Intentional fragmentation for material storage

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.Includes bibliographical references (p. 165-167).A novel technique (location-relaxed storage) of mixing products within warehouse storage bins is presented and evaluated. Analyses of warehouse operations, storage space efficiency, error sensitivity, and placement policies are presented and compared to traditional warehousing techniques. The major factors that drive the performance differences between traditional, highly organized storage and location-relaxed storage are shown to include the number of unique stock keeping units (SKUs) served by the warehouse and the picking lot size characteristic of demand. The analyses demonstrate traditional storage techniques have greater difficulty dealing with a large SKU base. Furthermore, location-relaxed storage is shown to have a lower sensitivity to operation errors and a greater opportunity for cost savings through optimization opportunities. Finally, a new placement strategy especially suited for location-relaxed storage is presented. As the popularity of Radio Frequency Identification (RFID) increases and the technical issues of widespread RFID implementation are addressed, new applications of RFID technology will change the way the world operates. An ongoing, industry-wide effort to implement RF-tags throughout the material goods supply chain has the support of manufacturers, retailers, and technology companies. RFID in the supply chain represents an enabling technology that will allow warehouse operations to break away from traditional methodologies and adopt revolutionary techniques, such as location-relaxed storage.by Stephen Ho.Ph.D

Update Tutorial: Big Data Analytics: Concepts, Technology, and Applications

In 2014, I wrote a paper on big data analytics that the Communications of the Association for Information Systems published (volume 34). Since then, we have seen significant advances in the technologies, applications, and impacts of big data analytics. While the original paper’s content remains accurate and relevant, with this new paper, I update readers on important, recent developments in the area

A Strategic Roadmap for Maximizing Big Data Return

Big Data has turned out to be one of the popular expressions in IT the last couple of years. In the current digital period, according to the huge improvement occurring in the web and online world innovations, we are facing a gigantic volume of information. The size of data has expanded significantly with the appearance of today's innovation in numerous segments, for example, assembling, business, and science. Types of information have been changed from structured data-driven databases to data including documents, images, audio, video, and social media contents referred to as unstructured data or Big Data. Consequently, most of the organizations try to invest in the big data technology aiming to get value from their investment. However, the organizations face a challenge to determine their requirements and then the technology that suits their businesses. Different technologies are provided by variety of vendors, each of them can be used, and there is no methodology helping them for choosing and making a right decision. Therefore, the objective of this paper is to construct a roadmap for helping the organizations determine their needs and selecting a suitable technology and applying this conducted proposed roadmap practically on two companies

Just-in-time Data Distribution for Analytical Query Processing

Distributed processing commonly requires data spread across machines using a priori static or hash-based data allocation. In this paper, we explore an alternative approach that starts from a master node in control of the complete database, and a variable number of worker nodes for delegated query processing. Data is shipped just-in-time to the worker nodes using a need to know policy, and is being reused, if possible, in subsequent queries. A bidding mechanism among the workers yields a scheduling with the most efficient reuse of previously shipped data, minimizing the data transfer costs. Just-in-time data shipment allows our system to benefit from locally available idle resources to boost overall performance. The system is maintenance-free and allocation is fully transparent to users. Our experiments show that the proposed adaptive distributed architecture is a viable and flexible alternative for small scale MapReduce-type of settings

Actes de la conférence BDA 2014 : Gestion de données - principes, technologies et applications

International audienceActes de la conférence BDA 2014 Conférence soutenue par l'Université Joseph Fourier, Grenoble INP, le CNRS et le laboratoire LIG. Site de la conférence : http://bda2014.imag.fr Actes en ligne : https://hal.inria.fr/BDA201

Optimal Supply Network with Vendor Managed Inventory in a Healthcare System with RFID Investment Consideration

Supply Chain Management in the healthcare sector faces several significant challenges, including complexity in healthcare systems, high supply chain costs, balancing quality and costs, delay in delivery, product availability from vendors, inventory waste, and unpredictability and uncertainty. Among those challenges, having an effective inventory management system with an optimal supply network is important to improve the match between supply and demand, which would improve the performance of for healthcare firms. Vendor Managed Inventory (VMI) system is a replenishment solution in which the vendor monitors and decides the time and the quantity of the inventory replenishment of their customers subject to their demand information exchange. A VMI contract in the location-inventory assignment problem is a decision tool for management in the healthcare industry, in which it enables the management to have a cost and service effective decision tool to critically re-evaluate and examine all areas of operations in a SC network looking for avenues of optimization. This dissertation is based on a real-world problem arising from one of the world\u27s leading medical implant supply company applied to a chain of hospitals in the province of Ontario. The chain of hospitals under study consists of 147 hospitals located in Ontario, Canada. The vendor is a supplier of three types of medical implants (a heart valve, an artificial knee, and a hip). In Chapter 2 of this dissertation, we present an optimal supply healthcare network with VMI and with RFID consideration, in which we shed light on the role of the VMI contract in the location-inventory assignment problem and integrate it with both the replenishment policy assignment and the Radio Frequency Identification (RFID) investment allocation assignment in healthcare SC networks using both VMI and direct delivery policies. A numerical solution approach is developed in the case of the deterministic demand environment, and we end up with computational results and sensitivity analysis for a real-world problem to highlight the usefulness and validate the proposed model. We extend our research of integrating the VMI contract in the location-inventory assignment problem with the replenishment policy assignment under a deterministic demand environment to include the stochastic demand environment. The impact of the uncertainty of the demand as a random variable following two types of distributions, normal and uniform distributions, is studied in Chapter 3. Motivated by the lack of investigations and comparative studies dealing with the preference of dealing with VMI contracts to other traditional Retailer Managed Inventory (RMI) systems, we provide in Chapter 4 of this dissertation a comparative study in which we compare the total cost of the VMI system with another two situations of traditional RMI systems: first, a traditional RMI system with a continuous replenishment policy for all hospitals and with assigned storage facilities and second, a traditional RMI system with a direct delivery policy for all hospitals without assigning a storage facility. Computational results, managerial insights, sensitivity analysis, and solution methodologies are provided in this dissertation. Keywords: Vendor Managed Inventory, healthcare system, location-inventory, RFID technology, supply-chain network, stochastic demand, location-inventory assignment problem, and retailer managed Inventory

Smart Technology Adoption’s Impact on the Value of Logistics Service Providers’ Firms

Although it took a pandemic to raise awareness about supply chain issues in the minds of the public at large, industry players have long understood supply chain complexities—particularly in the face of continually evolving technologies and ever-more interconnected global enterprises. With Logistics 4.0 and the rapid developments in smart technologies, these complexities make the ongoing need for technology adoption even more complicated for logistics providers. While the literature regularly reports on the adoption of specific technologies, there is little research on the adoption process and even less that might guide providers in prioritizing their technology targets. This research examined the literature for drivers and consequences of technology adoption among providers, then tested those concepts through in-depth interviews with 40 senior-level executives at global logistics provider firms. Among the study’s findings are that the drivers and consequences of smart technology adoption are similar among logistics providers. However, firm size, business tenure, and client relationships moderate the adoption of these innovations. The study identifies incumbent people, processes, and systems as “excess baggage” that slows adoption because of adjustments needed to accommodate new technologies and creates bottlenecks for these firms. However, when combined with new competencies, streamlined processes, and proper change management, this baggage may improve firm performance because of the legacy processes integrated with customers’ supply chains. The study also developed a framework to inform practitioners’ adoption efforts. The framework addresses the research questions. It also recommends that to realize quicker revenue gains when adopting smart technology. Providers focus on two key drivers: customer relationships and market demands. This research also suggests that providers adopting smart technology leverage their incumbent human resources, processes, and technologies to deliver customer value and improve firm performance

STRATEGIC DECISION MAKING IN SUPPLY CHAINS UNDER RISK OF DISRUPTIONS

Ph.DDOCTOR OF PHILOSOPH