Decision support system for industrial logistics distribution cost minimization

Modern manufacturing companies are currently seeking to improve time response to rapidly changing customer needs by employing decision support, expert or other knowledge engineering systems. Industrial logistics cost reduction is a concern of both manufacturing engineers and industrial logistics engineers. Manufacture in transit distribution network case is analyzed when certain carriers collect raw materials from several sources (manufacturing facilities, distribution centers, warehouses) and transport to manufacturing facility where products are produced Afterwards, output products are picked up and transported to either final customer or to some warehouse where assembly operations are performed Designing of manufacture-in-transit networks is extremely important for companies expanding their business to international level and executing cross-border operations. The research is aimed at two objectives that are the analysis of manufacture in transit distribution networks and formation of decision support system (DSS) framework which minimizes logistics network distribution costs. The input information of DSS is stored in database files and includes supplier location, quantity and types of raw materials, profiles, dimensions, price, qualitative requirements, plant location, supply deadlines. The proposed model of DSS evaluates industrial logistics distribution costs dependency on input values in common with alternatives of facility location and transportation channels. The DSS incorporates decision modeling structures such as influence diagrams and decision trees enabling to precisely assess and choose the best alternative solution evaluating all uncertainties of industrial logistics distribution cost minimization problem structures. The proposed DSS framework supports industrial logistics engineers with information flows and decision making techniques aimed at industrial logistics cost distribution minimization issue. Future research would determine implementation sensitivity analysis of the DSS framework applying it in mass and batch production companies. Furthermore, modeling of system and simulation will be considered.