Smart Computing and Systems Engineering - 2022 (SCSE 2022)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/25392

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Extraction of Sentiments in Tamil Sentences Using Deep Learning
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Loganathan, Hirushayini; Sakuntharaj, Ratnasingam
Sentiment analysis is the process of extracting information from the given text in which the text consists of various sensations such as happiness, perturbation, pride, worry, and so on about various functions, human beings, systems, and facts. Sentimental analysis or opinion mining uses data mining and natural language processing techniques to discover, retrieve and filter the information and opinions from the World Wide Web’s vast textual information. The sentiment analysers for European languages and some Indic languages are fully developed. However, Tamil, which is an under-resourced language with rich morphology, has not experienced these advancements. A few experiments have been conducted to determine the sentiments for Tamil text. An approach to doing the sentiment analysis for the Tamil language is proposed in this paper. The proposed approach uses Long Short-Term Memory, Convolutional Neural networks, and simple Deep Neural Network techniques. Test results show that the Long Short-Term Memory-based deep learning model performs well than the Convolutional Neural Network and simple Deep Neural Network for sentiment analysis of Tamil language with 94.10% accuracy.
Machine Learning Approach to Predict Mental Distress of IT Workforce in Remote
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Gamage, Sanduni Nilushika; Asanka, P. P. G. Dinesh
When considering online workers, due to the emergence of the coronavirus pandemic prevailing in the world, employees have been restricted to work remotely for a prolonged period. All the working arrangements are now based at home than before. Since this has been novel to society, the impact caused by this crisis on people is unknown in the short or long term. Since various factors can cause mental distress among online workers, periodic screening for mental distresses such as anxiety, depression, and stress is necessary for health and well-being. The causes of mental distress are multifactorial. They include socio-demographic, biological, economic, environmental, occupational, and psychological aspects. This paper proposes a concept of a screening system to predict mental distress given the external features associated with individuals, using supervised machine learning approaches and identifying the employees prone to higher risk and referring them early to professional assistance. The study was conducted concerning the circumstances in a pandemic era considering COVID-19 as the case study. The study was done with remote IT workers in Sri Lanka who work as a part of a software development team. 481 professionals participated in the study and were selected based on selection criteria and appropriate encoding techniques were utilized to encode categorical variables where most important 25 features were detected among 60 features using feature selection. Finally, classification techniques such as Random Forest, SVM, XGBoost, CatBoost, decision tree, and Naïve Bayes were used for modeling by which the CatBoost algorithm in overall measures outperformed other algorithms with a predictive accuracy of 97.1%, precision of 97.4%, recall of 99.7%, and f1 measure is 98.5%.
An Emotion Classification Model for Driver Emotion Recognition Using Electroencephalography (EEG)
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Gamage, T. A.; Kalansooriya, L. P.; Sandamali, E. R. C.
Road accidents have been a critical issue that has resulted in fatal injuries, disabilities, and deaths for many individuals worldwide. The notion of Human-Computer Interaction (HCI) is widely considered in monitoring drivers to safeguard their lives on roads. As a solution to the issue of the higher rate of road accidents, driver emotion recognition approaches have gained much attention, and the involvement of biological signals in detecting the emotional states of drivers is also significant. The authors have conducted a comprehensive literature review that concerns contemporary literature on the driver emotion recognition paradigm and comes up with four emotional states in this research to monitor the drivers' affective states. This paper presents a novel approach to detecting sad, angry, fearful, and calm emotional states of drivers with an emotion classification model using Electroencephalography (EEG) signals where the EEG data acquisition for the research is done using the Emotiv EPOC X device. The collected EEG data are preprocessed using the EEGLAB toolbox in Matlab, and feature extraction, selection, and emotion classification model training are done using Matlab. EEG acquisition and preprocessing have already been achieved, and as further work, the authors are to train the proposed emotion classification model as laid out in this paper. The findings of this research encourage the authors to continue towards the completion and provide further insights into enhancing research in the driver emotion recognition paradigm.
Deep Neural Architectures for Ethnicity Classification in Face Images
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Jayasekara, Buddhi G.; Hevapathige, Asela
Ethnicity is a key metric of an individual’s identity, social cluster, physical behaviour and cultural association. Accurate ethnicity identification of humans is required in numerous fields like security, legislation, social analysis and psychology. Ethnicity classification using machine learning is a complex, non-trivial and multi-dimensional research problem due to the feature complexity, class imbalance and the absence of rich data sets. In this research study, we have trained and compared four state-of-the-art deep neural models and their ensemble architectures on the problem of ethnicity classification in large-scale image data. The empirical results demonstrate that these end-to-end deep learning models and their ensemble architectures perform well in learning complex ethnic features in facial images and classifying them. From the evaluated models, Ensemble Convolutional Neural network provided the highest classification performance with 78.9% accuracy. Also, we have tested six prominent pre-trained models using transfer learning for ethnicity classification while being able to achieve comparable results.
Mapping of Sri Lankan Road Signs by Using Google Street View Images
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Kiridana, Y. M. W. H. M. R. P. J. R. B.; Weerarathna, P. L. M.; Wijesingha, W. P. D. Y.; Aashiq, M. N. M.; Kumara, W. G. C. W.; Haleem, M. A. L. A.
The development of autonomous vehicle driving systems and Intelligent Transportation System (ITS) have drawn massive attention since the 1980s. For the development of ITS, road sign detection and identification are considered to be very important due to the vital information provided by road signs. Generally, real-time video-based methods are used as the source of images for the operation of ITS. But they are inefficient and costly due to certain limitations like weather conditions, lighting conditions, and limited range in obtaining quality images. To overcome the limitations of the video-based approach, this research aims to develop techniques for detecting and identifying road signs by using Google Street View (GSV) as the image source, OpenCV for image processing and CNN for road sign identification. EdleNet, LeNet-5, and DenseNet were identified as accurate CNN models. Using images from GSV, generating a database of road signs with the relevant coordinates was possible, which is currently unavailable in Sri Lanka. In addition, this process leads to the generation of a valuable image dataset of Sri Lankan road sign images, and a web interface with mapped road signs. Consequently, this research would yield useful findings that may be applied to future research and provide the means to develop ITS, accident-avoidance systems, and driver assistance systems.
Common Object Request Broker-based Publisher-Subscriber Middleware for Internet of Things - Edge Computing
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Perera, Hansani; Jayakody, Anuradha
The edge computing layer in IoT reduces the flow of a massive amount of data directly to the cloud by processing some data in the local network. The middleware in the layer enables this processing of data and the communication between heterogeneous devices and services in the nearby layers. CORBA, which uses as a powerful middleware technology in developing middleware solutions in enterprise-level distributed applications, has been abandoned in the current generation. The paper presents the design, and the performance evaluation of a publisher-subscriber middleware implemented using CORBA that was studied when exploring the applicability of CORBA as an IoT edge computing middleware. The evaluation was continued in two steps to analyse several parallel connections (Load test) and handle requests in a unit time (burst test) via simulating an IoT environment in a cloud environment.
A Systematic Investigation on the Effectiveness of the Tabbert Model for Credit Card Fraud Detection
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Hewapathirana, Isuru; Kekayan, Nanthakumar; Diyasena, Deshanjali
As a result of rapid digitisation, online transactions using credit cards have become popular. With this, fraudulent activities have also increased considerably. Although many supervised and unsupervised machine learning techniques were proposed in past research for identifying fraudulent transactions, they do not fully utilize the tabular and hierarchical structure present in transaction datasets. Recently, the TabBERT neural network model was proposed to calculate row-wise embeddings that capture both inter and intra dependencies between transactions in tabular time series data. In this research, we present a systematic experimental framework to assess the effectiveness of applying the embeddings calculated using the TabBERT model for credit card fraud detection. We employ the calculated row embeddings for fraud detection using three unsupervised machine learning algorithms and two supervised machine learning algorithms. We perform our experiments on a synthetic dataset that has been generated using the TabGPT model. Overall, TabBERT-based embeddings increase the performance of the supervised learning models with the extreme gradient boosting model achieving a precision of 99% and an F1 score of 98%, and the multilayer neural network model achieving a precision of 97% and an F1 score of 95%. For unsupervised learning, the use of TabBERT embeddings increases the recall rate of K-means clustering algorithm by 0.19%.
Factors Influencing the Secondary Level Students’ Satisfaction in E-Learning: A Case Study of an Educational Institute in Sri Lanka
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Jayanett, W. I.; Jayalal, Shantha
With the covid-19 pandemic, e-learning has shown significant growth in Sri Lanka over the last few years. As a remedy to sudden school closure during the covid-19 outbreak, educational institutes have adopted e-learning to minimize the disruption of education. Even though there are benefits, teachers complained that the satisfaction of secondary level students is declining, and it has impacted the academic performance to become low. Therefore, this research is conducted to investigate the factors influencing the secondary level students’ satisfaction in e-learning at an educational institute in Sri Lanka from students’ perspectives. This study takes 211 students from secondary-level students in an educational institute as participants. The data were gathered through online questionnaires undertaking a Quantitative approach. Overall results indicate that flexibility is the most influencing factor. Respectively, the quality of the e-learning system/platform, Interactivity, quality of the Internet, and quality of the learning material influence students’ satisfaction. As per the recommendations, the educational institute is suggested to select a suitable e-learning platform and use Learning Management System (LMS). Also, they are suggested to provide a fixed timetable for teachers. The teachers are encouraged to be more interactive and to use computer-based learning materials to deliver the content. Also, an educational institute is suggested to provide adequate teacher training in creating resource materials. The Ministry of Education is suggested to provide a free e-learning system and data package for less cost. Also, the Ministry of Education is recommended to take strategic decisions to enhance school curriculums to be interactive. E-learning system designers should be aware of the school curriculum in designing e-learning systems. And the Government is encouraged to increase the coverage and infrastructure facilities to establish a satisfying e-learning environment.
An Ensemble Machine Learning Approach for Stroke Prediction
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Premisha, P.; Prasanth, Senthan; Kanagarathnam, Mauran; Banujan, Kuhaneswaran
Nowadays, one out of four people above 25 will suffer from a stroke. Especially this year, with the highest count of around 13.7 million people discovered with stroke for the first time. Out of 13.7 million, 5.5 million were fatalities. This was stated in a recent WHO study. It is estimated that if no action is taken, the number of fatalities will rise to 6.7 million yearly. The pandemic situation of COVID-19 will play a significant cause in the expanded death rate of stroke. Even for adults and patients with minor risk factors affected by stroke rather than in previous years. This study predicts the impact level of stroke with the development of an ensemble model by combining the various classifiers performed well in isolation. Predicting the stroke status in patients would help the physicians determine the prognosis and assist them in providing the targeted therapy in a limited time. During this study, an ensemble model was built by considering the base, bagging, and boosting classifiers: Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, Artificial Neural Network, Random Forest, XGBoost, LightGBM, and CatBoost. The dataset consists of 5110 patient details, along with 12 attributes that were analyzed in this research. The final ensemble model was developed by carrying out the methodology in two phases. During the first and second phases, the classifiers mentioned above were trained without hyper-parameter tuning and with hyperparameter tuning and tested against the fundamental evaluation matrices. During each phase, the classifier that produces the highest classification accuracy is discovered from the base, bagging, and boosting categories. From the results obtained, the final ensemble model was constructed using the Max Voting approach, which yielded an accuracy of 95.76%.
A Model to Optimize the Sales and Purchases Invoice Payments of Working Capital in the Fast-Moving Consumer Goods Industry
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Rathnasekara, J. P. D. T.; Wijayanayake, A. N.; Withanaarachchi, A.
Working capital optimization is critical in real business scenarios since it changes dynamically along with complex physical cash flows. In previous literature, working capital payment optimization mainly focused on the cash conversion cycle and cash on hand. In those studies, the objectives were to maximize the profit, maximize on hand cash flow or minimize the cost during the predefined period. However, in most cash maximization models, the time value of the money concept was not addressed. Further, in real-world scenarios, the time value of the money concept mainly affects the working capital and cash flow performances. In the proposed model, the time value of money concept was considered to get actual available cash at present. The objective of this proposed model is to maximize the current value of the money on hand while minimizing the cost within the considered time frame. The model was tested using Python along with CPLEX libraries. This study will be helpful to researchers, academics, and those working in the finance sector of the manufacturing industry to make better decisions on working capital invoice payments.
Designing of a Voice-Based Programming IDE for Source Code Generation: A Machine Learning Approach
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Nizzad, A.R. M.; Thelijjagoda, Samantha
Humans are precise in recognizing natural languages and responding contextually unlike machines. However, speech recognition or Automatic speech recognition often refers to converting human speech or voice to textual information with the help of artificial intelligence algorithms. With the advancement of Artificial Intelligence technologies and extensive research being conducted in AI, speech recognition has received much attention and has emerged as a subset of Natural Language Processing where the advancement and accuracy in speech recognition will open many ways to provide a high standard of human-computer interaction. In this study, using the pre-trained transformer model with a transfer learning approach, the English to Python dataset was used to train the transformer model to produce syntactically correct source code in python. Additionally, the Word2Vec model was used to generate voice-to-text as input for the model. For the purpose of demonstration, a custom Python IDE is developed to generate source code from voice input. The results and findings suggest that in the transformer model, with the use of transfer learning, any dataset can be trained to produce syntactically correct source code. The model’s training loss and validation loss were below 5 and 2.1, respectively. Future research can focus on generating valid source code from any human spoken language without restricting it to English only.
Analyzing Factors that Impact on Performance of Pickers in Third-Party Logistics Warehouses in Sri Lanka
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Prasadika, A. P. K. J.; Wijayanayake, A. N.; Niwunhella, D. H. H.
Order picking is the most crucial and expensive operation in a warehouse which affects customer satisfaction and the profitability of the warehouse. Picker is the employee who is responsible for the order picking process. So, picker performance is very important in improving the overall performance of the warehouse. Therefore, identifying the factors that have an impact on the performance of the pickers is advantageous. The main objective of this research is to identify the relationship between factors that has an impact on picker and picker performance through the Partial Linear Square – Structural Equation Modelling (PLS-SEM) technique using SmartPLS software. Initially, the most important twelve factors were identified by reviewing the past literature and industry experts’ opinions. They were divided into three main categories based on the characteristics and to reduce the complexity of the model which are picker-related factors, management-related factors, and warehouse-related factors. The data analysis was done in two steps to discover direct and moderator relationships, separately. The product type that the pickers handle is the moderator used in this study. The results of the PLS-SEM analysis show that picker-related factors and warehouse-related factors have a significant impact on picker performance at the significance level of 0.05, while management-related factors have a significant effect on picker performance at the significance level of 0.10. Further, the product type moderates all three relationships. The outcomes of the study help the managers of the warehouses to improve the performance of the pickers so that the overall performance of the warehouse can be improved.
Adding Commonsense to Robotic Application Using Ontology-Based Model Retraining
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Pradeepani, M. K. T.; Jayawardena, C.; Rajapaksha, U. U. S.
In terms of the level of technological capability in the world today, the use of automated robotics is common in various fields. There are large projects going on in many industries that collaborate between robots and other robots, as well as humans and robots. In hospital environments, care for people with medical needs and their needs and used to make appropriate suggestions to their problems. Robots can also be found in certain areas that can respond quickly as an emergency rescue agent. Furthermore, robots, which can be seen in the hotel industry as waiters and as farm assistants in agriculture, have a great tendency to be used as multi-tasking agents in many fields. In each of these areas, robots must co-operate with humans. In that situation, the importance of the exchange of mutual knowledge between robots-robots and between humans-robots comes into the picture. What matters here is not only the quantitative vastness of knowledge but also the ability to understand each other in the same medium. Although the common sense that people need in their day-to-day work is completely obvious to humans, the commonsense knowledge domain needs to be implanted in robots. Whatever concept is defined for adding commonsense to robotics, it should be a consistent concept that can be logically constructed so that it can be understood by a machine. As will be discussed later in the paper, different methods have been used in various related works to add a different kind of domain knowledge to robotics. The objective of this paper is to provide an improved retrained model for robotics in order to give them the ability to act more human-like when performing tasks. By using the proposed model robots are able to answer the incomplete command or inquiries related to a given context. One of the objectives of this work is to use the ontology-based, commonsense-support existing knowledge base as a mechanism to retrain and build a new model.
Detection of IoT Malware Based on Forensic Analysis of Network Traffic Features
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Nimalasingam, Nisais; Senanayake, Janaka; Rajapakse, Chathura
The usage of Internet of Things (IoT) devices is getting unavoidable lately, from handheld devices to factory automated machines and even IoT embedded automotive vehicles. On average, 100+ devices are connected to the IoT world per second, and the volume of data generated by these devices and added to the space is just too enormous. The value of the data costs more, and sometimes it is invaluable, and it may pull over the cybercriminals and eventually increases the number of cybercrimes. Therefore, the need to identify malware in IoT is a timely requirement. This research work applies Machine Learning (ML) models and yields an efficient lead to identifying the IoT malware using forensic analysis of their network traffic features by selecting the foremost unique features and combining them with the binary features of the malware families. An outsized dataset with many network traffic collections used various network traffic features. Thus, the proposed model's detection accuracy of almost 100% was achieved from the model during the experimental phase of the study, which was a result of the feature extraction process for each malware type. This model can be further improved by considering the fog level implementation of the IoT layer, where the learning will help identify a malicious packet transfer to the network at level zero.
Developing A User-Friendly Interface from Robotic Applications Development
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Fernando, W. A. M.; Jayawardena, C.; Rajapaksha, U. U. S.
In this research, we have developed a web-based Robot Operating System (ROS) learning environment with its own set of tools. Our system is a comprehensive learning environment where students can go through the tutorials using the web interface and use our web-based development environment for writing scripts. Furthermore, students can use the web-based Gazebo simulator to visualize the robots. In addition, our learning environment also has its own set of tools that students can utilize for testing and troubleshooting robots. One of the other benefits of our system is that it is platform independent, and hence it can be accessed from either computer, mobile phone or tablet. Our system also has a dropdown for selecting commands. In this, all the descriptions and syntaxes of the commands are predefined and populated whenever a command is added from the dropdown. In addition, we have developed multiple other features that make this system much easier to use and user-friendly. In order to verify the usability of the system, we have performed a heuristic evaluation, and our findings show that the system complies with nine of the ten heuristics in Nielsen's framework. In addition, our system complies with twelve of the fourteen heuristics in Zhang's framework. We performed a performance evaluation as well. In this, we compared the performance of simulating our web-based system against running the same simulation directly from a Linux-based ROS server using the Gazebo client. The results showed that our system was faster by a small margin.
A Comparative Study of Clustering English News Articles Using Clustering Algorithms
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Disayiram, N.; Rupasingha, R. A. H. M.
The news informs us of what is going on in the world. People nowadays read their interesting news on news websites. There are numerous categories of news. Each newsreader has a different preference for news categories. Sportspeople prioritize sports news, whereas technology fans pay attention to the technology segment of the news. At the end of the day, each news category is important. Every day, a large amount of information is released on news websites. News sites usually categorize the news however, not all of the categories are published on those sites. Some categories are given higher attention by news outlets, while others receive less coverage. As a result, finding an appropriate category of news is tough. These issues make it difficult for newsreaders and content seekers to find relevant sections on news websites. The clustering of English news articles by relative category provides solutions to these issues. This research aims to use clustering algorithms to cluster news articles depending on the relevant domain/cluster. We consider five news categories: politics, sports, health, technology, and business. The data collected online was converted into a vector format using the term frequency-inverse document frequency (TF-IDF) vectorization. Then, on the body of the news and the news heading, the three clustering algorithms: Expectation-Maximization (EM), Simple K-means, and Hierarchical Clustering based on an agglomerative approach were applied individually. The Waikato Environment for Knowledge Analysis (WEKA) tool's classes to clusters evaluation model are used to calculate the accuracy. The EM method had the maximum accuracy of 88.5% with the best results in terms of correctly clustered instances. The comparison between the heading of news and the body of news demonstrates that the body of news clustered the news items better than the heading of news.
Stock Market Prediction using Artificial Intelligence
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Dilhan, M. W. Sachin; Wagarachchi, N. Mihirini
This research focuses on predicting stock closing prices for one day or the future in specific economic conditions. Today, Sri Lanka faces a financial crisis due to the COVID-19 pandemic. Therefore, lots of investors are bankrupt due to unpredictable stock prices. This work mainly focuses on predicting stock prices in banking sector shares such as Commercial Bank (COMB.N), Hatton National Bank (HNB.N), Seylan Bank (SEYB.N), and Sampath Bank (SAMP.N) on Colombo Stock Exchange (CSE) in Sri Lanka. According to the hypothesis, All Share Price Index (ASPI) and Banking Sector indices have been taken as a numerical sentiment parameter other than the historical prices from each bank. Since ASPI shows overall market performance and Banking sector indices show banking sector capitalization changed over time. There can be a positive and negative sentiment when the ASPI and Sector Indices increase and decrease, respectively. Finally, a dataset is divided into 70% for training and 30% for testing. This study has used Recurrent Neural Networks (RNNs) such as Long short-term memory (LSTM) and Gated Recurrent Unit (GRU) using 25, 50, 100, 150, and 200 epochs. LSTM model has given the lowest Mean Squared Error (MSE) and Root Mean Square Error (RMSE). According to the LSTM model, COMB.N, HNB.N, and SAMP.N were given the lowest MSE, and RMSE for 100 epochs, and SEYB.N was given the lowest MSE and RMSE value for the 150 epochs.
Impact of Warehouse Management Factors on Performance Improvement of 3rd Party Logistics Industry
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Kodithuwakku, P.I.E.; Wijayanayake, A. N.; Kavirathna, C.A.
The alarming attention towards the warehousing performance improvement based on different aspects of warehouse management factors has proven to be more critical, especially for the growth of the third-party logistics (3PL) industry. Warehouse performance alleviates discrepancies between warehousing management criteria and performance indicators to assure that the smooth flow of the supply chain aggregates its demanding areas to a satisfactory level. For the last few years, urgent requirements for enhancing improvement have been prominent, especially in the service sector attached with the practical performance in the 3PL industry. Despite analysing the criterion and its effectiveness, identification of the direct relationship between warehouse management factors and how it has impacted the warehousing performance based on the different aspects of indicators in the Sri Lankan 3PL industry, is not yet taken into consideration. This study uses a systematic literature review and expert opinions to identify the key warehousing factors of 3PL industry in Sri Lanka. In total, 12 key success factors were obtained, and those factors were grouped into three categories: operational, economical and environmental factors. A conceptual model is developed to identify the relationship between the warehousing critical success factors and warehousing performance. The study reveals the lack of attention paid to warehouse performance criteria along with warehouse management factors. The findings of this study could inspire the decision-makers who wish to improve the 3PL industry performance while improving warehouse performance in the 3PL industry in Sri Lanka.
Network Design Optimization for Retail Distribution Supply Chain Considering Capacities of Distribution Centers under Disruptions
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Madhushan, K. G. Methmi; Kavirathna, C. A.
Distribution in the retail supply chain (SC) is a core function of taking products to the customer. Supply chain network design (SCND) plays a major role in retail distribution in determining the best ways to locate facilities in the SCs. 100% availability of facilities can be identified as the most common limitation of much research conducted in a similar context due to the vulnerability of facing disruptions. Disruptions in SC are considered differently in research and numerous strategies are applied. However, direct shipping from suppliers to retailers has not much been focused on by previous studies although it is abundant in practice. This study develops a model for SCND under disruptions considering the direct shipping from suppliers to the retailers in the distribution network considering different levels of disruptions that occurred at distribution centers (DC). Linear programming technique is used in python language to build up suggested optimization model. Results of the model show the optimum distribution networks to use in the different disruption situations such as partial disruptions to the DCs and full DC disruptions that minimize the costs considering direct shipment. Overall, direct shipment can use to optimize SCND not only under disruptions but also in general conditions with the identification of marginal values in different factors involved. Identification of the costs in DC failures using this method can be used to enhance the resiliency of the DCs according to their importance.
Examine the Impact of IoT for Supply Chain-Based Operations in ERP Systems: Systematic Literature Review
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Paththinige, Pavani; Thilakarathne, Kasun; Rathnasekara, Tenisha; Wickramaarachch, Ruwan; Withanaarachchi, A.
This study intends to look into the various ways that Industry 4.0 elevates the capabilities of ERP systems. Over recent years, it is evident that the Industry 4.0 concept has been the birthplace of many innovative technologies across various industries, and the practical implementation of Industry 4.0related technologies is also rapidly expanding across many industries. Moreover, the incorporation of Industry 4.0-related technologies has dramatically increased the performance of organizations and hence led to sustained profits in the long term. By integrating Industry 4.0 technologies such as IoT and RFID into ERP systems, the ERP systems will be able to capture realtime data from the work floor level, enabling ERP systems to provide more accurate analytics and predictions. Therefore, upgrading the existing traditional ERP systems to support these novel technologies presented by Industry 4.0 will undoubtfully contribute to enhancing the capabilities of ERP systems. Among the limited studies investigating ERP systems from the Industry 4.0 perspective, none have focused on conducting a systematic literature review on ERP from the Industry 4.0 standpoint to investigate how Industry 4.0 enhances the capabilities of ERP systems. Therefore, this study contributes to the theory by fulfilling that knowledge gap.