ABSTRACT
A life span forecasting model for KHS DMG-VF84 bottling line system was developed in this study to predict life span for a preventive maintenance planning. A Linear multiple regression analysis model and as well as the polynomial (second order) model were used. Eight independent variables were used for the model, but six key predictors were significant in developing the regression model and they are Availability (A), Reliability (R), Mean time to Repair (T), Failure rate (F), Operational time (O) and Mean time before failure (B).The eliminated variables were lost hour (H) and expected run time (E), implying that lost hour and expected run time did not significantly impact to Life span. The first and second model utilized linear multiple regression and second order polynomial respectively of Life span of the KHS DMG-VF84bottling line system as its dependent variables. The test data showed that the mean absolute percentage error for the first model is 8.3% and has the ability to predict Life span for bottling line with a good degree of accuracy of 75.68% with ± 0.24% error and the coefficients of determination R^2for the developed first model is 0.76. This indicates that the relationship between the dependent variable and the independent variables of the developed model is good and the predicted values from a forecast model fit with the real-life data. The test data also showed that the mean absolute percentage error for the second model is 7.5% and has the ability to predict Life span for bottling line with a good degree of accuracy of 79.65% with ± 0.20% error and the coefficients of determination R^2for the developed second model is 0.7965. This indicates that the relationship between the dependent variable and the independent variables of the developed second model is good and the predicted values from a forecast model fit with the real-life data as well. The average accuracy percentage for the first model is 91.7% while that of the second model is 92.5%, which shows that the second model is more accurate than the first one. This study established contributions to knowledge in the area of enhancing budget and control for production in a manufacturing company and support prompt and effective decision making.
TABLE OF CONTENTS
Title Page
Declaration i
Dedication ii
Certification iii
Acknowledgements iv
Abstract v
Table of Contents vi
CHAPTER 1: INTRODUCTION
1.1 Background of the Study 1
1.2 Statement of Problem 3
1.3 Objectives of the Study 4
1.4 Scope of the Study 4
1.5 Justification of the Study 5
CHAPTER 2: LITERATURE REVIEW 6
2.1 Introduction to maintenance management 6
2.2 Maintenance management 7
2.2.1 Breakdown maintenance 8
2.2.2 Preventive maintenance 9
2.2.3 Predictive maintenance 9
2.2.4 Corrective maintenance 9
2.2.5 Maintenance prevention 10
2.3 Maintenance practices planning and control systems 11
CHAPTER 3: MATERIALS AND METHODS 16
3.1 Materials 16
3.2 Methods 16
3.2.1 Machine descriptions and working principles 16
3.2.2 Evaluation procedure and model development 19
3.2.3 Determination of Life span (L) values 19
3.2.4 Determination of operational time (O) values in the bottling system 20
3.2.5 Determination of mean time to repair (T) values in the bottling system 20
3.2.6 Determination of Availability (A) values in the bottling line system 21
3.2.7 Determination of reliability (R) values of the bottling system 21
3.2.8 Determination of Failure Rate (F) values in the Bottling Line System 21
3.2.9 Time to recovery 22
3.3 Simple regression analysis 23
3.3.1 Multiple regression analysis 23
3.3.2 Development of second order model of the bottling line system 25
3.3.3 The correlation coefficient 25
CHAPTER 4: RESULTS AND DISCUSSION 27
4.1 Development of Life Span Forecasting Model for KHS DMG-VF84 Bottling Line System 27
4.2 Regression Analysis Model and Estimation Results 28
4.3 Testing the validity of the model 30
4.4 Confirmation test of the model 31
4.5 Development of second order model of the bottling line system 35
4.6 Testing the validity of the second model 36
CHAPTER 5: CONCLUSION AND RECOMMENDATIONS 40
5.1 Conclusions 40
5.2 Recommendations 41
References 43
Appendices 46
LIST OF TABLES
4.1: L versus R, F, T, A, O, B 26
4.2: Correlation between L, R, F, T, A, O, and B 27
4.3: The actual life span and the predicted life span. 30
4.4: The actual life span, the predicted life span and the
percentage error. 31
4.5: Regression Analysis: L versus R, F, T, A, O, B 33
4.6: The actual life span, predicted life span and the percentage error for second model. 34
4.7: The actual life span, predicted life span and the percentage error for second order model. 36
LIST OF FIGURES
3.1 Flow chart of basic bottling operations. 16
3.2 Time between failures 19
3.3 Time to recovery 21
4.1 Residual plots for R, F, T, A, O and B 32
4.2 Residual plots of (L) for second order model 35
LIST OF ABBREVIATIONS
KHS Kappert Holstein and Seitz
DMG Deckel Maho Gildemeister
VF84 Vapourizer Feed 84
CMMS Assets maintenance software
MPI Maintenance Performance Indicator
VBM Vibration Based Maintenance
CBM Condition Based Maintenance
SPC Statistical Process Control
SAS Statistical Analysis System
CM Corrective Maintenance
PM preventive maintenance
MINTAB Powerful statistical software
MAPE Mean Absolute Percentage Error
AA Average Accuracy
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND OF THE STUDY
A well-designed processing plant is not successful until it is operating safely and profitability. This requires a smooth start-up as well as a productive and safe environment for the operations. In order to sustain the operation, good maintenance practices are required. Troubleshooting is invariably required to detect and fix issues that occur when the performance of engineered equipment degrades (Saeid-Mokhatab, 2019).
Due to an increasing complexity of modern production systems, maintenance planning has become more and more important (Denkenaa et al; 2012). Manufacturing companies are generally aiming at more reliable production systems with higher availability performance (Tsu-Ming and Jia-Jeng, 2011). Reliability and maintainability play a crucial role in ensuring the successful operation of plant processes as they determine plant availability and thus contribute significantly to process economics and safety. In addition, maintenance and maintenance policy play a major role in achieving systems’ operational effectiveness at minimum cost (Ruiz et al., 2007).
In industrial systems, system maintenance is an important factor to retain high utilization of the equipment along with low levels of product failure. However, if equipment maintenance is not implemented on time, this might result in product failure and seriously affect the production and maintenance plans. For this reason, effectively predicting the equipment preventive maintenance time-point is important for semiconductor factories (Sheu and Kuo, 2006).
According to Tsu-Ming and Jia-Jeng (2011) equipment maintenance is classified into two types as corrective maintenance and preventive maintenance. In corrective maintenance, repairs are undertaken when equipment fails, restoring it to normal function. For the preventive maintenance, maintenance or replacement occurs during normal functioning of the equipment, which can restore it to a better functioning condition and reduce the probability of equipment failure. This makes for a sustained process. Preventive maintenance is a planned maintenance method developed in order to minimize all the operating machines and equipment breakdowns in an industry to the least extent (Korkut et al., 2010).
Hence, carrying out an effective maintenance operation requires efficient planning of maintenance activities and resources. Since planning is performed in order to prepare for future maintenance tasks, it must be based on good estimates of the future maintenance requirement. Estimates of the future maintenance requirement are obtained by forecasting, which can be simply defined as predicting the future. Clearly, good forecasts of the maintenance requirement are needed in order to plan well for maintenance resources. In terms of the time horizon, forecasts are typically classified as short-term which ranges from days to weeks; intermediate-term which ranges from weeks to months, and long-term which ranges from months to years.
Forecasting techniques are generally classified as qualitative and quantitative. Qualitative (subjective) techniques are naturally used in the absence of historical data (e.g. for new machines or products), and they are based on personal or expert judgment. On the other hand, quantitative (objective) techniques are used with existing numerical data (e.g. for old machines and products), and they are based on mathematical and statistical methods. Meanwhile, the qualitative forecasting techniques include historical analogy, sales force composites, customer surveys, executive opinions, and the Delphi method. For the quantitative techniques are classified into two types. The first is the growth or time-series models that use only past values of the variable being predicted, and the second type is the predictor-variable models that use data of other (predictor) variables.
For the challenges of unplanned disruption in production process, the development of an adequate time series model for forecasting preventive maintenance is of the essence. This study therefore is aimed atutilizing data-driven approach to predict equipment maintenance time point with a preventive maintenance model. Here, condition monitoring data from equipment are extensively analyzed. This is meant on the current status of equipment’s condition which is important to understand the capability of the equipment to perform its operation for the next cycle of production or ready for maintenance. Hence, in this research, the focused is on using time series data from incident logs to develop the time series model to forecast future preventive maintenance time. This predicts the future time points for preventive maintenance of the equipment, so that the resource plan at the time points can be well-arranged and production will not be significantly disrupted.
1.2 STATEMENT OF PROBLEM
Bottling line system can fail to function or function improperlywith age in utilization. During the time of bottle line systems breakdown or malfunctions, preventive maintenance planning application is always a traditional method to keep them in good conditions.
Preventive maintenance is one of the original proactive techniques that have been used since the start of researches on maintenance system. While in practice, preventive maintenance activities were either planned based on cost, time or failure. Research about preventive maintenance is known to be extensively conducted and majority of companies applied the policy in their production line. However, most analysis and method suggested in published literature were done based on mathematical computation rather than focusing on solution to real problems in the industry. This normally leads to the problems in understanding by the practitioner. The report where unscheduled plant repair costs were out of line with the repair cost budget allocated to each plant line. Seven Up budgets for both scheduled maintenance and unscheduled repair costs for its plants' equipment. Budgets for scheduled maintenance activities are easy to estimate and are based on the equipment manufacturer's recommendations. The unscheduled repair costs, however, are harder to determine. In this case, the unscheduled repair was determined by developing a model for predicting life span of KHS DMG – VF84 bottling system in the Seven Up Company using a multiple regression analysis to reduce unscheduled repair cost.
1.3 AIM AND OBJECTIVES
This study aimed at utilizing data driven approach to predict equipment maintenance time point with a preventive maintenance model with the following specific objectives;
i. to develop a multiple regression model that will determine the life span of KHS DMG-VF84 bottling line system for preventive maintenance planning.
ii. to use the developed model to predict life span of KHS DMG-VF84 bottling line system as well as testing and confirming the validity of the model.
1.4 SCOPE OF STUDY
This study is restricted to the development of life span forecasting model for KHS DMG-VF84 bottling line system. It also captured the utilization of the developed model to predict life span for KHS DMG-VF84 bottling line system as well as testing and confirming the validity of the model.
1.5 JUSTIFICATION FOR THE STUDY
The high throughput and efficiency of an entire manufacturing system are dependent on the performance of the equipment, which can result to improvements with respect to time, quality and cost. The sustainability, reliability and availability of the production line in the manufacturing system, contributes a major role in competing with a rival manufacturing organizations. Majority of the activities of the industries depend upon the future sales. Projected demand for the future assists in decision-making with respect to investment in plant and machinery, market planning and programmed.
Since breakdown of equipment makes the workers and the machines idle resulting in loss of production, delay in schedules and expensive emergency repairs, and the basic objectives of maintenance planning policies are to reduce unplanned system breakdowns and to increase available operational time, it will be necessary to plan for preventive maintenance for a bottling line system using multi-linear regression analysis. Regression is a typical supervised learning task; it is used in those cases where the value to be predicted is continuous. In this case. Hence, maintaining a system is extremely important as it requires a proper and effective maintenance policy to ensure that there is a capability for the system to perform its required functions.
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