An empirical analysis of humanitarian warehouse locations

Purpose: The purpose of this paper is to empirically verify characteristics of current warehouse locations of humanitarian organizations (based on public information) and to relate those to the model developed by Richardson et al. (2016). Design/methodology/approach: This paper is based on desk research. Public data such as (annual) reports and databases are used to empirically verify location characteristics. Findings: A significant portion of our sample co-locates their products at UNHRD premises. This indicates that organizations prefer to cluster warehouse activities, particularly when there is no fee involved for using the warehouse (as is the case in the UNHRD network). We find that the characteristics of the current warehouse locations are aligned with literature on location selection factors. Current location can be characterized by infrastructure characteristics (in particular closeness to airport and safety) and by low occurrence of disasters. Other factors for which we did not find evidence for were labor quality and availability as well as political environment. Research limitations/implications: We have used a limited sample of warehouses. We also focused our research on the countries where two or more organizations have their warehouses located. We did not account for warehouse sizes or product stored in our analysis. Practical implications: The geographic map of the current warehouses together with the quantified location factors provides an overview of current warehouse locations. Originality/value: We empirically verify characteristics of warehouse locations of humanitarian organizations. This differs from other studies that do not provide an empirically grounded perspective

The Impact of Data Quality Tags on Decision-Making Outcomes and Process

It has been proposed that metadata describing data quality (DQ), termed DQ tags, be made available in situations where decision makers are unfamiliar with the data context, for example, in data warehouses. However, there have been conflicting reports as to the impact of such DQ tags on decision-making outcomes. Early studies did not explicitly consider the usability and semantics of the DQ tag designs used experimentally or the impact of such tags on decision process, except in suggestions for future research. This study addresses these issues, focusing on the design of usable DQ tags whose semantics are explicitly specified and exploring the impact of such DQ tags on decision outcomes and process. We use the information quality framework InfoQual, the interaction design technique of contextual inquiry, and cognitive process tracing to address DQ tag semantics, usability, and impact on decision process, respectively. In distinct contrast to earlier laboratory experiments, there was no evidence that the preferred decision choice changed with DQ tags, but decision time was significantly increased and there were indications of reduced consensus. These results can be explained by understanding the impact of DQ tags on decision process using concurrent protocol analysis, which involves participants verbalizing thoughts while making a decision. The protocol analysis study shows that DQ tags are associated with increased cognitive processing in the earlier phases of decision making, which delays generation of decision alternatives

Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

The Role of Maintenance and Facility Management in Logistics: A Literature Review

Purpose - The purpose of this paper is to provide a literature review on the different ways of carrying out Facility Management and related topics in order to uncover that there is limited research regarding the impact of Facility Management on the logistics and operational performance of warehouses. Design/methodology/approach - Four different focus areas have been identified and for each one different methodologies and streams of research have been studied. Findings - The study underlines the importance of Facility Management for the logistics operations; therefore it supports the notion that investments aiming at preserving the status of the building and service components of warehouses are crucial. Originality/value - This paper aims to suggest to Facility Management managers that they can contribute to enhance business performance by designing effective Facility Management strategie

SODA: Generating SQL for Business Users

The purpose of data warehouses is to enable business analysts to make better decisions. Over the years the technology has matured and data warehouses have become extremely successful. As a consequence, more and more data has been added to the data warehouses and their schemas have become increasingly complex. These systems still work great in order to generate pre-canned reports. However, with their current complexity, they tend to be a poor match for non tech-savvy business analysts who need answers to ad-hoc queries that were not anticipated. This paper describes the design, implementation, and experience of the SODA system (Search over DAta Warehouse). SODA bridges the gap between the business needs of analysts and the technical complexity of current data warehouses. SODA enables a Google-like search experience for data warehouses by taking keyword queries of business users and automatically generating executable SQL. The key idea is to use a graph pattern matching algorithm that uses the metadata model of the data warehouse. Our results with real data from a global player in the financial services industry show that SODA produces queries with high precision and recall, and makes it much easier for business users to interactively explore highly-complex data warehouses.Comment: VLDB201

Linear integrated location-inventory models for service parts logistics network design

We present two integrated network design and inventory control problems in service-parts logistics systems. Such models are complicated due to demand uncertainty and highly nonlinear time-based service level constraints. Exploiting unique properties of the nonlinear constraints, we provide an equivalent linear formulation under part-warehouse service requirements, and an approximate linear formulation under part service requirements. Computational results indicate the superiority of our approach over existing approaches in the literature