International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5889-5898 © Research India Publications. http://www.ripublication.com
Influence of Overall Equipment Effectiveness on Print Quality, Delivery and Cost: A System Dynamics Approach Mr. Nagaraj Kamath H Department of PME, Manipal Institute of Technology, Manipal University, Manipal 576 104, Karnataka, India Corresponding Author Prof. Dr. Lewlyn L R Rodrigues, Department of H&M, Manipal Institute of Technology, Manipal University, Manipal 576 104, Karnataka, India The success of the printing industry can depends on the printing equipment performance, which can result in costeffective process (Kutucuoglu et al., 2001). And this is very much true in the present modern high end technology (Maggard and Rhyne, 1992). And also according to Ahmed et al., (2005), equipment maintenance can be considered as an essential function, even in printing industry.
Abstract Purpose-The success of the printing industry will depends on the printing equipment performance, which can result in costeffective process. Today equipment maintenance is considered as an essential function, even in printing industry. Hence the purpose of this paper is to propose a System Dynamic model for analysing the influence of Overall Equipment Effectiveness (TPM) on delivery, cost and quality of the printed product and thus also prove that TPM is a strategy and not a cost centre.
Challenges of Maintenance Function in Printing Industry According to Al-Hassan et al. (2000), companies consider maintenance function as cost centre, but in reality, if it is considered as a core competence, it can reduce the overheads and contribute for profit. And in any manufacturing firm, about 20-40 % value addition can result from strategy oriented management practice (Eti et al., 2006). In present manufacturing scenario, consistent and dependable equipment can enhance the organization’s core competency, And also to be competitive in global market, strategic investment in maintenance function becomes very much essential (Coetzee, 1999). Because of this organization are adapting operative and competent maintenance strategies such as Condition Based Maintenance (CBM), Reliability Centered Maintenance (RCM) and Total Productive Maintenance (TPM) (Knezevic, 2015; Anvari, 2011; Sharma et al., 2005). Hence equipment maintenance function, which can contribute to profit, productivity, availability, reliability and quality, is considered as an essential function even in printing industry (Ahmed et al., 2005).
Methodology-In this research System Dynamic methodology is used in which a causal loop diagram is constructed for Total Productive Maintenance and then the stock and flow diagram is developed. Findings-The SD model represented in this paper revel the influence of Total Production Management on required printing performance like: cost, delivery and quality. Research limitations-It is also found that the blend of TQM, JIT and TPM can reflect in reliable manufacturing practice, which is having the ability to produce efficient production. Hence, the present model can include the variables related to TQM and JIT. Practical implications-The practical implication concern the efficient decision making system in multi-colour sheet feed offset printing, regarding the maintenance plan and production parameters.
Literature Review and Hypothesis Formulation Preventive and condition based maintenance were not able to solve problems of manufacturing environment in a very effective manner, hence in 1971 Japanese invented the concept of Total Productive Environment (TPM), which apart from problem solving, also could develop synergistic relationship by synchronizing different functional department operations of the organization and the efficiency of TPM depends particularly on the performance of production and maintenance (Chan et al., 2005). Modern maintenance had begun with preventive maintenance (PM) and then evolved into TPM. TPM is a unique program practiced in the manufacturing industry with a key objective of maximizing
Introduction The growth rate of Indian Printing Industry is 12% per annum, which has been contributed from about 2,50,000 print industry. The present turnover of the industry is more than INR50,000 crores (USD 11 Billion). Even then, process color printing with the required tonal value and color LAB values can be still a major challenge for majority of the printers, due to the dynamic print operating environment. Many a times printers’ are still facing the problems of scheduling, material compatibility issues, lack of maintenance and relying on subjective judgment for attaining and maintaining color (Print Flair report, 2014).
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5889-5898 © Research India Publications. http://www.ripublication.com program and standard practices are important to obtain required printing performance. Hence in this research, as a part of EOM, we are using System Dynamics (SD) methodology, for developing a simulation model, which can capture the interactions of variable and solve the problem of simultaneity (mutual causation i.e. positive and balancing loop of same variable). The SD model can be used to conduct experiments (Forrester, 1968) and applied to socio-economic systems that change over time i.e. dynamic in nature (Lane, 2007). In such case, when it becomes difficult to predict the behaviour of system and virtually impossible to solve mathematically, SD simulation can be used, which can update the system with feedback loop (Ratnayake and Markeset, 2010; Kiani et al., 2009).
the Overall Equipment Effectiveness (OEE), which is a measure of TPM. Need of Total Productive Maintenance (TPM) in Printing Operation The primary goal of efficient utilization of printing machine and secondary goal of reducing and controlling the variation in the printing process can be met through TPM (Schipper, 2001). And also according to Ahuja and Khamba (2008), TPM concept can be used to optimize printing machine performance, which can result in economic efficiency or profitability (Benjamin and Marathamuthu, 2015). According to Seth and Tripathi (2005), in Indian manufacturing context, maintenance activity is considered as problem fixing strategy and operating cost reduction strategy. H1: There is a significant difference in printing cost before and after implementation of TPM From earlier literature it is found that, less information is available regarding the contribution of TPM initiatives towards coupling core competencies. Hence Ahuja and Khamba (2008), in their research revealed the contribution of various TPM implementation dimensions for achieving core competencies in Indian scenario. Willmot (1994) pictures TPM as real-world application of TQM, to develop a culture of ”ownership” of equipment, understand and develop skills for problem diagnostic and improvement projects. The concept of TPM also supports an effective communication system, in which the employees at different level, collectively understand each other’s language / problem / requirements (Witt, 2006). The entire structure of TPM is built on eight pillars / elements (Sangameshwaran and Jagannathan, 2002). This concept facilitates good forecasting, organizing, monitoring, reducing downtime and controlling practices through its unique eight pillar methodology (Rodrigues and Hatakeyama, 2006).
Methodology The objective of this paper is to capture the dynamic interaction of TPM variables and to analyse their impact on: Quality, Cost and Delivery. This requires following systematic steps to be followed (Wankhade and Dabade, 2006). 1. Problem definition and goals to be achieved 2. Construction of causal Loop Diagram (CLD) 3. Developing Stock and Flow Diagram (SFD) 4. Process study for initial data 5. Validation of model 6. Simulation test for different scenario Problem definition and goals to be achieved The present printing industry demands for a dynamic maintenance strategy and program, which could cope up with the dynamic needs of the customers like: Improved Quality, reduced cost, delivery period and realize the concealed and under-utilized important resources such as manpower, machine and material. Construction of causal loop diagram The first step in developing the System Dynamics model, is constructing the Causal Loop Diagram (CLD). This will represent the realistic scenario, with the help of feedback loops. This is a simple diagram in which the variables related to TPM are connected by arrows (figure 1). The arrows with positive sign will create a positive influence on the dependent variable and vice-a-versa in case of arrows with negative sign i.e. if a change in one variable generates a change in the same direction, in other variable (which are connected through arrow), then the relationship between the two variables is positive, if not it will be negative. To construct the causal loop diagram for TPM, it is necessary to analyse the components of TPM and also to know how they are interrelated. Total Productive Maintenance (TPM) is a unique program practiced in the manufacturing industry with a key objective of maximizing the Overall Equipment Effectiveness (OEE) which is obtained by united workforce participation, through proper channel of internal motivation (Nakajima, 1988). TPM is a production-driven improvement methodology that is designed to optimize equipment reliability and ensure efficient management of plant assets (Robinson & Ginder, 1995). The principal features of TPM are the pursuits of economic
H2: There is a significant difference in printing quality before and after implementation of TPM Total Productive Maintenance is a production-driven improvement methodology that is designed to optimize equipment reliability and ensure efficient management of plant assets (Robinson & Ginder, 1995). The principal features of TPM are the pursuits of economic efficiency or profitability, maintenance prevention, improving maintainability, improve delivery and participation of all employees of the organization (Ahuja & Khamba, 2008). Based on the conceptual work in the identification of TPM practices, it is found that, autonomous maintenance and planned maintenance, equipment technology emphasis, committed leadership, strategic planning, cross-functional training, and employee involvement as the most commonly cited practices of TPM (Nakajima, 1988; McKone et al., 1999). H3: There is a significant difference in printing delivery time before and after implementation of TPM 2.2 Applying System Dynamics to TPM Model According to Hill (2000), apart from strategic capabilities, relationship of manufacturing capabilities, design, quality
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5889-5898 © Research India Publications. http://www.ripublication.com Emphasis for maintenance is also dependent on the importance given to technological acquisition and improvement. The effectiveness of production facilities can be enhanced by improving the manufacturing performance through TPM (Dwyer, 1999; Dossenbach, 2006). TPM can be described as a structured equipment-centric continuous improvement process that strives to optimize production effectiveness by identifying and eliminating equipment and production efficiency losses throughout the production system life cycle through active team-based participation of employees across all levels of the operational hierarchy (Ahuja & Khamba, 2008). In the case of alternative equipment and materials, the resulting product quality becomes one of the major factors affecting the choice of the user. Such systems can be used to monitor the performance of a printing press and provide insight into which attributes of print quality are failing when the overall quality is insufficient and which technical parameters are responsible for the failure (Verikas et al., 2011).
efficiency or profitability, maintenance prevention, improving maintainability, the use of preventive maintenance, and total participation of all employees (Ahuja & Khamba, 2008). Based on the conceptual work in the identification of TPM practices, it is found that, autonomous maintenance and planned maintenance, equipment technology emphasis, committed leadership, strategic planning, cross-functional training, and employee involvement as the most commonly cited practices of TPM (Nakajima, 1988; McKone et al., 1999). According to Cua et al., (2001) in order to get a competitive advantage in manufacturing, we should also consider the use of proprietary equipment as a component of TPM. And to maintain equipment effectiveness, day-to-day maintenance by operators is a key factor. Sporadic breakdowns can be prevented through carefully-planned maintenance and the improvement or development of equipment. To achieve such an effective maintenance system, cross-functional training, involving all committed employees from three different levels of management and providing them time and resources to improve equipment performance / maintenance is necessary.
Poor equipment condition
Press Caliberation to pre-press
Press test run and diagnoistics
+ + + Equipment failure
+ + Performance rate -
+
Idling and minor + stops +
+
Wrong method
Reduced running speed +
+ Avaliability rate
Malfunction of equipment
+
More Job changeover time
Increased Make ready time
Press chaterization
Operation problem
Increased Setup and adjustment + + + -
-
+ +
-
+
Wrong tool used
Poor Poor operating performance rate speed rate
+
+
Press diagonistic and specification Poor registration
Poor drying
Press capability study
Material procurment and testing + + Quality rate
+ Poor raw material - Reduced yield + + +
SOP -
+ Defective products + Mechanical problem
Delivery + + Total Production Management +
Human error Print problem
+
+ Cost Bad equipment condition + Quality
Figure 1: Causal Loop Diagram of Overall Equipment Effectiveness (Total Productive Maintenance)
Developing Stock and Flow Diagram (SFD): In the second step of SD methodology, the Causal loop diagram (CLD) was transformed into Stock and Flow diagram, by developing a mathematical relationship between the variables (figure 2). With this model it will be possible to statistically analyze the model and also simulate the result for future periods. For a printing operation, OEE is the ratio of the percentage of available operating time to the percentage of equipment performance to the percentage of quality products produced. The aim of OEE is to produce more printed
products, which can be possible only when the six big losses related to the printing machine can be reduced. Printing Machine Availability rate is influenced by failure / breakdowns & downtime losses and increased machine makeready or setup time. Breakdown and downtime losses of the machine could be as a result of poor equipment condition (because of poor maintenance), wrong tools used on the machine, wrong working method adopted and poor or substandard raw materials used. The major contributors for increased make-ready or setup time of the printing machine could be the poor make-ready operations, poor machine
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5889-5898 © Research India Publications. http://www.ripublication.com condition and un-compatible raw material used. The machine’s reduced speed could be because of the printability issues like registration drying, machine’s condition and also the raw materials used. With press test run and diagnostics, the machine performance can be improved by reducing the idling and minor stops, because in this stage a pro-active measure is taken by adjusting the machine as per the required quality program, based on its original performance. Presscalibration to pre-press is also contributing to the Performance rate by reducing the stoppage, because with calibration we can achieve the required quality of dot and color density.
performance and poor operating speed rate. Press characterization and Diagnostics & specification can reduce the make-ready or setup time by giving quality reproduction. i.e. through profiling and calibration, it will be possible for us to achieve the required dot percentage and color density. With press diagnostics & specification, the machine part or components can be maintained as per the required standard of best practice or the quality program. Hence with this we can reduce the machine failure and setup time. Performance rate of the printing machine is influenced by the idling & minor stops and slow equipment speed. The main reasons for idling & minor stops could be the malfunction of the equipment, operation or process problem, poor equipment Index of Autonomus maintenance
Index of press optimization
Index of rawmaterial compatability Equipment failure and breakdown
Index of training and development
Equipment setup and adjustment
Total product Downtime Load time
Percentage of defective products
Avaliability rate
Quality Improvement percentage Delivery Time Reduction Percentage
OEE Quality rate
Cost reduction percentage Performance rate
Designed Cycle time
Processed amount Operating time
Idling and minor stops
Index of equipment condition
Index of reduced speed
Malfunctioning of machine
Figure 2: Stock and Flow Diagram of Overall Equipment Effectiveness (Total Productive Maintenance) Press capability study will determine the machine’s highest inking level or capacity, which will help to determine the machines ink and dot gain tolerance and thus contribute for quality products. The sequence for optimizing the printing system must begin with the press and work back through the pre-press system to color separation of films. In order to optimize the press performance, the printer must know the mechanical problem of machine, testing of materials, maximum ink density & dot gain characteristics, target ink density and dot gains and prepress proofing (Rizzo, 2008).
Quality rate of the printing machine is influenced by Reduced yield and defective products produced by that machine. Standard operating procedure, Mechanical problem of machine and Human error can result in defective products. Poor raw material, Human error, equipment condition and Printability problems can result in reduced yield of the machine. It is highly impossible to get a good output from poor raw material; hence with material procurement testing as per the standard quality program, defective products can be reduced and due to compatibility of raw material with machine, the machine throughput or yield can be increased.
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5889-5898 © Research India Publications. http://www.ripublication.com Process Study Data collection was done in a commercial printing press. In this case (Appendix 1) we have considered a 10 year old multi-color offset machine, which is having the average monthly availability rate as 52.25 %. For the base case we have assumed availability rate to be 50%, performance and quality rate to be 50 % and 90 % respectively.
Model Validation The current model was tested using the set of validations procedures laid out by Rodrigues et al. (2006) for testing a SD model. The details of the tests conducted and their respective results are described in Table 1 and Table 2.
Table 1: Validating Model Structure Test Purpose of Test Results a) Tests of Suitability StructureIs the model structure not in conflict to the The most relevant structures of TPM, as per empirical Verification Test understanding about the structure of the real research have been considered for model design. system, and have the most relevant structures of the real TPM system being modelled? DimensionalDo the dimensions of the variables in every The dimensions of all the variables were checked and the Consistency Test equation balance on each side of the equation? equations were verified and balanced e.g., erosion of OEE=Availability rate*Quality rate *Performance rate, LHS = RHS = 1/Month ExtremeDoes every equation in the model make sense Every equation has been tested for extreme values. For Conditions Test even if subjected to extreme (but possible) instance, the percentage of TPM will increase only when the values of variables? loss decrease. It means that, the model is capable of not losing its confirmations in the eventuality of using extreme values. BoundaryThis test verifies whether the model structure is As the model indicates, it considers all important variables Adequacy Test appropriate for the model purpose related to the TPM system as per the past available literature b) Tests of Consistency Face Validity Test Does the model structure look like the real The model has been developed in line with the real life system? Does a reasonable fit exist between the situations existing in successfully implemented TPM feedback structure of the model and the organizations, as analysed in literature review essential characteristics of the real system? ParameterThe numerical values of parameters should The parameters correspond conceptually and numerically to Verification Test Tests of Suitability have real system real life. e.g., an increase of 1% OEE can improve the equivalents. delivery reduction period by 0.33% is realistic from an Printing industry perspective. c) Test of Utility and Effectiveness Appropriateness The more the appropriate a model for the The model is made easy to understand by having four for the Audience audience, the more will be the audience’s separate views structures. All the terms used are appropriate perception of model validity to the context of TPM in offset Printing and easily understandable. Table 2: Validating Model Behavior Test Purpose of Test Results a) Tests of Suitability Parameter Do the modes of behaviour change with the parameter Behaviour change is observed when parameters are varied. Sensitivity Test variations e.g., when the independent variable index is increased OEE percentage is improved (Figure 4.1). Structural Is the behaviour of the model sensitive to reasonable Any reformulation to the model's structure would cause Sensitivity Test structural reformulation the results to change. b) Tests of Consistency BehaviourHere, the generated model behaviour is judged with This model has used hypothetical values to conduct the Reproduction the historical behaviour pattern. simulation. However, the patterns generated in the Test simulations are in tune with the patterns in real-life situations. Eg: After TPM implementation Quality can be improved from 15 % to 45% (Figures 4.4)
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5889-5898 © Research India Publications. http://www.ripublication.com BehaviourWhether or not the model generates patterns of future The model predicts the behaviour of the system up to a Prediction Test behaviour in terms of periods, shape or other period of 50 months and the patterns are revealed characteristics BehaviourWhat behaviour shown by the model is conflicting No erratic behaviour was observed during the course of Anomaly Test with the real system behaviour and how implausible the simulation and hence, anomaly of any kind does not behaviour arises if the assumptions are altered? exist. Family Member Parameter values are chosen to depict a particular Parameter values can be varied to analyse different Test situation. By choosing a different set of parameter situations. For example, a index of 0.5(Initial condition) values can the model be applied to other situation as denotes low support, whereas 0.9 (proposed condition) well? denotes highest support (Figure 4.3). Surprising Does the model under some test circumstances No surprising behaviours were observed under test Behaviour Test produce dramatically unexpected or surprise circumstances behaviour, not observed in the real system Extreme-Policy If the model behaves in an expected fashion even Model when subjected to extreme conditions behaves as Test under extreme policies, then it boosts confidence in expected. No abnormalities are observed. For example, the model. when all the variables are given equal weightage, that scenario reaches peak performance of OEE in the shortest possible time. Boundary If the extra model structure does not change the All the view considered are important & influences the Adequacy Test behaviour, then this extra structure is not necessary. model behaviour significantly and the behaviour of the Alternatively, if a model structure does not reproduce system in the absence any of these structures was desired model behaviour, it calls for inclusion of observed to be quite different additional model structure. BehaviourDoes plausible shift in parameters cause model to fail The model is quite sensitive to the variations in the policy Sensitivity Test behaviour tests previously passed? parameter. Statistical tests Does the model pass statistical tests based on the data Model values were used for testing Hypothesis using SPS from real system? software.
In the figure 3, it can be observed that, for the scenario Present Condition with index of 50 %, the level of OEE obtained was less than 10%. From the present condition, when the index was improved by 5%, accordingly the level of OEE also increased and finally for the last scenario, when the index was made 90%, the level of OEE was raised to about 35%, due to increase in Availability rate, Performance rate and Quality rate. Hence it is very clear from the graph, that when the values of the Independent variable of the model is kept at higher index, it will be possible to obtain greater level of TPM, expressed in term of OEE.
Model Simulation The simulation of the model was done with different scenarios pertaining to print operation condition. The first scenario, OEE index of 50% is the present Condition, which is based on the value obtained during the process study in the printing press. For the base condition, an index of 5% improvement was assumed and the simulation run was made for OEE with index from 55 to 90%.
Results The result is based on the simulation of the model for a period of 50 month, as follows:
Delivery Time reduction
OEE
0.4
0.4 0.3
6 6
0.3 Percentage
6
0.2
6
6
6
0.2
6
6 6 6
0.1
1 2 34 5
1
2 34
5
12
3
4
5 1
2
34
5
5
5 1
2
34
34
34
6 1 2 34 5
3 2
2
2 1
6
0.1
5
1
OEE : Index of 50% OEE : Index of 60% OEE : Index of 70%
10 1
15
20 25 30 Time (Month)
1 2
2 3
3
OEE : Index of 75% OEE : Index of 80% OEE : Index of 90%
35
40 4
45
50
4 5
1
2 34 5
12 3
5
10
45
15
Delivery Time Reduction P ercentage : Index of Delivery Time Reduction P ercentage : Index of Delivery Time Reduction P ercentage : Index of Delivery Time Reduction P ercentage : Index of Delivery Time Reduction P ercentage : Index of Delivery Time Reduction P ercentage : Index of
0 5
6
12
3
45
12
34
5 1
2
34
5 1
2
34
5 1
50% 60% 70% 75% 80% 90%
20 25 30 Time (Month) 1
1 2
3
5 6
6
2 3 4
5 6
1 3
4 5
50
2 3
4
45 1
2 3
4
40
1 2
3 5
35
5 6
6
5 6
2
0
1
0
0
6
6
Figure 4: Simulation of Delivery period reduction Figure 3: Simulation of OEE (Total Productive Maintenance)
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5889-5898 © Research India Publications. http://www.ripublication.com In the figure 4, we can observe that, for the scenario Present Condition with index of 50 %, the level of delivery period reduction obtained was about 5%. From the present condition, when the index was improved by 5%, accordingly the level of delivery period reduction also increased and finally for the last scenario, when the index was made 90%, the level of delivery period reduction was raised to about 22%, which can result due to increase in availability rate of the machine. In this case delivery improvement was considered to be 0.33%, for 1% increase of OEE (Ahuja and Khamba, 2008). Hence it is very clear from the graph that, when the values of the Independent variable of the model are kept at higher index, it will be possible to obtain higher percentage of delivery period reduction (Wankhade and Dabade, 2006). In the figure 5, we can observe that, for the scenario Present Condition with index of 50 %, the level of cost reduction obtained was less than 5%. From the present condition, when the index was improved by 5%, accordingly the level of cost reduction also increased and finally for the last scenario, when the index was made 90%, the level of cost reduction was raised to about 18%, due to increase in Availability rate, Performance rate and Quality rate. In this case improvement in cost reduction was considered to be 0.5%, for 1% increase of OEE (Ahuja and Khamba, 2008). Hence it is very clear from the graph, that when the values of the Independent variable of the model are kept at higher index, it will be possible to obtain higher percentage of cost reduction (Wankhade and Dabade, 2006).
0.6
6
6
0.15
0 Qualit y Improvement Qualit y Improvement Qualit y Improvement Qualit y Improvement Qualit y Improvement Qualit y Improvement
0.05
1 23 45
6
1 23
45
23
1
4
34
12
1
2
1
34
4
3
34
1
12
5 1
34
2
5
5
5 1
2
34
4
3
2
2 1
1
5
10
percentage : Index of percentage : Index of percentage : Index of percentage : Index of percentage : Index of percentage : Index of
15 50% 60% 70% 75% 80% 90%
20 25 30 Time (Month) 1
1 2
3 5
5 6
6
1 2 3
4 5
50
2 3
4
45 1
2 3
4
40
1 2
3 4
35
3 4
5 6
5 6
6
OEE60 Cost60 74 51 Mean .116322 .056951 Std. Deviation .0080160 .0041334 Most Extreme Differences Absolute .086 .065 Positive .076 .065 Negative -.086 -.065 Kolmogorov-Smirnov Z .743 .462 Asymp. Sig. (2-tailed) .638 .983 a. Test distribution is Normal. b. Calculated from data. N Normal Parametersa,b
2
2
2
34
Table 3: One-Sample Kolmogorov-Smirnov Test
5
5
5
5
1
5 234
Testing of Hypothesis Hypothesis testing was done using SPSS software. As the first step, the normality of the data was checked and then paired sample t-test was performed for the values obtained from the simulation model.
6
5
12
3 45
Figure 6: Simulation of Product quality improvement
6 6
1 23 45
6
0
6
6
6 6
6
0.1
6
0.3
0.2 0.15
6
0.45
1
0 0 Cost Cost Cost Cost Cost Cost
5
10
reduction percent age : Index of reduction percent age : Index of reduction percent age : Index of reduction percent age : Index of reduction percent age : Index of reduction percent age : Index of
50% 60% 70% 75% 80% 90%
15
20 25 30 Time (Month)
1
1 2
1 2
3
2 3 4
5 6
To check the normality of the data, randomly two set of data (OEE and Cost at index of 60%) was considered and the data was processed using one sample K-S test (KolmogorovSmirnov Z test). The significance level of both the values are above 0.05, hence the data is normally distributed. Due to the normal distribution of data, under parametric test, pair t-test was performed.
1 3
4 5
6
50
2 3
4 5
6
45 1
2 3
4 5
40
1 2
3 4
35
5 6
6
Figure 5: Simulation of Product cost reduction
In the figure 6 we can observe that, for the scenario Present Condition with index of 50 %, the level of quality improvement obtained was less than 15%. From the present condition, when the index was improved by 5%, accordingly the level of quality improvement also increased and finally for the last scenario, when the index was made 90%, the level of quality improvement was raised to about 46%, which can result due to increase in quality rate, availability rate and performance rate of the machine. In this case quality improvement was considered to be 1.33%, for 1% increase of OEE (Ahuja and Khamba, 2008). Hence it is very clear from the graph, that when the values of the Independent variable of the model are kept at higher index, it will be possible to obtain higher percentage of quality improvement (Kiani et al., 2009).
Table 4: Paired Sample Statistics Mean N Std. Deviation Std. Error Mean Pair 1 Delivery55 .067807 51 .0026206 .0003670 Delivery80 .093147 51 .0176890 .0024770 Pair 2 Cost55 .053476 51 .0020667 .0002894 Cost80 .073460 51 .0139503 .0019534 Pair 3 Quality55 .142245 51 .0054975 .0007698 Quality80 .195404 51 .0371079 .0051961
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5889-5898 © Research India Publications. http://www.ripublication.com Table 5: Paired Sample Correlations
Pair 1 Delivery55 & Delivery80 Pair 2 Cost55 & Cost80 Pair 3 Quality55 & Quality80
implementation, when the index was made as 90%, we could get the required print performance related to important parameters like: cost, quality and delivery. In this research we have shown how a standard maintenance practice like TPM, apart from improving production, can also contribute to improvement in cost and delivery, which is the important requirement of the customer. Hence in conclusion it can be stated that to get the required customer satisfaction, improve the business and get production advantage, standard maintenance practice like TPM, should not only considered as service centre, but also as profit or production centre (Simoes et al., 2011). From the past research it is found that the application of Total Quality Management (TQM), Just-in-Time (JIT) and Total Productive Maintenance (TPM) had begun way back in 1980s. All these programs have common goals of continuous improvement and waste reduction (Powell, 1995). It is also found that the blend of TQM, JIT and TPM can reflect in reliable manufacturing practice, which is having the ability to produce efficient production (Singh and Ahuja, 2015; Cua et al., 2001). Hence the future scope could be to obtain the production advantages, by combining the above quality and production programs.
N Correlation Sig. 51 1.000 .000 51 1.000 .000 51 1.000 .000
Table 6: Paired Sample Test Paired Differences t Std. Std. 95% Deviati Error Confidence on Mean Interval of the Difference Lower Upper Pa Delivery - .01506 .00211 ir 55- .02534 84 00 .02957 .02110 12.0 1 Delivery 02 83 22 10 80 Pa Cost55- .01188 .00166 ir Cost80 .01998 36 40 .02332 .01664 12.0 2 44 67 21 10 Pa Quality5 - .03161 .00442 ir 5.05315 04 63 .06204 .04426 12.0 3 Quality8 85 91 80 10 0 Mean
d Sig. f (2taile d)
5 .000 0
5 .000 0 5 .000 0
Social implications The ability of the SD model to simulate the real system have found its application in training and developing the workforce, without running the machine and save chemicals, energy raw materials, consumables and thus contribute towards Clean Green environment or sustainability issues. The model also can motivate the workforce and drive the fear or reluctance toward maintenance.
For testing H1, Cost at index of 55% was assumed to be the value before implementation of TPM, was compared with cost at index of 80%, which was assumed to be the value after implementation of TPM. From the Table 6 it is found that this pair is having a sig. value of 0.00 and also from Table 5 it is found that there existing a strong positive correlation between the pair of cost, hence it can be concluded that, There is a significant difference in printing cost before and after implementation of TPM. For testing H2, Quality at index of 55% was assumed to be the value before implementation of TPM, was compared with Quality at index of 80%, which was assumed to be the value after implementation of TPM. From the Table 6 it is found that this pair is having a sig. value of 0.00 and also from Table 5 it is found that there existing a strong positive correlation between the pair of Quality, hence it can be concluded that, There is a significant difference in printing cost before and after implementation of TPM. For testing H3, Delivery at index of 55% was assumed to be the value before implementation of TPM, was compared with Delivery at index of 80%, which was assumed to be the value after implementation of TPM. From the Table 6 it is found that this pair is having a sig. value of 0.00 and also from Table 5 it is found that there existing a strong positive correlation between the pair of Delivery, hence it can be concluded that, There is a significant difference in printing cost before and after implementation of TPM (Singh and Ahuja, 2014).
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Conclusion and Scope for future work Before implementation of the TPM, index of the independent variables were kept at 50% and as a part of successful TPM 5896
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