Articles

This case study focuses on the simulation of a soon-to-be-implemented automation system within a Walmart Canada warehouse. This new system's aim is more efficient warehouse operations. Many stock-keeping units (SKUs) cannot be sent to retail stores in full case quantities. They are slow movers and would require individual stores to carry excessive inventory.

Breakpack is the process of breaking cases down to individual eaches (pieces) and combining them into mixed SKU cartons. Automating breakpack offers significant labor and quality savings, that are important to ensure efficient warehouse operations, but also a high degree of complexity.

Due to various production and market factors, flexibility is a key point in semiconductor manufacturing supply chain design. However, the increased complexity associated with this flexibility must be effectively managed to leverage the benefits that flexibility provides. The product structure is one of the main factors for enabling the desired result. Product structure representations in the supply chain design include linear, tree, and network. In this paper, the researchers explain the problem by a real case merger where risk and opportunities based on the choice of product structure representation in the supply chain design were relevant and no final solution initially was determined.

Multi-resolution simulation models of a manufacturing system, such as a virtual factory, coupled with simulation-based analytics offer exciting opportunities to manufacturers to exploit the increasing availability of data from their corresponding real factory at different hierarchical levels. A virtual factory simulation model can be maintained as a live representation of the real factory and used to highly accelerate learning from data using simulation-based analytics applications.

This paper proposes a shared infrastructure for a virtual factory simulation-based analytics that can be employed by small and medium enterprises.

In many industrial manufacturing companies, energy has become a major cost factor. Energy aspects are included in the decision-making system of production line planning and control to reduce manufacturing costs. For this priority, the simulation of production line processes requires not only the consideration of logistical and technical production factors but also the integration of time-dependent energy flows. A hybrid (multimethod) simulation shows the complex interactions between material flow and energy usage in production close to reality. This paper presents a simulation approach combining System Dynamics, Discrete-Event and Agent-Based Simulation for energy efficiency analysis in production, considering the energy consumption in the context of planning and scheduling operations.

Fashion is one of the world’s most important industries, driving a significant part of the global economy representing, if it was a country, the seventh-largest GDP in the world in terms of market size. Focusing on the footwear industry, assembly line balancing and sequencing represents one of the most significant challenges fashion companies have to face.

This paper presents the results of a simulation-based optimization framework implementation in the fashion industry. It also highlights the benefits of the use of simulation together with a finite capacity scheduling optimization model. Production planning and scheduling optimization include a scenario analysis that compares production KPIs (in terms of average advance, delay, and resource saturation) related to different scenarios.

This paper addresses the problem of controlling the Work-In-Process (WIP) in semiconductor manufacturing by using a global scheduling approach and manufacturing scheduling software. A WIP balancing strategy is proposed to minimize the product mix variability in terms of throughput and cycle time. This strategy is enforced using a global scheduling optimization model which is formulated as a linear programming model. The global scheduling model is coupled with a generic multi-method simulation model built with manufacturing scheduling software for evaluation purpose.

During recent years, Autonomous Vehicle Storage and Retrieval Systems (AVS/RS) have been widely applied in warehouse optimization to meet the increasing demand for rapid and flexible large-scale storage and retrieval tasks. This paper focuses on the operations control strategies with regard to the conveyor system, rack storage system, and pick-up system in order to maximize the system’s throughput capacity and minimize the storage/retrieval times of items. The study is based on a large-scale shoe manufacturer’s warehouse optimization and provides insights for system management.

Klein Mechanisch Werkplaats Eindhoven (KMWE) is a precision manufacturing company situated in the Netherlands and recently relocated to a new location known as the 'Brainport Industries Campus' (BIC). This move allowed KMWE to improve the performance of its manufacturing facility by investing in vertical automated storage and retrieval systems (AS/RSs). However, these decisions needed to be made under input uncertainties since the move to BIC and modernization of existing equipment would cause changes in operating parameters inside the facility.

In this study, the researchers show how hybrid simulation modelling was used in production planning optimization, in particular to assess the impact of input uncertainties (such as operator productivity, vertical storage height) on the throughput performance of TSC.

We discuss the application of simulation to estimate a nominal, or target, processing times for work stations on a serial assembly line. The expectation is that having different processing times per station per product will increase the throughput of the line, compared to having a constant time for all stations. A demonstration case at ABB Robotics in Sweden will be presented. This is a small part in the “Process Automation for Discrete Manufacturing Excellence” project (PADME) involving five manufacturing industry partners and four research organizations, that aim at adapting Industrie 4.0 strategies and existing state-of-the-art technologies into new configurations, serving as a framework that can be used by similar industries.

Production plans are based on fair assumptions of process performance and all operation parameters are taken as averages. There are a number of events that happen in any manufacturing setup during the course of production like periodic delivery of raw materials or changeovers on a machine. The interaction between these events is non-linear and cannot be easily visualized. As a result of which most of the production plans in any company have only a limited realization. This paper provides an example of how simulation using AnyLogic has been applied in one such plant scenario to visualize the plan outcome.