) (37.25) Ave. Coef -0.002** 0.992*** (T-Stat) (-2.26) (4.54) Ave. Coef 0.004*** 0.018* 0.625*** (T-Stat) (4.51) (1.57) (36.44) Ave. Coef -0.002* 0.001 1.001*** (T-Stat) (-1.92) (0.0274) (4.51) Ave. Coef -0.001* 0.393*** 0.744*** (T-Stat) (-1.80) (22.48) (3.20) *p-value 10%, **p-value 5%, ***p-value 1% Birge (Chicago Booth) Supply Chain Structure July 8, 2014 15 / 20

Second-Order Effects Model: Characterize centrality by eigenvector centrality and in- and out-degree centrality Use average of industry if no relationship in dataset Split by NAICS code (3 for manufacturing, 4 for logistics) Split into quintiles of centrality. Observe trends and significance in returns across quintiles. Birge (Chicago Booth) Supply Chain Structure July 8, 2014 16 / 20

Second-Order Results: Manufacturing Table: Factor Sensitivities by In-degree Centrality for Manufacturing Firms. N3 Factor Loadings Portfolio Alpha(%) Rmt R ft SMB HML MOM Adj. R 2 (%) 1(Low) 0.340 1.250*** 92.13 (0.99) (15.71) 0.630* 1.119*** 0.327-0.366-0.145 93.25 (1.81) (9.90) (1.17) (-1.68) (-1.27) 2 0.077 1.220*** 91.10 (0.22) (14.70) 0.414 1.085*** 0.491-0.594** 0.025 93.63 (1.25) (10.07) (1.85) (-2.86) (0.23) 3 0.430 0.902*** 86.05 (1.26) (11.43) 0.175 1.091*** -0.561* -0.205 0.079 87.74 (0.50) (9.61) (-2.00) (-0.94) (0.69) 4 0.105 1.066*** 92.44 (0.37) (16.05) 0.127 1.098*** -0.079-0.338 0.022 92.51 (0.41) (10.83) (-0.31) (-1.73) (0.22) 5(High) 0.053 0.804*** 84.67 (0.16) (10.81) -0.170 1.006*** -0.659*** -0.431** 0.009 91.00 (-0.63) (11.52) (-3.05) (-2.56) (0.10) High-Low -0.287*** -0.446*** (-2.87) (-19.21) -0.800*** -0.113*** -0.986*** -0.065 0.153*** (-8.52) (-3.71) (-13.10) (-1.10) (5.03) *p-value 10%, **p-value 5%, ***p-value 1% Birge (Chicago Booth) Supply Chain Structure July 8, 2014 17 / 20

Second-Order Results: Logistics Table: Factor Sensitivities by In-degree Centrality for Logistics Firms. N4 Factor Loadings Portfolio Alpha(%) Rmt R ft SMB HML MOM Adj. R 2 (%) 1(Low) 0.061 1.072*** 79.57 (0.12) (9.10) -0.324 1.302*** -0.684 0.097 0.106 79.47 (-0.58) (7.19) (-1.53) (0.28) (0.58) 2 0.327 1.078*** 80.94 (0.72) (10.29) 0.327 1.153*** -0.875** -0.065-0.006 84.53 (0.78) (8.87) (-2.72) (-0.26) (-0.04) 3 0.493 0.973*** 77.60 (1.01) (8.59) 0.242 1.142*** -0.427-0.092 0.162 76.37 (0.44) (6.40) (-0.97) (-0.27) (0.90) 4 0.703* 0.893*** 83.31 (1.73) (9.50) 0.741 0.888*** 0.737* -0.571* 0.101 86.72 (1.67) (6.20) (2.08) (-2.07) (0.70) 5(High) 0.922** 0.638*** 75.37 (2.71) (8.08) 0.878** 0.735*** -0.140-0.549** 0.202* 82.76 (2.81) (7.26) (-0.56) (-2.81) (1.98) High-Low 0.861*** -0.434*** (6.60) (-14.37) 1.202*** -0.567*** 0.544*** -0.646*** 0.096** (8.80) (-12.82) (4.97) (-7.57) (2.15) *p-value 10%, **p-value 5%, ***p-value 1% Birge (Chicago Booth) Supply Chain Structure July 8, 2014 18 / 20

Conclusions 1 Supply chain network connections link firms with direct and indirect effects Birge (Chicago Booth) Supply Chain Structure July 8, 2014 19 / 20

Conclusions 1 Supply chain network connections link firms with direct and indirect effects 2 Nonlinear effects can allow for wide variation in topology Birge (Chicago Booth) Supply Chain Structure July 8, 2014 19 / 20

Conclusions 1 Supply chain network connections link firms with direct and indirect effects 2 Nonlinear effects can allow for wide variation in topology 3 Risk propogation from connections varies depending on chain level Birge (Chicago Booth) Supply Chain Structure July 8, 2014 19 / 20

Conclusions 1 Supply chain network connections link firms with direct and indirect effects 2 Nonlinear effects can allow for wide variation in topology 3 Risk propogation from connections varies depending on chain level 4 Data indicates wide range of degree distributions (that seem to imply some nonlinear relationships). Birge (Chicago Booth) Supply Chain Structure July 8, 2014 19 / 20

Conclusions 1 Supply chain network connections link firms with direct and indirect effects 2 Nonlinear effects can allow for wide variation in topology 3 Risk propogation from connections varies depending on chain level 4 Data indicates wide range of degree distributions (that seem to imply some nonlinear relationships). 5 Evidence of concurrent supplier and customer effects plus supplier momentum effects on returns. Birge (Chicago Booth) Supply Chain Structure July 8, 2014 19 / 20

Conclusions 1 Supply chain network connections link firms with direct and indirect effects 2 Nonlinear effects can allow for wide variation in topology 3 Risk propogation from connections varies depending on chain level 4 Data indicates wide range of degree distributions (that seem to imply some nonlinear relationships). 5 Evidence of concurrent supplier and customer effects plus supplier momentum effects on returns. 6 Evidence of decreasing returns to centrality in manufacturing and increasing returns to centrality in logistics. Birge (Chicago Booth) Supply Chain Structure July 8, 2014 19 / 20

Thank you! Any questions? Birge (Chicago Booth) Supply Chain Structure July 8, 2014 20 / 20

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