How to Generate Economic and Sustainability Reports from Big Data? Qualifications of Process Industry

Abstract

Big Data may introduce new opportunities, and for this reason it has become a mantra among most industries. This paper focuses on examining how to develop cost and sustainable reporting by utilizing Big Data that covers economic values, production volumes, and emission information. We assume strongly that this use supports cleaner production, while at the same time offers more information for revenue and profitability development. We argue that Big Data brings company-wide business benefits if data queries and interfaces are built to be interactive, intuitive, and user-friendly. The amount of information related to operations, costs, emissions, and the supply chain would increase enormously if Big Data was used in various manufacturing industries. It is essential to expose the relevant correlations between different attributes and data fields. Proper algorithm design and programming are key to making the most of Big Data. This paper introduces ideas on how to refine raw data into valuable information, which can serve many types of end users, decision makers, and even external auditors. Concrete examples are given through an industrial paper mill case, which covers environmental aspects, cost-efficiency management, and process design.

Smart production systems drivers for business process management improvement

“…In the SPS context, BDA offers opportunities for cost reduction, productivity and sustainability improvement (Hamalainen and Inkinen, 2017), and supporting innovation (Romero et al, 2017). Another critical application of BDA in SPS is related to the measurement, monitoring, and control of workers' operations (Seele, 2016) in enhancing their quality of life.…”

“…et al (2016);Ardito, et al (2018);Baek et al (2015);Bibri (2018);Bibri and Krogstie (2017a);Bibri and Krogstie (2017b);Bologa, et al (2017);Chang and Wills (2016);Chinnaswamy et al (2018);Cottrill and Derrible (2015);Diamantoulakis et al (2015);Dubey et al (2016);Hamalainen and Inkinen (2017);Han et al (2018);He et al (2017);Intezari and Gressel (2017);Kache and Seuring (2017);Kahn and Liu (2015);Khan and Vorley (2017); Liu et al (2017); Louhghalam et al (2017); Luo et al (2017); Mani et al (2017); Matthias et al (2017); Munshi and Mohamed (2017); Nobre and Tavares (2017); Al Nuaimi et al (2015); Pauleen and Wang (2017); Phillips-Wren and Hoskisson (2015); Rehman et al (2016); Romero et al (2017); Rothberg and Erickson (2017); Schuelke-Leech et al (2015); Seele (2016); Suciu et al (2016); Tu et al (2017); Verma and Singh (2017); Wamba et al (2017); Wang et al (2016a); Wang and Byrd (2017); Wu et al (2016); Yuan et al (2017); Zhang et al (2017a); Zhang et al (2017b); Zhou and Yang (2016). Blockchain Christidis and Devetsikiotis (2016); Li et al (2018).…”

The Big Data, Artificial Intelligence, and Blockchain in True Cost Accounting for Energy Transition in Europe

“…So far, within management accounting, the prediction function has been used the most often since estimation is the most common task in managerial accounting application of data mining [42]. AI uses data mining tools to build logic behind the data to forecast future outcomes and identify patterns for allocating impacts to activities [45]. In order to arrive at the true cost estimations, the interplay between discounting, uncertainty, damages, and risk aversion is important to consider [29].…”

Port Digitalization with Open Data: Challenges, Opportunities, and Integrations

“…The operational interlinkage in data management is therefore tightly embedded into the level of collaboration intensity and depth. The current development mindset in ports focuses on general improvement of data sharing, storing, and transfer (in the case of big data, see [22]).…”