The Effects of the Source of Policy Deviation in a Decentralized SC Joong Y. Son* Chwen Sheu** *MacEwan School of Business Grant MacEwan College **Department of Management Kansas State University Oct. 31, 2008 Research Forum

Contents  Policy Deviation – Examples & Issues  Research Questions  Literature Review  Model Descriptions  Policy Deviations in a Decentralized SC  Numerical Results & Managerial Implications  Future Research

Examples  Campbell’s winter sales promotion of Chicken noodle soup  Volvo’s special deal on green cars  Cisco’s over-reliance on its forecasting technology and misaligned incentives with partners  Mar most valuable company, MV of $555 bn  May 2001 inventory write-off ($2.2 billion)

Supply Chain Underperformance  Who/What is responsible? Misaligned incentives (Lee & Whang 1999) Information asymmetry (Corbett & de Groote 2000) Behavioral causes (Sterman 1987, 1989) Decentralized and myopic supply chain policies (Croson & Donohue 2002)  Remedies? SC coordination (Lee & Whang 1999; Klastorin et. al 2002) Information sharing/ exchange (Cachon & Lariviere 2001; Moinzadeh 2002; Huang et. al 2003) Team approach in ordering policy (Chen 1999; Chen & Samroengraja 2000)

Focus of this Research  Coordination (benchmark) vs. Decentralized repl. policies  Benchmark policy: base stock policy at each installation  Cost of policy deviation: decentralized replenishment policies Order for order policy MA based ES based  The impact of deviation based on the source (relative position with SC)  Managerial implications

Research Questions  What are the penalties (local & system-wide) for deviating from the benchmark policy?  Relationships between structural parameters (costs, demand variations) vs. policy parameters (replenishment policies and base stock levels) in a steady state.  What is the relationship between the source (relative position) of deviations and supply chain performance?  Incorporating incentive compatible design (stock outs in SC)

Related Works  Microeconomics perspective: Radner (1987), Marschak & Radner (1972)  SC coordination – incentive compatibility: Jeuland and Shugan (1983), Lee and Rosenblatt (1986), Balakrishnan et al (2004)  Information sharing: Lee & Whang (1999), Gilbert & Ballou (1999), Huang et al (2003), Chen et al (2000)  Setting: Clark & Scarf (1960), Sterman (1989, 1992), Steckel et al (2004), Chatfield (2004)  Replenishment Policies (smoothing algorithms) Dejonckheere et al (2002) Warburton (2004), Balakrishnan (2004)  Multi-agent based modeling: Kimbrough et al (2002) Sikora & Shaw (1998) Swaminathan et al (1998)

Supply Chain Structure A four-stage serial supply chain

Model Settings and Assumptions

 A four-stage decentralized serial supply chain Beer distribution game Incentive design on stockouts at each station Based on Clark & Scarf (1960) Different from Chen’s (1999) “team approach” or “cost centre” Stockout penalty is incurred at each position Upstream position is responsible for portion of stockouts at its immediate buyer  Downstream positions have better access to demand information. In case of policy deviations at multiple stations, deviations are more likely to occur from upstream

Benchmark Replenishment Policy  Benchmark policy for a four-stage serial supply chain Base stock policy at each installation with incentive to stock Minimize long run average supply chain costs Central coordinator/ planner Information availability  Base stock level at position i ( s i ) satisfies standard newsvendor results:

Sequence of Simulation Run

Results: Benchmark case

 The base stock level and SL at each station increases monotonically in unit backorder cost The base stock / SL: lowest at the retailer level highest at the factory  Service level remains relatively constant in demand variations Service level determined directly by the cost structure (b i /h i )  The base stock level increases in demand variations

Policy Deviations * Benchmark case: Base stock policy at all positions Replenishment Scenarios

Policy Deviations  Order for order policy (OFO/ LFL) Similar to the base stock policy (order = demand)  Moving average based policy N= 2, 4, 10  Exponential smoothing based policy  = 0.1, 0.5, 0.9  Total # of decentralized policies tested = 588  2,000 replications over 1,000 weeks

Results: Policy Deviations OFO Base stock vs. OFO

Results: Policy Deviations OFO OFO policy: σ=20, b i =$5.00

Results: Policy Deviations OFO  Deviating station (s): incurred the highest costs  Non-deviating stations: not significantly different from the benchmark case (in fact, slightly lower)  OFO: results in under-stocking at the deviating station (e.g., the distributor) Unfilled orders downstream  lower OH at the wholesaler The distributor accountable for much of backorders at downstream stations  With high b i, upstream deviation more costly to the entire SC

Results: Policy Deviations-MA based MA2 based σ=20, b i =$5.00

Results: Policy Deviations-MA based MA 10 based: σ=20, b i =$5.00

Results: Policy Deviations-MA based  Shorter MA period, N=2 or 4 Bigger order sizes and overstocking Little impact on other stations Downstream deviations more costly  Longer MA period N=10 Smoothing effects Frequent demand-supply misalignment Deviating party responsible for stock outs at downstream positions Upstream deviations more costly

Results: Policy Deviations-ES based ES based σ=20, b i =$5.00

Results: Policy Deviations-ES based  Consistent with MA based results  Small α=0.1 Significant under-stocking at the deviating station  demand – supply timing mismatch  high stock-out penalties Benefit from low OH outweighed by huge stock-out penalty borne by the deviating station  Large α=0.5, 0.9 Overstocking at the deviating station Downstream deviations more costly

Results: % increase SC cost with Policy Deviations  Potential cost savings for the SC by implementing the benchmark case

Results: % increase SC cost Policy Deviations - OFO OFO based σ=20

Results: % increase SC cost Policy Deviations – ES based ES based σ=20, α=0.9

Results: % increase in SC cost  Order-for-order based policy Tendency to understock With high b i  higher potential savings staying with BS  MA with small N/ ES with high α Overstock Downstream deviation costly With high b i  potential savings lower with BS

Managerial Implications  In a steady state, OFO policy deviations (from the benchmark base stock policy) at a given station result in Under-stocking at the deviating station Higher costs at the deviating station Non-deviating stations may benefit Especially at an immediate downstream position Some cases result in local costs lower than the benchmark

Managerial Implications  Decentralized policies with greater smoothing effects (larger N for MA, and smaller  for ES) tend to Display significant under-stocking/ misaligned D-S Result in the worst SC performance both locally and globally (could be parameter-specific)  Decentralized policies with higher responsiveness (smaller N for MA and greater  for ES) exhibit Overstocking Relatively strong SC performance

Managerial Implications  Downstream positions (e.g. the retailer or the wholesaler) have incentive NOT to share demand information with others When others deviate, the retailer exhibits the lowest local cost (even lower than the benchmark case) Need incentive compatible mechanism in SC to have the retailer share demand information with the rest of the stations  Regardless of policy deviations at other stations Staying with base stock policy warrants local protection against deviations at other positions

Managerial Implications  Regardless of whether companies have seemingly perfect operations or not, it is crucial for parties to coordinate and share information (Booker, 2001)  Think globally, act locally!

Future Research  Supply chain design Multiple stations at each stage Determining the number of buyers and suppliers Degree of heterogeneity of buyers and suppliers  Incorporating risk pooling and other coordination mechanisms (e.g. revenue sharing)