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Forecasting.

The case is intended for Postgraduate level management students.

The purpose of this case study is to explain various forecasting techniques, their applicability and the importance of forecasting to the students. This case also explains the management situations where the application of one technique may not be sufficient, thereby explaining the importance of simultaneous usage of qualitative and quantitative techniques for making crucial decisions. The case is focused on the district of Etah of the state of Uttar Pradesh in India. The real-life situation of elections in this district of an Indian state has been taken to explain the critical nature of forecasting accuracy in a management situation where the manager has only one chance to execute his project. Discussion in this case is limited to explaining various techniques available for forecasting and their applications and does not provide a solution to a management problem.

The students are expected to understand various forecasting methods and the managerial situations where these can be applied. The case also explains situations where it becomes extremely important to have fairly accurate estimates of future requirements and the application of one technique may not be sufficient, thereby explaining the importance of simultaneous usage of qualitative and quantitative techniques for making crucial decisions.

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CSS 9: Operations and Logistics.

Construction plant breakdown affects projects by prolonging duration and increasing costs. Therefore, prediction of plant breakdown, as a precursor to conducting timely maintenance works, cannot be underestimated. This paper thus sought to develop a model for predicting plant breakdown time from a sequence of discrete plant breakdown measurements that follow non‐random orders. An ARIMA (1,1,0) model was constructed following experimentation with exponential smoothening. The model utilised breakdown observations obtained from six wheeled loaders that had operated a total of 14,467 hours spread over a 300‐week period. The performance statistics revealed MAD and RMSE of 5.03 and 5.33 percent respectively illustrating that the derived time series model is accurate in modelling the dependent variable. Also, the F‐statistics from the ANOVA showed that the type and frequency of fault occurrence as a predictor variable is significant on the model's performance at the five percent level. Future work seeks to consider a more in depth multivariate time series analyses and compare/contrast the results of such against other deterministic modelling techniques.

The paper aims to evaluate different artificial neural network models and to suggest a suitable model for forecasting inflation in G-7 countries.

The study applies different combinations of neural networks with hyperbolic tangent function using backpropagation learning with the steepest gradient descent technique to monthly data on Consumer Price Index (a measure of inflation) of the USA, the UK, France, Germany, Italy, Japan and Canada.

Predictions of inflation based on the Consumer Price Index for all the seven countries divulged that it is expected that the rate of inflation will decline marginally in the near future.

The results proposed in this study will be a benchmark for policy-makers, economists and practitioners to forecast inflation and design policies accordingly.

The paper’s findings provide strong evidence for policy-makers that while constructing models for forecasting inflation, the suggested models can be used to track the future rates of inflation and, further, they can apply that model in framing policies.

Effective management and utilisation of plant history data can considerably improve plant and equipment performance. This rationale underpins statistical and mathematical models for exploiting plant management data more efficiently, but industry has been slow to adopt these models. Reasons proffered for this include: a perception of models being too complex and time consuming; and an inability of their being able to account for dynamism inherent within data sets. To help address this situation, this research developed and tested a web‐based data capture and information management system. Specifically, the system represents integration of a web‐enabled relational database management system (RDBMS) with a model base management system (MBMS). The RDBMS captures historical data from geographically dispersed plant sites, while the MBMS hosts a set of (Autoregressive Integrated Moving Average – ARIMA) time series models to predict plant breakdown. Using a sample of plant history file data, the system and ARIMA predictive capacity were tested. As a measure of model error, the Mean Absolute Deviation (MAD) ranged between 5.34 and 11.07 per cent for the plant items used in the test. The Root Mean Square Error (RMSE) values also showed similar trends, with the prediction model yielding the highest value of 29.79 per cent. The paper concludes with direction for future work, which includes refining the Graphical User Interface (GUI) and developing a Knowledge Based Management System (KBMS) to interface with the RDBMS.

This paper aims to analyze and give directions for advancing research in stock market volatility highlighting its features, structural breaks and emerging developments. This study offers a platform to research the benchmark studies to know the research gap and give directions for extending future research.

The author has performed the literature review, and, reference checking as per the snowballing approach. Firstly, the author has started with outlining and simplifying the significance of the subject area, the review illustrating the various elements along with the research gaps and emphasizing the finding.

This work summarizes the studies covering the volatility, its properties and structural breaks on various aspects such as techniques applied, subareas and the markets. From the review’s analysis, no study has clarified the supremacy of any model because of the different market conditions, nature of data and methodological aspects. The outcome of this research work has delivered further magnitude to research the benchmark studies for the upcoming work on stock market volatility. This paper has also proposed the hybrid volatility models combining artificial intelligence with econometric techniques to detect noise, sudden changes and chaotic information easily.

The author has taken the research papers from the scholarly journal published in the English language only and the author may also consider other nonscholarly or other language journals.

To the best of the author’s knowledge, this research work highlights an updated and more comprehensive framework examining the properties and demonstrating the contemporary developments in the field of stock market volatility.

Interfacial roughness in heterostructures critically degrade the optical and electrical properties of the devices fabricated with III‐V semiconductor compounds. Experimental work on the surface roughening processes during MBE growth by monitoring the reflection high energy electron diffraction (RHEED) intensity concluded that the surface roughening is a result of competition between surface roughening processes such as adsorption and evaporation and the surface smoothening process such as surface migration to stable sites. In this work, the stochastic model of MBE growth is employed to study the surface roughening kinetics in GaAs (100). The growth condition was chosen similar to that of experiments: temperature range of study: 773 – 873°K; cation to anion flux ratio in the range: 1 : 10 to 1 : 20. Diatomic arsenic molecular specie is employed for the study was As2. The time averaged RHEED intensity was obtained from the growth data and with the experimental results. The agreement between the results was excellent. A transition temperature at which the time averaged RHEED intensity is a maximum was observed for flux ratios 1 : 10 and 1 : 20. The RHEED intensity increases with temperature till the transition temperature due to surface smoothening resulting from the surface migration of Ga and As to energetically favorable sites. The RHEED intensity decreases beyond the transition temperature due to the evaporation of As from the surface. The transition temperature is observed to be a function of the flux ratio and can be explained by the difference in time for the formation of energetically stable surface adatom clusters resulting from the difference in the effective surface migration rates for various flux ratios.

This study aims to introduce a methodology for optimal allocation of spinning reserves taking into account load, wind and solar generation by application of the univariate and bivariate parametric models, conventional intra and inter-zonal spinning reserve capacity as well as demand response through utilization of capacity outage probability tables and the equivalent assisting unit approach.

The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern probability density function (PDF). The study also uses the Bayesian network (BN) algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

The results show that the utilization of bivariate wind prediction model along with reserve allocation adjustment algorithm improve reliability of the power grid by 2.66% and reduce the total system operating costs by 1.12%.

The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern PDF. The study also uses the BN algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

The life insurance industry has been exposed to high levels of longevity risk born from the mismatch between realized mortality trends and anticipated forecast. Annuity providers are exposed to extended periods of annuity payments. There are no immediate instruments in the market to counter the risk directly. This paper aims to develop appropriate instruments for hedging longevity risk and providing an insight on how existing products can be tailor-made to effectively immunize portfolios consisting of life insurance using a cointegration vector error correction model with regime-switching (RS-VECM), which enables both short-term fluctuations, through the autoregressive structure [AR(1)] and long-run equilibria using a cointegration relationship. The authors also develop synthetic products that can be used to effectively hedge longevity risk faced by life insurance and annuity providers who actively hold portfolios of life insurance products. Models are derived using South African data. The authors also derive closed-form expressions for hedge ratios associated with synthetic products written on life insurance contracts as this will provide a natural way of immunizing the associated portfolios. The authors further show how to address the current liquidity challenges in the longevity market by devising longevity swaps and develop pricing and hedging algorithms for longevity-linked securities. The use of a cointergrating relationship improves the model fitting process, as all the VECMs and RS-VECMs yield greater criteria values than their vector autoregressive model (VAR) and regime-switching vector autoregressive model (RS-VAR) counterpart’s, even though there are accruing parameters involved.

The market model adopted from Ngai and Sherris (2011) is a cointegration RS-VECM for this enables both short-term fluctuations, through the AR(1) and long-run equilibria using a cointegration relationship (Johansen, 1988, 1995a, 1995b), with a heteroskedasticity through the use of regime-switching. The RS-VECM is seen to have the best fit for Australian data under various model selection criteria by Sherris and Zhang (2009). Harris (1997) (Sajjad et al., 2008) also fits a regime-switching VAR model using Australian (UK and US) data to four key macroeconomic variables (market stock indices), showing that regime-switching is a significant improvement over autoregressive conditional heteroscedasticity (ARCH) and generalised autoregressive conditional heteroscedasticity (GARCH) processes in the account for volatility, evidence similar to that of Sherris and Zhang (2009) in the case of Exponential Regressive Conditional Heteroscedasticity (ERCH). Ngai and Sherris (2011) and Sherris and Zhang (2009) also fit a VAR model to Australian data with simultaneous regime-switching across many economic and financial series.

The authors develop a longevity swap using nighttime data instead of usual income measures as it yields statistically accurate results. The authors also develop longevity derivatives and annuities including variable annuities with guaranteed lifetime withdrawal benefit (GLWB) and inflation-indexed annuities. Improved market and mortality models are developed and estimated using South African data to model the underlying risks. Macroeconomic variables dependence is modeled using a cointegrating VECM as used in Ngai and Sherris (2011), which enables both short-run dependence and long-run equilibrium. Longevity swaps provide protection against longevity risk and benefit the most from hedging longevity risk. Longevity bonds are also effective as a hedging instrument in life annuities. The cost of hedging, as reflected in the price of longevity risk, has a statistically significant effect on the effectiveness of hedging options.

This study relied on secondary data partly reported by independent institutions and the government, which may be biased because of smoothening, interpolation or extrapolation processes.

An examination of South Africa’s mortality based on industry experience in comparison to population mortality would demand confirmation of the analysis in this paper based on Belgian data as well as other less developed economies. This study shows that to provide inflation-indexed life annuities, there is a need for an active market for hedging inflation in South Africa. This would demand the South African Government through the help of Actuarial Society of South Africa (ASSA) to issue inflation-indexed securities which will help annuities and insurance providers immunize their portfolios from longevity risk.

In South Africa, there is an infant market for inflation hedging and no market for longevity swaps. The effect of not being able to hedge inflation is guaranteed, and longevity swaps in annuity products is revealed to be useful and significant, particularly using developing or emerging economies as a laboratory. This study has shown that government issuance or allowing issuance, of longevity swaps, can enable insurers to manage longevity risk. If the South African Government, through ASSA, is to develop a projected mortality reference index for South Africa, this would allow the development of mortality-linked securities and longevity swaps which ultimately maximize the social welfare of life assurance policy holders.

The paper proposes longevity swaps and static hedging because they are simple, less costly and practical with feasible applications to the South African market, an economy of over 50 million people. As the market for MLS develops further, dynamic hedging should become possible.

This paper aims to provide a new method of generating relatively accurate and smooth saturated B-H curves based on reliable measurement data to improve the accuracy and efficiency of electromagnetic simulation.

The characteristics of different B-H curve extrapolation models are summarized, and an improved method is proposed. The fitting procedure in low fields and extrapolation procedure in high fields are presented in detail. The saturated B-H curves generated by various methods are compared and discussed. Finally, a simulation case study proved the advantages of the new method in terms of simulation accuracy and efficiency.

The B-H curve created by the new method avoids extrapolation from a single point and simultaneously smoothens the entire B-H curve, thereby improving the simulation accuracy and efficiency. The low magnetic potential requirements for closed measurements and the small deviation with open measurements indicate that this method is well-suited for implementation.

The results are applicable for materials subject to such excitation levels that saturation has to be taken into account.

While some extrapolation models of B-H curves have been investigated in reference papers, there is still room for improvement in accuracy and smoothness. The new method processes low fields and high fields magnetization data and then connects them based on third-order boundary equations for the first time. This method can generate saturated B-H curves with good accuracy and smoothness while retaining outstanding operability.

The purpose of this paper is the investigation of a new coordinated switching strategy to improve the transient performance of a fuel cell (FC)- supercapacitor (SC) electric vehicle. The proposed switching strategy protects FCs from large currents drawn during abrupt power variations. Furthermore, it compensates the poor FC transient response and suppresses the transient ripples occurring during power source switching instants.

Coordinated power source switching is achieved using three different transition functions. Vehicle model is fractioned into computational and console subsystems for its simulation using real time (RT) LAB simulator. Blocs containing coordination switching strategy, power sources models and their power electronics interface are placed in the computational subsystem that will be executed, in RT, on one of real time laboratory simulator central processing unit cores.

Coordination switching strategy resulted in reducing transient power ripples by 90% and direct current (DC) bus voltage fluctuations by 50%. Switching through transition functions compensated the difference between FC and SC transient responses responsible for transient power ripples. Among the three proposed transition functions, linear transition function resulted in the best transient performances.

The proposed coordinated switching strategy allows the control of the switching period duration. Furthermore, it enables the choice of adequate transition functions that fit the dynamics of power sources undergoing transition. Also, the proposed switching technique is simple and does not require the knowledge of system parameters or the complex control models.