Exploring the relationship between marketing and operations: Neural network analysis of marketing decision impacts on delivery performance

Abstract This study has the objective of exploring the relationship between two functional areas: marketing and operations. This relationship has been the focus of attention of other scholars, but much remains to be done in terms of providing a better understanding of it. To help fill this gap, we analyze the impact of marketing decisions on delivery performance. More specifically, we seek to know how sales, sellers, promotion and other variables that characterize marketing decisions impact delivery performance. We collected data about marketing decisions and delivery performance for a period of 30 months in a large manufacturing company in Brazil. We performed an artificial neural network analysis to assess how these marketing decision variables impact delivery performance variables in the company analyzed. Results show that seller characteristics are the variables that have the greatest impact on the performance of delivery operations.

[1]  Barry A. Wray,et al.  Using Neural Network Analysis to Evaluate Buyer‐Seller Relationships , 1994 .

[2]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[3]  Christopher S. Tang.  A Review of Marketing-Operations Interface Models: From Co-Existence to Coordination and Collaboration , 2010 .

[4]  Pedro Paulo Balestrassi,et al.  Design of experiments on neural network's training for nonlinear time series forecasting , 2009, Neurocomputing.

[5]  Cecil C. Bozarth,et al.  The impact of market requirements focus and manufacturing characteristics focus on plant performance , 1997 .

[6]  Sunil Vadera,et al.  A survey of AI in operations management from 2005 to 2009 , 2011 .

[7]  Manoj K. Malhotra,et al.  Spanning the continuum between marketing and operations , 2002 .

[8]  Amit Kumar Gupta,et al.  Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression , 2010 .

[9]  U. Karmarkar.  Integrative Research in Marketing and Operations Management , 1996 .

[10]  Mu-Chen Chen,et al.  Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks , 2012 .

[11]  C. W. Lim,et al.  Predicting the effects of physician-directed promotion on prescription yield and sales uptake using neural networks , 2005 .

[12]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[13]  Mukta Paliwal,et al.  Neural networks and statistical techniques: A review of applications , 2009, Expert Syst. Appl..

[14]  Luiz João Corrar,et al.  Análise multivariada para os cursos de administração, ciências contábeis e economia , 2007 .

[15]  Chris J. Piper,et al.  Value creation through enriched marketing–operations interfaces: an empirical study in the printed circuit board industry , 2002 .

[16]  Victoria L. Crittenden,et al.  Reducing conflict between marketing and manufacturing , 1993 .

[17]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[18]  P. Kotler,et al.  Marketing management : analysis, planning, and control , 1973 .

[19]  W. Cooley,et al.  Multivariate Data Analysis. , 1973 .

[20]  Albert Wenben Lai,et al.  Consumer Values, Product Benefits and Customer Value: a Consumption Behavior Approach , 1995 .

[21]  R. Yin.  Case Study Research: Design and Methods , 1984 .

[22]  Diane A. Mollenkopf,et al.  Creating value through returns management: Exploring the marketing–operations interface , 2011 .

[23]  Frederick Kaefer,et al.  A neural network application to consumer classification to improve the timing of direct marketing activities , 2005, Comput. Oper. Res..

[24]  Sovan Lek,et al.  Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .

[25]  J. Miltenburg.  Setting manufacturing strategy for a factory-within-a-factory , 2008 .

[26]  Barry A. Wray,et al.  Determinants of relationship quality: An artificial neural network analysis , 1996 .

[27]  Wei Wei,et al.  Testing the generalization of artificial neural networks with cross-validation and independent-validation in modelling rice tillering dynamics , 2005 .

[28]  Victor R. Prybutok,et al.  Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations , 2000, Eur. J. Oper. Res..

[29]  Aleda V. Roth,et al.  Research Paper Series Graduate School of Business Stanford University Why Should Marketing and Manufacturing Work Together? Some Exploratory Empirical Results Why Should Marketing and Manufacturing Work Together? Some Exploratory Empirical Results Why Should Marketing and Manufacturing Work Together , 2022 .

[30]  Steven Lysonski,et al.  Competitive Positioning in International Logistics: Identifying a System of Attributes Through Neural Networks and Decision Trees , 2007 .