Academic articles

Simulating Recovery Strategies to Enhance the Resilience of a Semiconductor Supply Network


Enhancing supply chain resilience is of vital importance in today’s business to manage and mitigate the risks, especially in the semiconductor industry challenged with intrinsic long cycle times and short product life-cycles. Transferring production from a primary site to an alternative site after a disaster is one of the strategies to ensure resilience of the supply network. In this study, different types of alternative sites with various levels of preparedness are investigated. A discrete-event simulation is used to evaluate their operational and financial impacts under four different disruption scenarios. The simulation outcomes demonstrate unexpected positive benefits of various alternative sites in terms of fast recovery and resilience building.

Optimizing Production Allocation with Simulation in The Fashion Industry: a Multi-Company Case Study


Production Planning and Control (PP&C) has been deeply analyzed in the literature, both in general terms and focusing on specific industries, such as the fashion one. The paper aims to add a contribution in this field presenting an optimization model for the Fashion Supply Chain (FSC), developed considering an interdependent environment composed by a group of focal companies that work with both exclusive and not-exclusive suppliers. The proposed framework will combine simulation and optimization models based on parameters, decision variables, constraints and Objective Functions (OFs) collected through a literature review. The framework has been developed in a parametrical way, in order to fit the peculiarities of the different actors operating along the FSC. The empirical implementation of the framework has been conducted using data coming from fashion companies belonged to the same network, considering rush orders as stochastics events for the scenario analysis and Key Performance Indicators (KPIs) assessment.

On Agent-Based Modeling in Semiconductor Supply Chain Planning


Supply chain (SC) planning in the semiconductor industry is challenged by high uncertainties on the demand side as well as a complex manufacturing process with non-deterministic failure modes on the production side. Understanding the complex interdependencies and processes of a supply chain is essential to realize opportunities and mitigate risks. However, this understanding is not easy to achieve due to the complexity of the processes and the non-deterministic human behavior determining supply chain planning performance. Our paper argues for an agent-based approach to understand and improve supply chain planning processes using an industry example. We give an overview of current work and elaborate on the need for integrating human behavior into the models. Overall, we conclude that agent-based simulation is a valuable method to identify favorable and unfavorable conditions for successful planning.

A Case Study for Simulation and Optimization Based Planning of Production and Logistics Systems


This paper introduces a practical approach for the comprehensive simulation based planning and optimization of the production and logistics of a discrete goods manufacturer. Although simulation and optimization are well-established planning aides in production and logistics, their actual application in the field is still scarce, especially in small and medium-sized enterprises (SMEs). This is largely due to the complexity of the planning task and lack of practically applicable approaches for real-life planning scenarios. This paper provides a case study from the food industry, featuring a comprehensive planning approach based on simulation and optimization. The approach utilizes an offline-coupled multilevel simulation to smooth production and logistics planning via optimization, to optimally configure the production system using discrete-event simulation and to optimize the logistics network utilizing an agent-based simulation. The connected simulation and optimization modules can enhance the production logistics significantly, potentially providing a reference approach for similar industry applications.

A simulation approach for multi-stage supply chain optimization to analyze real world transportation effects


The cost effective management of a supply chain under stochastic influences, e.g. in demand or the replenishment lead time, is a critical issue. In this paper a multi-stage and multi-product supply chain is investigated where each member uses the (s,Q)-policy for inventory management. A bi-objective optimization problem to minimize overall supply chain costs while maximizing service level for retailers is studied. Optimal parameter levels for reorder points and lot sizes are evaluated. In a first step a streamlined analytical solution approach is tested to identify optimal parameter settings. For real applications, this approach neglects the dynamics and interdependencies of the supply chain members. Therefore a simulation-based approach, combining an evolutionary algorithm with simulation, is used for the optimization. The simulation-based approach further enables the modelling of additional real world transportation constraints. The numerical simulation study highlights the potential of simulation-based optimization compared to analytical models for multi-stage multi-product supply chains.

A Simulation-Based Investigation of Freight Transportation Policy Planning and Supply Chains


Regional freight transportation policy planning is a difficult task, as few policy-planners have adequate tools to aid their understanding of how various policy formulations affect this complex, socio-technical system. In this paper, we develop a proof-of-concept model to simulate the impacts of public policies on freight transportation in a simulated region. We use the techniques of multi-disciplinary system design and optimization to analyze the formulation of regional freight transportation policies and examine the relative effects of policies and exogenous forces on the region in order to provide insight into the policy-planning process. Both single objective and multi-objective analysis is performed to provide policy-planners with a clear understanding of the trade-offs made in policy formulation.

Coordination of production and ordering policies undercapacity disruption and product write-off risk: ananalytical study with real-data based simulations of a fastmoving consumer goods company


Performance impacts of ordering and production control policies in the presence of capacity disruptions are studied on the real-life example of a retail supply chain with product perishability considerations. Constraints on product perishability typically result in reductions in safety stock and increases in transportation frequency. Consideration of the production capacity disruption risks may lead to safety stock increases. This trade-off is approached with the help of a simulation model that is used to compare supply chain performance impacts with regard to coordinated and non-coordinated ordering and production control policies.

The Urban Dynamics Educational Simulator: Tutorial for a Tool to Teach Agent-Based and Land Use and Transport Interactions Modeling


This paper describes a simple educational simulation tool to teach about agent-based modeling (ABM) and land use and transportation (LUT) interactions in urbanized regions. The relationship between land use and transportation in urban areas shows a dynamic complexity which is difficult to model with static or very aggregated approaches. Agent-based simulation is becoming a standard to model certain complex systems including LUT interactions. The LUT modeling with Agent-Based simulation has been evolving over the last 20 years and there are some complete models being applied to real metropolitan areas. In this paper, only some of the ingredients of those models are presented in an easy to explain agent-based model that can be used in classes.

Dynamic Recovery Policies for Time-Critical Supply Chains under Conditions of Ripple Effect


We consider time critical supply chains in the Australia dairy industry and re-covery policies in the presence of the ripple effect. Ripple effect is the impact of a dis-ruption on supply chain economic performance and disruption-based scope of changes needed in the supply structures and parameters to preserve the resilience. First, we de-scribe the ripple effect in general and one example of the ripple effect in the dairy supply chain in Australia. Second, we present a model for reactive recovery policies in the dairy supply chain under conditions of the ripple effect and exemplify them on a simulation example. The results of this study can be used in future for comparing proactive and re-active approaches to tackling the ripple effect from resilience and flexibility views.

Simulation-based Single versus Dual Sourcing Analysis in the Supply Chain with Consideration of Capacity Disruptions, Big Data and Demand Patterns


Sourcing strategy analysis in the settings of supply chain flexibility in regard to single vs dual sourcing has been a well explored area over the last two decades. In recent years, single vs dual sourcing analy-sis has been increasingly introduced in supply chain disruption management. Since most of the deci-sion-support models for supply chain sourcing strategy adaptation in the case of disruptions presume real-time information and coordination, the issues of Big Data and business intelligence needs to be included into the consideration. A supply chain simulation model with consideration of capacity dis-ruption and Big Data along with experimental results are presented. Based on both literature analysis and modelling example, managerial insights are derived. A set of sensitivity experiments allows to illustrate the model’s behaviour. The analysis suggest recommendation on using single sourcing, ca-pacity flexibility, and dual sourcing for different combinations of demand and inventory patterns. The paper is concluded by summarizing the most important insights and outlining future research agenda.