1Sakarya ev iew Safiye Turgay1, Samet Koç2 ed Big Data-Driven Preventive Maintenance Decision Support with Monte Carlo Simulation University/Department of Industrial Engineering, Sakarya, Turkey 1safiyeturgay2000@yahoo.com 2smtkoc58@gmail.com KEYWORDS- Stochastic Modelling, Preventive Maintenance Planning, Monte Carlo rr Simulation, Big Data ABSTRACT Production activities without interruption, growing the products on time, and producing the desired ee quality are essential for the sustainability of the enterprises. Today, with the developing technology, sustainability in the production process and analysis of production data has to integrate and the emerge of the big data and Internet of Things (IoT) concept. Continuously obtaining and analyzing tp the production data related to maintenance planning and revealing the rules to be used in the enterprise to analyze these data also support maintenance planning activities. They will make a significant contribution to the production process. However, most operating and maintenance costs no are wasted due to incorrect, unsystematic, and unplanned maintenance methods. Maintenance costs are known to account for between 15% and 60% of the total operating costs. This study aims to standardize the types of breakdowns and faults to estimate them during the int operation of the existing system by considering the frequency values of the breakdowns and faults obtained from dynamic structures from big data and encountered. It is one of the most important objectives to carry out the production process without interruption without increasing its pr performance with preventive measures and without deteriorating the production quality with a Pr e decision support system. 1 INTRODUCTION Preventing and predicting machine stoppages, disruptions, and breakdowns in production is very important in terms of sustainability of production and efficient and productive operation of the production process. Thanks to the sensors in the machines involved in the production, a continuous This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 flow of information during production and the minor changes in the system can instantly be noticed ed and analyzed, and relevant decision making in maintenance situations. This information is also possibly used with the concepts of big data and IoT[1,2,5]. The occurrence of breakdowns and faults are a necessity to maintenance activities which are causing a temporal loss in the production ev iew system for the reason of results in additional costs due to disruption. This study focuses on the negative impact on the enterprise's competitiveness and improves the production system efficiency. It is necessary to implement the appropriate periodic maintenance process and develop maintenance policies to prevent such damages. We perform proper periodical maintenance, determining which machine, period, and frequency of maintenance or proper maintenance policy is among the most critical goals in the suggested framework. The Monte Carlo method uses random variables and then moves them to find the probability of rr distribution. The Monte Carlo sample uses probability simulation models that are the stochastic model of an actual situation that is preparing sampling experiments for the model. Such simulations are used to study system outputs with a large number of interrelated variables in the stochastic ee structure. The remainder of the paper is structured as follows: the next section reviews some concerning tp notions of maintenance planning. The Monte Carlo simulation method and big data-driven maintenance model are discussed in Section 3. Section 4 includes the actual case application in the 2 no radiator factory. Then the last section covers the discussion of results and some concluding remarks. RELATED WORK Since the invention of machinery and tools, maintenance and repair activities have to be int developed at the same rate as technological developments, increasing needs, fast access of consumers, and sensitive production planning. Development of technology, gathering of sensors and information, and IoT simultaneously brought the big data event [3,4,9]. The decision support pr system proposed in this study also operates in a support system that enables big data processing from the sensors. It produces decisions for situations from historical data with the Monte Carlo technique. Pr e Big data structures and IoT structures are considered a whole. This type of work has been adopted to feed this structure in a dynamic environment and support a continuous decision support structure—machine stops and deteriorations terrible affect product quality and sustainability. The Monte Carlo simulation method is mainly preferred by accident risk situations in the literature [6]. Integer programming and linear programming approaches are not suitable for big data status since the system cannot perform the analysis process continuously, as it considers the only find This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 momentary values. The Monte Carlo simulation structure also ensures the system operates with a ed continuous structure and information flow within the decision support system. Mendes and others preferred the Monte Carlo simulation technique to maintain and repair cold-by stand systems [7]. The technique can also be used in the process of estimating unknown situations. For example, ev iew Gayrav and Sharma gave an example of the analysis of the deteriorating conditions of metals depending on environmental factors and material structure over time [8]. Inputs Manufacturing Machines Sensor Big Data rr Vibration Acoustic signals Power consumption Images Maintenance Demand ee Manufacturing Capacity Product Maintenance tp Fig. 1. Big Data, Maintenance and Manufacturing Relation The suggested framework provides the planning and scheduling, the ability to monitor the desired no levels of system performance. It is one of the fastest and most effective investments an organization can make to increase efficiency and availability, maintenance planning activities, especially in production planning, in balance or integration with other possible sub-activities. Maintenance Concept int 2.1 All components in a production system are subject to wear; faults and breakdowns may occur when performing the production tasks. The rate at which wear occurs and how often errors may force pr employees, equipment, and perhaps the entire system to be empty depends on the design and operating conditions of the process. Poor maintenance can lead to incorrect output, downtime, and increases in production costs due to repairs [10, 11]. Pr e Today, competitiveness is compulsory for companies around the world. New technologies and the continuous emergence of new markets create undeniable global competition. Then there are forced to complement the production plans quickly with resulting from these competition conditions that cause the short life of the used machines and put the system under stress. It is necessary to consider the cost and capacity of the equipment used in production and the technological trends. Proper This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 maintenance helps reduce the life cycle cost and allows the correction or reduction of internal ed system problems that provides a comprehensive and easily accessible technical solution with an appropriate maintenance policy. The suggested approach implies preventive maintenance. Also, the following sub-section gives the information briefly about maintenance activities and effects. Classification of Maintenance Activities Maintenance activities are classified as below: ev iew 2.2 2.2.2.1 Preventive (Periodic) Maintenance Activities: It significantly reduces capacity records and breakdowns resulting from unexpected failures. Before any failure occurs, the determined times are analyzed, and planning is done. It can be determined periodically and according to the working hours or working principles of the equipment. Fault measurement and analysis are essential to decide whether to choose preventive rr maintenance and incidental maintenance alternatives to the system. The failure rate and the average time between failures should be determined according to the appropriate.distribution type. The ee causes of breakdowns and faults may depend on each other and another component that can cause different faults. The conditional probabilities are vital for the production and maintenance planning to act as a result of the union of data by establishing a correct distribution model[12, 18]. tp 2.2.2.2 Predictive Maintenance: The predictive maintenance type, the values of some variables representing the operational capability of the machine or equipment, the status and capacity of the process should be defined in detail. Physical variables such as temperature, no vibration, power consumption can cause functionally are essential indicators for the implementation of the maintenance. This type of maintenance factor is used to analyze the system for subsequent maintenance plan decisions[24, 26]. int 2.2.3.3 Preventive (Corrective) Care: It covers the protection of the plant or equipment by providing satisfactory conditions during the early stage breakdowns and controls. Process reliability, affordability, and standard compliance factors occur form of preventive maintenance pr efforts. It is an essential factor for the general care organization's effectiveness to prevent damage to machinery and equipment such as design changes, vibration prevention, and isolation of heat- Pr e exposed areas, pre-regulation of equipment that may cause breakdowns and faults. It can be applied immediately after any breakdowns and faults can be postponed according to the maintenance policy that contains specific team rules [10, 13, 15, 17]. This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 3 THE MONTE CARLO SIMULATION ed The Monte Carlo method estimates a probability event by generating multiple scenarios on its statistical parameters. In other words, the Monte Carlo simulation models the random behavior of the event it is working. An ideal situation in which Monte Carlo applications are used is where the ev iew size of the problem to be solved is one or more than two. Size refers to the number of state variables. Because in multidimensional situations, it is either very difficult or impossible to obtain closed-form formulas, and the application of numerical methods may be equally difficult or impossible. In this section, simulation and random number generation could be examined [14]. X is the expected value of the random variable θ = E (X) with a probability distribution of 𝑃𝑥 estimated value of the sample mean of this variable; 𝑛 𝜃𝑛(𝑋) = 𝐸(𝑋) = 𝑛∑𝑖 = 1𝑋(𝑖) 1 rr can be predicted with: 𝑋 = (𝑋(1),𝑋(2)…….𝑋(𝑛)) (1) (2) ee It is a random vector consisting of independent and same dispersed components of the random variable, which depends on the same probability distribution (independent and identically tp distributed). Thus, 𝜃𝑛(𝑋)can be taken an approximate estimator of the expected value (θ) or, more precisely, as the expected value. The reason for this is that when a large number of these samples are taken, the no average of these samples will approach θ and the variance will be Var(𝜃) = 𝑉(𝑋)/𝑛. Another one is the central limit theorem. In other words, even if the distribution of a mass is not normally distributed, the mean and variance of the sample averages the standard distribution, provided that int the sample volume is large (n³ 30). (n 30). According to this theorem, the normalized sample means converge to the standard normal distribution; pr n→∞ while; 𝜃𝑛(𝑋) ‒ 𝜃 𝜎𝑛/ 𝑛 Pr e 𝑍= →𝑑𝑁~(0,1). (3) Standardization is a non-deviant estimator of actual standard deviation; 1 𝑛 (𝑋𝑖 ‒ 𝜃𝑛(𝑋))2. 𝜎𝑛(𝑋) = 𝑛 ‒ 1∑ 𝑖=1 (4) 2 The estimation error 𝜃𝑛(𝑋) ‒ 𝜃 is distributed by approximately 𝑁(0,𝜎𝑛/𝑛), allowing us to obtain the estimated value based on confidence intervals. This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 ed The standard error 𝜎𝑛/ 𝑛 is one of the most important elements of the Monte Carlo method. If we want to reduce the error value by half, we need to increase the number of samples by four times, or multiply the number of samples by a factor of 100. ev iew With the simplest Algorithm; 1. Select large a “n” number. 2. 𝑥 = (𝑥1,𝑥2…….𝑥𝑛)is the sample set. 3. 𝜃𝑛(𝑋)estimate the sample mean as E E(X)=𝜃𝑛(𝑋). 3.2 Simulation Model rr Random number generation is a result of deterministic processes and algorithms. Uniform distribution in the unit range can be produced in the appropriate numbers. These numbers can then ee be converted to random numbers. Let X ~𝐹𝑥 distribution uses the number of n and G_𝐺𝑥:[0,1]𝑛→𝑅 function represents the compound random number. tp The distribution of 𝐺𝑥(𝑈1,𝑈2……𝑈𝑛) can be obtained as 𝐹𝑥. The 𝐺𝑥function can be assigned 𝑃𝑥 to this function; Cumulative probability distribution function is 𝐹𝑥(𝑥). If 𝐹𝑥(𝑥) continuous, the probability density 𝑓𝑥(𝑥) = ∂𝐹𝑥(𝑥) ∂𝑥 no function is (5) If F_x is discrete, the discrete distribution function is (6) int 𝑃𝑥(𝑥) = 𝐹𝑥(𝑥) ‒ 𝐹𝑥(𝑥 ‒ ) A risk function, 𝑓𝑥(𝑥) pr ℎ𝑥(𝑥) = 1 ‒ 𝐹𝑥(𝑥) One of the essential elements of maintenance management is collecting, analyzing, and using data Pr e on equipment failures and repairs [16]. This study estimated the failures that can help determine the time of breakdowns and faults in the next term using the historic and now failure data. The results were obtained from the data analyzed by Macro Programming (VBA) in Excel with the Monte Carlo model given in Table 1. The suggested simulation model is schematized in Fig. 2. This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 ed In the application, the linear programming model has established the suitability of the data analyzed according to the road map to the normal distribution and the excess of nonparametric data. In the simulation stage of the problem, the random model was chosen, and the average values were ev iew generated by repeating the model n times. Breakdowns and fault assignments were made to the sections associated with the resulting probability values, and the performance indicators were int no tp ee rr determined by comparing the results in an excel environment. Pr e pr Fig. 2. Suggested Simulation Model This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 ev iew For j =1 to 5 if j=1 then blm=” WELDING”-Elseif j=2 then blm=”PRESS” … … Elseif j=5 then blm=”MACHINE” End If If y(1) > y(2) Then bsf = (y(1) - y(2)) * 24 Else bsf = (y(2) - y(1)) * 24 End If ' maintenance times difference If bsf <> 0 And bsf < Big Data Then obs = obs + bsf ' maintenance time calculated cumulatively End If set (b) = a End If Next i rr Sub Analysis() Dim set(Big Data) As Date Dim a As Date Dim y(2) As Date Dim obs As Double Dim obo As Integer Dim bsf As Double Dim blm As String Sheets("DATA").Select son=Range("E665536").End(xlUp).Row ed Table1. Monte Carlo Simulation Program Code in Macro Programming in Excel (VBA) Pr e pr int no tp ee For x = 1 To b If set (x + 1) > set (x) Then avgdiff = (set (x + 1) - set (x)) Else avgdiff = (set (x) - set (x + 1)) For i = 7 To son End If If UCase(Cells(i, 8)) = blm Then If avgdiff < 100 Then a = Cells(i, 12) + Cells(i, 13). 'Different constraint values 'maintenance date-time combined were used for each section. y(1) = Cells(i,19) + Cells(i, 20) (Purpose: closeness to reality ' combined 'maintenance start date )) c = c + (avgdiff / b) and time data y(2) = Cells(i, 17).Value + End If Next x Cells(i,18).Value ‘combined maintenance end date Sheets("parameters").Select time data For s=1 to 4 b=b+1 's used counter as (for set and Cells(s, j) = c Cells(s, j) = 1 / c frequency) Cells(s, j) = (obs / b) Cells(s, j) = 1 / (obs / b) Next s Next j End Sub Periodic (preventive) maintenance policy was determined by making maintenance assignments to minimize downtime for the estimated faults. Then, conformity analysis was performed in SPSS program for the model whose maintenance policy was determined, and the results were compared with the results of the ANOVA test. This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 ed Notations 𝑁𝑖: Sample Size ev iew C: Number of Faults 𝐴𝐴𝑆𝑘: Time between failures for each i component “k-1”through”k”)) 𝐴𝐴𝑂𝑆İ: Mean time between failures of component i 𝜆𝑖: failure rate of component i 𝑇𝐸𝐵𝑆𝑖: The component k of i. Maintenance time for repair efficiency (k = 1… ....... N_i) 𝑂𝐵𝑆𝑖 : average maintenance time for component i 𝜇𝑖 : Maintenance rate for component i rr 𝐴𝐴𝑂𝑆: Average time between failures of all components 𝜆: Average failure rate of all components OBS: Average maintenance time for all components ee μ: Average maintenance rate of all components M: Number of predictive maintenance activities (sample size) tp KBAS: Time between predictive maintenance activities (k = 1 ………… M) KBAOS: Average time between predictive maintenance activities OBS: Average preventive maintenance time π: The average rate of predictive maintenance activities no 〖CSF〗 _CM: Compound average time between all maintenance activities ψ: Average rate of all maintenance activities ODS: Average downtime int Functions related to these variables are shown below: the average time and rate of failure of component i; ∑ 𝐴𝐴𝑆𝑡 1 𝜆𝑖 = 𝑁𝑖 𝐴𝐴𝑂𝑆İ (8) 𝑘=1 pr 𝐴𝐴𝑂𝑆𝑖 = 𝑁𝑖 the average maintenance time and proportion of component i; ∑ Pr e 𝑂𝐵𝑆𝑖 = 𝑁𝑖 𝑇𝐸𝐵𝑆𝑖 𝑘=1 𝑁𝑖 𝜇𝑖 = 1 𝑂𝐵𝑆𝑖 (9) Mean duration and rate of failure of all components; This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 𝐶 1 𝐴𝐴𝑂𝑆 = 𝜆𝜆 = ∑𝑖 = 1𝜆𝑖 𝜆.𝑂𝐵𝑆 + 𝜋.𝑂𝐾𝐵𝑆 𝜆+𝜋 ed 𝑂𝐷𝑆 = (10) (11) 𝑂𝐵𝑆 = 𝐶 ∑ 𝑂𝐵𝑆𝑖 𝑖=1 𝜇= 𝐶 1 𝑂𝐵𝑆 ev iew Average maintenance time and rate of all components; (12) The average time and rate between predictive maintenance activities; 𝐾𝐵𝐴𝑂𝑆 = 𝑀 ∑ 𝐾𝐵𝐴𝑆 𝑀 𝑘=1 𝜋= 1 𝐾𝐵𝐴𝑂𝑆 (13) rr Average duration and average rate between all maintenance activities; 1 𝐵𝑂𝑆𝐶𝑀 = (1/𝐴𝐴𝑂𝑆) + (1/𝐾𝐵𝐴𝑂𝑆) 1 (15) ee  = 𝐵𝑂𝑆𝐶𝑀 (14) The structure of the big data-driven maintenance planning-based decision support system proposed in this study is given in Fig. 3. The sensors in this structure get the information from the machines, tp and it helps to decide which machine can be broken or break down in the future depending on the type of information received. Predictive maintenance is activated here according to the machine type, and maintenance planning is designed by considering the possible deterioration status of the Pr e pr int no machine. This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 ed ev iew rr ee 4 tp Fig. 3. Big data driven maintenance decision support model APPLICATION no In this section, collecting the necessary data is related to big data IoT & maintenance planning from sensors. We mentioned that the presence of automation systems that increase machine and product variety, continuous operation or failure conditions, and complexity in fast production enterprises increases the number and rate of failures by adding to the existential degradation tendency of the int equipment [22]. In this study, the data set has been kept in the 15 months of an organization with a production pr capacity of 2,200,000 meters/metal sheet, which has approximately 3000 employees operating in a 3-shift system and operating in Turkey (Table 2) examined. The system was stochastically handled. Pr e The Monte Carlo simulation technique was preferred for modeling due to the importance of the necessities to know the failures in the system, the downtimes caused by them, and the probability situations. This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 First of all, the problem was analyzed with VBA in Excel, and the performance results were ed revealed. In the next step, random numbers were generated with the Monte Carlo model, and it was estimated how the system would behave in the next year. ev iew Unplanned maintenance and predictive maintenance activities were calculated separately to obtain a combined average time in the analyzed data set. Since the operating conditions of each section of the enterprise consist of 5 different sections, which are different from each other, the faults occurring are calculated separately by indexing and subsequently adding combined average time. Various variables and formulations for failure and maintenance calculations are introduced below[20, 21, 23]. Table 2. Section Indexes WELDING 1 MACHINE 2 PACKAGING 3 4 PAINT 5 ee İ PRESS rr INDEX The planning process includes the tasks that are required to perform a job are determined before starting a job. The work is monitored, controlled, analyzed, and reported, as well as the planned tp work stages and times of execution. Maintenance needs three resources: labor, materials, and equipment. Both the most valuable and the most challenging resources to control are labor resources. Effective use of scheduling depends on labor productivity [25]. no It was observed that machine and equipment failures in different sections (welding, press, machine, paint, packaging) caused too much downtime, as observed in Table 3. Fig. 4, breakdowns and faults increased during increasing periods, and frequent breakdowns were observed. Although the number int of failures observed in the ongoing month after the periods in which the predictive maintenance policy is implemented, the direction of the failure trend did not change, and these maintenance Pr e pr practices were deemed insufficient. This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 Montly Breakdown-Maintenance Distribution ed 60 50 30 20 10 0 0 2 4 6 8 Breakdown 10 12 Maintenance ev iew 40 14 16 Fig.4. Monthly Breakdown and Maintenance Distribution PROBLEM JOB DIAGNOSIS DATE PRODUCTION AND HOUR HAULT DATE BREAKDOWN WELDING YES BREAKDOWN WELDING YES 13.05.2013 10.05.2013 PRESS YES 14.05.2013 BREAKDOWN WELDING YES 16.05.2013 BREAKDOWN WELDING NO WELDING YES WELDING YES BREAKDOWN WELDING YES MAINTENANCE WELDING BREAKDOWN 17.05.2013 ENDING OF AND HOUR DATE HOUR IS PROBLEM HOUR SOLVING? DATE HOUR 17:00 10.05.2013 17:00 14.05.2013 13:00 NO 13:00 13.05.2013 13:00 13.05.2013 13:20 YES 14:00 14.05.2013 14:00 14.05.2013 18:00 YES 11:30 16.05.2013 11:30 16.05.2013 11:50 YES 17.05.2013 11:05 17.05.2013 18.05.2013 06:45 18.05.2013 06:45 18.05.2013 18.05.2013 07:00 18.05.2013 07:00 11:05 20.05.2013 18.05.2013 11:35 YES 08:45 YES 07:30 YES 15:35 22.05.2013 15:40 22.05.2013 15:45 YES NO 21.05.2013 09:35 21.05.2013 16:00 21.05.2013 16:25 YES YES 22.05.2013 15:00 22.05.2013 15:05 22.05.2013 15:10 YES NO 23.05.2013 11:05 17.05.2013 11:05 23.05.2013 11:35 YES 06:45 no BREAKDOWN HOUR tp MAINTENANCE BEGINNING OF MAINTENANCE DATE MAINTENANCE DATE AND ee ORDER TYPE DEMANDED rr Table 3.Designed Data Set WELDING BREAKDOWN PACKING BREAKDOWN WELDING YES 23.05.2013 BREAKDOWN WELDING YES 24.05.2013 BREAKDOWN PAINT YES 25.05.2013 int BREAKDOWN MAINTENANCE PAINT NO 25.05.2013 BREAKDOWN WELDING YES 26.05.2013 06:45 07:00 15:35 09:35 15:00 18.05.2013 23.05.2013 08:45 YES 18.05.2013 07:00 24.05.2013 07:30 YES 22.05.2013 15:40 25.05.2013 15:45 YES 16:00 25.05.2013 16:25 YES 15:05 26.05.2013 15:10 YES 21.05.2013 22.05.2013 pr Maintenance planning is aimed to prevent downtime and quality defects caused by production failures, downtime, and repair activities according to the Monte Carlo approach in this study. The Pr e maintenance policy was modeled with the data analyzed, then developed by testing with the VBA program-based Monte Carlo simulation technique. With this solution approach, it is aimed to determine periodic preventive maintenance intervals. This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 4.1 Data analysis ed The columns of the multi-dimensional data that are analyzed are classified as simplified as follows. The section in which requests type occurs and when downtime is caused by maintenance, maintenance times, and downtime depending on repair activities are calculated from the columns. ev iew Data for the past two years are divided into monthly periods (Table 4), and the performance of the system is observed based on these periods. The installation of a new machine is considered an intuitive maintenance intervention. It can lead to breakdowns and faults in the system that require compulsory intervention and prediction of failure, or production downtime, and the distribution of the observed results over the 15-month period is shown in Fig. 5. rr Montly Breakdown-Maintenance Distribution 50 1 2 3 4 5 6 7 8 Compulsory Maintenance 9 ee 0 10 11 12 13 14 15 Heuristic Mantenance Fig. 5. Monthly Breakdown-Maintenance Distribution DIAGNOSIS PROBLEM DATE AND TIME no ORDER TYPE DEMANDED DEPARTMENT tp Table 4. Classified Data Set DATE BREAKDOWN WELDING BREAKDOWN WELDING 10.05.2013 13.05.2013 FINDING PROBLEM DEPARTMENT TIME 17:00 13:00 MAINTENANCE BEGINNING DATE AND TIME DATE MAINTENANCE ENDING DATE AND TIME TIME DATE TIME 14.05.2013 13:00 13.05.2013 13:20 14.05.2013 18:00 16.05.2013 11:50 17.05.2013 11:35 18.05.2013 08:45 18.05.2013 07:30 22.05.2013 15:45 21.05.2013 16:25 22.05.2013 15:10 1 Mechanic Maintenance 10.05.2013 7:00 1 Mechanic Maintenance 13.05.2013 3:00 MAINTENANCE BREAKDOWN PRESS WELDING WELDING pr BREAKDOWN int 1 NEW BREAKDOWN Pr e BREAKDOWN MAINTENANCE BREAKDOWN WELDING WELDING WELDING 14.05.2013 16.05.2013 17.05.2013 18.05.2013 14:00 Mechanic Maintenance 14.05.2013 4:00 1 11:30 Mechanic Maintenance 16.05.2013 1:30 1 11:05 Mechanic Maintenance 17.05.2013 1:05 0 06:45 Mechanic Maintenance 18.05.2013 6:45 0 18.05.2013 07:00 Mechanic Maintenance 18.05.2013 7:00 1 20.05.2013 15:35 Electrical Maintenance 22.05.2013 5:40 1 WELDING 21.05.2013 09:35 Machine Maintenance 21.05.2013 6:00 1 WELDING 22.05.2013 15:00 Electrical Maintenance 22.05.2013 5:05 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 As shown in Fig.6, the increase in the number of breakdowns in the ongoing periods proves how ed much it is necessary to plan this problem appropriately. Also, reducing the number of failures during the period following intuitive maintenance operations should not be overlooked. Breakdown Maintenance Linear (Breakdown) 60 50 40 30 20 10 0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 rr 1 ev iew Distribution Fig. 6. Breakdown with Increasing Tendency ee The model established by the data distribution is associated with macro programming (VBA) and maintenance and failure data for each section; breakdowns-between-maintenance times, maintenance times, and probabilities were calculated by the appropriate units (day, hour, etc.) on tp average. The VBA codes were used to measure the analysis, together with their descriptions. The request types included in the data set were calculated separately by the appropriate units (days, no hours), mean duration between failures, average maintenance time and rates, predictive maintenance activities, and maintenance intervals for each component. Each department’s performance values are shown in Table 5. int Tablo 5: Each department’s performance values WELDING PRESS PACKING PAINT MACHINE 1,0769 0,9286 3,1841 0,3141 26,2166 0,0381 9,9379 0,1006 45,6586 0,0219 Day Day 4,3515 7,4985 18,3854 29,1139 3,1250 Hour 0,2298 0,1334 0,0544 0,0343 0,3200 Hour AAOSi I pr OBSi i Pr e The performance indicators in Table 6 were determined by combining all sections due to the effect of faults occurring in all sections on the overall system performance starting from Table 7. The effect of the intuitive maintenance sections on the whole system is shown in Table 9. Table 6. Integrated Maintenance Performance Indicators This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 ed Day Day Hour Hour ev iew AAOS  OBS  ALL DEPARTMENTS 2,8955 0,3454 8,3401 0,2277 Table 7. Heuristic Maintenance Performance Indicators ALL DEPARTMENTS KBAOS 17,4704  0,0572 11,75 OKBS  0,0851 Hour rr 5 Day Day Hour RESULTS All cases that require compulsory or intuitive intervention are combined from the sections, and the ee average performance time (BOScm), maintenance rate (), and average downtime (ODS) are determined as the leading performance indicators in Table 8. tp Table 8. Whole System Performance Indicators 2,4838 Day  0,4026 Day ODS 5,9858 Hour no BOScm Table 13 shows the screen output of the Monte Carlo simulation. X is the expected value of the random variable θ = E (X) with a probability distribution of the Px estimated value of X the sample int mean of this variable; 𝑛 𝜃𝑛(𝑋) = 𝐸(𝑋) = 𝑛∑𝑖 = 1𝑋(𝑖) 1 (16) can be predicted by the formulation shown in the form. With this formulation, planned or unplanned pr maintenance that will occur in each section was estimated. We also mentioned that the number of failures in the next period decreased as maintenance was performed. The probability of a machine Pr e being maintained to not fail again in the remaining months is 40%. Estimated failures and maintenance operations were assigned to the months distributed randomly by simulation using the following formulation. 𝜋 𝑛 𝜃𝑛(𝑋) = 𝐸(𝑋) = 𝑛∑𝑖 = 1𝑋(𝑖).𝜓 1 (17) This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 The average rate of the predictive maintenance activities was used. The estimation coefficient ed and the average maintenance rate obtained from the whole system were run by five iterations (1000 times) to ensure the closeness to reality with the predicted failure data (Fig. 7). In the future periods and the periodic maintenance, numbers were determined from the data obtained. In each iteration, 10.00 1500 1000 9.80 1000 500 9.60 50 100 1 2 250 500 9.40 0 3 n 4 5 Periodic Maintenance ev iew approximately ten monthly maintenance numbers have emerged. rr Fig. 7. Periodic maintenance results with iterations The Monte Carlo model provides information about how much maintenance breakdown, halt and fault data performed in randomly generated numbers according to the observation values and 𝑁𝑖 𝐴𝐴𝑆𝑡 𝐴𝐴𝑂𝑆𝑖 = ∑𝑘 = 1 ee probability distribution of the historical data in Table 9. 1 𝜆𝑖 = 𝐴𝐴𝑂𝑆İ 𝑁𝑖 (18) Eq. 18 used for accumulated probability distribution was estimated. Moreover, again, the monthly tp breakdown distributions of the historical data are shown in Table 10, and monthly breakdown maintenance assignments are made. Table 9. Probability of Breakdowns and Failures in Departments OBSERVATION VALUE PROBABILITY no ORDER WELDER % RANGE 0,63 0-0,63 PRESS 135 0,25 0,64-0.88 PAINT 47 0,07 0,89-0,96 PACKAGING 10 0,02 0,97-0,99 MACHINE 7 0,01 0,99-1 int 350 Based on the data, the main performance indicator in which the Average Maintenance Time (OBS) pr and Average Preventive Maintenance Time (OBS) are combined in the system is the average results of the average downtime; 𝜆.𝑂𝐵𝑆 + 𝜋.𝑂𝐾𝐵𝑆 = 𝜆+𝜋 Pr e 𝑂𝐷𝑆 = = 3,591499. It was found that the average stop time obtained in the historical data is less than 2,5 hours. Even though the fault data in the simulation model arises from the number of failures in the historical data, it is possible to capture this performance value if the maintenance planning is done ten times a month and an assignment is made with a schedule outside the working hours that can be fully This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 controlled. Similarly, the system can be updated by making more periodic assignments to the ed department or machines where the number of failures is observed. It was found that the average stop time obtained in the historical data is less than 2,5 hours. Even though the fault data in the simulation model arises from the number of failures in the historical ev iew data, it is possible to capture this performance value if the maintenance planning is done ten times a month and an assignment is made with a schedule outside the working hours that can be fully controlled. Similarly, the system can be updated by making more periodic assignments to the department or machines where the number of failures is observed. Although it is assumed that the maintenance data is typically distributed in each section, it was decided to perform an ANOVA test based on the assumption that it was a dependent situation considering that it was distributed in 5 different sections. rr ANOVA method with a 95% confidence interval was used to test the proximity of the model obtained by using the Monte Carlo simulation technique with the data set, and the results given in Table 10 were obtained. According to these results, the Bias 0.000 value and the Sig. = 0.094 value ee was found to be significant. ANOVA test results are shown in Table 11. Table10. SPSS Statistical Analysis Results Descriptives tp ORDER TYPE Total N no Statistic Bootstrapa 95% Confidence Interval Std. Bias Error Lower Upper 549 0 0 549 549 1,09 ,00 ,01 1,07 1,12 Std. Deviation ,342 ,000 ,030 ,284 ,403 Std. Error ,015 Mean Lower Confidence Bound Interval for Upper Mean Bound 1,06 int 95% 1,12 NEW Maximum BREAKDOWN Pr e pr Minimum Table 11. ANOVA test results ANOVA ORDER TYPE Between Groups Within Groups Sum of Squares df Mean Square ,929 4 ,232 63,333 544 ,116 F 1,996 Sig. ,094 This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 64,262 548 ed Total In addition, with the large sample size, the monthly expected value (number of maintenance) for Monte Carlo simulation was increased and the Chi-Square test was applied in SPSS and the results Table12. SPSS Reliability Analysis (%95) DEPARTMENT PACKING TYPE DOWN MACHINE 8a 42a 337b 6a, b 1,6% 8,3% 66,5% 1,2% 80,0% 89,4% 96,3% 85,7% MAINT- Count 1a, b, c 4c 7b 1a, c NANCE 3,0% 12,1% 21,2% 3,0% 10,0% 8,5% 2,0% 1a 1a, b 6a, b % within ORDERTYPE 11,1% 11,1% 66,7% % within SECTION 10,0% 2,1% 10 47 1,8% 8,6% 100,0% 100,0% % within ORDERTYPE % within SECTION % within ORDERTYPE % within SECTION NEW Total WELDING Count Count % within ORDERTYPE 114a 507 22,5% 100,0% 84,4% 92,3% 20a, c 33 60,6% 100,0% 14,3% 14,8% 6,0% 0a, b 1b 9 0,0% 11,1% 100,0% 1,7% 0,0% 0,7% 1,6% 350 7 135 549 63,8% 1,3% 24,6% 100,0% 100,0% 100,0% 100,0% 100,0% tp % within SECTION PRESS rr BREAK- Count Total ee ORDER PAINT ev iew in Table 12 and Table 13 are demonstrated. Table 13. Monte Carlo Simulation Conformity Test no Monte Carlo Conformity Test df 35,194a 8 Likelihood Ratio 31,066 8 Fisher's Exact Test 34,835 pr Value 549 Chi- int Pearson Monte Carlo Sig. (2-sided) 95% Confidence Interval Asymptotic Significance (2-sided) Lower Significance Bound Upper Bound ,000 ,009b ,001 ,017 ,000 ,000b ,000 ,005 ,000b ,000 ,005 Square N of Valid Cases a. 7 cells (46,7%) have expected count less than 5. The minimum expected count is ,11. Pr e b. Based on 555 sampled tables with starting seed 743671174. It was found that the minimum expected value was 11; it was well-matched with the Monte Carlo simulation model with ten maintenance assignments in Table 13. This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4308323 6 CONCLUSION ed The preventive maintenance approach in the maintenance planning process aimed to develop a sustainable maintenance policy of the system without any disturbance and failure in the long term and without any decrease in production quality. In addition, it is aimed to determine the preventive ev iew maintenance policy by analyzing the existing system and predicting future situations through simulation. The results are classified according to the closeness to reality, and the duration of the monthly downtime, which is the main performance indicator, is calculated as approximately ODS = 6 hours for the historical maintenance data. This breakdown time is the actual loss of time that affects various actions such as production employee idle, product loss, and production time or unforeseen rr costs. Quality of production is reduced due to production losses, waste rate, and defective production due to machinery and workers working at a standstill. ee The system is determined as ODS = 2,5 hours with the simulation model, an order has been created in the complex system, and the periods considered idle have been reduced with ten tp maintenance assignments in each period for the coming months. The most relative benefit of the model to the system is renewable. As the real-time data continues to be stored, then historical system data will be refreshed, and authorized persons will no monitor new system performance. The model can be re-engineered at the same time, allowing advanced analysis for unwanted situations. References int [1] T. Nguyen, L. Zhou, V. Spiegler, P. Ieromoachou, Y. Lin, Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research Oct. 2018; 98, 254-364. pr [2] A. Siddiqa, İ. Abaker, T. Hashem, İ. Yaqoob, M. Marjani, S. Shamshirband, A. Gani, F. Nasaruddin, A survey of big data management: Taxonomy and state-of-the-art. Journal of Network Pr e and Computer Applications Aug. 2016; 71, 151-166. [3] K. Moharm, State of the art in big data applications in microgrid: A review. Advanced Engineering Informatics, V. 42 (Oct. 2019) 100945 [4] Z. You, C. 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Curran, Integrating maintenance work progress monitoring into aircraft maintenance planning decision support., Transportation Research Procedia no 2018; 29, 58-69 [13] S.O. Duffuaa, M. Ben-Daya, K.S. Al-Sultan, A.A. Andijani, A generic conceptual simulation model for maintenance systems. Journal of Quality in Maintenance Engineering 2001; 7(3), 207 – 219. int [14] D. Bumblauskas, A Markov decision process model case for optimal maintenance of serially dependent power system components. Journal of Quality in Maintenance Engineering 2015; 21(3) 271 – 293 pr [15] B. Laurel, (Ed.). The Art of Human-Computer Interface Design (1990);Reading, MA: Addison-Wesley. Pr e [16] M. Savsar, Analysis and modeling of maintenance operations in the context of an oilfilling plant. Journal of Manufacturing Technology Management 2011; 22 (5), 679 – 697 [17] T. Rosqvist, K. Laakso, M. Reunanen, Value-Driven Maintenance Planning for a Production Plant. 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