How to measure bullwhip effect by network data envelopment analysis?

Abstract One of the most complicated decision making problems in supply chain management is performance assessment which involves diverse criteria. One of the main criteria for evaluating supply chain performance is bullwhip effect (BWE). For supply chains’ managers, measuring bullwhip effect is of critical importance. However, BWE is measured in classical series or parallel structures of supply chains. This network structure is rarely found in real-life (Dominguez, Cannella, & Framinan, 2014; Dominguez, Framinan, & Cannella, 2014). To present an insight of BWE measure in different supply chain networks (SCNs), a novel mathematical approach is proposed. To deal with this issue, a new network data envelopment analysis (NDEA) model is developed to measure relative BWE of non-serial SCNs and their divisions. Our proposed model is based on slacks-based measure (SBM) model. Since bullwhip effect is undesirable, worst-practice frontier (WPF) approach is considered. Accordingly, a new network worst practice SBM model with undesirable outputs is developed to calculate BWE of non-serial SCNs. In addition, BWE of each division is computed. Finally, a case study in pharmaceutical industry validates applicability of the proposed model.

[1]  Stephen M. Disney,et al.  The impact of information enrichment on the Bullwhip effect in supply chains: A control engineering perspective , 2004, Eur. J. Oper. Res..

[2]  Frank Y. Chen,et al.  Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information.: The Impact of Forecasting, Lead Times, and Information. , 2000 .

[3]  Reza Farzipoor Saen,et al.  Using data envelopment analysis for estimating energy saving and undesirable output abatement: a case study in the Organization for Economic Co-Operation and Development (OECD) countries , 2015 .

[4]  P. Wangphanich,et al.  Analysis of the bullwhip effect in multi-product, multi-stage supply chain systems–a simulation approach , 2010 .

[5]  Hau L. Lee,et al.  Information distortion in a supply chain: the bullwhip effect , 1997 .

[6]  J. Sterman.  Misperceptions of feedback in dynamic decision making , 1989 .

[7]  P. K. Bagchi,et al.  Understanding the causes of the bullwhip effect in a supply chain , 2007 .

[8]  Chong Li,et al.  Controlling the bullwhip effect in a supply chain system with constrained information flows , 2013 .

[9]  D. Simchi-Levi,et al.  The Bullwhip Effect: Managerial Insights on the Impact of Forecasting and Information on Variability in a Supply Chain , 1999 .

[10]  H. T. Luong,et al.  A measure of the bullwhip effect in supply chains with stochastic lead time , 2008 .

[11]  Ralf W. Seifert,et al.  Quantifying the bullwhip effect using two-echelon data: A cross-industry empirical investigation , 2016 .

[12]  J. Fransoo,et al.  Measuring the bullwhip effect in the supply chain , 2000 .

[13]  Marc Lambrecht,et al.  Controlling bullwhip and inventory variability with the golden smoothing rule , 2007 .

[14]  Chiang Kao,et al.  Efficiency decomposition in network data envelopment analysis: A relational model , 2009, Eur. J. Oper. Res..

[15]  Young Hae Lee,et al.  The value of information sharing in a supply chain with seasonal demand process , 2010, The 40th International Conference on Computers & Indutrial Engineering.

[16]  Kaoru Tone,et al.  Network DEA: A slacks-based measure approach , 2009, Eur. J. Oper. Res..

[17]  Stephen Michael Disney,et al.  The effect of vendor managed inventory (VMI) dynamics on the Bullwhip Effect in supply chains , 2003 .

[18]  Robin De Keyser,et al.  Quantifying and mitigating the bullwhip effect in a benchmark supply chain system by an extended prediction self-adaptive control ordering policy , 2015, Comput. Ind. Eng..

[19]  Necmi Kemal Avkiran,et al.  Sensitivity analysis of network DEA: NSBM versus NRAM , 2012, Appl. Math. Comput..

[20]  Jose M. Framiñan,et al.  On bullwhip-limiting strategies in divergent supply chain networks , 2014, Comput. Ind. Eng..

[21]  Elena Ciancimino,et al.  On the Bullwhip Avoidance Phase: supply chain collaboration and order smoothing , 2010 .

[22]  Richard D. Metters,et al.  Quantifying the bullwhip effect in supply chains , 1997 .

[23]  R. Färe,et al.  Intertemporal Production Frontiers: With Dynamic DEA , 1996 .

[24]  Christopher S. Tang,et al.  An EOQ model for MRO customers under stochastic price to quantify bullwhip effect for the manufacturer , 2014 .

[25]  Dean C. Chatfield,et al.  Underestimating the bullwhip effect: a simulation study of the decomposability assumption , 2013 .

[26]  S. Disney,et al.  Reducing the bullwhip effect: Looking through the appropriate lens , 2007 .

[27]  Terry P. Harrison,et al.  The Bullwhip Effect—Impact of Stochastic Lead Time, Information Quality, and Information Sharing: A Simulation Study , 2004 .

[28]  K. Matthews.  Risk Management and Managerial Efficiency in Chinese Banks: A Network DEA Framework , 2011 .

[29]  Kaoru Tone,et al.  A slacks-based measure of super-efficiency in data envelopment analysis , 2001, Eur. J. Oper. Res..

[30]  William L. Weber,et al.  A slacks-based inefficiency measure for a two-stage system with bad outputs , 2010 .

[31]  Christopher S. Tang,et al.  The Incremental Bullwhip Effect of Operational Deviations in an Arborescent Supply Chain with Requirements Planning , 2011 .

[32]  Hashem Omrani,et al.  A performance evaluation model for supply chain of shipping company in Iran: an application of the relational network DEA , 2016 .

[33]  Nallan C. Suresh,et al.  An empirically-simulated investigation of the impact of demand forecasting on the bullwhip effect: Evidence from U.S. auto industry , 2016 .

[34]  Stephen M. Disney,et al.  On Replenishment Rules, Forecasting, and the Bullwhip Effect in Supply Chains , 2008, Found. Trends Technol. Inf. Oper. Manag..

[35]  J. Forrester.  Industrial Dynamics: A Major Breakthrough for Decision Makers , 2012 .

[36]  Yanfeng Ouyang,et al.  The bullwhip effect in supply chain networks , 2010, Eur. J. Oper. Res..

[37]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[38]  Susmita Bandyopadhyay,et al.  A review of the causes of bullwhip effect in a supply chain , 2011 .

[39]  Reza Farzipoor Saen,et al.  Assessing sustainability of supply chains by double frontier network DEA: A big data approach , 2017, Comput. Oper. Res..

[40]  R. Färe,et al.  PRODUCTIVITY AND INTERMEDIATE PRODUCTS: A FRONTIER APPROACH , 1995 .

[41]  S Eilon.  Management Science: An Anthology Volumes I–III , 1997 .

[42]  Hong Yan,et al.  Network DEA model for supply chain performance evaluation , 2011, Eur. J. Oper. Res..

[43]  W. Cooper,et al.  Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software , 1999 .

[44]  Stephen M. Disney,et al.  Measuring and avoiding the bullwhip effect: A control theoretic approach , 2003, Eur. J. Oper. Res..

[45]  Cheng-Li Chen,et al.  The worst-practice DEA model with slack-based measurement , 2009, Comput. Ind. Eng..

[46]  Stephen M. Disney,et al.  Taming the bullwhip effect whilst watching customer service in a single supply chain echelon , 2006, Eur. J. Oper. Res..

[47]  D. Simchi-Levi,et al.  The impact of exponential smoothing forecasts on the bullwhip effect , 2000 .

[48]  J. Framiñan,et al.  Serial vs. divergent supply chain networks: a comparative analysis of the bullwhip effect , 2014 .

[49]  Chiang Kao,et al.  Network data envelopment analysis: A review , 2014, Eur. J. Oper. Res..

[50]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[51]  Ying-Ming Wang,et al.  Measuring Malmquist productivity index: A new approach based on double frontiers data envelopment analysis , 2011, Math. Comput. Model..

[52]  Mu-Chen Chen,et al.  Evaluating the cross-efficiency of information sharing in supply chains , 2010, Expert Syst. Appl..

[53]  Ricardo Lüders,et al.  Order-up-to-level policy update procedure for a supply chain subject to market demand uncertainty , 2017, Comput. Ind. Eng..

[54]  Alireza Amirteimoori,et al.  Un-desirable factors in multi-component performance measurement , 2005, Appl. Math. Comput..

[55]  Kaoru Tone,et al.  Dynamic DEA with network structure: A slacks-based measure approach , 2013 .

[56]  Jan Olhager,et al.  Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration , 2012 .

[57]  Hirofumi Fukuyama,et al.  Estimating two-stage network Slacks-based inefficiency: An application to Bangladesh banking , 2013 .

[58]  Xun Wang,et al.  The bullwhip effect: Progress, trends and directions , 2016, Eur. J. Oper. Res..

[59]  Madjid Tavana,et al.  A new network epsilon-based DEA model for supply chain performance evaluation , 2013, Comput. Ind. Eng..

[60]  Jose M. Framiñan,et al.  Metrics for bullwhip effect analysis , 2013, J. Oper. Res. Soc..

[61]  Ming-Miin Yu,et al.  Assessment of airport performance using the SBM-NDEA model , 2010 .

[62]  Tzu-Yu Lin,et al.  Using independent component analysis and network DEA to improve bank performance evaluation , 2013 .

[63]  Reza Farzipoor Saen,et al.  Developing a worst practice DEA model for selecting suppliers in the presence of imprecise data and dual-role factor , 2012, Int. J. Appl. Decis. Sci..

[64]  Yeong-Dae Kim,et al.  A measure of bullwhip effect in supply chains with a mixed autoregressive-moving average demand process , 2008, Eur. J. Oper. Res..

[65]  Hirofumi Fukuyama,et al.  Production , Manufacturing and Logistics Identifying the efficiency status in network DEA , 2012 .

[66]  Terry P. Harrison,et al.  Quantifying the bullwhip effect in a supply chain with stochastic lead time , 2006, Eur. J. Oper. Res..

[67]  Joe Zhu,et al.  DEA models for supply chain efficiency evaluation , 2006, Ann. Oper. Res..

[68]  Pablo A. Miranda,et al.  A simulation model of a coordinated decentralized supply chain , 2015, Int. Trans. Oper. Res..

[69]  Juan R. Trapero,et al.  A novel time-varying bullwhip effect metric: An application to promotional sales , 2016 .

[70]  Reza Farzipoor Saen,et al.  Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement , 2015 .

[71]  Jose M. Framinan,et al.  The impact of the supply chain structure on bullwhip effect , 2015 .

[72]  Francesco Costantino,et al.  The impact of information sharing on ordering policies to improve supply chain performances , 2015, Comput. Ind. Eng..

[73]  Yanfeng Ouyang,et al.  Characterization of the Bullwhip Effect in Linear, Time-Invariant Supply Chains: Some Formulae and Tests , 2006, Manag. Sci..

[74]  Majid Azadi,et al.  A novel network data envelopment analysis model for evaluating green supply chain management , 2014 .