Feasibility of Warehouse Drone Adoption and Implementation

While aerial delivery drones capture headlines, the pace of adoption of drones in warehouses has shown the greatest acceleration. Warehousing constitutes 30% of the cost of logistics in the US. The rise of e-commerce, greater customer service demands of retail stores, and a shortage of skilled labor have intensified competition for efficient warehouse operations. This takes place during an era of shortening technology life cycles. This paper integrates several theoretical perspectives on technology diffusion and adoption to propose a framework to inform supply chain decision-makers on when to invest in new robotics technology

Improving lifecycle query in integrated toolchains using linked data and MQTT-based data warehousing

The development of increasingly complex IoT systems requires large engineering environments. These environments generally consist of tools from different vendors and are not necessarily integrated well with each other. In order to automate various analyses, queries across resources from multiple tools have to be executed in parallel to the engineering activities. In this paper, we identify the necessary requirements on such a query capability and evaluate different architectures according to these requirements. We propose an improved lifecycle query architecture, which builds upon the existing Tracked Resource Set (TRS) protocol, and complements it with the MQTT messaging protocol in order to allow the data in the warehouse to be kept updated in real-time. As part of the case study focusing on the development of an IoT automated warehouse, this architecture was implemented for a toolchain integrated using RESTful microservices and linked data.Comment: 12 pages, worksho

The necessities for building a model to evaluate Business Intelligence projects- Literature Review

In recent years Business Intelligence (BI) systems have consistently been rated as one of the highest priorities of Information Systems (IS) and business leaders. BI allows firms to apply information for supporting their processes and decisions by combining its capabilities in both of organizational and technical issues. Many of companies are being spent a significant portion of its IT budgets on business intelligence and related technology. Evaluation of BI readiness is vital because it serves two important goals. First, it shows gaps areas where company is not ready to proceed with its BI efforts. By identifying BI readiness gaps, we can avoid wasting time and resources. Second, the evaluation guides us what we need to close the gaps and implement BI with a high probability of success. This paper proposes to present an overview of BI and necessities for evaluation of readiness. Key words: Business intelligence, Evaluation, Success, ReadinessComment: International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 201

Antecedents and Catalysts for Developing a Healthcare Analytic Capability

Analytics is the most advanced component of business intelligence. An analytic capability enables fact-based decisions using quantitative models. These models draw on statistical and quantitative analysis of large data repositories. An analytic capability is especially critical in healthcare because lives are at stake and there is intense pressure to reduce costs and improve efficiency. This study proposes antecedents and catalysts for developing an analytic capability based on an in-depth study of the cardiac surgical programs of the Veterans Health Administration (VHA). The VHA has developed an analytic capability for patient treatment and administrative decision-making. The models rely on the input of clinical data from multiple facilities. However, a diversity of standards, infrastructure, staff and patient mix result in misunderstood data definitions, errors in data entry, incomplete data sets, and conflicts between multiple systems. Consequently, data aggregation and data interoperability at both the systemic and semantic levels are challenging. Catalysts for developing an analytic capability, derived from the VHA case study, include a community of practice and patient case reassessment practices. Antecedents of an analytic capability include robust data aggregation and cleaning practices and establishment of data standards followed by judicious tailoring of analytic outputs to decision making needs

Treatment of palm oil mill secondary effluent (POMSE) using ultrafiltration and nanofiltration membranes

Malaysian palm oil industry has grown rapidly over the last few decades, to becoming the world’s largest producer and exporter of palm oil. This success story however, comes with a greater challenge and equally required more sacrifices in order to maintain the tempo. In the year of 2004, it has been recorded that 26.7 million tons of solid biomass and approximately a 30 million tons of palm oil mill effluent (POME) were generated from 381 palm oil mills in Malaysia [1]. Although different kind of wastes are generated in the palm oil mills, the perceived harmful waste among all the waste generated is the palm oil mill effluent (POME) due to its associated harm if discharged into the environment untreated [2]. POME is a colloidal suspension originating from mixture of sterilizer condensate, separator sludge and hydro cyclone wastewater in a ratio of 9:15:1 respectively [3]. It is a brownish colored, thick liquid that is containing high amount of oil, solids, and grease with high Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD) values. Table 15.1 describes the characteristic of POME obtained from Malaysian Palm Oil Board