International Research Symposium on Pure and Applied Sciences (IRSPAS)
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Item Procurement optimization with Industry 4.0 in ERP based Sri Lankan apparel industry: a systematic review of literature(4th International Research Symposium on Pure and Applied Sciences, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Chandrasekara, H. A. S. Y.; Vidanagamachchi, K.; Wickramarachchi, A. P. R.Apparel industry is the Sri Lanka’s largest export industry and the highest net foreign exchange earner since 1992. It Provides wide range of clothing solutions for renowned international brands. Therefore, satisfying both customers and suppliers is a mandatory requirement in apparel industry. To survive in the market and acquire competitive edge, industries need to invest in advanced technology. However, most companies fail due to poor integration of technology in their supply chain. The concept of Procurement 4.0 has emerged to optimize supply chain performance developing new value propositions and meeting new business needs. It is the integration of Industry 4.0 concepts in procurement. To adapt these new technologies in an enterprise, a strong ERP system which can act as a platform for integration is required. Although Industry 4.0 concepts have been studied in different industrial contexts, a limited number of researches have been conducted on the use of emerging technologies such as Internet of Things (IoT), Robotic Process Automation (RPA), Big data and Cognitive analytics, Artificial Intelligence (AI) and Cloud technologies etc. in relation procurement while more existing researchers have focused on e-procurement. Therefore, this study explores how the ERP based apparel industry can optimize their procurement process with the emerging procurement technologies from Industry 4.0. In this study, a systematic review of literature was conducted based on the keyword-based search and content analysis, and 20 articles were selected out of 50 articles depending on the relevance to the major areas of study. Based on the findings, a framework was developed incorporating Industry 4.0 technologies that can be used in each step of the procurement process and their impact on procurement performance have been identified. In conclusion, this research provides a framework for implementing technology-based procurement practices. Apparel manufacturers can use the developed framework as a guideline to identify the current state of technology implementation in procurement process and to identify the next potential steps towards procurement 4.0. Further, the results of the study could be generalized and applied to any manufacturing industry. Future researches can be carried out to validate this model through a case study approachItem Application of industry 4.0 concepts to optimise workforce performance in human resource processes: context of Sri Lankan apparel industry(Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Weerasekara, M. M.; Wickramarachchi, R.; Vidanagamachchi, K.To cope with the new technologies driven by the fourth industrial revolution such as Cloud based systems, Internet of Things, Virtual Reality, Artificial Intelligence, skill development is vital in order to enhance the performance of the workforce. Advancement of the technology will affect disruptively for all organisational functions including human resource. There is a threat that humans will be replaced by advanced technological systems in the near future. It will badly affect all areas of operations including human resources, if organisations fail to absorb the technology. Even though these technologies have been widely used in many countries, developing countries such as Sri Lanka are far behind. This paper focuses on how to improve the performance of the employees by developing necessary skills, using various technologies included in Industry 4.0. Previous researchers have mentioned about how industry 4.0 applications disrupt human resource but not how to fulfil those gaps. Sri Lankan apparel industry has been identified as one of the key industries to implement these concepts to measure the workforce performance since it involves both human and machinery in its processes. By introducing advanced technologies to areas such as recruitment, talent on-boarding and off-boarding, training and development, both time and cost for unnecessary processes can be reduced while increasing the efficiency and effectiveness of the employees. Initial data collection has been conducted through two questionnaires based on a sample of 30 individuals from each level which are above executive and below executive from human resource departments. Five apparel firms were chosen to collect the data as a quantitative approach. Questionnaire was developed to discover the relationship between variables such as industry 4.0 application, skill development, job satisfaction and job performance to check how they intervene with enhancing performance. Data analysis was done through structural equation modelling using AMOS supporting software. The study suggests ways to optimise skills and satisfaction level of workforce performance using industry 4.0 applications with smart human resource concepts. In addition, an innovative model will be introduced to enable apparel industry to enhance the process of human resource development using technologies through industry 4.0 application.Item The strategic relationship building through value procurement: A systematic review of literature from the apparel industry.(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Karunarathna, K. N. P.; Rasanjani, P. M. D.; Weerasekara, M. M.; Rupasinghe, T. D.In the midst of the dynamics in the domain of supply chain management, most of the businesses are forced to strengthen the strategic relationships with their supplier base. Therefore, companies tend to move away from the traditional supplier-buyer relationship to a longer-term collaborative strategic relationship with a limited number of key suppliers. Strategic relationship building symbolizes the importance of enterprise wide thinking where functional units inside the firms and key suppliers from the firm’s supply chain all work in concert to bring value to the marketplace. The purpose of this study is to explore how the strategic relationship building with suppliers in the procurement process enhances the overall operational performance of apparel industry. For an example, in apparel industry, quality and the productivity should be calculated at the initial stage of the production. Value procurement models allow firms on selecting the best quality, cost and other factors instead of selecting the lowest cost supplier. Consequently, the study identifies the set of appropriate procurement strategies to minimize impact of costs to the production processes to retain long term relationship among suppliers. This study is based on a comprehensive, systematic review of literature published in relation to the areas of strategic supplier relationship and value procurement applicable to the apparel industry. The findings of this research is based on reviews of 17 articles which describe different models, frames, processes and appropriate theoretical terms. Through the effective categorization and integrative analysis of the above findings, this paper expects to introduce an innovative conceptual model of how to develop strategic supplier relationships to maximize the effectiveness of the industry.Item Factors affecting to the output efficiency of a production department: A case study in Sri Lankan apparel industry.(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Erandi, M. W. A.; Jayasundara, D. D.M.Output efficiency of a production department is essential for increasing the profit of an organization while satisfying the customers. A learning was regulated in a foremost apparel manufacturing factory in Western province. The main objective of the case study is identifying the factors which are affecting to the output efficiency of a production department while the specific objective is fitting a regression model to measure the effectiveness of those factors. A literature survey was conducted to get the expansive idea about the factors influencing and included them to a questionnaire to collect primary data from the factory. Six factors were selected from primary data analyses using Pearson correlation test and 5-point Likert scale method with 91% response rate. According to the correlation test, Absenteeism percentage, AQL output rate, Target efficiency, Average incentives, Hold quantity rate and Downtime rate were identified as significantly affected factors to output efficiency of the production department. Secondary data was collected from the factory based on those selected factors. 771 number of daily data were collected from the daily factory summary tables, attendance personal records, and preventive maintenance records of the factory. Line graphs were used to recognize the interaction effects of the variables. Variable centering method was utilized in order to remove the multicollinearity. The model was found at the 12th step of the forward selection procedure and model adequacy was examined using data subsetting lack of fit test. When checking the model diagnostics, the lack of fit tests between residuals versus predictor variables was applied to test the linear relationship between response and predictors. Residuals versus fitted plot was used to test the constant variance of the residuals. In order to investigate the normality of the residuals, Anderson Darling test and normal probability plot was used. It was confirmed that data not ill-conditioning by variance inflation factor values of predictors. According to the analysis of secondary data using the multiple linear regression main effects were identified from all first order explanatory variables except Accepted quality level output rate and Hold quantity rate. The interaction effects from the variables Absenteeism percentage, Accepted quality level output rate, Average incentives, Downtime rate were proved by the fitted model. Moreover, there were effects from second order terms of all explanatory variables except Hold quantity rate. As specified by the model, the predictors explain 55.2% of the variance in Output efficiency while the highest positive effect and the highest negative effect have made by target efficiency and downtime minutes respectively. Furthermore, Average incentives are affected slightly and there are considerable amounts of effects from interaction terms also. Using this model, the output efficiency of the production department can be increased by adjusting those factors necessarily and also it will ameliorate the productivity of the factory. Since the coefficient of multiple determination is 55.2% and the remaining 44.8% of the variability is still unaccounted, the model can be improved by adding more variables to the model. As a future work it will be worthwhile to use nonlinear regression method for curve fitting and measure the effects from these variables.Item Simulation-based modeling approach for collaborative supply chains from the perspective of apparel industry.(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Lakmal, R. D. S.; Rupasinghe, T. D.Supply chain collaboration has become a widely spoken and researched topic in every industrial standpoint. The depth and the width of the chain partners and activities are rapidly increasing, and have become difficult to achieve the best efficiencies by performing as isolated partners. Although the word collaboration is easy to understand, achieving it practically has become a difficult and complex task. Numerous researchers have investigated in areas such as identifying collaborative enablers, barriers of collaboration, developing indexes to measure collaboration, collaboration vs performance. However, a very few research studies have focused on understanding the practical aspect of improving collaboration in supply chains. Therefore, in this study the authors have utilized a simulation-based approach to assess how collaborative practices among different partners in the supply chain affect the collaboration level of an industry and time dependent variants for an industry to achieve maximum benefits of collaboration. The simulation models were developed using Netlogo open source modelling platform, focusing on three types of agents in the supply chain where the suppliers, manufacturers, retailers and behavior of these different partners was modeled. The study utilizes the apparel industry as the tested and thus, the Netlogo simulation models determine the effects of collaborative practices across those aforementioned partners. The outcomes of this study will facilitate the identification of critical factors which the industry should focus on, in order to enhance the collaboration in supply chains in apparel industry, and that can be further improved to enhance the collaboration of various other industries as well.Item Supply chain collaboration for sustainable Industry 4.0: A case study from the apparel industry.(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Gamage, D. D.; Rupasinghe, T. D.Due to the advancement of technology and the dominance of the consumer economy, it has become important for businesses to adopt sustainable practices and innovative technologies to boost productivity in their business processes. The purpose of such sustainable practices is in bringing in balance between the economic, social and environmental spheres of a business. The advancement of technology has brought the 4th industrial revolution to the world in which businesses around the world are integrating the use of cyber-physical systems for improved outcomes. There are studies conducted on the impact of Industry 4.0 applications on collaborative supply chains. The focus of this is study is to simulate the applications of Industry 4.0 in supply chain collaboration in driving sustainability in the apparel industry using NetLogo – a programmable modelling environment. It will use an existing framework of collaboration characteristics supported through Industry 4.0 application and will consider the behavior of supply chain agents under varying conditions of certain specified characteristics. It will study how information sharing, sense making, resource pooling, goal congruency, empowerment and cross functionality of the collaborative supply chain impact on its sustainability in terms of carbon emissions with Life Cycle Assessment. This study shows that improved levels of collaboration through Industry 4.0 applications have a positive effect in reducing the carbon footprint of the supply chain of the apparel industry.Item A conceptual framework to assess supply chain risk in the apparel industry.(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Erandi, H.; Peter, S.Along with the advent of globalization and the championing of free trade together with improved communication and transportation systems, enterprises have the ability to source supplies from a globally distributed supply chain. However, on the flip side, due to the complexities in dealing with a dispersed network of suppliers, manufacturing companies are facing risks of disruption to their supply chains. As risk is very context dependent, it is important to identify supply chain risks in different contexts and industries. Sri Lankan apparel industry plays a major role in the country’s economy, making it vital for companies to engage in proper identification and assessment of these risks. Therefore, the objective of this study is to conceptualize a model to assess identified supply chain risks and thereby to generate an overall risk assessment score for an apparel manufacturing company. The initial base for risk identification is through the use of the Ishikawa model. The cause and the effect for supply chain risks were established by analysing the initial data collected and via industry experts, a list of risk classes and sub classes were formed. Thereafter, a modified version of risk numeric analysis model is used to setup each and every class weight where industry experts’ opinion is taken for calculating the appropriate weights. Instead of using Analytical Process Hierarchy (AHP) which was used in the original model, Analytical Network Process (ANP) is used to prioritize the identified risk classes. The decision to use ANP is due to its ability to consider the complex inter relationships and linkages between risk classes and sub classes during the prioritization process. Finally, an aggregate score is developed for the overall company in terms of the supply chain risks by using the scores obtained for each risk class. The model will highlight the different types of supply chain risks that an apparel manufacturing company may face and how a proper mechanism can be developed to quantify these risks. The model would facilitate the company to directly identify the magnitude of each and every supply chain risk and the risk distribution via the overall risk score of the company. The risk score can be used by managers as a flag or an indicator that signals the company about potential risks. Apart from that, this model can be used to compare historical figures to monitor and evaluate the overall risk scores of the company. Furthermore this risk score can be used to compare the company performance with other competitors’ score values and to analyse how competitive the company is within the industry.