An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption

This paper introduces an integrated algorithm for forecasting electricity consumption (EL) based on fuzzy regression, time series and principal component analysis (PCA) in uncertain markets such as Iran. The algorithm is examined by mean absolute percentage error, analysis of variance (ANOVA) and Duncan Multiple Range Test. PCA is used to identify the input variables for the fuzzy regression and time series models. Monthly EL in Iran is used to show the superiority of the algorithm. Moreover, it is shown that the selected fuzzy regression model has better estimated values for total EL than time series. The algorithm provides as good results as intelligent methods. However, it is shown that the algorithm does not require utilization of preprocessing methods but genetic algorithm, artificial neural network and fuzzy inference system require preprocessing which could be a cumbersome task to deal with ambiguous data. The unique features of the proposed algorithm are three fold. First, two type of fuzzy regressions with and without preprocessed data are prescribed by the algorithm in order to minimize the bias. Second, it uses PCA approach instead of trial and error method for selecting the most important input variables. Third, ANOVA is used to statistically compare fuzzy regression and time series with actual data.     

Strategic human resource management and supply chain orientation

Most SHRM research has concentrated on single, focal organizations and on activities taking place within the firm. The purpose of this article is to lay a foundation for studying SHRM in the supply chain. We present a framework which identifies factors that influence whether adopting a supply chain orientation (SCO) is effective, and articulates the contingencies that shape SHRM practices needed to achieve and capitalize on SCO. We make several contributions to the strategic human resource management and supply chain literatures. First, we highlight this neglected area of research. Second, we expand the boundary conditions of strategic human resource management and HR systems from a primarily single firm, intra-organizational focus to one which includes both intra- and inter-organizational relationships. Third, we provide a framework for understanding the links between HR systems, SCO, and strategic outcomes. Fourth, we build upon previous theorizing in strategic human resource management and provide a framework for research in the supply chain context. And finally, we offer propositions for future research, along with a decision making model which has implications for both research and management practice.     

On the properties of the hotelling��s T2 control chart with variable sampling intervals

When T ² control chart is used to monitor a process, it is usually assumed that the samples of size n ₀ is taken at constant intervals t ₀ . In this paper, we investigate the T ² control chart for monitoring the process mean vector when the sampling intervals are variable. Recent studies have shown that the variable sampling interval (VSI) scheme helps practitioners detect process shifts more quickly than the classical scheme Fixed Ratio Sampling (FRS). In this paper, it is assumed that the length of time the process remains in control is exponentially distributed.     

Corporate financial distress: The case of publicly listed firms in an emerging market economy

Occurrences of financial distress (FD) are not readily obvious yet can span several periods. This paper examines episodes of FD using industry‐relative (IR) firm‐/ accounting‐, market‐ and macro‐level information. Mixed logit regressions reveal that firm‐ and market‐based measures, as well as macro‐level variables explain the likelihood of FD in 263 publicly listed non‐banking firms in the Philippines during the period 1995 to 2018. Rates of identification of firms in financial distressed states of close to 69 percent are obtained at a cutoff probability of 0.30 in the model with time‐varying intercept and slope. This study shows the importance of recognizing heterogeneous firm behavior. The ability to more accurately predict the probability of FD and to determine the financial health of firms can help financial institutions in allocating funds and policy makers in predicting crises episodes.