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Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management Kuala Lumpur, Malaysia, March 8-10, 2016

Cost Based Failure Consequence Approach for Identifying Critical Components for Maintenance

Support Decision

Dr. Pravin P. Tambe*

Industrial Engineering Department RCOEM, Nagpur, India

[email protected], [email protected]

Dr. Makarand S. Kulkarni Mechanical Engineering Department,

IIT Delhi, Hauz Khas, New Delhi, India

Abstract - Failure Mode and Effect Analysis (FMEA) is a technique popularly used in reliability engineering for product design improvement. Many industries use the current FMEA technique for risk assessment. The failure consequence is quantified in terms of Risk Priority Number (RPN), a product of occurrence, severity, and detection difficulty. In many situations, the RPN measurement provides no meaning where the quantification on cost basis is more important and meaningful. This paper presents a cost based failure consequence analysis approach for quantification of equipment failure. Three consequences of component failure are considered; immediate stoppage of machine, reduction in production rate and increase in rejection rate. These failure consequences are quantified in terms of Expected Annual Cost (EAC). Based on the EAC, the criticality of component is decided. The approach is demonstrated using a case study of a high pressure die casting machine. The method proposed in this paper will help in making effective cost-driven decisions while making any improvement in the maintenance planning of the system based on tradeoffs between the cost of improvement and failure cost.

Keywords- Failure consequence, Cost basis, Critical components, Maintenance support

NOTATIONS

E(NCM)Replace Expected No. of corrective replacement E(NCM)Repair Expected No. of corrective repair E[N]Inspection Expected No. of Preventive Inspections E[N]PReplace Expected No. of Preventive Replacement FC1 Immediate Stoppage of machine FC2 Reduction in Production FC3 Increased Rejection Rate PFC1 Probability of FC1 PFC2 Probability of FC2 PFC3 Probability of FC3 MTTCA Mean Time to Corrective Action [Cfix]CA Fixed cost of Corrective Action CFC1 Cost of FC1 RPR Reduction in Production Rate TFC2 Time to detect FC2 CFC2 Cost of FC2

284© IEOM Society International


IRR Increase in Rejection Rate TFC3 Time to detect FC3 CFC3 Cost of FC3 E[CCA] Expected Cost of Corrective Action MTTPInspection Mean Time to Preventive Inspection [Cfix]PInspection Fixed cost of Preventive Inspection MTTPReplace Mean time to Preventive Replacement [Cfix]PReplace Fixed Cost of Preventive Replacement E[C]PInspection Expected Cost of Preventive Inspection E[C]PReplace Expected Cost of Preventive Replacement E[CPA] Expected Cost of Preventive Action E[NCA] Expected number of Corrective action PR Production rateCLP Cost of Lost production CL Cost of Labor CRej Cost of Rejections EAC Expected Annual Cost

I. INTRODUCTIONThe Failure Modes and Effects Analysis (FMEA) is one of the most practical design tools implemented in the

product design to analyze the potential failures and to improve the design. FMEA is a structured approach to identify, analyze and prioritize the potential failure modes of a process and their causes [1]. FMEA technique has been widely and successfully used in industries for improving the manufacturing reliability and the product reliability. The two types of FMEA; product-oriented or process-oriented, have been considered as one of the most popular tools used to identify, prioritize, and eliminate possible failures in products and processes before they are released. FMEA provides several benefits to the users as given below [8]: • Improvement of product/process reliability & quality• Increment of customer satisfaction and safety• Early identification and elimination of potential product/process failure modes• Prioritization of product/process deficiencies for development• Capture engineering/organization knowledge• Emphasis of problem prevention• Documents risk and actions taken to reduce risk• Provide focus for improvementThe process of FMEA includes the identification of key product characteristics, potential failure modes, likelycauses of those failures and the associated controls that currently exist. The failure mode, its effects are thenassigned severity, frequency of occurrence and probability of detection rankings respectively. The product of thethree values produces a risk priority number (RPN) for each failure mode, effect and control combination. Byprioritizing the FMEA by RPN, the significant and critical characteristics are identified. However, the traditionalFMEA involves occurrence (O), detection difficulty (D), and severity (S) subjectively measured in a 1 to 10range. The three indices used for RPN are scale variables and they do not provide any quantitative measure interms of actual cost that may be incurred due to the failure of the machine component and therefore may not proveto be effective in making cost-driven decisions in many cases. To overcome the difficulty associated with theconventional RPN based FMEA, several researchers have suggested modifications to the existing FMEA andproposed cost based FMEA models [2-4]. However, these models mainly consider the cost of material(components) and labor while the failure of machine may cost much more to the users in terms of breakdowns,rejections, rework, and slower production, which is not adequately modeled in the literature. Lad and Kulkarni [5]proposed a methodology for machine tool reliability analysis that will help the machine tool manufacturers inmaking cost driven decisions while improving the performance of their machines in the field. The methodologyconsists of three parts viz., modified fault tree diagram, simulation based data analysis and cost based FailureModes and Effects Analysis. Rhee and Ishii [6] presented a Life Cost-Based FMEA, which measures risk in termsof cost for predicting life cycle failure cost, measuring risk and planning preventive, scheduled maintenance andultimately improving up-time. They applied a Monte Carlo simulation to the cost-based FMEA to account for theuncertainties in detection time, fixing time, occurrence, delay time, down time, and model complex scenarios.



Jamshidi and Kazemzadeh [7] presented a Fuzzy method based on cost, considering firm budget as a limit and assessing weight for each RPN factors and each expert. A pair wise comparison among Severity, Occurrence and Detection by the AHP method has been done to obtain a new fuzzy membership function. Wang [8] proposed the adoption of quality cost factors that are used to replace the ambiguous factors used in the traditional FMEA calculation. Lillie et al. [9] used cost-based FMEA to determine the projected cost of failure consequence for a technology insertion control plan for the adoption of lead-free solder for the assembly of electronic systems in critical applications that previously used tin–lead solder. A case study of the lead-free implementation of a power supply demonstrated the return on investment of the control plan for the same product under to different risk scenarios. Zhao et al. [10] analyze the defects of traditional Risk Priority Number method in Process Failure Mode Effect and Critically Analysis (PFMECA) and proposed a new method based on cost and probability. The improved RPN method makes the result of the RPN analysis more objective and accurate. The new RPN method is applied to the PFMECA of a circuit board and observed good results. Rahimi et al. [11] proposed an integrated approach to identify, evaluate and improve the potential failures in a service setting. This integrated approach combines Fuzzy cost-based service-specific FMEA (FCS-FMEA), Grey Relational Analysis (GRA) and profitability theory for better prioritization of the service failures by considering cost as an important issue and using the profitability theory in a way that the corrective actions costs are taken into account.

The practice of FMEA is diversified and different approaches are proposed by different organizations and researchers from one application to another. In this paper, we extend the methodology of cost based FMEA introduced in [5] to the problem of identifying the critical components for maintenance decision. The objective is to present a quantitative approach for the failure of machine component and its consequence in terms of annual cost used to identify the criticality. The Expected Annual Cost (EAC) is expressed on the basis of down time cost, reduced speed cost, reduced quality cost and repair/replacement cost. The cost based consequence analysis approach is demonstrated using the case study of a die casting machine.

II. COMPONENT FAILURE DISTRIBUTION AND PARAMETER ESTIMATION Maintenance decision analysis requires the use of failure distribution of equipment. The Weibull distribution

is one of the most useful probability distribution which is widely used in reliability, life data analysis and quality control due to its versatility. The Weibull distribution is a very flexible life distribution model that can be used to characterize failure distributions in all three phases of the bathtub curve. The Weibull plot is extremely useful in maintenance planning. The basic Weibull distribution has three parameters, a scale parameter (η), a shape parameter (β) and the location parameter (γ). The scale parameter influences both the mean and the spread of the distribution. The scale parameter, determines when, in time, a given portion of the population will fail, i.e. 63.2%. The shape parameter, beta, is the key feature of the Weibull distribution that enables it to be applied to any phase of the bathtub curve. Depending on the values of the parameters, the Weibull distribution can be used to model a variety of life behaviors. It is a versatile distribution that can take on the characteristics of other types of distributions, based on the value of the shape parameter (β). For β<1, the Probability Density Function (PDF) is similar in shape to the exponential and for larger value of β (e.g., β >3) the PDF is somewhat symmetrical, like the normal distribution. Additionally, the shape parameter shows which class of failure is present at any given time based on its value. β < 1 indicates a failure rate that decreases with time, as in the infant mortality period, β =1 indicates a constant failure rate and β >1 models an increasing failure rate, as during wear-out.

The probability density function for two parameter Weibull distribution is given as, β

ηβ

ηηβ ⎟⎟

⎠

⎞⎜⎜⎝

⎛−−

×⎟⎟⎠

⎞⎜⎜⎝

⎛×=

t

ettf1

)( (1)

The two parameters; scale parameter (η) and shape parameter (β) are determined based on the failure data and the distribution function. However, many times such data is not available in proper recorded format. In such cases, the knowledge and experience of maintenance personnel and service engineers, who have vast experience of attending the machine failures and repairs can serve as alternative source for getting the useful information about the machine components. These people are called experts and such a technique is called an expert judgment technique for parameter estimation [5, 12]. Experts are first asked about the time when the components mostly fail, which in this case becomes X. Experts are also asked about any incidence when he/she witnessed the longest failure-free operation of the component. The longest failure-free time then becomes the Y. Once the value of X and Y are known, the corresponding value to scale parameter (η) and shape parameter (β) can be obtained using equations given below.



β

ββη

/11⎥⎦

⎤⎢⎣

⎡ −×=X (2)

βη )/(01.0 Ye−= (3)

III. COST BASED FAILURE CONSEQUENCE ANALYSIS

Cost based failure consequence analysis, as considered in this paper, differs from the conventional FMEA in a sense that, it considers the failure of component and quantifies the risk of component/ subassembly failure consequence in terms of cost. The steps to be followed in this approach are as follows:

1. Identify the system to be analyzed.

2. Identify the system failure and allocate the same to the lower level

Define the failure in terms of one of the consequence. In this paper the following three consequences of component/sub-assembly are considered

FC1: The failure leads to machine completely down, machine producing excessive noise, heat etc., where it needs to be stopped immediately

FC2: The failure results in slower production

FC3: The failure is in terms of a degraded state where the machine will run, but lead to deterioration of the product quality being manufactured on the machine resulting in the increased rejections.

The system failure is then allocated to lower level components/subassemblies, for which a fault tree is generally used. 3. Identify the Cost structure and other shop floor policy parameters -

The next step is to identify the cost structure and the shop floor operational parameters related to the failure consequence like, machine operating hours per year, designed production rate, cost of lost production, cost of rejections, labor cost, process quality control scheme parameter like sample size, sampling interval, etc.

4. Quantify the Failure Risk

Failure risk is quantified in terms of cost of failure and repair which include, cost of downtime, cost of slower production, cost of rejections, cost of repair/replacement, etc. the following models are used to quantify the failure risk in terms of the failure consequence.

The expected Annual Cost Index is calculated as : [ ] [ ]PACA CECEEAC += (4)

where-

E[CCA] – Expected Cost of Corrective Action

[ ] [ ]CAFCFC

FCFCFCFCCA NE

CPCPCP

CE ×⎥⎦

⎤⎢⎣

⎡×+

×+×=

33

2211 (5)

CAfixLPFC ]C[)CLCPR(MTTCAC ++××=1 (6)

122 FCLPFCFC CCTRPRPRC +×××= (7)

1Re33 FCjFCFC CCTIRRPRC +×××= (8)

SFC TT ×⎥⎦

⎤⎢⎣

⎡−

=β1

13 (9)

where, β=beta error of control plan



Ts = Time between samples

E[CPA]- Expected Cost of Preventive Action

[ ] [ ] [ ][ ] [ ] placePplaceP

InspectionInspectionPA

NECE

NECECE

ReRe ×+

+= (10)

{ }

{ }PReplace

PReplacefixRe

Inspection

nPInspectiofix

[N]

][C)(

[N]

][C)(

E

CCPRMTT

E

CCPRMTT

LLPplaceP

LLPnPInspectio

×

++××+

×

++××=

(11)

5. Prioritize the components based on Expected Annual Cost

The component based on EAC can be categorized for its criticality, which can be used to prioritize the further decisions.

In the next section, the details about the case study are presented.

IV. CASE STUDY

In this section, we consider a case study of high pressure die casting machine (TOSHIBA 800). The machine under consideration operates for eight hours per shift and three shifts per day. The machine component related data like, time to failure distribution parameters, maintenance time, cost, etc. is given in Table I. Also the data for the failure consequences (FC1, FC2 and FC3) is given in Table I. ‘NA’ in Table I indicates ‘Not Applicable’. In Table 1, the expected number of inspections, corrective and preventive action is determined through a simulation approach using ReliaSoft BlockSim7 software (http://www.reliasoft.com/), for a given preventive maintenance policy. The simulations are run considering the machine life as 10 years, however during calculations; the expected annual cost is obtained by dividing the total cost by 10. In the present case, the critical to quality characteristics are the casting defects and the industry follows acceptance sampling for process quality control using a single sampling plan. The normal defective percentage is 8 % while in the case of failure of the components, the defective rate increases to maximum 20 % for some components. To demonstrate the methodology, sample calculations for one of the components, Arm bearing is given below.

V. RESULTS AND DISCUSSION Sample Calculations of the Expected Annual Cost for Arm bearing

Here the cost is calculated based on the expected number of corrective and preventive actions using simulation approach for 10 years using Reliasoft Blocksim software and converted to annual value by dividing it by 10. PR=72, CLP=8.2 , CL=200 , CRej=161.5

E[NCA] = 21.98 , E[N]Inspection =119,

E[N]PReplace = 39

MTTCA = 0.5 , [Cfix]CA = 6000 , IRR= 0.12,

CFC1=MTTCA× (DPR×CLP+CL)+ [Cfix]CA

= 0.5 × [72×8.2+200] + 6000

=6395.2

CFC3=PR×IRR×TFC3×CLP + CFC1

As mentioned before, the industry follows acceptance sampling for process quality control using a single sampling plan. The normal defective percentage is 8% while in the case of failure of the component, the defective rate increases to 20 %. For Sampling plan of n=5, c=1, β=0.737 for p=0.2, therefore,



TFC3= . × 2 = 7.6 hour

CFC3 = 72 × 0.12×7.6×161.5 +6395.2 =17000

E[CCA] = 17000 × 21.98 = 373660

E[CPA] = {MTTPInspection × (PR×CLP+CL) +[Cfix]PInspection} × E[N]Inspection

+ { MTTPReplace × (PR×CLP+CL) +[Cfix]PReplace } × E[N]PReplace

In the above equation,

MTTPInspection = 1 hour, [Cfix]PInspection = 200, E[N]Inspection= 119

MTTPReplace = 0.5 hour, [Cfix]PReplace = 12000, E[N]PReplace = 39

E[CPA] = {1 × (72×8.2+200) +200 } × 119 +

{ 0.5× (72×8.2+200) +12000 } × 39

= 601270.4

Therefore, EAC = E[CCA] + E[CPA] /10 = (373660+601270.4) /10 = 97493

In the similar way, the EAC is calculated for all the components in Table I and results for the EAC values for all the components are given in Table II. The variation in EAC for the components is shown in Fig.1.



TABLE I. COMPONENT DATA

r – repair , R- Replace

Component Eta (η)

in Hrs.

Beta (β) E

[NC

A]

E[N

] Insp

ectio

n

E[N

] PR

epla

ce

PFC1 PFC2 PFC3

Arm Bearings 1837 2.83 21.98 119 39 1 0 1

Limit switch 14027 3.32 4.56 NA NA 1 0 1

Block 1106 4.63 61.71 59 NA 0 1 1

Seal 6680 4.40 5.90 119 11 0 1 1

Dia. Valve 3265 5.55 20.41 119 NA 0 1 1

Teflon seal 1624 5.94 24.87 NA 39 0 1 1

Header 1106 4.63 187.35 119 NA 0 1 1

Connector 7738 2.49 8.78 39 NA 0 1 1

Valve screw 22803 2.59 13.79 59 NA 0 1 0

Gear box 20364 3.93 80.92 19 NA 0 1 0

Proximity Sensor 7738 2.49 8.78 NA NA 0 1 0

Limit switch 3744 2.69 18.49 119 NA 0 1 0

Inj.Piston 22803 2.59 13.67 NA NA 1 0 0

Shot sensor 6840 3.76 9.76 39 NA 1 0 0

Cartridge 7738 2.49 8.77 NA NA 1 0 0

Seal 3674 2.83 10.95 NA 19 1 0 0

Hose pipe 7738 2.49 8.78 NA NA 1 0 0

Seal & O’ring 14027 3.32 4.56 NA NA 1 0 0

DC unit DCV 5108 5.24 12.93 NA NA 1 0 0

Locking nut 29379 2.83 1.98 NA NA 0 1 1

bush 26746 4.36 2.11 19 NA 0 1 1

wiper seal 3674 2.83 10.96 NA 19 0 1 1

Toggle Bush 26746 4.36 2.12 9 NA 0 1 1

Pin 26746 4.36 2.12 NA NA 0 1 1

Ejector DCV 5108 5.24 12.93 NA NA 1 0 0

Proximity sensor 14027 3.32 4.56 NA NA 1 0 0

Ej. Piston seal 13093 5.37 4.74 NA NA 1 0 0

Acc. seal 1837 2.83 37.68 NA 19 0 0 1

Chain 1623 6.04 116 (r) 29 (R)

119 NA 1 0 0

Piston 1829 2.86 27 (r)

27 (R) NA NA 0 1 1

Couplings 7738 2.49 12 (r) 4 (R)

29 NA 1 0 0

Filter 1106 4.63 222 (r) 37 (R)

39 NA 1 0 0

Safety valve 36023 3.00 2 (r)

1 (R) NA NA 0 0 1



TABLE I. (CONTINUED…..)

Component Name MTTCA [Cfix]CA RPR TFC2 IRR TFC3

MT

TPI

nspe

ctio

n

[Cfix

] P In

spec

tion

MT

TPR

epla

ce

[Cfix

] PR

epla

ce

Arm Bearings 0.5 6000 0 0 0.12 7.6 1 200 0.5 12000

Limit switch 1 20000 0 0 0.12 7.6 0 0 0 0

Block 1 10050 0.1 2 0.03 7.6 2 0 0 0

Seal 2 7000 0.1 2 0.03 7.6 1 0 2 7000

Dia. Valve 2 8000 0.1 2 0.03 7.6 1 0 0 0

Teflon seal 0.25 150 0.1 2 0.03 7.6 0 0 1 150

Header 2 50 2 2 0.03 7.6 2 50 0 0

Connector 0.33 250 0.1 2 0.03 7.6 1 0 0 0

Valve screw 3 10000 0.1 2 0 0 0.5 200 0 0

Gear box 10 12000 0.1 2 0 0 1 1000 0 0

Proximity Sensor 0.5 2000 0.1 2 0 0 0 0 0 0

Limit switch 1 20000 0.1 2 0 0 0.5 0 0 0

Inj.Piston 13 10000 0 0 0 0 0 0 0 0

Shot sensor 0.5 105000 0 0 0 0 0.5 5000 0 0

Cartridge 2 50000 0 0 0 0 0 0 0 0

Seal 1.5 500 0 0 0 0 0 0 1.5 500

Hose pipe 0.25 2500 0 0 0 0 0 0 0 0

Seal & O’ring 3 40000 0 0 0 0 0 0 0 0

DC unit DCV 0.5 25000 0 0 0 0 0 0 0 0

Locking nut 4 80000 0.1 2 0.03 7.6 0 0 0 0

bush 210 550000 0.1 2 0.03 7.6 2 2000 0 0

wiper seal 4 3000 0.1 2 0.03 7.6 0 0 4 3000

Toggle Bush 210 170000 0.1 2 0.12 7.6 8 3000 0 0

Pin 10 60000 0.1 2 0.12 7.6 0 0 0 0

Ejector DCV 0.5 25000 0 0 0 0 0 0 0 0

Proximity sensor 0.5 2000 0 0 0 0 0 0 0 0

Ej. Piston seal 8 30000 0 0 0 0 0 0 0 0

Acc. seal 4 50000 0 0 0.2 7.6 0 0 4 50000

Chain 4(r)

3(R) 1000(r) 6000(R

0 0 0 0 2 800 0 0

Piston 2(r)

2(R) 50(r)

5050(R 0.1 2 0.03 7.6 0 0 0 0

Couplings 5(r)

1(R) 1200(r)

16200(R 0 0 0 0 0.5 500 0 0

Filter 0.5(r)

0.5(R) 0(r)

1000(R 0 0 0 0 0.5 0 0 0

Safety valve 1(r)

1(R) 500(r)

1000(R 0 0 0.2 7.6 0 0 0 0

r – repair , R- Replace



TABLE II. EXPECTED ANNUAL COST VALUES FOR COMPONENTS

Component EAC (Rs.) Component EAC (Rs.) Component EAC (Rs.)

Arm Bearings 97493 Limit switch 43362 Toggle Bush 82173

Limit switch 14347 Inj.Piston 27728 Pin 16730

Block 93323 Shot sensor 123975 Ejector DCV 32845

Seal 24679 Cartridge 45275 Proximity sensor 1093

Dia. Valve 34617 Seal 5049 Ej. Piston seal 17237

Teflon seal 11421 Hose pipe 2370 Acc. seal 367941

Header 101843 Seal & O’ring 19357 Chain 100882

Connector 5965 DC unit DCV 32334 Piston 34186

Valve screw 20744 Locking nut 17026 Couplings 15574

Gear box 165432 bush 159157 Filter 48776

Proximity Sensor 2104 wiper seal 21500 Safety valve 4872

Fig.1. EAC values for components

From Fig.1, the components with high values of EAC are identified as critical components. The critical components/subassemblies need to be analyzed for their failure cause and total downtime. The analysis of the critical components will help in identifying the improvements in maintenance support decision. Also, the critical components with higher EAC will be further analyzed in terms of their failure cost due to FC1, FC2 and FC3, and necessary action for higher cost component.

0

50000

100000

150000

200000

250000

300000

350000

400000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

EAC

Component No.



In the case of multi-component system, it is not possible to consider all the components of the system due to limited

time, budget and/or resources. The purpose of selective maintenance policy is to help select a maintenance action from a set of desired actions like, repair, replace or do-nothing for maintenance program so as to minimize the cost while keeping system availability at a specified level or to maximize system availability at a predefined maintenance cost rate. The cost based failure consequence analysis can be used for identifying components for selective maintenance decision in case of multi-component system.

Also, in the case of integrated approaches for selective maintenance with quality and production scheduling, the optimization procedure for the decision parameters take a large computational time even for a moderate number of components and scheduling problem size [13,14]. However, it is observed that integrated approaches give better performance as compared to the conventional practice of independent planning. Hence, for systems with large number of components, the cost based failure consequence analysis can be used to first identify the critical components and then consider only those critical components in the decision optimization. This will help to reduce the computational time with more cost effective results.

VI. CONCLUSION This paper has presented the cost based failure consequence analysis to identify the criticality of components for further

decision. The component failure and its consequence are quantified on the cost basis and an Expected Annual Cost (EAC) is calculated for each component. The approach is demonstrated using a case study of high pressure die casting machine. The components with high EAC can be further analyzed for failure cause and downtime analysis, which can be utilized for further maintenance policy and support decision. EAC value will give better economic performance of the machine to the users. Thus the proposed cost based failure analysis approach is a modification of the conventional FMEA approach and help the manufacturer in making cost driven decision while improving the performance of their machines through effective maintenance decision and utilization of resources. The approach will help in reducing the computational time of maintenance decision optimization, such as selective maintenance decision where the time increases due to inclusion of the large number of components in decision optimization. In such situation, first the critical components can be identified using the cost based failure consequence analysis and then only the critical components can be considered for maintenance decision optimization. It can also be applied in the approaches for integrating selective maintenance decision with quality control, production planning & scheduling, inventory, etc., based on the interrelationship between these elements in the operational context.

REFERENCES

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Vol.10 No.5, 1993, pp.16-23. [3] M. Ben-Daya and. Raouf. A, “A revised failure mode and effects analysis model”, International Journal of Quality &

reliability Management, Vol. 13 No. 1, 1996, pp. 43-47. [4] S. Kamenta and K. Ishii, “Scenario-based failure modes and effects analysis using expected cost”, Journal of

Mechanical Design, Vol. 126 No.6, 2004, pp. 1027-1035. [5] B.K. Lad and M.S. Kulkarni, “An Expert Based Methodology for Cost Oriented Analysis of Machine Tool Reliability”,

International Journal of Performability Engineering, Vol. 6 No. 2, 2010, pp. 137-148. [6] S.J. Rhee and K. Ishii, “Using cost based FMEA to enhance reliability and serviceability”, Advanced Engineering

Informatics, Vol. 17, 2003, pp. 179–188 [7] A. Jamshidi and R. Kazemzadeh, “A Fuzzy Cost-based FMEA Model” , Proceedings of the 2010 International

Conference on Industrial Engineering and Operations Management Dhaka, Bangladesh, January 9 – 10, 2010 [8] M.H. Wang, “A Cost-Based FMEA Decision Tool for Product Quality Design and Management”, IEEE International

Conference on Intelligence and Security Informatics (ISI), Beijing, China, 10-12 July 2011, pp. 297-302. [9] E. Lillie, P. Sandborn and D. Humphrey Assessing the value of a lead-free solder control plan using cost-based FMEA,

Microelectronics Reliability, Vol. 55 No.6, 2015, pp.969–979. [10] Y. Zhao, G. Fu, B.Wan and C. Pei, “An improved cost-based method of Risk Priority Number”, IEEE Conference

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for Minimizing Failures at a Healthcare Diagnosis Service”. Quality and Reliability Engineering International, Vol. 31, 2015, pp. 601–615.



[12] P. Jager and B. Bertsche, “A new approach to gathering failure behavior information about mechanical components

based on expert knowledge”, 50th Annual Reliability and Maintainability Symposium (RAMS), Los Angeles, USA. Jan. 2004, pp. 90-95.

[13] P.P. Tambe and M.S. Kulkarni, “A novel approach for production scheduling of a high pressure die casting machine subjected to selective maintenance and a sampling procedure for quality control”, International Journal of System Assurance Engineering and Management, Vol. 5 No. 3, 2014, pp. 407-426

[14] P.P. Tambe, S. Mohite and M.S. Kulkarni, “Optimization of opportunistic maintenance of a multi component system considering the effect of failures on quality and production schedule: A case study”, International Journal of Advanced Manufacturing Technology, Vol. 69 No 5-8, 2013, pp 1743-1756



BIOGRAPHY Pravin P. Tambe is an Assistant Professor in Department of Industrial Engineering at RCOEM, Nagpur India. He earned his Master’s degree in Industrial Engineering from VNIT Nagpur, India and PhD from Indian Institute of Technology (IIT) Delhi, India. He has published research papers in reputed international and national journal and conferences. He is actively involved in the activities of Indian Institution of Industrial Engineering (IIIE). He is the member of IIIE, IAENG and IEOM. He is reviewer for international journals of Elsevier, Emerald, and Springer. His research interest mainly focuses on maintenance planning, quality control, production scheduling, reliability and productions & operations management.

Makarand S. Kulkarni is currently an Associate Professor with the Department of Mechanical Engineering at the Indian Institute of Technology Delhi. He is associated with the Industrial Engineering group of the Department. He graduated in Production Engineering and later did his Masters in Materials Technology from the Department of Metallurgical Engineering and Materials Science at the Indian Institute of Technology Bombay. Subsequently, he completed his Ph.D. in the area of Manufacturing Engineering from the Department of Mechanical Engineering at the Indian Institute of Technology Bombay. His post Ph.D. industry experience includes application of quality and reliability engineering techniques in manufacturing and service industry. His current research interest mainly focuses on Quality, Reliability, Life Cycle Costing and Analysis, Maintenance Management, Product Service Systems, Lean Manufacturing, Warranty Management and Operations Planning & Control.
