Introduction

Tire has gone through many stages of evolution, since it was developed first time about 100 years ago. In the beginning, solid rubber tires were used mostly for bicycles and horse-driven carts. First, John Dunlop made a tire which consist a tube mounted on a spoked rim. Then, in 20th century with the arrival of motor vehicles, the use of pneumatic tires was started. The manufacturing process of tires begins with selection of rubber as well as other raw materials including special oils, carbon black, etc. These various raw materials are shaped with a homogenized unique mixture of black color with the help of gum. The mixing process is controlled by the computerized systems to insure uniformity of the raw materials. Furthermore, this mixture is processed into the sidewall, treads, or other parts of the tire. The tire bead wire is used as a reinforcement inside the polymer material of the tire. Bead wire is made up of high carbon steel and the main function of bead is to grasp the tire on the rim. The bead wire of functional tire can work at pressures of 30–35 psi (Palit et al. 2015). Bead wires help to transfer the load of vehicle to the tire through the rim. Due to the increase demand of tires, maintaining the quality and reliable performance becomes priority. In addition, there is need for maintaining the quality in the era of technological advancements in design of pneumatic tires.

The companies have to analyze, monitor, and make improvements of their existing manufacturing systems to comply with the market competition. Different companies use different methodologies, approaches, and tools for implementing programs for continuous quality improvement. Besides these, each company certainly required to use a proper selection and combination of different approaches, tools, and techniques in its implementation process (Sokovic et al. 2010). Variations are generally observed during the manufacturing process of any product. The prime objective of process management or process capability analysis in any organization is to investigate the variability during the manufacturing process of product (Pearn and Chen 1999) which helps organization to monitor and measure the potential of process (Wu et al. 2004). The process capability is determined when the process is under statistics control (i.e., the sample mean on X-bar and R-chart lies within three-sigma limits and varies in random manner). Sometimes, a process which is under statistical control may not produce the products within the specifications limits. The reason for this problem is the presence of common cause or this can be happened due to lack of centering of process mean (i.e., there is a significant different between mean value and specified nominal value). Process capability procedure uses control charts to detect the common causes of variation until the process not comes under statistical control (Boyles 1994; Chen et al. 2009). Process capability indices are used in many areas, i.e., continues measure of improvement, prevention of defects in process or products, to determine directions for improvement, etc. (Kane 1986). Process capability indices are measures of the process ability for manufacturing a product that meets specifications. Three basic characteristics (i.e., process yield, process expected loss, and process capability indices) had been widely used in measuring process potential and performance. Among various process, capability indices C p and C pk are easily understood and could be straightforwardly applied to the manufacturing industry (Chen et al. 2001, 2002).

Literature review

The quality improvement tools and total quality management (TQM) are still used in modern industry. However, industries tried to incorporate strategic and financial issues with this kind of initiatives (Cagnazzo and Taticchi 2009). After inception of TQM in the early 1980s, six sigma came in picture as an element of TQM that could be seen as current state of evolution in quality management. Six sigma is a strategy that helps to identify and eliminate the defects which leads to customer dissatisfaction in tire industries (Gupta et al. 2012). An organization working on direction of implementing six sigma into practice or working to build six-sigma concepts with improvement in process performance and customer satisfaction is considered as six-sigma company (Kabir et al. 2013). General Electric and Motorola are two well-known companies who implemented six sigma successfully. For successful implementation of six sigma in organization, one must have to understand the barriers and motivating factors of the six sigma (Hekmatpanah et al. 2008). Six sigma aimed to achieve perfection in every single process of a company (Narula and Grover 2015). The term six sigma means having less than 3.4 defects per million opportunities (DPMO) or a success rate of 99.9997%. In six sigma, the term sigma used to represent the variation of the process (Antony and Banuelas 2002). If an industry works as per the concept of three-sigma levels for quality control, this means a success rate of 93% or 66,800 DPMO. Due to less rejections, the six-sigma method was a very demanding concept for quality control, where many organizations still working on three-sigma concept. In this regard, the six sigma is a methodology that enables the companies to review their existing status and guide them in making improvements by analyzing their status via statistical methods (Erbiyik and Saru 2015). For most of the industries, sigma is a level that measures the process improvement and thus can be used to measure the defect rate. Six-sigma define–measure–analysis–improve–control (DMAIC) methodology is a highly disciplined approach that helps industrial world to focus on developing perfect products, process, and services. Six sigma identifies and eliminates defects or failures in product features concerned to the customers that affect processes or performance of system.

The literature reveals that most of the waste in developing countries comes from the automobiles (Rathore et al. 2011; Govindan et al. 2016), and out of the total automobile waste, most of the waste comes in the form of tires. There are several barriers faced during the remanufacturing these wastes (Govindan et al. 2016). Around the world, only few studies have been carried out for the tire industries and these studies are focused on analyzing the profitability of car and truck tire remanufacturing (Lebreton and Tuma 2006), system design for tire reworking (Sasikumar et al. 2010), value analysis for scrap tires in cement industries (de Souza and Márcio de Almeida 2013), and analyzing the factors for end-of-life management (Kannan et al. 2014). In addition, some researchers proposed methodologies for improving the process in tire-manufacturing companies out of which few industries implemented lean and six-sigma methodologies (Gupta et al. 2012, 2013; Visakh and Aravind 2014; Wojtaszak and Biały 2015). Other studies also found implementing just in time (Beard and Butler 2000) and Kanban (Mukhopadhyay and Shanker 2005).

However, numerous studies are available for process improvement in the automobile industries using various methods (Dangayach and Deshmukh 2001a, b; Chen et al. 2005; Dangayach and Deshmukh 2004a, b, 2005; Laosirihongthong and Dangayach 2005a, b; Sharma et al. 2005; Radha Krishna and Dangayach 2007; Krishna et al. 2008; Cakmakci 2009; Prabhushankar et al. 2009; Mathur et al. 2011; Dhinakaran et al. 2012; Dangayach and Bhatt 2013; Muruganantham et al. 2013; Sharma and Rao 2013; Kumar and Kumar 2014; Venkatesh et al. 2014; Surange 2015; Bhat et al. 2016; Dangayach et al. 2016; Jain et al. 2016; Gidwani and Dangayach 2017; Meena et al. 2017).

A review of the literature shows that the DMAIC method is the superb practice for improving the process capability in automobile industries. Hence, the current research concentrates on the use of DMAIC method aimed for process capability enhancement of the bead splice appearing in a tire-manufacturing industry.

Methodology

In this study, the six-sigma DMAIC phases were applied to enhance the process capability (long term) for bead splice. In every phase of DMAIC method, a compound of both techniques qualitative as well as quantitative was utilized. The DMAIC steps followed in the current research are as follows:

Define

In the first phase, the goals were defined to improve the current process. The most critical goals were acquired using the voice of customer (VOC) method. These goals would be helpful for the betterment of the company. In addition, the goals will direct to bring down the defect level and increase output for a specific process.

Measure

Without measuring the performance attributes, the process cannot be improved. Therefore, the ultimate target of measure phase was to establish a good measurement system to measure the process performance. Process capability index C pk was selected to measure the process performance. To compute the process capability index, observations of bead splice variation were taken and MINITAB (version 16.0) was used for analysis.

Analyze

In the analyze phase, the process was analyzed to identify possible ways of bridging the gaps between the present quality performance of the process and the goal defined. In addition, it was started by determining the existing performance statistics obtained with the help of six-sigma quality tools (process capability index). The further analysis of these data was done for finding root cause of the problem using Ishikawa diagram.

Improve

In improvement phase, the alternative ways were searched creatively to do things better and faster at low cost. Different approaches (i.e., project management, other planning and management tools, etc.) were used to establish the new approach and statistical methods were proposed for continuous improvement.

Control

The improvement gained through the previous steps needs to be maintained for continuous success of the organization. Control phase was used to maintain these improvements in process. The new process/improved process was proposed for sustaining the quality control in the organization.

Case study

Company profile

Company A was the leading Indian tire manufacturing who started exclusive branded outlets of truck tires. Company started its first manufacturing plant at Perambra, Kerala state of India in the year 1977. Furthermore, the company started its second manufacturing plant in Limda, Gujarat. Company expanded its business and established third plant at Kalamassery, Kerala in year 1995, where premier-type tires are produced. Then, company established a special tubes plant in the year 1996 at Ranjangoan, Maharashtra. Company increased its capacity to produce exclusive radial tires at Limda, Gujarat plant in the year 2000. In year 2004, company initiated production of high-speed rated tubeless radial tires for passenger cars.

Implementation of DMAIC methodology

Problem definition

In the current research, the problem was identified on the basis of VOC data. The customer complaints on wastage of material due to variation in the bead splice of a particular product were recorded. Table 1 shows the specification of the product (tire).

Table 1 Bead splice specification
Full size table

This wastage increases financial loss to the organization. Therefore, the problem is variations in the bead splice which has to be reduced to minimize the wastages.

Establishment of measures

Initially, the normality test for the collected data was performed and Fig. 1 shows the normal distribution curve for the bead splice data. After passing the normality test, process capability index C pk was calculated to measure the present process performance using the observations of bead splice variation, which is presented in Table 2.

Fig. 1
figure 1

Normality test of bead splice

Full size image
Table 2 Initial observations
Full size table

These data were used to create an overall baseline for the system to assess its performance based on the necessary improvement areas established in the define phase. Figure 2 shows that the value of process capability index C pk is 0.94 which is less than 1; hence, the process is not capable.

Fig. 2
figure 2

Process capability diagram of bead splice: before improvement

Full size image

Data analysis

In this phase, the data were analyzed and control charts were constructed. Figure 3 shows the X-bar and R-chart for the existing data. From the figure, it is clear that the few points are outside the lower control limit; however, the process is in statistical control.

Fig. 3
figure 3

X- and R-bar chart of present data

Full size image
Identification of root cause

The Ishikawa diagram was used for finding the root cause of the problem, which is shown in Fig. 4. The identified causes of the problem are as follows:

Fig. 4
figure 4

Ishikawa diagram

Full size image

  • First cause of the problem was bead splice setting on higher side caused by slippage of bead tape from gripper. The slippage of bead tape from gripper was generated due to worn out of the griper key.

  • Second cause was variation in the advancer setting caused due to change in skill of worker. This man-to-man variation was caused due to lack of the standard setup guidelines available.

  • The third cause was related to the frequency of sensor setting. Setting of sensor is required frequently as the former diameter changes. However, due to non-availability of guideline, sensor setting could not change frequently.

  • The last cause was identified that the workers were not using the measuring tape.

Improve

After finding the root causes, the corrective actions were taken, which are presented in Table 3. After implementing these corrective actions, again observations were taken to measure the process performance.

Table 3 Corrective arrangements
Full size table

The collected data are shown in Table 4 and run chart for bead splice variation was drawn for the observations taken before and after corrective actions (Fig. 5). From Fig. 5, it is clear that variability in the process reduced drastically.

Table 4 Final observations
Full size table
Fig. 5
figure 5

Run chart for bead splice

Full size image

The process capability index was also computed after implementing corrective actions. Figure 6 shows that after improvement in process, the capability index C pk value is improved to 2.66 which shows that process is capable.

Fig. 6
figure 6

Process capability diagram of bead splice: after improvement

Full size image

Control

To maintain the achieved process performance of the six-sigma quality level, the above four steps of DMAIC methodology must be applied periodically.

Conclusion and Discussion

In this research, DMAIC approach was implemented for process improvement in tire industry. First, process capability index C pk of the current process was computed which was found less than 1. Therefore, to improve the value of process performance, the root causes of problem were determined with the help of cause and effect diagram. In addition, substantial analysis of existing system was done for finding the solution of root cause identified. Finally, in the improve phase, statistical analysis was done for identifying the process capability index value which was improved after taking corrective actions. From outcomes of the study, it can be concluded that process performance of a tire-manufacturing plant can be improved significantly by implementing six-sigma DMAIC methodology.

Cause and effect diagram was also used in an Indian study by Gupta et al. (2012), although no manufacturing aspects were discussed. One more exploratory research was implemented for finding the enablers for successful implementation of lean tools in radial tire-manufacturing company in India (Gupta et al. 2013); however, no manufacturing aspects were discussed in this study also. In the current study, six-sigma DMAIC method is used for improving the process performance.

The main aim of this study was to improve the process capability index of the bead splice, which is achieved by increasing the value of process capability index up to 2.66. This study is based on six-sigma DMAIC quality methodology which provides information about the decision-making power for particular type of problem and the most significant tool for improvement of that type of problem in which data used must come from a stable process (under statistical control: Chen et al. 2017).

Six sigma is a standard of measurement of the product or process quality, also having a caliber for improvement in efficiency and excellence of process. The main aim of implementing six-sigma approach is delivering world-class quality standards of product and service while removing all internal as well as external defects at the lowest possible cost. For proper and successful implementation of a six-sigma project, organization must have the required resources, the guidance to the employees by top management, and leadership of top management. The case company follows several quality standards, which have research and development cell, and good coordination system for managing the issue faced on shop floor. Hence, the corrective actions were implemented successfully.