Modelling Framework to support Manufacturing System diagnosis for Improvement

Introduction-The high competitiveness of modern industry lead companies to a continuous refinement of their manufacturing processes. There are high number of useful strategies and techniques like JIT, QFD etc. which can be implemented for this purpose. These techniques however make the analysis of manufacturing systems difficult due to complexity of these systems and the high number of implied factors. In many cases the results obtained from conventional analysis are lacking in a detailed description of systems current state. The effort that implies the use of process analysis charts, data summary panels, or use of quality tools is wasted due to lack of integration of this information in subsequent phases.

Literature Review- Jim Davis, Thomas Edgar et al has explained smart manufacturing and its goal. They discussed how Smart Manufacturing Leadership Coalition (SMLC,2009) has developed a road map for achieving smart manufacturing as a collective effort in the years to come. Sebestian Engell and Iiro Harjunkoski have made a study on the challenges for todays production systems and analyses the functional hierarchy. J.C. hernandez- matias, A.Vizan proposed in their work an integrated modelling framework for the analysis of manufacturing systems that can increase the capacity of modelling tools for rapidly creating a structured database with multiple detail levels and thus obtain key performance indicators. Problem Identification- One of the major problems is data obtained from the shop floor of manufacturing system lacks integration which makes manufacturing analysis a complex task. Analysis of the data such as quality, time, costs, productivity has widely divergent goals which hinder the development of standardised methods and tools. Specially for SMEs (small and medium enterprises) its a challenge to implement strategies for excellence due to high cost of traditional system diagnosis consultant services.

Objective-The main aim of the work is to develop a framework which supports manufacturing system for improvement. Framework developed will be an Information model which will show how data will be collected from different levels of manufacturing. Data obtained from various sections of manufacturing unit will lead to continuous improvement of manufacturing system. This continuous improvement will finally lead towards smart manufacturing.

Methodology-The complete thesis work will be done in basically three steps Identification of KPIs (Key Performance Indicators). Development of information models on the basis of identified KPIs. Validation of system with a case study.

Identification of KPIs-KPIs represent a set of measures focusing on those aspects of organizational performance that are the most critical for the current and future success of the organization. There are hundreds of KPIs and which one are required for a manufacturing system are decided in accordance with what are the target goals to be achieved. Targets can be proper utilistaion of assets, optimisation of working capital, managing energy costs, increasing customer satisfaction and so on. Here 4-5 KPIs will be identified and focus will mainly lie on optimisation of manufacturing systems and energy cost management.

Development of Information Model-After the KPIs are developed the next step will be to develop an information model. This will ensure that the information gathered from the various machine cells in factorys floor area is integrated at high level. Data collected in real time will ensure that required steps are taken to

achieve the targets may it be financial, resource optimisation, environment related, sustainability etc. First of all a reference information model to be used as a reference for structuring and classifying the information will be made which will show the complete basic structure of manufacturing plant. Further modelling techniques will be used, may be IDEF modelling technique will be used to create information model to compile all the information from manufacturing system. IDEF is a descriptive method based on graphical and text description of functions, information and data.

Validation of Developed System-Finally to demonstrate and validate the developed model a particular case study will be taken related to some manufacturing industry.

References-Jim Davis, Thomas Edgar, James porter, John Bernaden, Michael Sarli. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computer and Chemical Engg. 2012; 47: 145-156.Perera T, Liyanage K. Methodology for rapid identication and collection of input data in the simulation of the manufacturing systems. Simul Pract Theory 2000;7:64556.Al-Ahmari A, Ridway K. An integrated modelling method to support manufacturing systems analysis and design. Comput Ind 1999;38:22538.J.C. Hernandez-Matias, A. Vizan, J. Perez-Garcia, J. Rios. An integrated modelling framework to support manufacturing system diagnosis for continuous improvement. Robotics and Computer- Integrated Manufacturing 2008; 24: 187-199.

Under the Guidance of-Submitted by-Dr. Jatinder MadanVivek sharmaPGMSE-136015Dr. Pardeep Gupta