Prioritization of barriers in industrial symbiosis implementation in automotive industry - Using ISM and MICMAC Analysis

The continuous increase in production, lack of flexibility of organizations, and lack of knowledge on sustainability have led to the depletion of raw materials and increased waste generation. Industrial symbiosis now has become a very effective solution and an essential strategy for responsible consumption and waste utilization. This strategy helps different organizations to blend their resources, share information, logistics, and waste materials to solve their problems by forming a network to increase profits. This study was directed towards identifying the barriers towards applying Industrial Symbiosis in an organization with probable solutions to them. ISM modeling and MICMAC analysis were used to visualize the impact of different barriers for implementing Industrial symbiosis in an organization and improve efficiency in terms of eco-innovation. The results of this study give experiences and rules to practicing managers in medium and small-scale industries to effectively execute Industrial Symbiosis. The study also adds to the improvement of a basic model for examining the barriers affecting IS with regards to eco-innovation and sustainable frameworks and contributes to ongoing researches on this eco-friendly idea of Industrial Symbiosis.

Traceability of Fruits and Vegetables Supply Chain towards Efficient Management: A Case Study from Sri Lanka

The necessity for food traceability has been increased over the years with the expansion of food supply chains globally over these years due to stringent of food safety regulations. Enhancing the access to quality food safely is one of the essential requirements of food supply chain traceability. Conversely, significant percentages of postharvest losses available especially in developing countries due to poor supply chain and logistics practices thereby threatening food security. Unless there is a possibility to trace the Supply chain, it is difficult to take remedial actions. When it comes to Sri Lanka, currently it is harder to have the traceability in most of the foods supply chains commonly on most of the elementary supply chains such as fruits and vegetables. This has led to postharvest losses since it is harder to identify when and where damages occur, who are accountable, harvested and transient times, supply demand mismatch too. Therefore, this paper aims to investigate the feasibility of tracing of fruit and vegetable supply chain in Sri Lanka and contribute theoretically to facilitate authorities and decision makers for future traceability improvement. Availability of secondary information on fruits and vegetables traceability was examined referring to government agencies. Basic structure of supply chain was identified based on secondary data and a case study was conducted based on supply chains linked to Thambuththegama and Keppetipola Dedicated Economic Centers to gather primary data. To quantify the feasibility of tracing, a feasibility index was developed. Developed index was used to assess the feasibility towards improved traceability of selected chains where it can be applied for other food and non-food supply chains as well. The feasibility index can be used for other fruits and vegetables supply chains too to assess the feasibility prior to implementation of a traceability system. Furthermore, it can be used for non-food supply chains with some modifications. Analysis revealed that poor feasibility of wholesalers compared to farmers and retailers. Product identification technologies, awareness and willingness for traceability improvement were ranged low to fair for all the entity categories. Hence, enhancement of record-keeping and information sharing, adopting product identification and quality measurement technologies, and strengthening of legislation were identified as key improvements for enhanced fruits and vegetable traceability and efficient postharvest management of studied supply chains

Predicting Average Wait-Time of COVID-19 Test Results and Efficacy Using Machine Learning Algorithms

Due to the rising number of confirmed positive tests, the global impact of COVID-19 continues to grow. This can be attributed to the long wait times patients face to receive COVID-19 test results. During these lengthy waiting periods, people become anxious, especially those who are not experiencing early COVID-19 symptoms. This study aimed to develop models that predict waiting times for COVID-19 test results based on different factors such as testing facility, result interpretation, and date of test. Several machine learning algorithms were used to predict average waiting times for COVID-19 test results and to find the most accurate model. These algorithms include neural network, support vector regression, K-nearest neighbor regression, and more. COVID-19 test result waiting times were predicted for 54,730 patients recorded during the pandemic across 171 hospitals and 14 labs. To examine and evaluate the model’s accuracy, different measurements were applied such as root mean squared and R-Squared. Among the eight proposed models, the results showed that decision tree regression performed the best for predicting COVID-19 test results waiting times. The proposed models could be used to prioritize testing for COVID-19 and provide decision makers with the proper prediction tools to prepare against possible threats and consequences of future COVID-19 waves.

Maximum Coverage Location Model for Fire Stations with Top Corporate Risk Locations

The fire station location is a critical decision to optimize the coverage level as measured in terms of the response time. This paper focuses on optimizing the coverage problem, especially in the fire protection field, with new model features to incorporate realistic business challenges such as location criticality and secondary coverage. We extend the deterministic Maximum Coverage Location Problem to account for Top Corporate Risk locations being covered by different fire stations as primary and secondary coverage. To deal with the response time uncertainty arising in practice, we propose a new binary linear problem based on the Maximum Expected Covering Location Problem. By exploiting the model structural characteristics, we prove that the model complexity can be substantially reduced to yield an efficient solution. In the numerical experiments, we use a real case study with five years of historical data. The optimization results of the models yield a priority ranking of the fire stations to open and show the value of incorporating the coverage uncertainty. Finally, we also compare our model with uncertainty with the standard scenario-based optimization to extend the numerical results.

Assessment of Critical Failure Factors for Implementing Lean Six Sigma in Manufacturing Industry: A case study

In the present scenario manufacturing industries have been facing problem-related to cost, quality, and customer satisfaction. To overcome such problems, the organizations are ready to adopt continuous improvement (CI) approaches such as Lean Six Sigma (LSS) which keeps them stable when the demand for products or services fluctuates. LSS is a breakthrough improvement approach that helps to improve the bottom-line result of the company by utilizing its tools and techniques. The successful adaptation of the LSS approach provides a significant improvement in key metrics but deficiency of proper implementation shows a negative effect. To prevent such a situation, need to know about their failure factors. The objective of the present study is to assess the critical failure factors (CFFs) for LSS framework implementation in manufacturing organizations. The leading CFFs for LSS have been identified and selected through a structured literature review and expert opinion. The CFFs based model for LSS implementation has been developed using the Interpretative Structural Modelling and Matrice d’ Impacts Croises Multiplication Appliquee a un Classement (ISM-MICMAC) approach. Previous studies related to such concerns have not developed a structural hierarchical model that is necessary to tackle CFFs towards the LSS implementation process. Such an interrelation helps decision-makers, planners to systematically guide about the barriers that affect the implementation process and help for further implementation success. The developed structured model will also help LSS practitioners, consultants, researchers to anticipate the potential CFFs to implement the LSS framework in their industry for continuous improvement and achieve a leading position in a competitive market.

Predicting Patient Waiting Time in the Queue System Using Deep Learning Algorithms in the Emergency Room

Many hospitals consider the length of time waiting in queue to be a measure of emergency room (ER) overcrowding. Long waiting times plague many ER departments, hindering the ability to effectively provide medical attention to those in need and increasing overall costs. Advanced techniques such as machine learning and deep learning (DL) have played a central role in queuing system applications. This study aims to apply DL algorithms for historical queueing variables to predict patient waiting time in a system alongside, or in place of, queueing theory (QT). We applied four optimization algorithms, including SGD, Adam, RMSprop, and AdaGrad. The algorithms were compared to find the best model with the lowest mean absolute error (MAE). A traditional mathematical simulation was used for additional comparisons. The results showed that the DL model is applicable using the SGD algorithm by activating a lowest MAE of 10.80 minutes (24% error reduction) to predict patients' waiting times. This work presents a theoretical contribution of predicting patients’ waiting time with alternative techniques by achieving the highest performing model to better prioritize patients waiting in the queue. Also, this study offers a practical contribution by using real-life data from ERs. Furthermore, we proposed models to predict patients' waiting time with more accurate results than a traditional mathematical method. Our approach can be easily implemented for the queue system in the healthcare sector using electronic health records (EHR) data.

Strategic and Sustainable Implementation of 5S in a Beef Abattoir

In recent time workplace organization and easy information retrieval help in achieving optimum productivity through maximum utilization of the resources available, significantly reducing industrial lead-time and waste thus resulting in low production cost and increase return-on-investment (ROI). This paper is a study of the effective and efficient implementation of 5S processes in a beef abattoir. Thus, the paper employs both qualitative (case study) and quantitative (statistical analysis like 5S scorecard and 5S audit performance) methods. This research identifies and outlines 5S “best practice” issues overlooked such as unneeded items lying around, torn sign displays, labels and shelves not partitioned, bins not clearly stored in demarcated areas and storage tools not clearly shown with sign panels and labels. Furthermore, budget constraints and the abattoir unreadiness to adopt the 5S system inhibits the smooth implementation of the 5S phases required. Therefore, this research managed to map out a 5S lean-system implementation framework for the case company X beef abattoirs. Finally, the research recommended effective process on how 5S can efficiently save the industry on planning to reduce waste in processes such as lead-time in effective information retrieval system, safety issues to mitigate non-value adding activities and space utilization, for improved productivity.

The Effect of Information sharing and Inventory Management in the Supply Chain Practices on Firms’ Performance: Empirical Evidence from Some Selected Companies of Ethiopia

The objective of this paper is to investigate empirically the effect of information sharing and inventory management practice on firms’ performance. To achieve the stated objective the study targeted supply chain practices of some companies operating in Ethiopia. Data were collected from 170 respondents including employees, suppliers, and distributors of the companies under investigation. Before the analysis of data, the accuracy of data entry, the existence of missing values, normality of data distribution and outliers checked and proved the nonexistence of serious issues. The specified objective and proposed hypotheses in this study tested by structural equation modelling (SEM). The result shows information sharing and inventory management practices have a direct and significant effect on the firm’s performance. Similarly, the higher share of information leads to better inventory management practice, which in turn leads to a greater performance of firms. The study concludes that information sharing has both direct and indirect effects on a firm's performance in the supply chain practices; whereas inventory management practices have a direct effect on the firm's performance. Generally, the results of the study have major theoretical and practical implications. Theoretically, it offers concrete evidence on the significant effects of information sharing and inventory management in the supply chain practices on firm’s performance in developing countries; and hence contributes to the scarce body of literature and reduces the gaps of knowledge in the developing countries on the specified area of study. Besides the theoretical implication, practically the study allows the companies and industries under the considerations to recognize the significant effects of information sharing and inventory management practices on firm’s performance and to use this information to develop and enhance culture of information sharing and usage of sound inventory management techniques in the supply chain practices for the enhancement of organizational performance.

Optimization on the shop floor - A business simulation Approach

Assembly processes can be optimized with various methods. However, it is difficult to evaluate the effectiveness and interaction of these methods. To date, shop floor improvement methods in series production, such as the methods under investigation 5S, Poka Yoke, Kanban, and Standard Work Sheet, have not been scientifically analyzed using a business simulation. This study is aimed at closing this research gap by conducting a business simulation and analyzing the generated data using the design of experiments. This combination represents a new form of research. For the design of experiments, full factorial design with four factors was used. Lead time is selected as the KPI. By analyzing the lean methods that were investigated in this study, both main effects and interactions were found. Results show that it is useful to apply at least one optimization method, whereby Poka Yoke has the most significant impact on the lead time. Researchers in the field of optimization methods can base their investigations on this study.

Analysis of Cargo Release Services with Process Mining

Process Mining (PM) uses event logs extracted from process-oriented IS in order to uncover, analyse, diagnose and improve processes. However, the number of studies demonstrating PM applicability is limited, particularly in the field of logistics. This paper presents a methodological framework for a multi-faceted analysis of real-life event logs based on PM and the usefulness of its techniques, combined with traditional IE&M methods, thus offering an innovative approach on multiple levels by combining the use of PM and more traditional methods; using PM to demonstrate the actual movement of goods and generate a physical map of movements inside the warehouse; and enabling continuous tracking. A case-study, implemented on the cargo release process of a large Israeli logistics company, demonstrates this approach. Results reflect a major gap between the actual and the described processes, as an automatic creation of the process from logs shows that 64% of the customers received their goods after 4.5 hours (instead of 90 minutes, as service standard requires). Practical implications include detailed steps and a recommendation for additional analyses. Research value analysis shows that PM techniques constitute an ideal means to tackle organizational challenges by reflecting real-time situations, suggesting process improvements and creating companywide process awareness.