Impacts of marketing decisions on delivery performance and flexibility of the operations area

PurposeThe purpose of this paper is to investigate the relations in the Marketing/Operations interface through the analysis of data of the operational reality of a Brazilian company with a low technological intensity. The study aims to quantify and determine the impacts of marketing decisions on delivery performance and on flexibility of the operations area.Design/methodology/approachA longitudinal case study was conducted and the variables used in the model were derived from established theories and were evaluated with artificial neural networks. The case of a food manufacturing company was selected to reflect the relations in the marketing/operations interface of a low technological intensity enterprise.FindingsThe results show that the decisions on Place/Channel, Price and Product dimensions of marketing exert a significant impact on flexibility and delivery performance of the operation area.Research limitations/implicationsThe findings of the case study cannot be generalised and the outcomes are specific to just one firm. However, the approach lends itself to replication, particularly within low technological intensity companies.Originality/valuePrior studies have focussed on coordination among functional areas as marketing and operations at higher levels of abstraction. The study contemplate empirical propositions through the data analysis of a company with a low technological intensity that can be used to improve managers' decisions and alignment in the Marketing/Operation Interface.

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

[2]  Dharma Lesmono,et al.  On the evaluation of product customization strategies in a vertically differentiated market , 2013 .

[3]  Arch G. Woodside,et al.  Mapping strategic decision-making in cross-functional contexts , 2006 .

[4]  K. R. Kumar,et al.  Resource allocation to defensive marketing and manufacturing strategies , 1996 .

[5]  Ana Beatriz Lopes de Sousa Jabbour,et al.  Alignment of operations strategy: exploring the marketing interface , 2013, Ind. Manag. Data Syst..

[6]  Evangelos L. Psomas,et al.  A comparative analysis of competitive priorities and business performance between manufacturing and service firms , 2017 .

[7]  Julia Miyaoka,et al.  Managing Production and Distribution for Supply Chains in the Processed Food Industry , 2013 .

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

[9]  Vidyaranya B. Gargeya,et al.  Alignment between business environment characteristics, competitive priorities, supply chain structures, and firm business performance , 2009 .

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

[11]  Christopher A. Voss,et al.  Case research in operations management , 2002 .

[12]  T. Eng,et al.  International R&D partnerships and intrafirm R&D–marketing–production integration of manufacturing firms in emerging economies , 2014 .

[13]  Stephen Eldridge,et al.  Improving productivity through strategic alignment of competitive capabilities , 2019, International Journal of Productivity and Performance Management.

[14]  Daniel Pacheco Lacerda,et al.  Economic measuring of losses derived from inventory management at an oil refinery , 2020 .

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

[16]  P. Nath,et al.  The impact of marketing capability, operations capability and diversification strategy on performance: A resource-based view , 2010 .

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

[18]  M. Szwejczewski,et al.  Performance trade‐offs in manufacturing plants , 1997 .

[19]  Daniel Pacheco Lacerda,et al.  Do the improvement programs really matter? An analysis using data envelopment analysis , 2018, BRQ Business Research Quarterly.

[20]  Michal Tkác,et al.  Artificial neural networks in business: Two decades of research , 2016, Appl. Soft Comput..

[21]  Daniel Pacheco Lacerda,et al.  Exploratory analysis of the variables prevailing on the effects of product modularization on production volume and efficiency , 2017 .

[22]  Kripa Shanker,et al.  Integrating operations and marketing decisions using delayed differentiation of products and guaranteed delivery time under stochastic demand , 2014, Eur. J. Oper. Res..

[23]  Sakun Boon-itt,et al.  Marketing-operations alignment: A review of the literature and theoretical background , 2018 .

[24]  Jifeng Mu,et al.  Dynamic Capability and Firm Performance: The Role of Marketing Capability and Operations Capability , 2017, IEEE Transactions on Engineering Management.

[25]  Anthony Arundel,et al.  Exploring innovation success recipes in low-technology firms using fuzzy-set QCA , 2016 .

[26]  Jifeng Mu.  Marketing capability, organizational adaptation and new product development performance☆ , 2015 .

[27]  Y. Hsiao,et al.  Branding vs contract manufacturing: capability, strategy, and performance , 2013 .

[28]  Özalp Özer,et al.  Integrating Dynamic Time-to-Market, Pricing, Production and Sales Channel Decisions , 2015, Eur. J. Oper. Res..

[29]  Wantao Yu,et al.  The impacts of marketing and operations capabilities on financial performance in the UK retail sector: A resource-based perspective , 2014 .

[30]  W. L. Berry,et al.  ALIGNING MARKETING AND MANUFACTURING STRATEGIES WITH THE MARKET , 1999 .

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

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

[33]  Sununta Siengthai,et al.  Competitive strategies and firm performance: the mediating role of performance measurement , 2013 .

[34]  P. Wanke,et al.  A performance analysis of Brazilian public health: TOPSIS and neural networks application , 2018, International Journal of Productivity and Performance Management.

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

[36]  Chih-Hsuan Wang,et al.  Using quality function deployment for collaborative product design and optimal selection of module mix , 2012, Comput. Ind. Eng..

[37]  Line Dubé,et al.  Rigor in Information Systems Positivist Case Research: Current Practices , 2003, MIS Q..

[38]  M. Terziovski,et al.  The impact of strategic alignment and responsiveness to market on manufacturing firm's performance , 2016 .

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

[40]  Susu Cheng,et al.  Dynamic Control of Product Innovation, Advertising Effort, and Strategic Transfer-Pricing in a Marketing-Operations Interface , 2019, Mathematical Problems in Engineering.

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

[42]  Cecil C. Bozarth,et al.  Factory focus: Segmenting markets from an operations perspective , 1991 .

[43]  I-Hsuan Hong,et al.  Equilibrium pricing and lead time decisions in a competitive industry , 2012 .

[44]  Maria Teresa Cuomo,et al.  The (r)evolution of wine marketing mix: From the 4Ps to the 4Es , 2016 .

[45]  Daniel Pacheco Lacerda,et al.  Exploring the relationship between marketing and operations: Neural network analysis of marketing decision impacts on delivery performance , 2014 .

[46]  Daniel Pacheco Lacerda,et al.  Effects of product modularity on productivity: an analysis using data envelopment analysis and Malmquist index , 2020 .

[47]  Z. Rahman,et al.  Marketing capabilities and firm performance: literature review and future research agenda , 2015 .

[48]  Moxi Song,et al.  Information sharing, operations capabilities, market intelligence responsiveness and firm performance , 2018, Baltic Journal of Management.

[49]  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 .

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