Information Sharing In Supply Chains: An Empirical and Theoretical Valuation

Transcription

1 Case 11/17/215 Information Sharing In Supply Chains: An Empirical and Theoretical Valuation Ruomeng Cui (Kelley School of Business) Gad Allon (Kellogg School of Management) Achal Bassamboo (Kellogg School of Management) Jan Van Mieghem (Kellogg School of Management) 1 Motivation: Collaboration with a CPG Company Variety of products: Supplier (CPG Company) Retailer (Retail Chains) Customer Order Demand Sports Drinks Orders 1 Demands week week

2 The Promise of Information Sharing Collaborative Planning, Forecasting and Replenishment (CPFR) programs GlobalNetXchange reported a 5% to 2% reduction in inventory costs and 2% to 12% increase in shelf availability. Costco and 7-Eleven share warehouse-specific, daily, item level POS data with suppliers through the SymphonyIRI group. it helps explain a point that I try to make about sharing demand data in fact a few weeks ago I was making the exact same point to a partner about the difference in sharing the demand information vs sharing the output of a process. I don t believe he understood, and now I ll likely refer him to your research, and hopefully they ll make a few minor changes in their architecture to allow sharing of both data points. A Partner & Consultant of IVT Solutions 3 Literature Review Theoretical Work Customer Demand ARMA: Lee et al. (2), Raghunathan (21), Aviv (23, 27), Zhang (24), Miyaoka and Hausman (24), Gilbert (25), Kovtun et al.(212) Gaur et al. (25) and Giloni et al. (212) MMFE: Heath and Jackson (1994), Graves et al. (1998), Aviv (21a), Chen and Lee (29) Replenishment Policy Linear : Chen et al. (2), Graves et al. (1998), Aviv (21b), Balakrishnan et al. (24), Miyaoka and Hausman (24), Chen and Lee (29) Nonlinear : Caplin (1985), Cachon and Fisher (2) Empirical Work Impact of information sharing Boute (23), Dong et al. (211) Bullwhip effect Cachon et al. (27), Bray and Mendelson (212), Bray and Mendelson (213) Using other indicators to improve forecast Gaur et al. (29), Kesavan et al. (29), Curtis et al. (212) 4 2

3 Research Question How much does downstream demand improve the supplier s order forecast accuracy? What are the theoretical explanations? How do important variables, e.g., product characteristics, affect the value of information sharing? 5 Agenda Model and Empirical Results Revisit the Literature Propose a New Theoretical Model Properties of the Value of Information Sharing Value of Operational 6 3

4 Model and Empirical Results Revisit the Literature Propose a New Theoretical Model Properties of the Value of Information Sharing Value of Operational Model and Empirical Results 7 Supply Chain Model Supplier Retailer Two-echelon supply chain Order Demand Customer The supplier needs to forecast future orders, i.e. the one-step-ahead forecast O t,t+1 made in week t. The forecast error O t+1 O t,t+1 Out-of-sample forecast: Test period Update the available information history Root mean squared error (RMSE) Mean absolute percentage error (MAPE) 1 N N i=1 O t+i O t,t+i /O t+i 8 4

5 Cases $/unit Cases $/unit 11/17/215 Data Sports Drinks Orange Juice 6 Sales Orders Point of Sale Price 5 4 Sales Orders Point of Sale Price /7/29 6/27/29 11/14/29 4/3/21 8/21/21 1/8/211 5/28/211 Week 2/7/29 6/27/29 11/14/29 4/3/21 8/21/21 1/8/211 5/28/211 Week Choose low promotional products Promotional depth: Price discount Low (<=.15) promotional products: 14 items (2% ordering volume) 9 Structure of Empirical Study Orders Sales Orders Time Series Analysis Inventory Policy No Info Sharing Forecast Info Sharing Forecast Value of Information Sharing 1 5

6 Forecast Without Information Sharing Time Series Model Fit the autoregressive integrated moving average model, ARIMA(p,d,q), to the order process. O d d t = μ + ρ 1 O t 1 O t d = (O t O t 1 ) (O t d O t d 1 ) d + ρ 2 O t 2 d + + ρ p O t p + η t + λ 1 η t λ q η t q 11 Forecast With Information Sharing: Replenishment Policy ConDOI policy: keep the constant days of inventory (DOI) over time. Order up to Γm t, where m t is the retailer s future demand forecast, and Γ is the target DOI level. Retailer s demand forecast is a weighted moving average of historical demands 3 m t = β j D t j j= In practice, the retailers also smooth inventory I t = γγm t + (1 γ)i t 1 Order process O t = (1 + γγβ )D t + γγβ 1 D t 1 + γγβ 2 D t 2 + γγβ 3 D t 3 γi t

7 Estimated Replenishment Policy Parameters Smoothing level DOI level Brand Product Orange 128 OR Juice 128 ORCA OR ORCA ORST ORPC Sports 5 BR Drinks 5 GP PD LL PD OR PD FRZ GAL GLC GAL FRT GAL OR Company's Claimed DOI level 1-2 weeks DOI Level 2-3 weeks DOI Level O t = c D t + c 1 D t 1 + c 2 D t 2 + c 3 D t 3 γi t 1 + δ t 13 Empirical Results: Relative RMSE Orange Juice Forecast Error OR 128 ORCA 12 OR 12 ORCA 59 ORST 59 ORPC Sports Drinks Forecast Error No Info Sharing Info Sharing 5 BR 5 GP PD LL PD OR PD FRZ 1GAL GLC 1GAL FRT 1GAL OR The accuracy increases for almost all products. 14 7

8 Empirical Results: MAPE 25.% 2.% Orange Juice The accuracy increases for almost all products. 15.% 1.% 5.%.% 8.% 7.% 6.% 5.% 4.% 3.% 2.% 1.%.% 128 OR 128 ORCA 12 OR 12 ORCA 59 ORST 59 ORPC Sports Drinks No Info Sharing Info Sharing 5 BR 5 GP PD LL PD OR PD FRZ 1GAL GLC 1GAL FRT 1GAL OR Average Improvement: 23% at the SKU level 17% at the Pack level CPG company: 3% at the Pack level 15 Model and Empirical Results Revisit the Literature Propose a New Theoretical Model Properties of the Value of Information Sharing Value of Operational Revisit Theoretical Results in the Literature 16 8

9 Demand and Order Process Demand follows an ARIMA(p,d,q) process. D d d t = μ + ρ 1 D t 1 d + ρ 2 D t 2 d + + ρ p D t p + ε t + λ 1 ε t λ q ε t q Back shifter notation, π B = 1 B d and φ B = 1+λ 1B+ +λ q B q π B D t = μ + φ B ε t 1 ρ 1 B ρ p B p The order is affine in historical demand, O t = ψ B D t π B O t = μ + φ B ψ B ε t Demand Parameter Policy Parameter 17 Theoretical Conclusions in Gaur et al. (25) and Giloni et al. (212) π B O t = μ + φ B ψ B ε t π B O t = μ + φ B ψ B ω t Noninvertible Invertible ω t has the largest variance Invertibility: The process shock can be written as an absolutely summable series of historical observations, or equivalently, all roots of φ B ψ B = lie outside the unit circle. The value of information sharing is zero if Var(O t+1 O t,t+1 order history = Var(O t+1 O t,t+1 demand + order history) Proposition: The value of information sharing is zero if and only if the parameter φ B ψ(b) is invertible. 18 9

10 Model and Empirical Results Revisit the Literature Propose a New Theoretical Model Properties of the Value of Information Sharing Value of Operational Empirical Observations Show High Value Theoretical Literature Suggests Zero Value What is the Driver of This Difference? 19 Key Driver: Decision Deviations Difference between the empirical model and the theoretical model Empirical Expression: O t = c D t + c 1 D t 1 + c 2 D t 2 + c 3 D t 3 + c inv I t 1 + δ t Theoretical Expression Operational Explanations of Decision Deviations from the Target Policy: Batching delivery due to the truck load constraints Delay in shipment due to the transportation scheduling and the batching effect Ordering products that are above the target DOI level The daily replenishment policy State Variable Observed by Retailers But Not by Suppliers (Rust 1994). 2 1

11 Order Process with Decision Deviations O t = c D t + c 1 D t 1 + c 2 D t 2 + c 3 D t 3 + c inv I t 1 + δ t Theoretical Expression π B O t = μ + φ B ψ B ε t Evolution of decision deviations π B O t = μ + φ B ψ B ε t + π B κ(b)δ t Demand Signal Coefficient Decision Deviation Coefficient 21 Information Loss in a General Setting Consider two MA processes χ 1 B ε t and χ 2 B δ t χ 1 B = 1 λ 1 B λ q1 B q 1 and χ 2 B = 1 μ 1 B μ q2 B q 2 Forecast χ 1 B ε t + χ 2 B δ t With information sharing: observe the parameters and signals Without information sharing: observe the aggregate process Theorem: If χ 1 B = χ 2 B, the value of information is zero. If χ 1 B χ 2 B, the value of information sharing is positive for any finite forecast lead time h max {q 1, q 2 }

12 Our Theory: Strictly Positive Value With Decision Deviations O t μ = φ B ψ B ε t + π L κ B δ t = κ(l)η t Demand Signal Decision Deviation Order Signal Demand Signal Process ARIMA(,1,1) Decision Deviations Order Process week week week Proposition: We have φ B ψ B π B κ B If the demand shock and the decision deviation are nonzero, then the value of information sharing is always positive for any forecast lead time. 23 Key Insights and Generalization φ B ψ B π B κ(b) Demand Shock Coefficient Decision Deviation Coefficient O t = D t + I t I t 1 Innately different evolution patterns of demand signals and decision deviations induce information loss. Preserve such evolution patterns under any stationary and affine replenishment policy and demand process. Remain under the general order-up-to policy (GOUTP) and the martingale model of forecast evolution (MMFE) demand process

13 Theoretically Predicted RMSE MSE Percentage Improvement Theoretically Predicted RMSE 11/17/215 Numerical Example Based on Theory and Consistent With Empirical Findings 51.6% 43% σ δ 2 σ δ 2 +σ ε 2 Demand follows an ARIMA(,1,). γ=.24 and Γ=3. 25 Our New Theory Is Supported by Our Empirical Findings Without Information Sharing With Information Sharing y = 1.x R² = y = 1.14x R² = Empirically Observed RMSE Empirically Observed RMSE 26 13

14 MSE Percentage Improvement Cases Cases 11/17/215 Model and Empirical Results Revisit the Literature Propose a New Theoretical Model Properties of the Value of Information Sharing Value of Operational Properties of the Value of Information Sharing 27 Impact of Demand Characteristics Sports Drink Orange Juice λ < Week Week High seasonal products benefit more. An ARIMA(,1,1) demand process D t = D t 1 + ε t λε t 1, where λ [,1) Exponential smoothing expression D t = (1 λ) λ i 1 i=1 D t i + ε t Weighted Moving Average = (.5, (1.5, -.5) DOI Level = 2 Smoothing Level =.5 Decision Deviation Weight 28 14

15 MSE Percentage Improvement 11/17/215 Impact of Forecast Lead Time The accuracy improvement decreases in the forecast lead time. Forecast Lead Time 29 Impact of Promotional Depth No significant correlation between the value and promotional depth Improvements Accuracy Without sharing Accuracy With sharing High order uncertainty Non-stationarity y = -.19x +.45 R² = Promotional Depth 3 15

16 Model and Empirical Results Revisit the Literature Propose a New Theoretical Model Properties of the Value of Information Sharing Value of Operational Value of Operational 31 Structure of Empirical Study Orders Sales Orders Sales Orders Time Series Analysis Inventory Policy Forecast Without Information Sharing Forecast Without Operational Forecast With Information Sharing 32 16

17 Value Decomposition Forecast Without Information Sharing Forecast With Information Sharing Value from only sales history Additional Value from Operational Forecast Without Operational Forecast Without Information Sharing Forecast Without Forecast With Operational Information Sharing 33 Statistical Methods: Without Operational Forecast without Information Sharing O d d t = μ + ρ 1 O t 1 d + ρ 2 O t 2 d + + ρ p O t p + η t + λ 1 η t λ q η t q Forecast without operational knowledge: Capture correlations between demand and orders Reg D O t = c D t + c 1 D t c 5 D t 5 + ε t Reg D and O O t = c D t + + c 5 D t 5 + b O t + + b 5 O t 5 ε t Vector ARIMA O d d t = μ + ρ 1 O t 1 d + ρ 2 O t 2 +c D d d t + c 1 D t 1 d + + ρ p O t p + η t + λ 1 η t λ q η t q d + + c p D t p + ε t + b 1 ε t b q ε t q Forecast with Information Sharing O t = c D t + c 1 D t 1 + c 2 D t 2 + c 3 D t 3 + c inv I t 1 + δ t 34 17

18 Empirical Results: Relative RMSE 9.% 8.% 7.% 6.% 5.% 4.% 3.% 2.% 1.%.% Orange Juice 128 OR 128 ORCA 12 OR 12 ORCA 59 ORST 59 ORPC No Info Sharing Reg D Vector ARIMA Reg D and O Info Sharing Information Orders Orders and Sales Orders, Sales and Replenishment Rules 9.% 8.% 7.% 6.% 5.% 4.% 3.% 2.% 1.%.% Sports Drinks 5 BR 5 GP PD LL PD OR PD FRZ 1GAL GLC 1GAL FRT 1GAL OR 35 Operational is as Important as Only Using Sales History Forecast Without Information Sharing 23% Forecast With Information Sharing Value from only sales history 12% 11% Additional Value from Operational Forecast Without the Operational No Info Sharing Vector ARIMA Reg D Reg D and O Info Sharing MAPE 56.45% 42.72% 45.94% 42.18% 33.36% 36 18

19 Contributions: Empirically show a statistically significant value of information sharing. Theoretically reconcile the discrepancies with decision deviations. Different evolution patterns are the key driver of information loss. Properties of the value of information sharing Decreases in the degree of seasonality. Decreases in the forecast lead time. Insignificant relationships with the promotional depth. Show the importance of the value of operational knowledge