Search

Growing competition market situations have emerged the requirement of the real-time data, understanding data behaviour, and maintenance actions in the manufacturing system. The future decision-making process in manufacturing needs to be more flexible to adapt to various methods for maintenance decision support systems (MDSS). This paper classifies various application areas of MDSS through a systemic literature review. Specifically, it identifies the relationship between the machine maintenance areas and the processes in which it integrates different tools and techniques to develop MDSS. The accumulated information helps in analyzing trends and shortcomings to concentrate the efforts for future research work. The reviewed papers are selected based on the contents, application tool assessments and clustered by their application areas. Furthermore, it proposes a structure outlined based on the functional knowledge as well as the information flow design during the development of MDSS, along with the relationship among application areas.

The present competitive market is focusing on industrial efforts in producing high-quality products with the lowest possible cost. In every real-life system, there are a number of factors that cause disturbance in the process performance and their output. Process improvements through minimizing or removing such factors provide advantages such as reduced wastage or re-machining and improved market share. To help in accomplishing these objectives, various quality improvement philosophies have been put forward in recent years that can maximize the quality characteristics to ensure the enhancement of product and process. Six Sigma is an emerging data-driven approach that uses methodologies and tools that lead to improved quality levels and fact-based decision-making. This paper presents the application of the Six Sigma methodology to reduce defects in a cutting process of a garment manufacturing company in India, which is concluded through an action plan for improving product quality level. The define–measure–analyze–improve–control (DMAIC) approach has been followed here to solve the underlying problem of reducing defects and improving sigma level through continuous improvement process. The process helps in establishing specific inspection methods adapted for defect type which causes maximum rejection and to prevent their appearance in product.

It is important for modern businesses to search the ways for continuous improvement in performance of their supply chains. The effective coordination and integrated decision making across the supply chain enhances the performance among its various partners in a multi stage network. The partners considered in this paper are product suppliers, processing points (PP), distribution centres (DC) and retail outlets (RO). The network addresses an uncertain environment threatenedby different sources in order to captivate the real world conditions. The uncertain demand of deteriorating products and its dependent costs develop uncertainties in the environment. On the other hand, suppliers and processing points have restricted capacities for the retail outlets’ order amount happened in each period. A bi-objective non-linear fuzzy mathematical model is developed in which the uncertainties are represented by the fuzzy set theory. The proposed model shows cost minimization and best supplier selection coordination under the conditions of capacity constraints, uncertain parameters and product’s deteriorating nature. The fish and fish products give good examples for the proposed model. To solve, the model is converted into crisp form and solved with the help of fuzzy goal programming.

In this paper, an approach for integration of data from different sources and from a population of similar monitored entities is presented with evaluation procedure based on multiple machine learning methods that allows selection of a proper combination of methods for data integration and feature selection. It is exemplified on the real-world case from manufacturing industry with application to double ball-bar measurement from a population of machine tools. Historical data from the period of four years from a population of 29 similar multitask machine tools are analysed. Several feature selection methods are evaluated. Finally, simple economic evaluation is presented with application to proposed condition based approach. With assumed parameters, potential improvement in long term of 6 times reduced amount of unplanned stops and 40% reduced cost has been indicated with respect to optimal time based replacement policy., Skapad 2018-05-02 som submitted till: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, EISSN 1362-3052