Analyzing supply chain operation models with the PC-algorithm and the neural network

Research highlights? We study the relations and magnitudes of influences among key factors in a supply chain models. ? Our method is a two-stage approach using (i) the PC-algorithm and (ii) the neural network. ? Using (i), we obtain the skeleton graph describing relations among the factors. ? Internal operation and collective efficacy are deemed the most critical factors based on the graph. ? Using (ii), we quantify the relative importance of other factors in predicting the critical factors. Understanding how the various factors in a supply chain contribute to the overall performance of its operation has become an important topic in management science research nowadays. In this paper, we propose and apply a two-stage methodology to an industrial survey data set to investigate relations among the key factors in a supply chain model. Precisely, we use the PC-algorithm to discover the connectivity relation among the factors of interest in the supply chain model. Critical factors in the model are then identified, and we then utilize the neural network to quantify the relative importance of some of the factors in predicting the critical factors. An advantage of our proposed method is that it frees up the researcher from making subjective decisions in his or her analysis, for example, from the needs of specifying plausible initial path models required in a structural equation modeling analysis (which is usually used in business and management research) and of selecting factors for the subsequent predictive modeling. We envision that the analysis results can aid a decision maker in optimizing the system performance by suggesting to the decision maker which ones of the factors are the important ones that he or she should devote more resources and efforts on.

[1]  Johan Frishammar,et al.  Managing External Information in Manufacturing Firms : The Impact on Innovation Performance , 2005 .

[2]  V. Sridharan,et al.  Alternative approaches for reducing schedule instability in multistage manufacturing under demand uncertainty , 1995 .

[3]  Vicki L. Goodwin,et al.  Meta-Analysis of the Antecedents of Personal Goal Level and of the Antecedents and Consequences of Goal Commitment , 1992 .

[4]  Robert Biernacki,et al.  Modeling of manufacturing processes by learning systems: The naïve Bayesian classifier versus artificial neural networks , 2005 .

[5]  Qiyuan Jiang,et al.  LOT-SIZING RULE AND FREEZING THE MASTER PRODUCTION SCHEDULE UNDER CAPACITY CONSTRAINT AND DETERMINISTIC DEMAND , 2009 .

[6]  Michael Y. Hu,et al.  Explaining consumer choice through neural networks: The stacked generalization approach , 2003, Eur. J. Oper. Res..

[7]  R. M. Monczka,et al.  Global Sourcing: A Development Approach , 1991 .

[8]  Dean H. Kropp,et al.  Alternative approaches to schedule instability: a comparative analysis , 1987 .

[9]  Angappa Gunasekaran,et al.  The role of forecasting on bullwhip effect for E-SCM applications , 2008 .

[10]  L. Ellram.  A Managerial Guideline for the Development and Implementation of Purchasing Partnerships , 1991 .

[11]  Angappa Gunasekaran,et al.  A strategic model for agile virtual enterprise partner selection , 2007 .

[12]  Jinxing Xie,et al.  Freezing the master production schedule under single resource constraint and demand uncertainty , 2003 .

[13]  Wen Pei,et al.  Fuzzy neural based importance-performance analysis for determining critical service attributes , 2009, Expert Syst. Appl..

[14]  Peter Bühlmann,et al.  Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm , 2007, J. Mach. Learn. Res..

[15]  James D. Wright,et al.  Handbook of Survey Research. , 1985 .

[16]  P. Spirtes,et al.  Causation, Prediction, and Search, 2nd Edition , 2001 .

[17]  Gianmario Verona.  A Resource-Based View of Product Development , 1999 .

[18]  S. Vickery,et al.  The effects of an integrative supply chain strategy on customer service and financial performance: an analysis of direct versus indirect relationships , 2003 .

[19]  Yi-Chang Chiu,et al.  Determinants of guest loyalty to international tourist hotels—a neural network approach , 2002 .

[20]  Denis Royston Towill,et al.  Integrating the automotive supply chain: where are we now? , 2002 .

[21]  Kit Yan Chan,et al.  A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach , 2009, Expert Syst. Appl..

[22]  Soushan Wu,et al.  Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..

[23]  Fabrizio Salvador,et al.  Supply chain interactions and time‐related performances: An operations management perspective , 2001 .

[24]  Joseph W.K. Chan,et al.  Competitive strategies and manufacturing logistics: An empirical study of Hong Kong manufacturers , 2005 .

[25]  K. Eisenhardt,et al.  PRODUCT DEVELOPMENT: PAST RESEARCH, PRESENT FINDINGS, AND FUTURE DIRECTIONS , 1995 .

[26]  Tor Guimaraes,et al.  Integrating artificial neural networks with rule-based expert systems , 1994, Decis. Support Syst..

[27]  R. Shibata,et al.  PARTIAL CORRELATION AND CONDITIONAL CORRELATION AS MEASURES OF CONDITIONAL INDEPENDENCE , 2004 .

[28]  Andrew H. Sung,et al.  Ranking importance of input parameters of neural networks , 1998 .

[29]  G. David Garson,et al.  Interpreting neural-network connection weights , 1991 .

[30]  I Nyoman Pujawan,et al.  Schedule nervousness in a manufacturing system: a case study , 2004 .

[31]  Xiande Zhao,et al.  Freezing the master production schedule for material requirements planning systems under demand uncertainty , 1993 .

[32]  Dean H. Kropp,et al.  A comparison of strategies to dampen nervousness in MRP systems , 1986 .

[33]  Botond Kádár,et al.  Stability-oriented evaluation of rescheduling strategies, by using simulation , 2007, Comput. Ind..

[34]  Gerald Heisig,et al.  Comparison of (s,S) and (s,nQ) inventory control rules with respect to planning stability , 2001 .

[35]  V. Valsamakis,et al.  Supplier‐Customer Relationships and Company Performance , 1998 .

[36]  Eve D. Rosenzweig,et al.  The influence of an integration strategy on competitive capabilities and business performance: An exploratory study of consumer products manufacturers , 2003 .

[37]  E. A. Locke,et al.  A theory of goal setting & task performance , 1990 .

[38]  C. Armistead,et al.  The Impact of Supply Chain Integration on Operating Performance , 1993 .

[39]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[40]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[41]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[42]  Don A. Dillman,et al.  Chapter 10 – Mail and Other Self-Administered Questionnaires1 , 1983 .

[43]  Sita Bhaskaran.  Simulation Analysis of a Manufacturing Supply Chain , 1998 .

[44]  Wen-Chin Chen,et al.  Back-propagation neural network based importance-performance analysis for determining critical service attributes , 2008, Expert Syst. Appl..

[45]  Julian D. Olden,et al.  Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .

[46]  Edwin A. Locke,et al.  Effects of Leader Role, Team-Set Goal Difficulty, Efficacy, and Tactics on Team Effectiveness , 1997 .