Examination of Risk Factors in the Healthcare Product Development Process by using Fuzzy based MCDM Method

Healthcare product development (HCPD) process is generally long and time taking process because of complex manufacturing systems. These intricacy systems, the amount of risks involved in product development is also high. It is very much essential in controlling/minimizing risk in this process. The aim of this paper is to investigate risks in an herbal soap manufacturing company under HCPD to suggest a ranked risk structure (RRS) portrayal for obtaining the subjective risk assessment to change the magnitude of risks. For transforming the linguistic data into numeric risk ratings, a fuzzy based MCDM technique is used in this paper and also computing the ‘Level of risk’ regarding crisp ratings, a method of ‘Incentre of Centroids’ for GTrFN has been used. Lastly, a structure to sort dissimilar risk factors was suggested based on the notable extent of risk ratings (crisp). Subsequently, an action strategy was proposed to provide instructions to company managers to effectively control risks. A case study tactic is employed.

Digital Twins for Simulation Systems

Complex Manufacturing Systems can be better engineered with simulation techniques Relaying only on physical system to collect real world and capturing models is out of date. Moreover, a complex system has recursive model design, which leads to consume more time and more maintenance, unplanned down times and poor operating efficiency. The New Industry 4.0, digital twin creates virtual mirror of actual system. Here we have demonstrated digital twinning of UAV(Parrot ARDrone 2.0). Digital twin is virtual replica of physical assets and it can be simulated with real time data using industrial Internet of Things. Simulation with real time data improves operating efficiency, reduces unplanned down time hence increased revenue to manufactures

A new data analytics framework emphasising preprocessing of data to generate insights into complex manufacturing systems

Recent emphasis has been placed on improving the processes in manufacturing by employing early detection or fault prediction within production lines. Whilst companies are increasingly including sensors to record observations and measurements, this brings challenges in interpretation as standard approaches do not highlight the presence of unknown relationships. To address this, we have proposed a new data analytics framework for predicting faults in a large-scale manufacturing system and validated it using both a publicly available Bosch manufacturing dataset with a focus on preprocessing of the data and the open-source SECOM industrial dataset. This paper is an extension to the work presented at International Conference on Intelligent Manufacturing and Internet of Things. The additional material includes a detailed focus on feature selection and the various approaches for identifying important features in the data, an updated framework methodology and description, an extension of XGBoost to allow this model to be used for prediction/classification and a comparison for classification with a Random Forest, tree-based model. The framework was used to explore two public manufacturing datasets and successfully identified the most influential features related to product failure in each production line data.