Informed Business Decisions Research Articles

FlashP

Interactive response time is important in analytical pipelines for users to explore a sufficient number of possibilities and make informed business decisions. We consider a forecasting pipeline with large volumes of high-dimensional time series data. Real-time forecasting can be conducted in two steps. First, we specify the part of data to be focused on and the measure to be predicted by slicing, dicing, and aggregating the data. Second, a forecasting model is trained on the aggregated results to predict the trend of the specified measure. While there are a number of forecasting models available, the first step is the performance bottleneck. A natural idea is to utilize sampling to obtain approximate aggregations in real time as the input to train the forecasting model. Our scalable real-time forecasting system FlashP (Flash Prediction) is built based on this idea, with two major challenges to be resolved in this paper: first, we need to figure out how approximate aggregations affect the fitting of forecasting models, and forecasting results; and second, accordingly, what sampling algorithms we should use to obtain these approximate aggregations and how large the samples are. We introduce a new sampling scheme, called GSW sampling, and analyze error bounds for estimating aggregations using GSW samples. We introduce how to construct compact GSW samples with the existence of multiple measures to be analyzed. We conduct experiments to evaluate our solution its alternatives on real data.

Cross-border crosswalk: An overview of Canadian and US banking and consumer financial services regulators

Canada and the United States are neighbours, each with its own ‘alphabet soup’ of banking and consumer financial services regulators. Many institutions in each country are under the purview of both federal and state/provincial regulators, and some institutions may be supervised by multiple financial regulators. For businesses engaged in financial services on either side of the border, it is important to understand which agencies regulate the products and services they offer and how agencies policies change over time. Understanding how local regulatory environments differ should inform business decisions. For example: There may be costs associated with expanding to a new jurisdiction as there are likely different compliance requirements. A product that is viable in one area may be untenable in another due to differing regulations (eg varying usury limits). Litigation risk may differ between jurisdictions: various US regulators can file lawsuits in federal court whereas Canadian regulators often have supervisory and regulatory powers that do not include prosecution. US companies are also often able to insulate themselves from class action liability through the operation of arbitration clauses and class action waivers. Companies seeking to do business in both the United States and Canada should consider engaging legal and expert teams during product development to harmonise where possible. Additionally, when facing regulatory scrutiny or litigation, similar harmonisation may be beneficial as well. Navigating the oversight of each agency is nuanced within each country, and one’s knowledge, experience and jargon are often specific to their area of expertise. Regulated entities’ incentives may differ due to differences in regulation or licensing requirements. Engaging with and retaining expertise (eg staff, counsel or external experts) in one country who have the tools and language to work with people in another can be valuable. To help with that, this paper provides an overview of the legal framework, financial services landscape and key regulators in Canada and the United States.

Exploring Futuring and Predictive Analytics for Developing Organizational Strategy

The study addresses the type of leadership styles that promote Innovation, transformational, and adaptive leadership—in this study of futuring, discussing the evolving role of the futurist within the organization. The discussion of the history of futurists within the organization for the public and private sectors looks at the part of the futurist to determine the relevance of today compared to 20 years ago. Also, it looks at how the futurist can utilize the futurist's techniques in their organization. The study of predictive analytics addresses the use of predictive analytics with examples of companies that use predictive analytics, and the reasons they use predictive analytics—examining predictive analytics to determine if they are driving more informed business decisions. The article explores the relationship between big data and predictive analytics and if big data is responsible for the popularity of predictive analytics.

When Sino-American Struggle Disrupts the Supply Chain: Licensing Intellectual Property in a Changing Trade Environment

Through legislative changes, tariff wars, and executive actions, the Trump Administration has injected new urgency into international technology and supply chain management, particularly between the United States and China. Analytically, the situation invites a perspective that links practical/on-the-ground responses by commercial actors to the politics of technological competition between superpowers. This paper approaches the management of supply chain disruption in terms of a key security issue motivating recent changes to the trade environment: the protection of intellectual property. After reviewing critical policy developments on both sides of the Pacific in conjunction with recent trade statistics, we turn to a discussion of the opportunity that intellectual property licensing provides for managing supply chain linkages between the United States and China. Viewing intellectual property as both a driver and a solution of trade difficulties highlights the sorts of cross-jurisdictional nuances that can better inform policy and business decisions alike in the broader international trade regime.

A multi‐criteria sustainable supplier selection framework based on neutrosophic fuzzy data and entropy weighting

AbstractThe gains in corporate sustainability are important for business development under the increasing competition and climate change. The importance of the green and sustainable supplier selection has increased due to the environmental concerns and regulations. The information for business decisions is often vague and imprecise. Therefore, this paper develops a fuzzy methodology for sustainable supplier selection based on fuzzy information. Single‐valued neutrosophic set (SVNS) is a very popular tool for processing potentially uncertain information provided by decision‐makers. Thus, SVNS is considered as a useful extension of the existing methods in uncertain complex situations. In order to facilitate the multi‐attribute decision‐making (MADM), this paper develops a new similarity measure for the SVNSs and explores its application possibilities. For achieving the aim, a single‐valued neutrosophic (SVN) hybrid weighted similarity (SVNHWS) measure is presented to reflect degree of similarity of SVNSs more effectively. Moreover, a method based on the SVNHWS and entropy measures is constructed for handling SVN MADM problems, in which, the entropy measure is used to derive the unknown weights information of attributes. Finally, an illustrative mathematical example dealing with the sustainable supplier selection is provided. The reliability of the proposed technique is tested by means of the comparative analysis.

Research on the application of big data technology in government management

With the advent of the new technology times, big data technology has been widely used as an important strategy of comprehensive social governance. The development of big data not only represents huge amount of data, advanced information technology and huge business opportunities, but also is an effective method to solve problems, transform the world and explore new ideas and scientific applications in the future. However, big data in economic development does not mean that it can replace all the rational thinking of social problems. The logic of scientific development cannot be annihilated in the mass data. In this era of rapid development of intelligent hardware, an important problem for application developers is how to find the delicate balance between power, coverage, transmission rate and cost. Enterprise organizations can use relevant data and analysis to help them reduce costs, improve efficiency, develop new products and make more informed business decisions.

Open banking experiences set the stage for Open X

As open banking regulations encouraged established firms to collaborate with FinTech newcomers as solution partners, the banking ecosystem became more convenient for customers through greater transparency in data exchange. Now an emphasis on delivering experiences versus only products is driving banks to focus on the entire customer journey and touchpoints in customers’ daily lives. Within this evolving banking ecosystem, data is becoming a critical asset, with front runners holding and storing information while also using it strategically to inform business decisions, create value and unlock revenue. Collaboration with financial services (FS) has become commonplace as firms pursue new capabilities by working with best-fit partners versus building or buying new resources — a shift that allows them to innovate without losing the focus on essential activities. As ecosystem partnerships promote shared access versus complete ownership, banks and other players will be able to refine their products, adopt offerings from across ecosystems and create new sources of revenue. This paper demonstrates how the inevitable new FS era, Open X, will feature a shared marketplace with a seamless exchange of resources, improved experiences for customers and others and expedited product innovation to create a winning go-to-market platform for all ecosystem participants.

A case-based reasoning system for recommendation of data cleaning algorithms in classification and regression tasks

Recently, advances in Information Technologies (social networks, mobile applications, Internet of Things, etc.) generate a deluge of digital data; but to convert these data into useful information for business decisions is a growing challenge. Exploiting the massive amount of data through knowledge discovery (KD) process includes identifying valid, novel, potentially useful and understandable patterns from a huge volume of data. However, to prepare the data is a non-trivial refinement task that requires technical expertise in methods and algorithms for data cleaning. Consequently, the use of a suitable data analysis technique is a headache for inexpert users. To address these problems, we propose a case-based reasoning system (CBR) to recommend data cleaning algorithms for classification and regression tasks. In our approach, we represent the problem space by the meta-features of the dataset, its attributes, and the target variable. The solution space contains the algorithms of data cleaning used for each dataset. We represent the cases through a Data Cleaning Ontology. The case retrieval mechanism is composed of a filter and similarity phases. In the first phase, we defined two filter approaches based on clustering and quartile analysis. These filters retrieve a reduced number of relevant cases. The second phase computes a ranking of the retrieved cases by filter approaches, and it scores a similarity between a new case and the retrieved cases. The retrieval mechanism proposed was evaluated through a set of judges. The panel of judges scores the similarity between a query case against all cases of the case-base (ground truth). The results of the retrieval mechanism reach an average precision on judges ranking of 94.5% in top 3 (P@3), for top 7 (P@7) 84.55%, while in top 10 (P@10) 78.35%.

Application of Big Data Copula-Based Clustering for Hedging in Renewable Energy Systems

In this paper, we formulate an optimisation-hedging model which demonstrates how operational research methods and analytics can take advantage of big data sources to inform business decisions in the renewable energy sector. This is achieved by incorporating an analytical technique called co-cluster (copula clustering) algorithm in measuring risks confronting a renewable energy producer. The model development and co-cluster methodology are illustrated using an empirical case study under three market scenarios for an ethanol producer. Our results show that adopting the co-cluster algorithm gives the ethanol processor an improved risk management strategy by capturing marginal relationships among the input and output prices; hence highlighting the advantages of big data and data analytics in business decision making within the renewable energy sector.

Market Research for Digital Promotions & Sales for EDTech Industry

Market research is the process of gathering, analyzing, and interpreting information about a market, product, or service, in order to make informed business decisions. In the edtech (education technology) industry, market research can be particularly important for developing digital promotions and sales strategies. Digital promotions refer to marketing efforts that use the internet, social media, and other online platforms to reach and engage with customers. These promotions can be particularly effective in the edtech industry, as they allow companies to target specific demographics and reach a large, dispersed audience. In order to effectively execute digital promotions, edtech companies should conduct market research to understand the needs and preferences of their target customers. This may involve surveying potential users, analyzing data on customer behavior, and studying industry trends. By conducting market research, edtech companies can better understand what types of digital promotions will be most effective, and how to optimize their sales strategies for the online environment. This can help them to more effectively reach and engage with customers, and ultimately drive sales and revenue growth.

A review of data science in business and industry and a future view

AbstractThe aim of this paper is to frame Data Science, a fashion and emerging topic nowadays in the context of business and industry. We open with a discussion about the origin of Data Science and its requirement for a challenging mix of capability in data analytics, information technology, and business know‐how. The mission of Data Science is to provide new or revised computational theory able to extract useful information from the massive volumes of data collected at an accelerating pace. In fact, besides the traditional measurements, digital data obtained from images, text, audio, sensors, etc complement the survey. Then, we review the different and most popular methodologies among the practitioners of Data Science research and applications. In addition, because the emerging field requires personnel with new competences, we attempt to describe the Data Scientist profile, one of the sexiest jobs of the 21st Century according to Davenport and Patil. Most people are aware of the need to embrace Data Science, but they feel intimidated that they do not understand it and they worry that their jobs will disappear. We want to encourage them: Data Science is more likely to add value to jobs and enrich the lives of working people by helping them make better, more informed business decisions. We conclude this paper by presenting examples of Data Science in action in business and industry, to demonstrate the collection of specialist skills that must come together for this new science to be effective.

Cost of leukopenia and neutropenia in metastatic breast cancer within last 12, 36, and 60 months using a curated disease model

Background: Evaluating changes to historical treatment costs is critical for healthcare professionals to make informed business decisions. However, real-world clinical and cost outcome data is challenging to use regularly without significant data science knowledge or resources. Aims: This study sought to demonstrate the potential value in user-friendly analytics tools to identify drivers of costs and outcomes. Methods: The IBM Access and Value Connect solution was used to analyze a patient cohort of metastatic breast cancer (mBC) patients treated in the most recent 12, 36, and 60 months in the IBM MarketScan; Commercial and Medicare Supplement Database. We used the interactive visual explorer tool to quickly (<15 min) determine the mean total per-patient-per-month (PPPM) cost associated with mBC overall and for select side-effects by age group (45–54, 55–64, 65–74, and 75+), and generated histograms for mean total PPPM overall and for leukopenia and neutropenia by age group for each study period. Results: The mean total PPPM across all mBC patients ranged from $6,562 for the 75+ age group at 60 months to $14,201 for the 45–54 age group at 12 months. For those who experienced leukopenia, the mean total PPPM ranged from $10,319 for the 75+ age group at 60 months to $19,598 for the 45–55 age group at 60 months. Similarly, for those who experienced neutropenia, the mean total PPPM ranged from $10,593 for the 65–74 age group at 60 months to $21,784 for the 45–54 age group at 12 months. Conclusions: These methods show that it is possible to make PPPM costs easily available without data science, clinical, or programming knowledge with interactive, visual analytics. The results showed that in general PPPM costs are higher for younger patients overall and among those who experience leukopenia or neutropenia. This is likely due to the practice to aggressively treat younger patients.

Islamic Banks’ Financing and Employment Generation: The Case of Punjab, Pakistan

The objective of this study is to assess the impact of Islamic financing modes on employment through asset formation and value creation of Islamic Banking in Punjab, Pakistan. Data was collected from two randomly selected Islamic Banks in Pakistan namely Meezan Bank and Al-Baraka Bank (Pakistan) Ltd. Multistage sampling procedure was used for selection of sample respondents from three regions of Punjab. List frame of the study consisted of respondents/firms utilizing finance from selected Islamic Banks located in each district. The sample of the study comprised of 206 small and medium firms who utilized Islamic modes of financing to get financing for their business. Variables of the model comprised of employment generation, asset formation, technology, firm age and business size. Least square regression model was used for analysis of the cross sectional data. All the independent variables of the model have significant effect on dependent variable. Empirical results revealed that only asset formation has direct relationship with employment generation. Technology, firm age and business size have inverse relationship with employment generation capacity of the borrowing firms utilizing Islamic finance. The study concluded that in Pakistan, the SME’s used financing tool to enhance the assets by adopting the technological change. The results of the study will support policy makers, practitioners and planners to make more informed business decisions.

Tennis superstars: The relationship between star status and demand for tickets

Akin to other sports, professional tennis is urged to adopt a consumer-centred strategy and understand the influence of the star status of elite players on demand for its core product. Measuring the impact that tennis players have on demand for match attendance remains a key element towards achieving that goal. Using data from the Australian Open ticket sales, the authors demonstrate how individual players have influenced stadium attendance at the Grand Slam. Findings indicate that some players are associated with a strong positive impact on demand for tickets, above and beyond their performance ratings, reflecting their value to the Australian Open. The authors discuss how this star status can be used to inform business decisions related to tournament management, match scheduling, and determining player appearance fees, to ultimately drive better commercial outcomes and deliver a world-class sporting event. The findings have implications for tournament organisers, player managers and those that market player activities.

Shale oil resource potential and oil mobility characteristics of the Eocene-Oligocene Shahejie Formation, Jiyang Super-Depression, Bohai Bay Basin of China

Oil mobility in liquid-rich shale is crucial for economic success of resource development. The characteristics of the host rock and the properties of oil fluids are two primary factors controlling fluid mobility. Organic geochemistry data allows examination of both resource potential and oil mobility. We propose data-driven empirical models in this paper to describe hydrocarbon resource potential with respect to oil mobility. The proposed methods were applied to a dataset collected from the Eocene-Oligocene Es3 and Es4 source rock units in four recent shale oil exploration wells in the Jiyang Super-Depression of the Bohai Bay Basin. The dataset, results from standard programmed pyrolysis of whole rock samples as well as solvent extracted replicates of 139 core samples, was studied to examine the source rock characteristics and shale oil resource potential. Bulk geochemical characteristics and thermal decomposition trends allow for sub-division of the samples into two groups; a) samples with high initial hydrogen index (>800 mg HC/g TOC) and average present day TOC of 4.07% from LY1 and NY1 wells in a distal facies; and b) samples with slightly lower initial hydrogen index (about 700 mg HC/g TOC) and average TOC of 3.11% from FY1 and L69 wells in a transitional facies. Three different categories of oil resource with respect to oil mobility can be defined: adsorbed, restricted and movable based on organic geochemical characteristics and their variations in the stratigraphic intervals represented by the samples in the four wells. We show that expulsion efficiency is a good indicator of hydrocarbon retention in source rock reservoir and that the two wells in the distal facies have higher total resource potentials with abundant movable oil resource than the two wells in the transitional facies. The total oil potential is separated into three types of oil resource to show oil mobility, thus providing more information for business decision.

The Value of US Government Data to US Business Decisions

The US government is a major producer of economic and financial data, statistics, analysis, and forecasts that are gathered, compiled, and published as public goods for use by citizens, government agencies, researchers, nonprofits, and the business community. There is no market transaction in the publication and dissemination of these government data and therefore no market-determined value. The purpose of this paper is to outline and augment our understanding of the value of government data for business decision-making. We provide an overview of the topic, including results from government reports and a private sector survey. We then provide concrete examples of how these government data are used to make business decisions focusing on three sectors: automotive, energy, and financial services. Examples of new initiatives by the federal government to open access to more data, exploiting technology advances associated with the internet, cloud storage, and software applications, are discussed. With the significant growth in the digital economy, we also include discussion and insights around how digital platform companies utilize government data in conjunction with their privately generated data (or “big data”) to foster more informed business decisions.

Economic Decision Making and Risk Management

The definition of long-term objectives in corporate management as well as means of achieving them is often associated with uncertainty. The reason for this is apparent: managers in corporate entities cannot predict all circumstances, whether positive or negative, that is likely to occur in the future. Management during the decision-making process must be able to make informed decisions given the existence of insecurities and uncertainties in the course of business operations. Management must, therefore, take into account the risks that might occur in the future. As a result, this article aims at discussing the risks affecting corporate entities. The paper also defines and analyzes the risks, thus explaining how business entities can tackle them through making informed business decisions.

Discussion of “Relating Product Prices to Long‐Run Marginal Cost: Evidence from Solar Photovoltaic Modules”

AbstractMy discussion of Reichelstein and Sahoo (2018) is organized into three parts. First, what is the context for this work? That is, what are the energy and climate stakes? Second, what does the paper do? I will re‐work the equation defining the components of long‐run marginal cost to better reflect what is actually estimated and to better highlight some important choices about how the different components are modeled. Third, and most importantly, how do the results inform business decisions and policy choices regarding energy and climate? What more do we need?

An integrated approach to sustainability and delivery of improved health outcomes for Children’s Health Queensland community based services and programs

Introduction : A population health and locality based planning approach has been used to design and implement a digital reporting and monitoring platform to inform service improvement, guide strategic planning activity and support the implementation of CHQs key strategic priorities. The approach is framed within a Results Based Accountability (RBA) model that provides a stepped and disciplined approach to improving patient outcomes. Aim and theory of change : Taking a population health and locality based planning approach to the implementation of service improvement strategies ensures that child health services and programs are provided in the right location and consumers have improved outcomes. Targeted population and stakeholders : For all child health localities the service activity, workforce, consumer feedback and socio-demographic data were collated into a respository and was made available to all child health services and programs through an interactive digital dashboard. The information is rendered and presented via a series of performance indicators across five domains: Service activity; Economic sustainability; Healthy and engaged workforce; Experience of care; Results based accountability. The interactive dashboards are made available through ‘QlikView’ software and used to establish a baseline prior to implementing an intervention to improve service delivery. The same suite of tools are used to monitor the impact of the intervention. In each locality interventions were implemented to reduce the variation in specific performance indicators and the intent to increase home visiting, decrease failure to attend rates, increase new to review ratio, and increase partnerships with local services. Highlights : Each of the child health team accessing the dashboard has identified areas for improvement based on the digital dashboard reporting. Models of service delivery have been adjusted based on data e.g. Home visits to the consumers’ home have increased from 17% to 23% of all visiting with a target of 25%. Failure to attend rates across teams has decreased from 18% to 12% with significant reduction in variation with one team able to reduce from 30% to 14%. New to review ratio has improved across all teams to 1:4 with a reduction in repeat appointments. Consumers over a one month period have responded (n=652) to how well did we treat them, did we help them with their problems, why did they rate us this way, and is there any way we could have improved what we did. Comments and Conclusion : Child health services and program areas now have the capacity to make informed business decisions about clinical services, utilisation of resources and planning for longer term facility requirements with services and partners. Wider use of the dashboard reporting is now being used in Child Development Services, School Based Youth Health Nursing Service and other community based services.

Communicating airport concession operating costs

The long-term success of a concessions programme can depend on well-constructed business plans through which concessionaires demonstrate a thorough understanding of the cost of doing business at an airport. Sharing information upfront can allow concessionaires to make informed business decisions and is the first step to successful airport and concessionaire relationships. The layers of complexity and associated costs of operating a restaurant or shop in an airport are factors that prospective concessionaires need to fully understand in order to judge the potential for success and profitability of business opportunities. Operating at an airport presents a unique set of challenges not found in traditional street operations. Aside from basic operating costs associated with the business, airport concessionaires must operate longer hours to accommodate passenger traffic, comply with federal and airport laws and regulations, pay minimum rents and capital investment requirements, as well as a myriad of other fees associated with operating at the airport. Unfortunately, there is not an industry manual available that clearly defines the operating costs of doing business at airports, nor is there a consistent list of fees common to all airports. Each airport is unique, with their own set of specific operating requirements. Furthermore, only some airports offer a comprehensive list of anticipated concession operating costs at the onset of the request for proposals (RFP) process — at the point at which interested concessionaires are evaluating business opportunities. A prospective concessionaire — particularly new, small and/or inexperienced concessionaires — may have insufficient information to evaluate an airport business proposition. Consequently, these concessionaires may incur unexpected costs that challenge their financial viability and affect the customer service they deliver to airport passengers. Unison Consulting, along with the Airport Cooperative Research Program (ACRP), completed a synthesis to help identify gaps in the communication of cost data from airports to retail and food and beverage concessionaires. Armed with more complete information regarding the full costs of operating in the airport environment, prospective concessionaires — particularly less experienced businesses — will be better able to gauge the opportunity being offered by airports. This will result in better proposals submitted for airport consideration, featuring more accurate business plans and pro formas.