An integrated supply chain model with dynamic flow and replenishment requirements

Integration and coordination of the informational and logistical functions of the supply chain have the potential to increase a company's competitiveness. In this paper, an integrated supply chain framework is developed that considers three main dimensions of information: customer demand, transportation, and inventory data. These dynamic supply chain data are integrated in an algorithm capable of producing high-quality, minimal cost solutions for the integrated supply chain system. The integrated supply chain model's primary goal is to react effectively and quickly to supply chain disruptions and changes, thereby adding unique flexibility to current supply chain decision support systems. The performance of the proposed algorithm is tested in a dynamic environment by embedding it in the decision-making framework of a supply chain simulation model. Model results demonstrate the algorithm's ability both to improve system performance and to reduce total supply chain costs.

[1]  Khalid Bichou,et al.  The ISPS Code and The Cost of Port Compliance: An Initial Logistics and Supply Chain Framework for Port Security Assessment and Management , 2004 .

[2]  J. G. Saw,et al.  Choosing the Optimum Lot Size for a Reordering Policy with Instantaneous Emergency Deliveries , 1972 .

[3]  Heung-Suk Hwang,et al.  Design of supply-chain logistics system considering service level , 2002 .

[4]  Ujval Gandhi.  Vendor Managed Inventory: A new approach to supply chain management , 2003 .

[5]  Vaidyanathan Jayaraman,et al.  Transportation, facility location and inventory issues in distribution network design: An investigation , 1998 .

[6]  Sven Axsäter,et al.  Exact Analysis of Continuous Review (R, Q) Policies in Two-Echelon Inventory Systems with Compound Poisson Demand , 2000, Oper. Res..

[7]  Forrest B. Green,et al.  Managing the unmanageable: integrating the supply chain with new developments in software , 2001 .

[8]  Arnoldo C. Hax,et al.  Production and inventory management , 1983 .

[9]  L. Bai.  Inventory Control and Demand DistributionCharacterization , 2005 .

[10]  Wallace J. Hopp,et al.  Factory physics : foundations of manufacturing management , 1996 .

[11]  Hong Yan,et al.  Benefits of information sharing with supply chain partnerships , 2001, Ind. Manag. Data Syst..

[12]  Frank Grange.  Challenges in modeling demand for inventory optimization of slow-moving items , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[13]  David F. Pyke,et al.  Inventory management and production planning and scheduling , 1998 .

[14]  Nilay Shah,et al.  Object-oriented dynamic supply-chain modelling incorporated with production scheduling , 2006, Eur. J. Oper. Res..

[15]  Ross Robinson,et al.  Port-Oriented Landside Logistics in Australian Ports: A Strategic Framework , 2006 .

[16]  Chu‐Hua Kuei,et al.  Designing and Managing the Supply Chain Concepts, Strategies, and Case Studies , 2000 .

[17]  Russell D. Meller,et al.  The interaction of location and inventory in designing distribution systems , 2000 .

[18]  Michael Roe,et al.  Towards Supply Chain Integration Through Multimodal Transport in Developing Economies: The Case of Bangladesh , 2005 .

[19]  Luca Bertazzi,et al.  Deterministic Order-Up-To Level Policies in an Inventory Routing Problem , 2002, Transp. Sci..

[20]  Togar M. Simatupang,et al.  The knowledge of coordination for supply chain integration , 2002, Bus. Process. Manag. J..

[21]  Scott J. Mason,et al.  INTEGRATING THE WAREHOUSING AND TRANSPORTATION FUNCTIONS OF THE SUPPLY CHAIN , 2003 .

[22]  N. M. Steiger,et al.  MODELING AND ANALYSIS OF A GENERIC CROSS-DOCKING FACILITY , 2005 .

[23]  Saif Benjaafar,et al.  Demand Allocation in Multiple-Product, Multiple-Facility Make-to-Stock Production Systems , 2004, Manag. Sci..