Since 80-90% of the costs of holding a piece of the material predetermined by the way it was projected and manufactured, there are tremendous opportunities to reduce those costs by doing a good job up front and by applying reliability engineering concepts.

Input from managers and applied scientists working with operators and maintenance people are critical. The second stage (Shahzad Ahmd, 2015), the action point, deals with operating and maintaining the equipment in the best fashion possible. Autonomous maintenance and training play important roles at this point. Neglect and by ensuring operators have the best skills to take to the woods and arrange up their equipment. In addition to maintaining basic (Nazim Baluch, 2013) care of the equipment by keeping it clean, properly lubricated, and physically secured, the operator can serve a vital and dynamic use in providing on-site detection by looking for signs of impairment. The final point, the exhaustion stage, is the point when the equipment's performance is affected by wear and rip. By using Preventive and Predictive Maintenance (PPM), the effects of deterioration minimised. (I.P.S. Ahuja, 2008). A good preventive maintenance program provides interval or timed servicing of the equipment so the divisions can replace as they break. Cleaning, lubricating, adjusting, inspecting, repairing, replacing, and testing can decrease deterioration.

Most machine failures do not come about by luck; most failures are either made by or indicated by deterioration or drift in operating shape, conditions that can frequently be watched or evaluated. The ability to cut breakdowns comes from preventing them. Eliminating breakdowns mean detecting signs of deterioration or drift. About 75% of all breakdown can be avoided by having the operator closest to the equipment cleaning and inspecting it on a regular planned basis. Equipment failure prevented if we can distinguish them in the early levels of trickery. Spotting them means detecting changes in operating conditions, trends that point to future problems. The remaining 25% of breakdowns can be noticed by maintenance people conducting regularly scheduled (Pratesh Jayaswal, 2008) Maintenance tasks and apply Predictive Maintenance Technology. Modern engineering has greatly improved our ability to monitor critical conditions for both failure diagnostics and failure prediction. Higher asset investment costs and ever-increasing use of more innovative process technology, organisations are aggressively implementing these techniques and that permit detection of the impending failure or degradation of execution.

2. PROBLEM IDENTIFICATION:

To satisfy the customer demand, existing assembly line set to meet the current schedule as the innovative flow line machines were spare to other new products. Additional pay paid for added working hours to meet the customer schedule. No equal time for proper preventive maintenance hence leads to more equipment failure. Employee self-confidence, reduced due to additional working hours. More scrap and rework during the setting changeover, extra working hours.

The problematic area was the Grinding machine, where the setting changeover took once in a week for machining two different item code per customer schedule. Due to this frequent setting change, the losses were more. The losses include the setting evolve over time loss, jigs, and fixture. These losses led to low cell effectiveness, and this resulted in the following difficulty: Additional running hours needed. Delivery hindrance and sometimes bankruptcy. High machining defects. Low operator morale. OEE is the best practices way to monitor and improve the efficiency of manufacturing cells/machines in manufacturing operations. OEE Systems deliver: universally accepted performance measurement motivating continuous improvement, recognising opportunities to increase output, supporting energy deficiencies integral part of world-class manufacturing.

Figure 1 Grinding Machine Loss tree

3. METHODOLOGY:

To apply the TPM method in maintenance organisation based in TPM system, it was adopted the Three Stage Autonomous maintenance, ECRS and quality improvement tool. This methodology shows to be adequate for making out the defect and quality matters. It proves to be simple and objective planned improvement in the TPM maintenance management in this constitution.

3.1 Quality improvement tools:

Ishikawa 7QC tools / Quality Management - Recognise, rank and investigate failures, FMEA -Recognise and assess probable weaknesses, PM-analysis - Diminish all unrelieved loss to zero. Fault tree analysis - recognise causes of failures and their reason links.

3.2 Autonomous maintenance:

Train the operators to respond to the campaign instead of the solution.

· By increasing competence and understanding, the operators may: Eliminate minor stoppages, prevent breakdowns, safely implemented improvements, improve quality, safety, and the surroundings.

· In the long run, operators start to execute maintenance profession,

· Daily inspections replace, restore and low occurrence controls

· The seven levels of autonomous maintenance Implemented.

· Takes long time to carry out, often years

3.3 Focus Improvement:

It focused equipment improvement and reduces losses that impede equipment efficiency, human work effectiveness and efficient use of production resources. It should aim at improving the efficiency of utilisation of equipment, operator material and energy. Kaizen is for small improvements machine losses, but carried out on a continual basis and involved cross-functional teams comprising in production, maintenance engineering and operator. Kaizen requires no or little investment. The Focus Improvement aim is reducing losses in the place of work that affects our efficiencies. By using a thorough and careful procedure, we remove losses in a systematic method using a variety of Kaizen tools.

3.4 ECRS

This is a unique approach towards the action process optimisation with supporting core principle. Elimination of motion is the best; One shot setup is typical, and no change over is the last. If migration is not possible, Combine the usual method. If the combination is not feasible, combine the process, Reduce the activity time. If activity time cannot decrease, Do Kaizens to simplify the actions

Figure 2 Grinding Machine Setup time

Figure 3 Grinding Machine guide plate Setup time

Figure 2 shows how the Grinding Machine setup time brought down from 210 minutes to 100 minutes.

Figure 3 shows the how the guide plate setup time brought down to 12 minutes from 35 minutes. By applying the ECRS techniques. However, there is scope for further refinement of fine tuning the process. In the above example, the major share of the time consumed for eliminating the fixture and again installing the required one. By clearing out so much time consumed for that action was more likened to other measures. Hence this was brought up and was fetched down to minimise possible. However, still, we find some scope to cut inventory time. Hence by applying the ECRS technique by a precise way, we can reduce the setup time.

Figure 4 Grinding Machine Defects

Figure 5 Minor stoppages of Grinding Machine

Figure 5 shows a small stoppage loss, i.e. frequents stoppages for a short time from instants to less than 5 minutes of recovery. Losses that occurs when the equipment temporarily stops or idles due to sensor actuation or blocking of the work, chute bottleneck. The equipment operate through simple measures. To diminish minor stoppage. It is important enough to study the phenomena, complex and thoroughly get rid of minor flaws. The target quantity of minor defects is zero.

Figure 6 Breakdown trend of Grinding Machine

Figure 6 presents breakdown maintenance is continuance performed on equipment that has broken down and is unusable. It is observed on a breakdown resources trigger. It would either plan or un-plan. A model of intended maintenance is run-to-failure maintenance, while cases of unplanned maintenance include corrective maintenance and responsive maintenance. Breakdown carrying lead costlier than preventative maintenance.

4. RESULT AND DISCUSSION

Many losses observed during the grinding operation; they are mainly downtime losses, speed losses, and quality losses which affect Overall Equipment Effectiveness. To decrease these losses and to meet world-class OEE. There should arrest these losses by utilising the TPM concept. The critical limits of the six big losses need detailed performance data. This result, to begin with in too complex data gathering requirements since the OEE achievement data need. Such data collected from machine experience, the working time of each downtime and speed loss investigated. The detailed approach of the OEE factors discussed below.

Machine utilisation is a ratio between operating time and net available time.

Table 1 Availability of Grinding Machine Before / After TPM Implementation

	Total utilised hours	Total non-utilized hours
Before TPM Implementation	170	25
After TPM Implementation	195	09

Figure 7 Availability of Grinding Machine

Table 1 depicts the Availability of the grinding machine in terms of both utilized and non-utilized hours after implementation of TPM. It is interesting to mention that out of the total, Utilized hours of the machine raise from 170 hours to 195 hours. The downtime of 25 hours is diminished from 25 to 09 hours. The enhancement time clearly reveals the benefits of the implementation of TPM.

Machine Availability = Total Time–Down Time/Total time.

Availability (A): Operating time / Net available time

Where, Planned production time

=Shift length – Break time hrs – 0

= 24 hours (for 1 day) 24 * 30 days

= 720 hours = 43200 min

Operating time = planned production time – downtime

= 43200 - 6700 = 36500 min

Availability = 36500 / 43200

= 0.855 = 85.5 % (Before TPM)

Operating time = 43200 - 2800 = 40400 min

Availability = 40400 / 43200 = 0.9350= 93.5 % (After TPM)

The performance drives into report everything that causes the manufacturing process to run at less than the maximum achievable speed when it is operating.

Figure 8 Performance of Grinding Machine

Figure 8 shows the production efficiency of the grinding machine regarding the parts produced and the process time. The actual cycle time, processed amount and production time are thought to account for the execution efficiency.

Table 2 indicates the performance of the Machine No. of Parts Produced Process Time in Minutes

Before TPM

Performance efficiency = Actual cycle time * processed amount/Operation Time= (55*162) / (170*60) = 0.87

= 87 %

After TPM Implementation

Performance efficiency = Actual cycle time * processed amount/Operation Time= (70*162) / (195*60) = 0.9690 = 96.90 %

Performance efficiency indicates the achievement of the grinding machine by considering the numbers of parts produced and the associated process time after the implementation of TPM.

Table 2 Performance Efficiency

	Number of Parts Produced	Process time in Minutes
Before TPM Implementation	55	162
After TPM Implementation	70	162

Figure 9 Quality Component Produced by Grinding Machine.

Figure 9 showed the processed quantity of the components and correlated with the deficiencies of the processed parts regarding percent of rejection are estimated to assess the calibre. Value is check Before/After the TPM implementation.

Table 3 Percentage of Rejection

	Number of Parts Produced	No. of parts rejected	Percentage of rejection
Before TPM	962000	9620	0.1%
After TPM	1006600	290	0.02%

Table 3 signifies the quality of the parts made from grinding machine after the implementation of TPM. The processed amount is boost to the same defective level.

Rate of Quality = (Processed Amount–Defective Amount) / (Processed amount)

Before TPM

Quality = (962000-9620) /962000 = 0. 9910 = 99.10 %

After TPM Implementation

Quality = (1006600-290) /1006600 =0. 9999 = 99.99 %

Figure 10 OEE of the Grinding Machine

Before TPM

OEE = Machine availability * performance Efficiency *

Rate of quality

= 0.86 *0.96905 * 0.9983 = 0.7415 = 74.15 %

After TPM Implementation

OEE = 0.9359 *0. 87 * 0.9910 = 0.9055 = 90.55 %

5. CONCLUSION

Total productive maintenance efficiently provides the enhancement in the availability, performance and the quality rate results in the improvement of the overall equipment effectiveness of the equipment. TPM is the useful tool to increase the productivity of Indian industries.OEE improve from 74% to 90%; MTBF increased from 30 minutes to 8 hours. Quality Scrap reduced by 90%, Cost of quality control reduced to 70%, Breakdowns reduced by 90%, Customer complaints reduced by 80%, Costs Production cost reduced by 30%, Delivery precision capital bound in WIP and finished produce decreased by 55%, Delivering accuracy, actual/promised time increased to 100%. Safety measure leads zero accidents, Dust free environment, reliability ten times as many suggested improvements. Time for education and training boosted by 100%.

6. REFERENCES:

1. Ahuja, I.P.S. and Khamba, J.S. (2008b), “An evaluation of TPM initiatives in Indian industry for enhanced manufacturing performance”, IJQ&RM, Vol. 25 No. 2, pp. 147-72.

2. Ahuja, I.P.S. and Khamba (2008),"Total productive maintenance: literature review and directions", IJQ&RM, Vol.25 Iss: 7 pp. 709 -756. Emerald Article: Total productive maintenance: Literature review and guidance

3. http://dx.doi.org/10.1108/02656710810890890

4. Broglie M., M. Forskolin and F. Zamora, “Overall equipment effectiveness of a Manufacturing line (OEEML): An integrated approach to assessing system performance,” J. Manuf. Technol. Manag., vol. 20, no. 1, pp. 8–29, 2009.

5. Charles J. Robinson and Andrew P. Ginder. (1995). Implement TPM: The North American Experience”, Productivity Press Portland, Oregon.

6. C. Andersson, M. Belgian,“Managing Production Performance with Overall Equipment Efficiency (OEE - Implementation Issues and Common Pitfalls”, A White Paper, 2011

7. C.K Jha, Ashish Singh (2016),” Study of total productive maintenance: A case study of OEE improvement in the automobile industry, benefits and barriers in TPM implementation” International Journal For Technological Research In Engineering, Volume 3, Issue 9, Pp. 2000-2006

8. Ignatio Madanhire, and Charles Mbohwa(2015) “Implementing Successful Total Productive Maintenance (TPM) in a Manufacturing Plant” Proceedings of the World Congress on Engineering Vol II, London, U.K.

9. Jose Arturo Garza-Reyes, Steve Eldridge, et, al (2010)., International Journal of Quality & Reliability Management, Vol. 27 No. 1, pp. 48-62, Emerald Group Publishing Limited: DOI 10.1108/02656711011009308

10. J. A. Garza-Reyes, S. Eldridge, et, al(2010), “Overall equipment effectiveness (OEE) and process capability (PC) measures”, International Journal of Quality & Reliability Management, 27-1, pp. 48-62.

11. Karina Nielsen,* and Raymond Randall (2012) “The importance of employee participation and perceptions of changes in procedures in a team working intervention”’.d oi: 10.1080/02678373.2012.682721

12. Nazim Baluch, Che Sorry Abdullah et, al., (2013),” Impelling Innovation at a Canadian Automotive Plant “, Innovative Systems Design and Engineering, Vol.4, No.3, Pp 1-12.

13. Pratesh Jayaswal, A. K. Wadhwani (2008), “Machine Fault Signature Analysis” International Journal of rotating Machinery Volume 2008 (2008), Article ID 583982, 10 pages
http://dx.doi.org/10.1155/2008/583982

14. Ranteshwar Singh, Ashish M. Gohil et, al (2013)., Total Productive Maintenance (TPM) Implementation in a Machine Shop: A Case Study, Volume 51, Pp. 592-599. doi:10.1016/j.proeng.2013.01.084

15. R.K. Sharma, D. Kumar et, al (2013) “ Manufacturing excellence through TPM implementation: a practical analysis” Industrial Management & Data Systems, 106, Pp. 256–280

16. Rodrigues, M. and Hatakeyama, K. (2006), “Analysis of the fall of TPM in companies”, Journal of Materials Processing Technology, Vol. 179 Nos 1-3, pp. 276-9

17. Ravikant, V.P., Shrikant, R.J. and Prasad, A.H. (2011), “Implementing approach of Total Productive Maintenance in Indian industries & theoretical aspect: an overview”, International Journal of Advanced

18. Engineering Sciences and Technologies, Vol. 6 No. 2, Pp. 270-6.

19. Shahzad Ahmad, Syed Md. Shahwaz, (2015)“ Implementation of Total Productive Maintenance in Thermal Power Station (Barauni Refinery)”, Vol-1, Issue-3, Pp 7-15.

Received on 23.08.2016

Modified on 12.10.2016

Accepted on 20.01.2017

Research J. Humanities and Social Sciences. 8(1): January - March, 2017, 52-58.

DOI: 10.5958/2321-5828.2017.00008.0