A stochastic linear programming approach for the production, distribution and inventory systems of the nigerian bottling company (nbc):

Thesis Overview

<p> <strong>INTRODUCTION</strong><br><strong>1.1 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</strong><strong>BACKGROUND OF THE STUDY</strong><br>For nearly three decades, multi-echelon supply chains have constituted a focal research area. As a result, models for the control of supply chain of several forms and operating disciplines are now available. Due to the shear volume and variety of these models, surveys of varying scope or focus often appear (Inderfurth, 1994; Van Houtum et al., 1996; Diks et al., 1996; de Kok and Fransoo, 2003 ; Mula et al., 2006). Manufacturing and Production companies are profit-oriented; as a result, a well-defined mathematical model should be established and formulated, so that solving this model can generate an optimal strategy. In recent years, the mathematical theory of production, inventory and distribution has been extended to cover many of the situations that arise in practice. Mathematical programming has been applied frequently and successfully to a wide variety of production, inventory and distribution problems for a variety of industries. For example, Camm and Moni (1997) used integer programming and network to improve Procter and Gambleâs distribution system; Arntzen and Luka (1995) used mixed integer linear programming to determine Digital Equipment Corporationâs distribution strategy: Martin et al (1993) used linear programming to assist in distribution operations for Libbey-Owens-Ford; Robinson et al (1993) used optimization in designing a distribution decision support system for Dow Brands, Inc; Mehring and Gutterman (1990). <br></p>