Lean Manufacturing

Lean Manufacturing appeared as a solution to reduce waste in production processes by implementing the concepts originated in the Toyota Production System (TPS), which were developed by Eiji Toyoda, Taiichi Ohno and Shigeo Shingo in the early 1940s. It has been directly applied to discrete industries, that is, assembly industries. However, applicability to process industries, that is, continuous industries, is still under development. Recently, scholars such as ^{Abdulmalek and Rajgopa (2007}); ^{King (2019}), ^{Mahapatra and Mohanty (2007}); ^{Marodin et al. (2018}); and ^{Pool et al. (2011}) have been analyzing and implementing some of the Lean concepts in process industries and have achieved remarkable results. The goal of Lean Manufacturing is to reduce waste at every stage of the production process.

For ^{Hu et al. (2015}), Lean Manufacturing methodology is increasingly applied in manufacturing companies, regardless of their size, in order to make them more competitive and sustainable and to achieve positioning in the modern market. The application of this methodology also resulted in great agility in production processes. Therefore, the applicability of Lean Management in SMEs is recognized as crucial for the development of economies around the world.

Powell et al. (2009) acknowledge that, although many of the Lean practices and tools have been successfully applied in all types of industrial processes, there is a significant lack of the application of control practices of Lean production in process industries.

^{Abdulmalek and Rajgopal (2007}) present a case-based approach to show how lean manufacturing practices, when used appropriately, help eliminate waste in process industries, keep a better inventory control, improve product quality, and contribute to a better overall financial and operational control. Not all lean tools are applicable in the same manufacturing environments; therefore, it must be identified which lean tools are applicable in each manufacturing environment within the process industries.

There are lean methodologies that are applicable to all types of manufacturing environments in the process sector, regardless of their specific characteristics. These tools offer the possibility to obtain significant profit with relatively low investments since they require simple application (^{Abdulmalek & Rajgopal, 2007}; Powell et al., 2009).

Manufacturing flexibility is very important for agility and can be improved with an appropriate Lean implementation. It means that industries make what the customer orders as soon as possible after the order has been placed (^{Saleeshya et al., 2012}).

TPM and OEE

According to ^{Jasiulewicz-Kaczmarek (2016}), total productive maintenance (TPM) is a set of strategic initiatives focused on maintaining and improving production and quality systems through machines, equipment, processes, and employees that add value to an organization.

TPM has eight pillars aimed mainly at proactively improving machine reliability. People are at the center of this system and must be continuously trained to identify and eliminate waste (^{Zarreh et al., 2018}).

The publications on TPM agree that this methodology has a participatory work philosophy, whose objective is to maximize the effectiveness of machinery and equipment by eliminating errors, defects and other negative phenomena.

According to ^{Moreira et al. (2018}), OEE is a performance indicator that allows measuring the overall effectiveness of the equipment that makes it possible to know the productive efficiency of a company, department or machine.

^{Nakajima (1988}) defines six types of losses and gathers them in three groups, associating each group to an indicator; thus, he mentions that there are: (a) losses due to the unavailability of equipment, (b) losses due to poor equipment functioning or performance, and (c) losses due to manufacturing low-quality products. Nakajima associates these groups to their indicators availability (A), performance (P) and quality (Q) respectively.

The overall equipment effectiveness (OEE) indicator is obtained by multiplying the three indicators previously mentioned. Figure 1 illustrates the procedure to calculate OEE and shows its relationship with the six types of losses mentioned above. Given the emergence of new production schemes with increasingly complex systems, the OEE calculation must also be supported by new technologies in order to guarantee the quality of the information, de igual manera éste debe estructurarse bajo una buena estrategia de manufactura como lo es la filosofía “Lean” y debe de ser complementado por la metodología de mejora continua [likewise, it must be structured under a good manufacturing strategy such as the Lean philosophy and must be complemented by the continuous improvement methodology] (^{García et al., 2011}).

Figure 1 Overall Equipment Effectiveness and its link to the six major losses in production. 

Failure Analysis

The Weibull distribution is one of the most used distributions in reliability and maintenance engineering. Depending on the value of the shape parameter, β (beta), the failure rate can decrease, be constant or grow. It is used to model the failure behavior of various real-life systems. (^{Chaurasiya et al., 2017}; ^{Bistouni & Jahanshahi, 2015}).

MTBF and MTTR

According to ^{Saetta, and Caldarelli, (2018}), Mean Time Between Failures (MTBF) is a KPI related to the average production time elapsed between a failure and the next time it happens.

They also indicate that the Mean Time To Repair (MTTR) is the time it takes to make a repair after the failure has occurred. In other words, it is the time used during the intervention in a given process.

Unit of Analysis

The unit of analysis of this research is a part manufactured in the metalworking company under study.

Study Population

The population chosen for this study was the total spare parts production in 2018, that is, the 789 parts manufactured, as shown in Table 1.

Sample Size

For the calculation of the sample size, a population N of 789 pieces (total production in 2018) has been considered. The value of Z refers to the confidence level, which has been selected at 95%; consequently, the error (E) will be 0.05. Table 2 shows the sample size calculation.

Sample Selection

The sample was selected from the historical database of and only those parts manufactured with defects. The sample represents 57.71% of the population, as shown in Table 3 below.

Data Collection Techniques

The company database, which was provided by the production control and planning area, was used. The production reports, equipment failure reports, and preventive and corrective maintenance records were used to describe the processes and to analyze the variables provided, which belonged to the production line of spare parts for mining equipment of the company. Figure 2 shows the scheme used for data processing and analysis.

Figure 2 Data processing and analysis scheme. 

By analyzing the information, it has been detected that the operation of the company under study is characterized by the following results:

It is inferred that equipment failure is caused by the lack of preventive maintenance work on them, which has generated losses of time, resources, materials and money; also, the use of additional human resources to cover inspections in reprocessing means a great loss in productivity.

Having identified the problem, total productive maintenance and the detection, prevention and elimination of waste were selected as techniques of Lean Manufacturing methodology to be used to improve the performance of the company under study.

Hypothesis Proposition and Testing

In order to have the data processed for the current study, Microsoft Excel software was used, specifically its statistical tools and those created to build customized tables and graphs. For the interpretation of the statistical results, the books by ^{Chue et al. (2009}) and ^{Córdova (2003}) were used.

The hypothesis tests of failure analysis, reliability and demand were performed, as well as the general hypothesis test, which, in order to be validated, requires the previous validation of the three previous hypotheses. The results of the general hypothesis test will be shown, since the four mentioned have the same scheme.

General Hypothesis Test

This hypothesis must be validated by means of the three specific hypotheses, since these are required to accept or reject the general hypothesis by means of the OEE values.

Null hypothesis-H₀:The use of the TPM-Lean Manufacturing methodology does not improve the OEE in the production of spare parts for mining equipment in the company Frecep SAC.

Alternate hypothesis-H₁:The use of the TPM-Lean Manufacturing methodology does improve the OEE in the production of spare parts for mining equipment in the company Frecep SAC.

To validate the hypotheses, a Student's t-test for paired samples will be used, selecting a reliability of 95%. Once the result is obtained, the null hypothesis will be rejected ift< -2.776 and ift> 2.776. The data taken before and after TPM implementation corresponding to current and improved OEE values will be used as shown in Table 4.

Where, t stat=-20.446, that is, it is less than -2.776; then, the null hypothesis is rejected, and the alternative hypothesis is accepted. Since there is a confidence level of 95%, the alternate general hypothesis is accepted, which states: “The use of the TPM-Lean Manufacturing methodology does improve the OEE in the production of spare parts for mining equipment in the company Frecep SAC”.

Application of the TPM Technique.

For the implementation of TPM, the pillar planned maintenance and the pillar focused improvement were executed, which refers to analyze failures and improve the maintenance management process. When analyzing the preventive maintenance schedule, it was observed that in 2018 no planned maintenance (PM) was performed on the cutting, lathe and milling equipment. Due to the non-compliance of PM, which was 93.75%, a break in the operation was caused, which originated a series of corrective maintenance (CM). When only 8.47 hours of PM were fulfilled, 1917.33 hours of CM and 1177.64 hours of excess of CM were originated, that is, for each PM performed, approximately 226 CM were executed.

An Excel sheet was generated to determine the x-axis and y-axis in order to use the regression tool and thus calculate the Weibull failure parameters β and α, using the Weibull equations.

Likewise, the MTBF and MTTR indicators were calculated: The MTBF value was 50.86 hours, which means that every 50.86 hours a failure occurs in any of the 4 machines, and the average repair time for each CM is 7.76 hours on average. With this data we can calculate the reliability of the equipment as shown in Table 5.

Table 5 Reliability Calculation - Production Line (4 machines). 

Source: Prepared by the author

The calculation of the reliability of the line is low, since at 6 hours it is 0.7139, at 10 hours it is only 0.2668 and at 19 hours after a failure it is 0.

By applying the pillar focused improvement, a new preventive maintenance schedule is generated, in which it is calculated that, due to the wear of the equipment, preventive maintenance should be performed every 13 days (MTBF/8 hours per day). The work has been considered 365 days, so there is a frequency of 28 PM in a year.

It has been calculated that the probability of execution of a PM is 0.25, so it is assumed that out of a frequency of 28 PM for each equipment, 7 must be executed, and in the case of autonomous maintenance (AM), 13 must be executed. This means that when a total of 320 maintenance tasks are programmed, between PM and AM, 80 must be executed. The planning of the PM works is complemented with the creation of the forms and logs to be used to record the verifications in the execution of the works.

Based on the current demand (year 2018) a forecast of the future demand with the improved conditions (year 2019) was made. Thus, it has been calculated that the daily demand for products is an average of 3.52 units per day for 2018 and an average of 3.29 units per day for 2019. Considering that the production per product takes 1.52336 days, the production time for the products in 2019 is very similar when using the four machines for manufacturing.

There was an improvement in the reduction of the delivery times of on time finished products, which was a total of 719 units. This means that of the annual production of 742 units, 23 units were delivered late, due to reprocesses (22) and processes that generated waste (20). Thus, the number of operators was reduced from an average of 8 to 6, as well as the theoretical hours from 17,908 to 12,420 hours (due to the difference in the number of operators and the decrease in maintenance hours); the programmed hours were reduced from 14 700 to 10 200 hours per year; and the hours of classes and meetings from 220.50 to 153 hours. The number of PM hours was increased from 8.47 to 247.90 hours, which reduced CM from 1917.33 to 65.51 hours, eliminating CM overtime. Production delay was reduced from 2156.49 to 112.37 hours and production time was reduced from 9602.80 to 9028.33 hours. Downtime was reduced from 794.40 to 592.89 hours by adding 40 hours of MA.

Complying with the PM reduced the CM, since due the lack of PM, mechanical failures of the equipment and the need for the CM occur; therefore, by forecasting the frequency and hours of PM and CM, the following OEE has been calculated:

Consequently, by improving compliance with the MPs and MAs by 30.84%, the quality factor has improved from 49.44% to 94.64%, the performance factor from 76.68% to 93.34%, and, therefore, the availability factor has also increased from 86.70% to 96.88%. The OEE was increased from 32.86% to 85.58%, exceeding the world class rate of 85%. It was concluded that non-compliance with PMs generates CMs and it results in project delivery delay. The change in MTBF and MTTR rates are also significant, as shown in Table 6.

Table 6: OEE Calculation: Current-Improvement. 

Source: Prepared by the author.

When calculating the annual MTBF, it is observed that it has increased from 50.86 hours to 237.65 hours and the MTTR has been reduced from 7.76 hours to 0.27 hours.

It is possible to quantify the savings obtained by implementing the TPM methodology, which amount to S/ 590,353.55 as a result of reducing the number of operators and the hours and delay per CM. In turn, the investment is S/ 119,317.15 for the increase in PM and AM and for the training of operators for proper management of maintenance work. This is shown in Table 7.

Table 7 Savings and Investment. 

Source: Prepared by the author.