Linking Supply Chain Strategy and Processes to Performance Improvement

AbstractThis paper proposes a model that will assist companies, particularly the small and medium-sized enterprises, assess their performance by prioritizing supply chain processes and selecting an adequate strategy under various market scenarios. The outlined model utilizes and integrates the SCOR framework standard processes and AHP approach to construct, link, and assess a four level hierarchal structure. The model also helps SMEs put more emphasis on supply chain operations and management. The use and benefits of the proposed model are illustrated on a case of a family owned, medium-sized manufacturing company

Multi-attribute Performance Models for Small Manufacturing Enterprises

Nowadays, there are huge environmental changes in the business world. These changes have resulted in tremendous growth and opportunities for new markets but also in challenges that threaten the operations and survival of firms. These competitive pressures are driving firms to re-evaluate their competitive strategies, supply chains, and manufacturing technologies in order to improve performance and survive long term. Small and medium-sized enterprises also face these challenges, which influence their operations and existence. They are significantly constrained by remarkable limitations in terms of financial resources as well as non-financial factors, such as informal strategic decisions and actions. Reports have revealed that small enterprises are vulnerable to failure. Only around 50% of them in Canada and the United States survive for more than five years. Focusing on financial measures alone is not a good strategy for guaranteeing the long term success of a business. The absence of objective and formal strategic decisions and performance measurement systems in small enterprises increase their chances of failure. Therefore, models have been developed that assess and translate informal and qualitative in small enterprises into measurable, quantitative data. This allows for the evaluation and measurement of decisions and actions, which increases the chances of success for a small enterprise. Using the multi-criteria decision methodology (MCDM) allows for the following: integrating and linking various levels of decision-making and processes, converting subjective information into objective decision making, executing individual business preferences, and ranking strategic attributes and business processes. An analytical hierarchy process approach was first used to develop a simple model. Using the case of a small manufacturing enterprise, it was found that the business did not emphasize financial measures alone; they also paid attention to non-financial measures, such as reliability and responsiveness. It was observed that the business was willing to rank strategic attributes and supporting business processes each time there was a change in the external environment. Finally, an analytical network process approach to express the links and effects among the supply chains of a small business were established, and an overall business performance formula was created

Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things

Industrial automation uses robotics and software to operate equipment and procedures across industries. Many applications integrate IoT, machine learning, and other technologies to provide smart features that improve the user experience. The use of such technology offers businesses and people tremendous assistance in successfully achieving commercial and noncommercial requirements. Organizations are expected to automate industrial processes owing to the significant risk management and inefficiency of conventional processes. Hence, we developed an elaborative stepwise stacked artificial neural network (ESSANN) algorithm to greatly improve automation industries in controlling and monitoring the industrial environment. Initially, an industrial dataset provided by KLEEMANN Greece was used. The collected data were then preprocessed. Principal component analysis (PCA) was used to extract features, and feature selection was based on least absolute shrinkage and selection operator (LASSO). Subsequently, the ESSANN approach is proposed to improve automation industries. The performance of the proposed algorithm was also examined and compared with that of existing algorithms. The key factors compared with existing technologies are delay, network bandwidth, scalability, computation time, packet loss, operational cost, accuracy, precision, recall, and mean absolute error (MAE). Compared to traditional algorithms for industrial automation, our proposed techniques achieved high results, such as a delay of approximately 52%, network bandwidth accomplished at 97%, scalability attained at 96%, computation time acquired at 59 s, packet loss achieved at a minimum level of approximately 53%, an operational cost of approximately 59%, accuracy of 98%, precision of 98.95%, recall of 95.02%, and MAE of 80%. By analyzing the results, it can be seen that the proposed system was effectively implemented

Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing

Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%