INTEGRATING COMPREHENSIVE INDUSTRIAL RAW MATERIAL DELIVERY PLANNING AND PRODUCT-SERVICE SYSTEM INVENTORY CONTROL
Keywords:Product Service System (PSS) Inventory Control; Industrial Raw Material Delivery Planning.
Product Service Systems (PSS) are integrated product and service offerings that provide superior customer value in industrial applications for planning, inventory control, delivery planning and a use jointly defined. Delivery planning is a particular challenge in providing personal security services. A good delivery planning is can minimize problem from great complexity subject to various constraints within a large solution space. Organizations offering PSS services or industrial services suffer from decision support to put in place robust capacity planning strategies in highly dynamic and uncertain environments. This paper presents a simulation-based approach to capacity planning. The goal of this project to identify potential routes of integration between two factors deliveries planning and PSS inventory control that could improve the delivery performance based on the raw material industry. Emphasis is placed on capacity planning for PSS inventory control and delivery planning. By using IDEFØ (Integration Definition for Function) generic modelling and Delmia Quest simulation software in this research able to achieve the time required. The advantage of IDEFØ includes: a well-tested language and a comprehensive systems modelling technique. The resulting IDEFØ models are well-defined, easy to understand, well-structured, easy to change and use, and can be extended to any depth of detail. Meanwhile, Delmia Quest software will provide real time simulation for production process and generated report for actual operational situation. After a few general considerations on robust capacity planning for the control and delivery of technical support inventory using scenario simulations, the main elements of the agent-based simulation method are presented. The most important parameters given by company A, the control parameters and the performance indicators are discussed and the scenario planning process based on the simulation is described.