Effectiveness and Challenges of Green Environmental Planning in Apparel Companies of Sri Lanka

Globalization has made people deviate from the natural environment day by day, and it has also increased global warming with consequences for both the living and non-living environment. To address this issue, organizations have undertaken different kinds of environmental planning. Green Environmental Planning (GEP) is one of them. The very first green factories are owned by the massive garment producers in Sri Lanka. However, their success was challenged by many limitations in various ways. This study investigates the effectiveness of GEP and challenges of GEP in Sri Lankan apparel companies. This research was conducted through both qualitative and quantitative research methods. Five main components of GEP were examined in this study as the independent variables while the dependent variable was the effectiveness of GEP. The sample was based on two companies by using stratified random sampling method. One hundred and twenty employees were selected from the total population of each company. The primary data was collected through a questionnaire and face-to-face interviews and the secondary data were also used. All variables of GEP are highly correlated with the dependent variable of Effectiveness of Green Environmental Planning while temperature controlling is the strongest predictor of the dependent variable. The main challenges of improving GEP are technological weaknesses, lack of awareness of employees, conventional mindsets towards traditional buildings, less governmental contribution, less inter-sectorial coordination among related sectors and lack of professionals in the field. 50 Recommendations were proposed towards a more effective green environmental planning in Sri Lankan apparel companies.Key Words: Challenges, Effectiveness, Global Warming, Green Environmental Planning, Apparel Companie

Novel Tsetlin Machine Mechanisms for Logic-based Regression and Classification with Support for Continuous Input, Clause Weighting, Confidence Assessment, Deterministic Learning, and Convolution

publishedVersio

Eco-Sustainable Practices to Improve Industrial Efficiency: Case Study at a Medium Scale Apparel Manufacturing Company in Sri Lanka

Today, we live in a world which is materialistic, profit-oriented and industrial in nature. Butenvironmental degradation has increased up to a higher extent, and the anthropogenic climate change has taken place with many bad effects. People have deviated from the natural environment day by day and it has resulted in an increase of the work stress and frustration. It directly affects to the organisational productivity as human resource is the most important resource to achieve competitive advantages. That is why eco-sustainable practices are becoming so important in order to achieve efficiency. This paper is based on a case study of Eco Sustainable Practices to improve the efficiency in a selected apparel company. The major problem identified was the low efficiency due to unsustainable points in the process flow such as wastage of resources, high temperature level, high electricity/water/fossil fuel consumption and work-related employee stress. The main objective of the study is to suggest appropriate eco-sustainable practices to enhance the efficiency. Three different primary data collection methods were employed and analysed using qualitative and quantitative methods. The study found practically applicable solutions which direct the company to higher efficiency and standards after analyzing each part of the process flow and the facilities of the factory. Implementing green roofs with solar panels can reduce temperature and support carbon neutralisation while fulfilling the energy requirement throughout the year. Windows can be replaced with a special prismatic glass to avoid heat flow. LED task lights can be replaced in each machine to save energy compared to the conventional lighting system. Grass pavers are suggested for the driveway and the parking lot which absorb the surface heat as well as provide a pleasant green entrance way for the employees. Also, there are some specific plants suggested to grow inside of the factory to reduce heat, provide fresh air and lower the stress. Empty thread cones can be returned to suppliers after collecting them separately. Rain water harvesting for cleaning purposes reduces the water consumption considerably. A biological treatment for waste water can be introduced to protect the neighboring wetland to ensurethe quality of the ground water system. Waste food could collect and send to animal farms. Office waste papers can be reused as medicine bags and provided to nearest base hospital located. The arrangement of eco-friendly color themescan be recommended for making pleasant working environment. A well-maintained monitoring system under the top management is vital for all the eco-sustainable implementations. The study finally concluded with the suggestion to improve Eco Sustainable Practices on a long-term basis to improve efficiency in the factory selected.Keywords: Eco-Sustainable Practices; Efficiency, Industrie

Glass Ceilings, Sticky Floors or Sticky Doors? A Quantile Regression Approach to Exploring Gender Wage Gaps in Sri Lanka

Recently developed counterfactual techniques that combine quantile regression with a bootstrap approach allow for the interpretation of lower quantiles of the "simulated unconditional wage distribution" as if they related to poor people. We use this approach to analyse gender wage gaps across the wage distribution in Sri Lanka using quarterly labour force data from 1996 to 2004. Male and female wages are equal at the overall mean, but differ greatly between public and private sectors and across the wage distribution. We find that differences in the way identical men and women are rewarded in the labour market more than account for the difference in wages throughout the distribution. We find evidence of wider wage gaps at the bottom of the distribution in both sectors (indicative of "sticky floors"), but little evidence of larger gaps at the top of the distribution ("glass ceiligs"). Conditional wage gaps increase when controls for occupation, industry and part-time employment status are included, consistent with females selecting into occupations that better reward their characteristics. Policies that address gender bias in wage setting - especially in the low and unskilled occupations - are indicated, while policies that address gender bias in hiring and in workplace practices are likely to be more appropriate than policies that seek to improve womens' productivity-enhancing characteristics in reducing the gender wage gap.Gender gap, glass ceilings, sticky floors, quantile regression, public sector

Financial Risk, Financial Risk Management Practices and Performance of Sri Lankan SMEs: Special Reference to Anuradhapura District

All type of business organizations need to have careful monitoring of current expenses and forecast potential costs, which could be caused by risky actions. Risk is inherent in all business functions and in every kind of activities. This is especially true for Small and Medium sized Enterprises (SMEs) that are most exposed to the harmful effects of the risks, due to limited resources and structural features. Therefore, the objective of this study is to identify the relationship and impact of financial risk and quality of financial risk management towards performance of SMEs in Anuradhapura district. There are around 5000 registered SMEs in district which have been registered under divisional secretarial office and chamber of commerce branch. From them, 30 SMEs were selected by using purposive sampling method. Financial risk such as Capital Structure risk, Insolvency risk, and Liquidity risk and quality of financial risk management are the independent variables and performance of SMEs is the dependent variable. Data were collected from financial statements to calculate financial risk and some qualitative data were collected to measure quality of financial risk management through questionnaire. Collected data were analyzed by mean testing, correlation and regression analysis. The results reveal that there is no any significance relationship between financial risk and performance of the SMEs and there is a strong positive relationship between quality of financial risk and performance of the SMEs. Keywords: Small and Medium size Enterprises, capital structure risk, insolvency risk, liquidity risk, profitability growth rat

Escape Local Minima with Improved Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) is a powerful meta-heuristic technique which has been maneuvered to solve numerous complex optimization problems. However, due to its characteristics, there is a possibility to trap all particles in a local minimum in the solution space and then they cannot find the way out from the trap on their own. Therefore, we modify the traditional PSO algorithm by adding an extra step so that it helps PSO to find a better solution than the local minimum that they undesirably found. We perturb all the particles by adjusting parameter values in the traditional algorithm when there is no improvement of the objective value over the training iterations, assuming that particles have stuck in a local minimum. In this research, we mainly focus on adjusting the learning factors. However, the parameter values have to be used in an effective way to perturb the particles. The behavior of the proposed modification and its parameter adjustments are studied using a function which has a large number of local minima - Schwefel’s function. Results show that 2 out of 3 PSO attempts trap in local minimum and slight changes on learning factors do not help them to get out from the traps. However, perturbances made with large learning factors can find better solutions than the local minima that they stuck in and help to find the global minimum eventually

Multi-Label Classification Using Higher-Order Label Clusters

Multi-label classification (MLC) is one of the major classification approaches in the context of data mining where each instance in the dataset is annotated with a set of labels. The nature of multiple labels associated with one instance often demands higher computational power compared to conventional single-label classification tasks. A multi-label classification is often simplified by decomposing the task into single-label classification which ignores correlations among labels. Incorporating label correlations into classification task can be hard since correlations may be missing, or may exist among a pair or a large subset of labels. In this study, a novel MLC approach is introduced called Multi-Label Classification with Label Clusters (MLC–LC), which incorporates label correlations into a multi-label learning task using label clusters. MLC–LC uses the well-known Cover-coefficient based Clustering Methodology (C3M) to partition the set of labels into clusters and then employs either the binary relevance or the label powerset method to learn a classifier for each label cluster independently. A test instance is given to each of the classifiers and the label predictions are unioned to obtain a multi-label assignment. The C3M method is especially suited for constructing label clusters since the number of clusters appropriate for a label set as well the initial cluster seeds are automatically computed from the data set. The predictive of MLC–LC is compared with many of the matured and well known multi-label classification techniques on a wide variety of data sets. In all experimental settings, MLC–LC outperformed the other algorithms