53,647 research outputs found

    How do top- and bottom-performing companies differ in using business analytics?

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    Purpose Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA. Design/methodology/approach Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies. Findings Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment. Practical implications Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities. Originality/value This study provides useful management insights into the effective use of BA for improving organizational performance

    Determining the indirect value of a customer

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    The issue of accountability in marketing has led to a substantial and growing body of work on how to value customer relationships. Net present value methods (customer lifetime value / customer equity) have emerged as generally preferred ways to assess the financial value of customers. However, such calculations fail to take account of other important but indirect sources of value noted by previous researchers, such as advocacy. This paper examines the development and application of three processes to determine indirect value in business-to- business and business-to-consumer contexts. The research shows that indirect value has a measurable monetary impact not captured by conventional financial tools, and that understanding this changes the way in which customers are managed

    Collinsville solar thermal project: energy economics and dispatch forecasting (final report)

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    The primary aim of this report is to help negotiate a Power Purchase Agreement (PPA) for the proposed hybrid gas-Linear Frensel Reflector (LFR) plant at Collinsville, Queensland, Australia.  The report’s wider appeal is the discussion of the current situation in Australian National Electricity Market (NEM) and techniques and methods used to model the NEM’s demand and wholesale spot prices for the lifetime of the proposed plant. Executive Summary 1        Introduction This report primarily aims to provide both dispatch and wholesale spot price forecasts for the proposed hybrid gas-solar thermal plant at Collinsville, Queensland, Australia for its lifetime 2017-47.  These forecasts are to facilitate Power Purchase Agreement (PPA) negotiations and to evaluate the proposed dispatch profile in Table 3.  The solar thermal component of the plant uses Linear Fresnel Reflector (LFR) technology.  The proposed profile maintains a 30 MW dispatch during the weekdays by topping up the yield from the LFR by dispatch from the gas generator and imitates a baseload function currently provided by coal generators.  This report is the second of two reports and uses the findings of our first report on yield forecasting (Bell, Wild & Foster 2014b). 2        Literature review The literature review discusses demand and supply forecasts, which we use to forecast wholesale spot prices with the Australian National Electricity Market (ANEM) model. The review introduces the concept of gross demand to supplement the Australian Electricity Market Operator’s (AEMO) “total demand”.  This gross demand concept helps to explain the permanent transformation of the demand in the National Electricity Market (NEM) region and the recent demand over forecasting by the AEMO.  We also discuss factors causing the permanent transformation.  The review also discusses the implications of the irregular ENSO cycle for demand and its role in over forecasting demand. Forecasting supply requires assimilating the information in the Electricity Statement of Opportunities (ESO) (AEMO 2013a, 2014c).  AEMO expects a reserve surplus across the NEM beyond 2023-24.  Compounding this reserve surplus, there is a continuing decline in manufacturing, which is freeing up supply capacity elsewhere in the NEM.  The combined effect of export LNG prices and declining total demand are hampering decisions to transform proposed gas generation investment into actual investment and hampering the role for gas as a bridging technology in the NEM.  The review also estimates expected lower and upper bounds for domestic gas prices to determine the sensitivity of the NEM’s wholesale spot prices and plant’s revenue to gas prices. The largest proposed investment in the NEM is from wind generation but the low demand to wind speed correlation induces wholesale spot price volatility.  However, McKinsey Global Institute (MGI 2014) and Norris et al. (2014a) expect economically viable energy storage shortly beyond the planning horizon of the ESO in 2023-24.  We expect that this viability will not only defer investment in generation and transmission but also accelerate the growth in off-market produced and consumed electricity within the NEM region. 2.1     Research questions The report has the following overarching research questions: What is the expected dispatch of the proposed plant’s gas component given the plant’s dispatch profile and expected LFR yield? What are the wholesale spots prices on the NEM given the plant’s dispatch profile? The literature review refines the latter research question into five more specific research questions ready for the methodology: What are the half-hourly wholesale spots prices for the plant’s lifetime without gas as a bridging technology? Assuming a reference gas price of between 5.27/GJto5.27/GJ to 7.19/GJ for base-load gas generation (depending upon nodal location;) and for peak-load gas generation of between 6.59/GJto6.59/GJ to 8.99/GJ; and given the plant’s dispatch profile What are the half-hourly wholesale spots prices for the plant’s lifetime with gas as a bridging technology? Assuming some replacement of coal with gas generation How sensitive are wholesale spot prices to higher gas prices? Assuming high gas prices are between 7.79/GJto7.79/GJ to 9.71/GJ for base-load gas generation (depending upon nodal location); and for peak-load gas generation of between 9.74/GJto9.74/GJ to 12.14/GJ; and What is the plant’s revenue for the reference gas prices? How sensitive is the plant’s revenue to gas as a bridging technology? How sensitive is the plant’s revenue to the higher gas prices? What is the levelised cost of energy for the proposed plant? 3        Methodology In the methodology section, we discuss the following items: dispatch forecasting for the proposed plant; supply capacity for the years 2014-47 for the NEM; demand forecasting using a Typical Meteorological Year (TMY); and wholesale spot prices calculation using ANEM, supply capacity and total demand define three scenarios to address the research questions: reference gas prices; gas as a bridging technology; and high gas prices. The TMY demand matches the solar thermal plant’s TMY yield forecast that we developed in our previous report (Bell, Wild & Foster 2014b).  Together, these forecasts help address the research questions. 4        Results In the results section we will present the findings for each research question, including the TMY yield for the LFR and the dispatch of the gas generator given the proposed dispatch profile in Table 3; Average annual wholesale spot prices from 2017 to 2047 for the plant’s node for: Reference gas prices scenario from 18/MWhto18/MWh to 38/MWh Gas as a bridging technology scenario from 18/MWhto18/MWh to 110/MWh High gas price scenario from 20/MWhto20/MWh to 41/MWh The combined plants revenue without subsidy given the proposed profile: Reference gas price scenario 36millionGasasabridgingtechnologyscenario36 million Gas as a bridging technology scenario 52 million High gas price scenario $47 million 5        Discussion In the discussion section, we analyse: reasons for the changes in the average annual spot prices for the three scenarios; and the frequency that the half-hourly spot price exceeds the Short Run Marginal Cost (SRMC) of the gas generator for the three scenarios for: day of the week; month of the year; and time of the day. If the wholesale spot price exceeds the SRMC, dispatch from the gas plant contributes towards profits.  Otherwise, the dispatch contributes towards a loss.  We find that for both reference and high gas price scenarios the proposed profile in Table 3 captures exceedances for the day of the week and the time of the day but causes the plant to run at a loss for several months of the year.  Figure 14 shows that the proposed profile captures the exceedance by hour of the day and Figure 16 shows that only operating the gas component Monday to Friday is well justified.  However, Figure 15 shows that operating the gas plant in April, May, September and October is contributing toward a loss.  Months either side of these four months have a marginal number of exceedances.  In the unlikely case of gas as a bridging scenario, extending the proposed profile to include the weekend and operating from 6 am to midnight would contribute to profits. We offer an alternative strategy to the proposed profile because the proposed profile in the most likely scenarios proves loss making when considering the gas component’s operation throughout the year.  The gas-LFR plant imitating the based-load role of a coal generator takes advantage of the strengths of the gas and LFR component, that is, the flexibility of gas to compensate for the LFR’s intermittency, and utilising the LFR’s low SRMC.  However, the high SRMC of the gas component in a baseload role loses the flexibility to respond to market conditions and contributes to loss instead of profit and to CO2 production during periods of low demand. The alternative profile retains the advantages of the proposed profile but allows the gas component freedom to exploit market conditions.  Figure 17 introduces the perfect day’s yield profile calculated from the maximum hourly yield from the years 2007-13.  The gas generator tops up the actual LFR yield to the perfect day’s yield profile to cover LFR intermittency.  The residual capacity of the gas generator is free to meet demand when spot market prices exceed SRMC and price spikes during Value-of-Lost-Load (VOLL) events.  The flexibility of the gas component may prove more advantageous as the penetration of intermittent renewable energy increases. 6        Conclusion We find that the proposed plant is a useful addition to the NEM but the proposed profile is unsuitable because the gas component is loss making for four months of the year and producing CO2 during periods of low demand.  We recommend further research using the alternative perfect day’s yield profile. 7        Further Research We discuss further research compiled from recommendation elsewhere in the report. 8        Appendix A Australian National Electricity Market Model Network This appendix provides diagrams of the generation and load serving entity nodes and the transmission lines that the ANEM model uses.  There are 52 nodes and 68 transmission lines, which make the ANEM model realistic.  In comparison, many other models of the NEM are highly aggregated. 9        Appendix B Australian National Electricity Market Model This appendix describes the ANEM model in detail and provides additional information on the assumptions made about the change in the generation fleet in the NEM during the lifetime of the proposed plant

    An Integral Evaluation of the Financial State of the Regional Enterprises

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    The subject matter of the article is the development of theoretical positions and methodical approaches to the integral evaluation of the financial state of the region’s metallurgical enterprises. The purpose is to show the possibility of dividing the integral evaluation into separate elements for using this tool to build individual models based on the forecasting of the various coordinates of the financial position of enterprise. The hypothesis of the study is based on the objective need to improve the integral evaluation of the financial position of enterprises. This involves the modernization of existing theoretical and methodological approaches to the increase of the quality of analysis by eliminating certain shortcomings of discriminant models in order to clarify the algorithm of constructing the integral index. The methodological bases of systemic approach and mathematical modelling in economics are applied: the methods of financial analysis, grouping, abstraction, comparison which give the possibility of determining the financial indicators needed to build the predictive models of financial state; the methods of correlation and regression analysis, which allow to improve the integral value and to build the mathematical forecasting models. With the purpose of improving the integral evaluation of the financial condition of enterprise, the geometric interpretation is used, which involves the dividing of the integral indicator on the individual elements. The special feature of the proposed methodological approach consists in the implementation rules for the certain procedures of the evaluation of financial position and generalization of the analysis results. The proposed approach can be used by financial analysts to elaborate the strategic plans of company development and structure optimization of financial resources. This research allows to define the quantitative influence of separate parameters on the general assessment of the financial position for the purpose of its forecasting, which is understood as the system of the evidence-based probabilistic assumptions of the basic and alternative structural changes of the enterprise’s assets and liabilities

    An economic model of the manufacturers' aircraft production and airline earnings potential, volume 3

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    A behavioral explanation of the process of technological change in the U. S. aircraft manufacturing and airline industries is presented. The model indicates the principal factors which influence the aircraft (airframe) manufacturers in researching, developing, constructing and promoting new aircraft technology; and the financial requirements which determine the delivery of new aircraft to the domestic trunk airlines. Following specification and calibration of the model, the types and numbers of new aircraft were estimated historically for each airline's fleet. Examples of possible applications of the model to forecasting an individual airline's future fleet also are provided. The functional form of the model is a composite which was derived from several preceding econometric models developed on the foundations of the economics of innovation, acquisition, and technological change and represents an important contribution to the improved understanding of the economic and financial requirements for aircraft selection and production. The model's primary application will be to forecast the future types and numbers of new aircraft required for each domestic airline's fleet

    Competition and the Provision of Rail Passenger Services: A simulation exercise

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    This paper presents the results of simulating the effects of introducing competition on a long distance international rail passenger route where there is also a strong domestic market served by high speed trains. We are aware of a number of proposals to introduce new services in such circumstances. It has allowed for the fact that on such a service seat reservations are likely to be compulsory and yield management practiced, so that whatever is initially assumed about fares there will be further endogenous changes in average fares to maintain high load factors. It is found that on-track competition has benefits to consumers, in terms of fares and services, but that it would reduce the profitability of the incumbent and that it would be difficult for the new entrant to attain profitability unless its costs were significantly lower than those of the incumbent. A large part of the revenue of the entrant on this route would come from the domestic market, and if open access competition were permitted then the entrant might seek to run a frequent service offering head on competition on this part of the route. However, again it would appear that both operators would make heavy losses in this situation. One way of restoring profitability might be to reduce track access charges, but that would require additional government subsidy to the infrastructure manager, as the additional train kilometres run would not compensate for the lower charges. An alternative way of seeking to achieve the same result as on track competition in terms of reduced costs and innovation whilst preserving economies of density would be to award a monopoly franchise by means of competitive tendering. Franchising has generally succeeded in raising rail demand and reducing costs, although in the one example where inter-city services were franchised – Britain – costs have actually risen. Thus unless this is due to peculiarities of the British situation which would not exist elsewhere, on track competition may still have a role in reducing costs

    Predicting Customer Lifetime Value in Multi-Service Industries

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    Customer lifetime value (CLV) is a key-metric within CRM. Although, a large number of marketing scientists and practitioners argue in favor of this metric, there are only a few studies that consider the predictive modeling of CLV. In this study we focus on the prediction of CLV in multi-service industries. In these industries customer behavior is rather complex, because customers can purchase more than one service, and these purchases are often not independent from each other. We compare the predictive performance of different models, which vary in complexity and realism. Our results show that for our application simple models assuming constant profits over time have the best predictive performance at the individual customer level. At the customer base level more complicated models have the best performance. At the aggregate level, forecasting errors are rather small, which emphasizes the usability of CLV predictions for customer base valuation purposes. This might especially be interesting for accountants and financial analysts.forecasting;value;customer relationship management;customer lifetime value;customer segmentation;database marketing;interactive marketing

    Fifty years of Research on Accuracy of Capital Expenditure Project Estimates: A Review of the Findings and their Validity.

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    Capital budgeting research has traditionally focused on ever improving the methods used for evaluating projects. Since it seems futile to use sophisticated evaluation techniques if their input data – that is, estimates of cash inflows and outflows – are of inferior quality, it is justifiable to call this focus into question by exploring forecasting accuracy. In order to do so, the article analyzes the empirical findings on estimation error gathered in 35 studies published between 1954 and 2002. As the review shows, over-optimism seems to be a relevant problem in capital expenditure project forecasting. This calls the traditional research focus into question. More research effort targeted at the misestimation bias in capital budgeting and at ways to improve forecasting accuracy seems necessary.Capital budgeting, Capital Expenditures, Estimation Accuracy, Forecasting, Post-Audit.

    Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (1/4)

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    Technical report about sustainable urban freight solutions, part 1 of
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