ANALYSIS, DESIGN AND OPTIMIZATION OF QRM ASPECTS IN PRODUCTION SYSTEMS

Abstract

In today's turbulent business environment, global competition characterized by both a technology push and a market pull had forced the organizations to compete themselves on various platforms such as faster delivery, price tags, state of art-technology and higher quality dimensions. Various innovative techniques and management practices such as TPM, TQM, BPR, MRP, and JIT etc. are being practiced by various business houses across the globe. However, benefits accrued from them have often been limited because of unreliable or inflexible nature of systems/components/parts. In reliability and maintainability studies only few researchers have seriously addressed the issue of handling uncertainties related with Quality, Reliability and Maintainability (QRM) data (Fonseca and Knapp, 2001; Sergaki and Kalaitzakis, 2002; Majumder, 2004). The present study is an attempt to resolve such uncertain issues related with QRM aspects of systems. The central focus of the present work is to analyze, design and optimize QRM aspects of production systems. A comprehensive review of literature was conducted to identify the gaps and relevant research issues in these areas. Based upon the critical review, a framework (using both qualitative and quantitative techniques) has been developed to abridge the gaps. Owing to its sound logic, effectiveness in quantifying the vagueness and imprecision in human judgment, the fuzzy methodology has been used as an effective tool in the study to synthesize the information related to QRM aspects of production systems. To cope with the complex, uncertain and subjective relationships between various cost segments and to help managers to set up/improve various quality improvement initiatives, the application of fuzzy methodology (FM), is proposed to elicit, aggregate and synthesize various quality costs under the four cost categories ( Prevention, Appraisal, Internal iii Failure and External Failure). Treating quality as a fuzzy notion, the information obtained from wide range of sources (supplier, operators experience, manufacturer's specification and expert opinions etc.) is synthesized with the help of well-defined fuzzy set principles. Capitalizing on the literature studies and identified gaps, an integrated, structured and systematic approach is proposed to plan, implement and sustain a quality-costing program, aimed at helping the managers to provide (i) a structured framework for implementing, sustaining and managing a Quality Cost Accounting System (QCAS) in industry (after prioritization of alternatives under each cost category) (ii) framework to implement Quality Costing System (QCS) based on Process Cost Modeling (PCM) (after prioritizing the processes) In particular, the fuzzy logic approach used in the study to address the quality aspects related to the system is mainly concerned with the following three issues: (i) Translation of linguistic/subjective assessments related to quality cost information under various cost segments into Fuzzy Number Representation (FNR) (ii) Operation on Triangular Fuzzy Numbers (TFN) (iii) Information aggregation using Choquet Fuzzy Integral (CFI) With respect to the issue of handling uncertainties, related with failure data of the production systems, only limited research studies have been undertaken seriously (Fonseca and Knapp 2001, Sergaki and Kalaitzakis 2002). To this effect, the study provides application of non-probabilistic methods (Fuzzy and Grey theory) in conjunction with reliability analysis tools (Fault tree, Petrinets, FMEA) to treat the element of uncertainty associated with the data related to system performance. A unified and structured framework to model, analyze and predict the system iv behavior more realistically has been developed. The framework makes use of both qualitative and quantitative techniques to analyze the failure behavior of an industrial system (paper mill). In the Quantitative framework first the Petri net model of the system is obtained from its equivalent fault tree model and then system failure rate and repair times have been computed (based on the steps as discussed in Section 4.3.3). For the system components, the fuz'zification of data (failure and repair time) is done using Triangular Membership Function (TMF). After knowing the input fuzzy triangular numbers for all the components shown in Petrinet model the corresponding fuzzy values of failure rate (A.) and repair time (r ) for the system at different confidence levels (a) were determined using fuzzy transition expressions. Various system parameters are quantified in terms of fuzzy, crisp and defuzzified values. Depending upon:the value of confidence level, the analyst can predict and analyze the behavior of the system. In the Qualitative framework the in-depth qualitative analysis of all the subsystems is carried out using Root Cause Analysis (RCA) and Failure Mode and Effect Analysis (FMEA). Using the selected experts, possible failure modes, their causes and effect on system performance, with the values of failure of occurrence (Of), likelihood of non-detection of failure (Oa), and severity (S) of failure of various components has been ascertained and resulting Risk Priority Number (RPN) is computed. The limitations of traditional RPN procedure are addressed by using fuzzy decision making system (FDMS) and Grey Relation Analysis (GRA). Finally, the results so obtained from traditional, fuzzy and grey approach are compared. After knowing the behavior of system both in qualitative and quantitative terms, the management is highly concerned with reliable operation of the process / production systems. Thus, it becomes customary to plan and adapt a suitable maintenance strategy which ensures the reliable and trouble free functioning of the system. To this effect, a framework based on Fuzzy Linguistic Methodology (FLM) is developed to assess and identify the effectiveness and efficiency of various maintenance strategies. As a case, three input parameters i.e. historical data [ II], present data [I2], and competence of data [13] related to failures of a component (gears in paper machines) has been taken to judge the effectiveness of nature of maintenance strategies followed in the mill. These parameters are represented as members of fuzzy set, combined by matching them against (If-Then) rules in rule base, evaluated in fuzzy inference system and then defuzzified to assess the capability or effectiveness of maintenance strategy. The various maintenance strategies considered were Frequency Based or Breakdown Maintenance (BDM), Preventive Maintenance (PM), Total Productive Maintenance (TPM), Condition Based Maintenance (CBM), and Reliability Centered Maintenance (RCM). From the results, it is observed that aggressive (TPM) and proactive (CBM) maintenance strategy gives high FIS output (0.859) and high performance index score (0.315) as compared to traditional, reactive (BDM) maintenance strategy. As evident from F