Optimization of the air cargo supply chain.

Purpose: This paper aims to evaluate and optimize the various operations within the air cargo chain. It pursues to improve the efficiency of the air cargo supply chain and to provide more information to the decision-makers to optimize their fields. Design/methodology/approach: The method used is a process simulation modelling software, WITNESS, which provides information to the decision-makers about the most relevant parameters subject to optimization. The input for the simulation is obtained from a qualitative analysis of the air cargo supply chain with the involved agents and from a study of the external trade by air mode, given that their behaviour depend on the location. The case study is focused on a particular location, the Case of Zaragoza Airport (Spain). Findings: This paper demonstrates that efficiency of the air cargo supply chain can increase by leveraging several parameters such as bottlenecks, resources or warehouses. Originality/value: It explores the use of a simulation modeling software originally intended for manufacturing processes and extended to support decision making processes in the area of air cargo

Applied business analytics approach to IT projects – Methodological framework

The design and implementation of a big data project differs from a typical business intelligence project that might be presented concurrently within the same organization. A big data initiative typically triggers a large scale IT project that is expected to deliver the desired outcomes. The industry has identified two major methodologies for running a data centric project, in particular SEMMA (Sample, Explore, Modify, Model and Assess) and CRISP-DM (Cross Industry Standard Process for Data Mining). More general, the professional organizations PMI (Project Management Institute) and IIBA (International Institute of Business Analysis) have defined their methods for project management and business analysis based on the best current industry practices. However, big data projects place new challenges that are not considered by the existing methodologies. The building of end-to-end big data analytical solution for optimization of the supply chain, pricing and promotion, product launch, shop potential and customer value is facing both business and technical challenges. The most common business challenges are unclear and/or poorly defined business cases; irrelevant data; poor data quality; overlooked data granularity; improper contextualization of data; unprepared or bad prepared data; non-meaningful results; lack of skill set. Some of the technical challenges are related to lag of resources and technology limitations; availability of data sources; storage difficulties; security issues; performance problems; little flexibility; and ineffective DevOps. This paper discusses an applied business analytics approach to IT projects and addresses the above-described aspects. The authors present their work on research and development of new methodological framework and analytical instruments applicable in both business endeavors, and educational initiatives, targeting big data. The proposed framework is based on proprietary methodology and advanced analytics tools. It is focused on the development and the implementation of practical solutions for project managers, business analysts, IT practitioners and Business/Data Analytics students. Under discussion are also the necessary skills and knowledge for the successful big data business analyst, and some of the main organizational and operational aspects of the big data projects, including the continuous model deployment

Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the USN Operating Forces

NPS NRP Technical ReportThe SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover.N3/N5 - Plans & StrategyThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

A Software Radio Challenge Accelerating Education and Innovation in Wireless Communications

This Innovative Practice Full Paper presents our methodology and tools for introducing competition in the electrical engineering curriculum to accelerate education and innovation in wireless communications. Software radio or software-defined radio (SDR) enables wireless technology, systems and standards education where the student acts as the radio developer or engineer. This is still a huge endeavor because of the complexity of current wireless systems and the diverse student backgrounds. We suggest creating a competition among student teams to potentiate creativity while leveraging the SDR development methodology and open-source tools to facilitate cooperation. The proposed student challenge follows the European UEFA Champions League format, which includes a qualification phase followed by the elimination round or playoffs. The students are tasked to build an SDR transmitter and receiver following the guidelines of the long-term evolution standard. The metric is system performance. After completing this course, the students will be able to (1) analyze alternative radio design options and argue about their benefits and drawbacks and (2) contribute to the evolution of wireless standards. We discuss our experiences and lessons learned with particular focus on the suitability of the proposed teaching and evaluation methodology and conclude that competition in the electrical engineering classroom can spur innovation.Comment: Frontiers in Education 2018 (FIE 2018

A concept of water usage efficiency to support water reduction in manufacturing industry

Increasing pressures on freshwater supplies, continuity of supply uncertainties, and costs linked to legislative compliance, such as for wastewater treatment, are driving water use reduction up the agenda of manufacturing businesses. A survey is presented of current analysis methods and tools generally available to industry to analyze environmental impact of, and to manage, water use. These include life cycle analysis, water footprinting, strategic planning, water auditing, and process integration. It is identified that the methods surveyed do not provide insight into the operational requirements from individual process steps for water, instead taking such requirements as a given. We argue that such understanding is required for a proactive approach to long-term water usage reduction, in which sustainability is taken into account at the design stage for both process and product. As a first step to achieving this, we propose a concept of water usage efficiency which can be used to evaluate current and proposed processes and products. Three measures of efficiency are defined, supported by a framework of a detailed categorization and representation of water flows within a production system. The calculation of the efficiency measures is illustrated using the example of a tomato sauce production line. Finally, the elements required to create a useable tool based on the efficiency measures are discussed

Achieving manufacturing excellence through the integration of enterprise systems and simulation

This paper discusses the significance of the enterprise systems and simulation integration in improving shop floor’s short-term production planning capability. The ultimate objectives are to identify the integration protocols, optimisation parameters and critical design artefacts, thereby identifying key ‘ingredients’ that help in setting out a future research agenda in pursuit of optimum decision-making at the shop floor level. While the integration of enterprise systems and simulation gains a widespread agreement within the existing work, the optimality, scalability and flexibility of the schedules remained unanswered. Furthermore, there seems to be no commonality or pattern as to how many core modules are required to enable such a flexible and scalable integration. Nevertheless, the objective of such integration remains clear, i.e. to achieve an optimum total production time, lead time, cycle time, production release rates and cost. The issues presently faced by existing enterprise systems (ES), if properly addressed, can contribute to the achievement of manufacturing excellence and can help identify the building blocks for the software architectural platform enabling the integration

Designing Enterprise Resources Planning Application for Integrating Main Activities in a Simulator Model of SCM Network Distribution

Collaborative supply chain is a specific topic in supply chain management and studied by industrial engineering students in supply chain management course. Unfortunately, conventional learning media cannot explain the phenomenon of collaborative supply chain to the students. This study aimed to design a dynamic learning media so that inter-company collaboration and information sharing on the activities of Supply Chain entities can be explained effectively to the students. The problem was solved using 3 (three) steps. First, the distribution network was described using mock up. It consists of miniature trucks, miniature network and miniature of the manufacturer-distributor-retailer embedded with tag and reader of RFID. Second, the Enterprise Resources Planning application was developed for supporting business activities. Third, we developed the integrator consists of monitor’s user interface and practice modules. The result of the research - an SCM-Simulator – will be able to improve learning skills of industrial engineering graduates, especially abilities to identify, formulate, and solve the activities of tactical plan & operational routines of Supply Chain entities. However, distribution module designed is for limited scale laboratory study of simple objects. Keywords: Distribution Network, Enterprise Resource Planning, Industrial Engineering Education, SCM Simulator,and Learning Media

Digital twin-driven real-time planning, monitoring, and controlling in food supply chains

There needs to be more clarity about when and how the digital twin approach could benefit the food supply chains. In this study, we develop and solve an integrated problem of procurement, production, and distribution strategies (PPDs) in a medium-scale food processing company. Using the digital twin approach, the model considers the industrial symbiosis opportunities between the supplier, manufacturer, and customer using interval and sequence variables operating in a constrained environment using mixed-integer linear programming (MILP) and agent-based simulation (ABS) methodology. The study optimizes the make-span and lead time, simultaneously achieving a higher level of digitalization. The analysis demonstrates how digital twin accelerates supply chain productivity by improving makespan time, data redundancy (DR), optimal scheduling plan (OSP), overall operations effectiveness (OOE), overall equipment effectiveness (OEE), and capacity utilization. Our findings provide compelling evidence that the seamless integration PPDs enormously enhance production flexibility, resulting in an excellent service level of 94 %. Managers leverage real-time simulation to accurately estimate the replenishment point with minimal lead time, ensuring optimized operations. Furthermore, our results demonstrate that implementing PPDs has yielded considerable benefits. Specifically, we observed a remarkable 65 % utilization of the pasteurizer and aging vessel and an impressive 97 % utilization of the freezer. Moreover, by applying the DT model, the present model found a notable 6 % reduction in backlog, further streamlining operations and enhancing efficiency