Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Assessment of Cleaner Production Level in Agro based Industries – A Fuzzy Logic Approach

AbstractCleaner Production (CP) is a preventive environmental strategy and across the globe there are plenty of success stories of its initiatives which have not successfully transcended to realize the fruits. In this context, there is a need to promote CP initiative to help the industry, society and environment. Hence, present level of CP is to be assessed which facilitates exploring the potential for further CP. This underscores the need for developing a methodology to estimate the CP level. Generalizing the evaluation of assessment of CP is difficult due to heterogeneous nature of operations in industries. The application of fuzzy logic assists in addressing this complexity by offering a methodology that can be adapted to any type of organization by identifying the appropriate variables. This paper presents a model developed to assess the CP status and implementation of this model on 22 cashew processing units. In the assessment of CP level, contributing parameters viz., process efficiency, environmental degradation, and sustainability aspects are considered. Various dimensions of each contributing parameter is identified and measured in the overall estimation of CP level. The result of the study has useful policy implications for catalyzing the CP initiatives

Supply chain risk analysis

A new decision support system is proposed and developed that will help sustaining business in a high-risk business environment. The system is developed as a web application to better integrate the supply chain entities and to provide a common platform for performing risk analysis in a supply chain. The system performs a risk analysis and calculates risk factor with each activity in the supply considering its interrelationship with other activities. Bayesian networks along with fault tree structures are embedded in the system and logical rules are used to perform a qualitative fault tree analysis, as the data required to calculate the frequency of occurrence is rarely available. The developed system guides the risk assessment process: from asset identification to consequence analysis before estimating the risk factor associated with each activity in the supply chain. The system is tested with a sample case study on a highly explosive product. Results show that the system is capable of identifying high-risk threats. The system further needs to be developed to add a safeguard analysis module and to enable automatic data extraction from the enterprise resource planning and legacy databases. It is expected that the system on complete development and induction will help supply chain managers to manage business risks and operations more efficiently and effectively by providing a complete picture of the risk environment and safeguards required to reduce the risk level