What Should Businesses Know About Social Media Analytics?

Big social media data generated by customers can be employed start to identify which customer behaviour and actions creates more value. A study by conducted by MIT Sloan Management Review found that 67% of the total 2,500 survey respondents reported that by employing analytics their companies gained a competitive advantage as well it helped them to innovate. Still, many businesses struggle to plan and create value from social data analytics. Hence, using a thematic analysis of current social media analytics (SMA) literature and focus group interview with SMA experts, the aim of this article is to provide an executive overview of SMA concepts, theories, and tools that are vital for understanding and strategically using social media analytics for business intelligence purposes

Industry 4.0 Maturity Assessment: A multi-dimensional indicator approach

Purpose - Industry 4.0 has offered significant potential for manufacturing firms to alter and rethink their business models, production processes, strategies, and objectives. Manufacturing organizations have recently undergone substantial transformation due to Industry 4.0 technologies. Hence, to successfully deploy and embed Industry 4.0 technologies in their organizational operations and practices, businesses must assess their adoption readiness. For this purpose, a multidimensional analytical indicator methodology has been developed to measure Industry 4.0 maturity and preparedness. Design/methodology/approach- A weighted average method was adopted to assess the Industry 4.0 readiness using a case study from a steel manufacturing organization. Findings- The result revealed that the firm ranks between Industry 2.0 and Industry 3.0, with an overall score of 2.32. This means that the organization is yet to achieve Industry 4.0 mature and ready organization. Practical Implications- The multi-dimensional indicator framework proposed can be used by managers, policymakers, practitioners, and researchers to assess the current status of organizations in terms of Industry 4.0 maturity and readiness as well as undertake a practical diagnosis and prognosis of systems and processes for its future adoption. Originality/ value- Although research on Industry 4.0 maturity models has grown exponentially in recent years, this study is the first to develop a multi-dimensional analytical indicator to measure Industry 4.0 maturity and readiness

Integrating location and logistics models

Economic geographers investigate facility location problems by applying procedures derived from the classical location theory. These conventional procedures focus on the analysis of the tradeoffs between facility and transportation decisions, and provide solutions to location problems that minimize distance-based cost components. The classical approach leads to basic shortcomings when applied to physical distribution problems. Furthermore, the GIS industry shows little attention to physical distribution problems and the manipulation of logistics data is difficult in out-of-box GIS products. Logistics decision-makers attempt to optimize their distribution systems by performing the tradeoff analysis among facility, inventory and transportation decisions. In addition, the minimization of distribution costs is generally performed analyzing time-sensitive costs only. This study aims to improve the contribution of geographic methodologies to distribution problems by analyzing inventory and transportation decisions, and then integrating time-sensitive cost components with distance-based system costs. An implemented formulation of the p-median problem is developed for this purpose in this study. A heuristic algorithm based on the swapping location criterion is used for the achievement of the solution of the physical distribution problem. Conventional swapping algorithms consider the best feasible solution by analyzing the spatial configuration of facilities. In this study, the implemented heuristic algorithm analyzes both savings obtained applying a change in the spatial configuration of the facilities and in the average speed of the transportation provider. This formulation is embedded as an extension available for the package ArcView 3.2 by ESRI. The logistics extension also includes a customized GUI for the input, editing, analysis, and output of logistics data. The major outcome from this study is that average speed of service and value of shipped item play a key role in the determination of the total distribution costs. The minimization of these costs, from a shipper\u27s perspective, suggests number and location of warehouses that optimize the physical distribution system

Prescriptive Control of Business Processes - New Potentials Through Predictive Analytics of Big Data in the Process Manufacturing Industry

This paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics to big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain for the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products’ qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company – a typical example of the process industry – the work at hand outlines which data becomes available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

Game Theory and Prescriptive Analytics for Naval Wargaming Battle Management Aids

NPS NRP Technical ReportThe Navy is taking advantage of advances in computational technologies and data analytic methods to automate and enhance tactical decisions and support warfighters in highly complex combat environments. Novel automated techniques offer opportunities to support the tactical warfighter through enhanced situational awareness, automated reasoning and problem-solving, and faster decision timelines. This study will investigate how game theory and prescriptive analytics methods can be used to develop real-time wargaming capabilities to support warfighters in their ability to explore and evaluate the possible consequences of different tactical COAs to improve tactical missions. This study will develop a conceptual design of a real-time tactical wargaming capability. This study will explore data analytic methods including game theory, prescriptive analytics, and artificial intelligence (AI) to evaluate their potential to support real-time wargaming.N2/N6 - Information WarfareThis 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.