Introduction OTD-NET and LAS: Order-to-delivery network simulation and decision support systems in complex production and logistics networks

Abstract

Global Sourcing adds to a company's value by realizing the best prices on a global market and making new markets accessible. However, increasing global orientation of the companies is associated with new challenges in planning and controlling the network processes to obtain a high service level and a guaranteed availability of supply goods. Order-to-Delivery Network simulation (OTD-NET) offers a simulation based approach for gaining insight in global networks. The OTD-NET suite provides services for modelling, simulating and analyzing complete logistic networks including the specifics of the different network participants like suppliers or producers. With the specialized automotive edition, OTD-NET enables a precise specification of complex products considering its configuration, bill of materials and supports an easy analysis of various network strategies like network design, structure and process concepts, supply strategies as well as different production strategies. OTD-NET is developed by Fraunhofer Institute for Material Flow and Logistics in Dortmund (Germany). Within the paper OTD-NET is presented and exemplarily demonstrated by a joint project with Volkswagen. 1 439 978-1-4799-3950-3/13/$31.00 ©2013 IEEE Liebler, Beissert, Motta, and Wagenitzcy and competitiveness depend essentially on a company's ability to purposefully restructure existing processes and organizational structures based on a detailed evaluation of the current status and forecasts of future demands. Hence, it is becoming more and more important to simulate the company or network processes and to evaluate it under a variety of perspectives. A thorough approach to planning and control of value-adding processes across the entire value network is needed to remain competitive and expand existing benefits of a network structure (Arnold et al 2008). The objective of such an approach is• continuous optimization of existing processes and structures through the analysis and evaluation of strategy, structure and process alternatives. • constant improvement of relevant performance and cost characteristics of the production and logistics network like on-time delivery, reduction of stocks and thereby implied costs and shorter lead and delivery times. Looking at the automotive industry, requirements for an adequate planning system to handle complex supply chain network can be derived. Automotive industry is characterized by a broad production network with various participants involved like original equipments manufacturer (OEM), suppliers as well as various logistic service providers to execute transports between the supply chain partners. The automotive industry is characterized by a high product variety; German premium brands in particular offer a wide variety of customer configurable options. Almost every aspect of the car's interior and exterior may be changed or adapted. Meyr (2004) states that cars are available in more than 1030 variants (Meyr 2004). Therefore the number of customer configured build-to-order (BTO) cars increases. On the other hand...

Ensuring Supply for Emergency Services – Modeling Supply Chains with Incomplete Sets of Data

“…OTD NETWORK, which was developed by Fraunhofer IML, is a discreteevent simulation environment designed for modeling, simulation, and analysis of supply chains. Its abstract, object-oriented architecture allows for versatile applications across various industries and a wide range of specific issues [16].…”

Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management

“…With the advent of modern computational methods, the focus has since shifted to construct more complex policies. These can be based on simulation tools, like the static OTD-Net (Liebler et al, 2013) simulator, Monte-Carlo-based discrete event simulators such as SimPy (Scherfke & Lünsdorf, 2009), or optimal control techniques. Within the latter category, Reinforcement Learning (RL) can be used to find dynamic policies that do not require assumptions about the environment.…”