Dynamic Bid Pricing for an Optimized Resource Utilization in Small and Medium Sized Enterprises

Abstract Sales revenues of small and medium-sized enterprises are subject to seasonal fluctuation. This leads often to overloaded or underutilized manufacturing resources. Either way, this results in revenue losses. Therefore, companies have to optimize their resource utilization. This paper describes a new methodology for a dynamic bid price system by using correlations of revenue management in production planning to level the resource utilization. The methodology supports especially small and medium-sized enterprises, which are often affected by additional work shifts across seasons. Furthermore, the proposed method points out dependencies between costs and capacity to avoid financial losses. The method has been developed and is being tested in collaboration with two small and medium-sized enterprises.

[1]  Brian G. Kingsman,et al.  Modelling input-output workload control for dynamic capacity planning in production planning systems , 2000 .

[2]  Brian G. Kingsman,et al.  Responding to customer enquiries in make-to-order companies Problems and solutions , 1996 .

[3]  Dan Zhang,et al.  Dynamic Pricing for Network Revenue Management: A New Approach and Application in the Hotel Industry , 2017, INFORMS J. Comput..

[4]  Thomas Volling,et al.  Revenue Management in Make-To-Order Manufacturing: Case Study of Capacity Control at ThyssenKrupp VDM , 2010 .

[5]  Shingo Komatsu,et al.  Demand forecasting for production planning in remanufacturing , 2015 .

[6]  Thomas Spengler,et al.  A two-stage bid-price control for make-to-order revenue management , 2012, Comput. Oper. Res..

[7]  Itir Z. Karaesmen,et al.  Revenue management: Models and methods , 2008, 2008 Winter Simulation Conference.

[8]  T. Fiig,et al.  Dynamic pricing – The next revolution in RM? , 2016 .

[9]  Paolo Renna Dynamic pricing of excess capacity in production networks by fuzzy logic , 2016, Int. J. Comput. Integr. Manuf..

[10]  Stefan Rehkopf,et al.  Revenue Management Konzepte zur Entscheidungsunterstützung bei der Annahme von Kundenaufträgen , 2005 .

[11]  Hermann Lödding Handbook of Manufacturing Control , 2013 .

[12]  Chen-Yang Cheng,et al.  Dynamic cost forecasting model based on extreme learning machine - A case study in steel plant , 2016, Comput. Ind. Eng..

[13]  Cristovao Silva,et al.  Three decades of workload control research: a systematic review of the literature , 2011 .