An Integrated Lean Six Sigma Approach to Modeling and Simulation: A Case Study from Clothing SME

This stage allows understanding the process and determining performance indicators. Interviews, meetings, observations, project charters, and spreadsheets were realized as primary tools to achieve this stage. The industry consists of a cutting unit, a manufacturing unit, and a finishing unit. To collect enough data, >100 h of observation were required, divided between these units. Four months were spent for the project application. As management commitment is the most important key to LSS implementation success, a meeting was prepared by the head managers to explain to the employees the importance and the need for the LSS project and its deliverables. Meetings were held before several observations. The team is composed of the leader (quality management manager), lean facilitator (the author), quality manager, and production manager. A project charter consists of detailing the problem statement: Project justification, objectives, definition, and risks were developed.

During the Sim-Lean Educate, the lean facilitator gives lean, Six Sigma, and LSS lessons to the participants. In addition, a brainstorming session was conducted, which focused on the means to understand and improve the current process.

At the end of the Define stage, the main objective of the project was determined: to reduce the “trousers” process flow time to 14 min. A project charter—including a problem statement and project scope—was developed.

In this step, data on measurable indicators of production processes are collected. The primary outputs from the Measure step were detailed process maps. In our case, we used the BPMN standard to model our process, process activities, and the current system simulation model. To achieve this, the methods used included interviews, observations, time studies, and summarizing data in a spreadsheet.

Since BPMN and BPSim are integrated into the BiZagi software, the analyzed process was the production flow of a Tunisian clothing SME. Using the BiZagi software, the draft process map is represented in the form of a belt in BPMN. Figure 4 shows the BPMN model: the chronological sequence of assembly operations needed to transform raw materials into sewed products. The “trousers” production cycle includes different steps of assembly operations to transform raw materials into the finished product, namely trousers. There are mainly four sequences of steps, namely: (i) pre-preparation of pockets; (ii) production of the back of trousers; (iii) production of the front of trousers; and (iv) assembling of fabric parts.

Figure 4

This model retains interviews, and observations were used initially to gain more insights into the process and determine which data were relevant for the time study. Informal interviews with the stakeholders were also carried out during the observation and even during the time study to gather information about the process, as well as staff activities between steps. The standard time can be calculated using the following formula: (2)Standard time=normal time * (1+allowance){\rm{Standard}}\,{\rm{time}} = {\rm{normal}}\,{\rm{time}}\,*\,\left( {1 + {\rm{allowance}}} \right)

The results of the reference layout model are shown in Table 1 according to the performance measures. From Table 1, it can be observed that the average time for the “trousers” process (lead time) is 14.77 min, the average number of finished trousers in a day is 419.5, and the average staying time of jobs in queues is 657.8 min.

In this stage, the causes of the non-value-added activities were determined, the time study was analyzed, and data were analyzed and tested for distribution fit and goodness. This latter is used as input to drive, verify, and validate the simulation model.

Our simulation model was developed to be as near as possible to the real process to ensure the calibration of our system. The Stat fit Student Version program is used to determine the statistical distributions. Figure 5 shows the histogram of this process distribution. Table 2 summarizes the distributions estimated for all tasks.

Figure 5

Table 3 shows that the difference between the simulation model and the real process is almost 6%, which is lower than the maximum allowable error (±10%), confirming the validity and calibration of the model to be used to test scenarios.

It was observed that post 2 “Sew back pocket hem” and post 12 “Put together waistband and lining” blocked the system. Machines are busy with 98.9% and 98.3%, respectively.

Therefore, in the current model, post 2 and post 12 were identified as a bottleneck. The usage of the resources indicates some sub-utilization and over-utilization. Therefore, we confirm the hypothesis about a possible problem of resource capacity.

To improve the process efficiency, LSS techniques were applied. A Kaizen activity event was planned and directed to ensure that each team member could participate in finding solutions and propose suggestions for the implementation.

The improvement activities begin with the presentation of the objectives by the team members. LSS approach and the results obtained after the measure phase were presented to ensure that all the Kaizen members received the same information; then, models were run, the results were analyzed, and the developed system was validated. The modification of the process mapping can be possibly made when necessary. Several scenarios of the sewing process were tested and are tried by what-if analysis to check if the model still performs adequately. Among the considered decision factors in suggestions, we list the resource utilization, the average waiting time for each sewing process, and the total production. In addition, bottlenecks and weaknesses were determined to construct alternative models to improve the system's performance. Several suggestions from Kaizen members are identified for improving the system. For brevity, only three are listed and tested for the sewing process as given in the following. The results of the three scenarios are provided in Table 4.

Scenario 2 has the largest positive impact on the fabrication process. As seen in Table 5, the daily production of trousers has increased to 450.3 with Scenario 2 (up by according to the reference system). Also, with the same scenario, the lead time was lower than that of the reference layout, and the average staying times (min) of work waiting in queues were observed to be lower than those of the reference layout.

The daily output of the system has increased to 422.5 with Scenario 1 and 435.7 with Scenario 3. Furthermore, the average staying time with Scenario 3 has decreased to 635.7 min from 657.8 min. To sum up, with these scenarios, the efficiency of the line has increased. Results were presented to the group members, who are interested in the projected flow time. The last step in the improvement stage is implementation.

Scenario 2 is implemented because of its features such as high daily output, low waiting time, and low lead time. The group members conducted an action plan to quickly implement those changes with other lean tools in 30 working days. Lean tools like 5S and FMEA were implemented. Based on the analysis of the spaghetti plot diagram, physical flow is proposed to create a new handling tool by the realization of a finished product evacuation sheath that eliminates the movement of workers and accelerates the flow in all security aspects. Twenty weeks after implementing the modifications, new data were collected. The results before and after improvement are presented in Table 6.

Trousers’ production lead times were reduced from 14.77 min to 14 min (a 5% reduction). The daily production of trousers is increased to 450. (Up by according to the reference system), the average staying time (min) of jobs waiting in queues was found to be 560 min. The gap between what was estimated and the real value after implementation can be explained by limits of the discrete-event simulation model, which did not take into account machine breakdowns, changing apparatus, and the fabric loss.

The applied improvements have been fully integrated into the training regime and the process documentation, and the information related to company performance was shared among the employees. Visual management, total productive maintenance, and process FMEA are to be implemented after completion of the project to provide a visual aid for controlling the relevant key input and output variables and to ensure that the team could not revert to old habits. The control charts are a powerful tool for achieving process control and stability.