Supply Chain Disruptors and their Impact on the Future of Manufacturing, Logistics, and Distribution

In this paper, we outline some of the changes occurring in the world and how businesses are adapting to these changes. We also list disruptors – factors or new ideas that disrupt the status quo – and their impact on manufacturing, supply chain, logistics, and distribution

Performance Comparison of Automated Warehouses Using Simulation

The purpose of this study is to compare the performance of two types of warehouses, both of which use autonomous vehicles (AVs). One warehouse uses movable racks (MR) for storing mini-loads, whereas the other uses fixed racks (FR). In general, warehouse automation not only increases the speed of the fulfillment process but also makes the picking process more accurate. We simulate three scenarios for the MR and FR systems using Simio. Four performance measures are considered for the comparison – the average order processing time (WR), the average utilization of AVs (U), the average order processing queue length (Nq) and the average distance travelled by AVs (d). We also estimate the capital costs of both systems and use it to compare the two systems. On the basis of our assumptions and simulation results, we find that the FR system not only requires an average 56 % less capital investment than the MR system, but it also provides a more efficient warehousing automation option with relatively lower utilization of AVs, lower order processing time and lower average number of orders waiting to be processed

Simulation and Optimization Modeling for Drive-Through Mass Vaccination – A Generalized Approach

Proper planning and execution of mass vaccination at the onset of a pandemic outbreak is important for local health departments. Mass vaccination clinics are required to be setup and run for naturally occurring pandemic outbreaks or even in response to terrorist attacks, e.g., anthrax attack. Walk-in clinics have often been used to administer vaccines. When a large percentage of a population must be vaccinated to mitigate the ill-effects of an attack or pandemic, drive-through clinics appear to be more effective because a much higher throughput can be achieved when compared to walk-in clinics. There are other benefits as well. For example, the spread of the disease can be minimized because infected patients are not exposed to uninfected patients. This research extends the simulation modeling work that was done for a mass vaccination drive-through clinic in the city of Louisville in November 2009. This clinic is the largest clinic set up in Louisville with more than 19,000 patients served, over two-thirds via ten drive-through lanes. The intent of the model in this paper is to illustrate a general tool that can be customized for a community of any size. The simulation-optimization tool will allow decision makers to investigate several interacting control variables in a simultaneous fashion; any of several criterion models in which various performance measures are either optimized or constrained, can be investigated. The model helps the decision maker determine the required number of Points of Dispense (POD) lanes, number and length of the lanes for consent hand outs and fill in, staff needed at the consent handout stations and PODs, and average user waiting time in the system

Simulation-based Performance Improvement of a Defense Logistics Warehouse

Warehouses play a critical role in supply chains. They serve as a vital link between manufacturers and customers. In this study, we investigate performance of the largest warehouse (Eastern Distribution Center - EDC) of the Defense Logistics Agency (DLA) located in New Cumberland, PA and propose a near optimum design for the receiving area of the warehouse to improve its performance. First, we develop a simulation model of the system. Second, we interface an optimization model with the simulation in order to optimize the number of people working at the induction stations. In the optimization model, our goal is to minimize the average cycle time of a material type. Last, we re-design the existing system based on results from the optimization. The simulation and the optimization models are developed through the use of ARENA 13.9 and Opt Quest

Design for manufacturing and assembly/disassembly: joint design of products and production systems

Design for Manufacturing, Assembly, and Disassembly is important in today’s production systems because if this aspect is not considered, it could lead to inefficient operations and excessive material usage, both of which have a significant impact on manufacturing cost and time. Attention to this topic is important in achieving the target standards of Industry 4.0 which is inclusive of material utilisation, manufacturing operations, machine utilisation, features selection of the products, and development of suitable interfaces with information communication technologies (ICT) and other evolving technologies. Design for manufacturing (DFM) and Design for Assembly (DFA) have been around since the 1980’s for rectifying and overcoming the difficulties and waste related to the manufacturing as well as assembly at the design stage. Furthermore, this domain includes a decision support system and knowledge base with manufacturing and design guidelines following the adoption of ICT. With this in mind, ‘Design for manufacturing and assembly/disassembly: Joint design of products and production systems’, a special issue has been conceived and its contents are elaborated in detail. In this paper, a background of the topics pertaining to DFM, DFA and related topics seen in today’s manufacturing systems are discussed. The accepted papers of this issue are categorised in multiple sections and their significant features are outlined

Warehouse design and planning: A mathematical programming approach

The dynamic nature of today's competitive markets compels organizations to an incessant reassessment in an effort to respond to continuous challenges. Therefore, warehouses as an important link in most supply chains, must be continually re-evaluated to ensure that they are consistent with both market's demands and management's strategies. A number of warehouse decision support models have been proposed in the literature but considerable difficulties in applying these models still remain, due to the large amount of information to be processed and to the large number of possible alternatives. In this paper we discuss a mathematical programming model aiming to support some warehouse management and inventory decisions. In particular a large mixed-integer nonlinear programming model (MINLP) is presented to capture the trade-offs among the different inventory and warehouse costs in order to achieve global optimal design satisfying throughput requirements.(undefined)info:eu-repo/semantics/publishedVersio