CENTRO DE ENSEÑANZA TÉCNICA Y SUPERIOR Engineering College Industrial Engineering Global Program Final project: Six Sigma-DMAIC applications Presents: Benito Pablo Barajas Loaiza 32487 Francisco Xavier Bastidas Moreno 30646 Mexicali, B.C. May 19th of 2021 Introduction In this summary we will present four case studies each one applied on a different sector of the industry, the main objective of this project is to identify how de DMAIC lean tool is applied and analyzed in real life projects. Up next we will introduce the DMAIC methodology as stated by Anderson & Kovach on a study directed to minimizing welding defects on pipelines. “The Lean Six Sigma methodology follows a five-phase approach, known as DMAIC (define, measure, analyze, improve, and control). The purpose of the Define phase is to identify the project goals and understand the potential value that the improvement project will generate for the organization. This phase includes obtaining approval of the project charter and developing a high-level map of the current process. During the Measure phase, the measurement system is verified, and data are collected to establish a baseline measurement for the current process. In the Analyze phase, information about the problem and underlying process are analyzed to identify potential causes of the problem. The objective of the Improve phase is to generate solutions for the vital few root causes found in the previous phase and implement these solutions to create an improved process. In addition, the performance of the improved process is measured and compared against the baseline established in the Measure phase to determine the degree of improvement achieved through the project. The goal of the Control phase is to hold the gains made in the improved process. This includes developing a control/process management plan that documents, monitors, and controls the improved process.” (Anderson & Kovach 2014). “A six sigma case for process optimization in water treatment plant” Background: water treatment consists of separating impurities from raw water, in order to do this, several chemicals are added to water in order to create bigger compounds that are easier to take out. In this case, several actions are being made in order to reduce the cost of the process; as part of this: new materials are added to water and they are being analyzed to see the relationship between the NTU and the cost. Process to optimize: The main goal of water treatment is to remove the suspended solids, in order to achieve this raw water must go through several process. Starting with the pre-treatment process, which includes: coagulation, flash mixing, flocculation and clarification. Coagulation: in this process molecules of aluminum or iron-based coagulants are added to raw water in order to destabilize the particle, therefore it can be mixed. Flash mixing: in order to assure the combination of aluminum coagulants with raw water, mechanical flash mixing process must be done. This step creates the environment that will facilitate efficiently water treatment. Flocculation: this is a low step because it combines each particle with an aluminum molecule, this new compound is called flocs. Clarification: in this process the flocs are settled at the bottom of the tank and clear water is suspended in the upper part. Figure 1 & 2 (Chemical process and in-production process) DMAIC Six Sigma Application: Define: The scope of the projects aims to optimize the cost process of raw water treatment. The company look towards improved coagulation and enhanced softening process through experimental analysis. Furthermore, turbidity was taken as a variable to determine responses of coagulants. Measure: In order to determine the focus area, a SIPOC was made which allows the company to have a clearer view of the process and how to improve it. Once the issue was located, a pareto chart was developed, with this tool the company can look towards how the process is done and which things add or does not add value. Figure 3 & 4 The pareto chart, which is made regarding data of the process part of the SIPOC, states that the results are controlled 80% due to the chemicals that are being used in the process, therefore the company must look to more commons chemicals treatments (liquid alum and lime, as stated in the case study). Analyze: A run chart is analyzed taking 270 samples of Nephelometric Turbidity Units (NTU) from all the year. In the chart it can be determine that the average NTU is 17, being the highest value 43. Also, a control chart is set up regarding 40 ppm, as well as a process capability chart. (Figure 5) In the control chart it is notable that the process is not under control, as seen in the Run chart the process tends to have higher values as time goes. (Figure 6) To conclude, a process capability chart is designed in order to determine the process variability. Since the values for process capability and performance are much beyond the benchmark of 1.33, it can be assumed that the process archives six sigma levels therefore it has low variation. Figure 7 & 8 Improve: As demonstrated with the pareto chart, in order to reduce the costs, there must be a change (optimization) in the dozing rates. The improve phase started with test protocols for jar test, these jars will be the pilots of the study since each jar will have certain amount of chemicals (alum flakes and lime). The studio shows that for 40ppm alum dosing has optimum chemical consumption with a turbidity of 5 NTU. (Figure 9) Control: With the results of the jar test new protocols are being recorded and a training program is being develop in order to demonstrate how the new dozing work and why it is better. Conclusion: With Six Sigma methodologies and DMAIC a new process that optimize water treatment as well as reducing the cost for this process. In order to determine which part of the treatment had to be optimized, a SIPOC and Pareto chart demonstrate that the chemical dozing was the most expensive part of the process, therefore new compounds were added to the water in order to assure lower costs and a process capability that fulfill six sigma yields. “Six Sigma-based to optimize the diffusion process of crystalline silicon solar cell manufacturing” Background: Silicon-based photovoltaics have become one of the most necessary items in the manufacturing process of solar cells. Among the process, there are p-type and n-type doped silicon material, nowadays most of the cells used phosphor (p-type) and its creation begin with boron-doped silicon wafers. In this case Six sigma-DMAIC tools are use to analyze the process of phosphorus doping, aiming to optimize the process in order to reduce manufacturing time. Problem Statement: Problem Statement: This study deals the Six Sigma-DMAIC approach on throughput improvement by optimizing the POCl3 tube diffusion process. Six Sigma investigates the functional form, Y = f (x), where Y is the output or result (dependent variable) of the process and x’s are the causes (independent variables) which affect the output. In the present case study, Y is the throughput of the diffusion process. (Diffusion refers to the process of creating p-type silicon wafers by introducing phosphorous into it) (Figure 10) Define: In order to know the root of the diffusion issue, a pareto chart was created in which 4 causes were analyzed giving the following results: (Figure 11) Diffusion process time: the time in which te wafers move into the paddle and then into the quartz tube. Rework and reprocess taking a material that it is out of the specifications limits into another process in order to meet the specifications. Bubbler change over time: this refers to POCL3 which is the liquid used for n-type dopant for silicon wafers. When a silicon wafer is being dope with this compound, a toxic chemical is being produce in the quartz tube therefore several safety protocols are taken in action in order to replace the tube. The pareto chart states that the major issue is regarding the processing time, therefore a p diagram was develop taking in account that processing time is the biggest problem in the diffusion process. (Figure 12) Measure: In order to set the sigma rating, 100 runs and processing time in minutes were recorded. These were the values: (Figure 13) Since p value is less than .05 it can be assumed that the data does not follow normal distribution. Since these data does not fit any known distribution, the process capability was analyzed taking in consideration the process time. By analyzing the ppm, we can assume that the process, right know, have a sixsigma value of 0. (Figure 14) Analyze: (Figure 15 & 16) In order to determine what affects the CTQ of the process, an Ishikawa diagram was made in which all the possible causes are enlisted. To add more value to each of the roots, the company created a cause validation plan in which all the roots are explained, regarding their solution or how are they calculated. Based on the cause validation plan, few causes were validated using regression analysis. In the cases where regression analysis was used, all of the causes were independent variables (potential causes), which are: Loading Unloading Transferring Temperature Using those variables, multiple linear regression analysis were made in order to identify the root cause. Best subset regression was used to study the effect of each of these variables on throughput time, these are the output of the regression analysis: (Figure 17) Improve: The team decide to conduct a DoE using all the variables from the “measure” part of the root-cause diagram, in which each factor was experimented using 3 levels of experimentation: (Figure 18) Once the factors and the levels of experimentation where determined, a Signal to Noise analysis was being made in order to know the minimum process time, these were the results: (Figure 19 & 20) With the new process optimum levels, new data was given after calculation. The observed ppm is 0 and the sigma level just approached 6, also the process time was improved from 74 to 62 minutes, lastly, the standard deviation move from 1.744 to .959. (Figure 21) Control: Since the process was reduce several minutes, new control limits were set, within this new specifications it is important to state that all the inspections were under control. (Figure 22) Conclusion: By applying six sigma DMAIC methodology the diffusion process has been increased. Furthermore, the sigma level of the process has increase and, due to optimization, the time has been reduce several minutes. Reducing Welding Defects in Turnaround Projects: A Lean Six Sigma Case Study In this case study lean manufacturing techniques were used to improve metrics that the customer is requiring, a DMAIC process was followed in addition of other external tools ad hoc the instrument to create pivotal data gathering and useful information. The company in the study is called “JV industrial companies (JVIC)” located in Houston, Texas it prides itself as an industry-leading turnaround, construction, and fabrication services organization headquartered near Houston, Texas. This company provides complete construction solutions for industrial clients across the United States. The company's core values include superior safety, quality, service, integrity, personal responsibility, and personal accountability. In search of keeping these values and the continuous improvement in quality and customer satisfaction the company looked for help from a third-party company, the research specifically addresses the need to reduce welding defects in turnaround projects. Using an action research approach to address an increase in the number of weld repairs that occurred during turnaround projects completed in 2011. Little documented research exists on the use of this methodology in the turnaround industry; hence, this research attempts to fill this gap in the literature by providing a case study that demonstrates how Lean Six Sigma can be applied in service-based environments such as turnaround projects. Background Welding is commonly used in industry to join two base metals either on the manufacturing of parts or in the repairment of them, in this case butt welds for pipelines will be studied. By nature, welding creates stress concentration on the weld area that are reinforced by filler material, welding is a complex process that requires constant and precise inspections that become more demanding depending on the level of damage that a failure on it could affect its surroundings. Given the severity of a failure on the junction, welding operators must be qualified technicians who understand the process not only by practicing and empirical knowledge but also from theoretical knowledge, operators in industry are commonly certified b the American Welding Society (AWS) which certifies them as skilled leveled workers and keep track of their welds through a welders log which can't exceed a time lapse of 6 months without a welding operation or their certification would expire. Unfortunately, the high cost of training welders in the proper way affects companies in the long run creating constant welding issues. There are many causes of defects on a welding process, external and internal variables may affect the final part, pores are common ones and have a tolerance interval as they are simply oxygen molecules trapped inside or outside the weld, so they are categorized as defects, in the other hand when cracks are detected the part becomes a defective. “A turnaround strategy is backing out or retreating from the decision wrongly made earlier and transforming from a loss-making company to a profit-making company”. (Business Jargons 2021) The company implemented this type of strategy to reduce the average weld repair rate; the total number of rejected butt welds divided by the total number of butt welds inspected by X-ray. The company implemented six sigma tools primarily DMAIC analysis to get to the customers specified 2% repair rate, if the rate is higher than that the company will assume repair costs, currently their monthly average is 3.3% generating high costs which were compromising the company’s future. DMAIC JVIC conducted a Lean six sigma project through participatory action research which required employees to be involved and to know what were the objectives of the project. Define The first step to tackle this problem was a project charter which described the dutys of each participant with the overall objective of reducing the weld repair rate for turnaround projects. The problem statement was the following: “Problem Statement: JVIC's butt weld repair rate for the La Porte division has averaged 3.66% over the last 9 months (January 2011–September 2011), resulting in increased repair costs.” Mission Statement: Reduce the average butt weld repair rate for the La Porte division to 2.75% in the next 6 months (by April 2012), resulting in an estimated savings of $75,000- $100,000 per year.” The team implemented six sigma tools like mapping techniques and flowcharts of the process to prevent leaks and missing errors that inspections didn’t took into account, the result of this was a well-executed traveler with all the process mapped and rerouted if a defect was found. (Figure 23) Measure Information about the current process was gathered to quantify the weld repair rate. Again, the team used mapping techniques to identify the QC inspections that are made during the welding process, they realized that the inspections recorded only binary information which only helped the process to re route it and not to create a record of the defects on the process. To improve this a defect log was created where the defect was investigated and recorded as soon as the inspection showed defects, these defects will also be registered on the welders log of the operator for improvement purposes. the focus of the project, data from detailed project weld logs for a 9-month period ranging from January through September 2011 were reviewed. This information was summarized by the project team using histograms and Pareto charts (Figure 25). With these tool they concluded that the part of the company that they will focus on was La Porte division given that it was the facility with the most welds performed and the second place on inspections required for parts. Analyze The team identified potential causes for hugh burr welds repair rates through brainstorming and the 5 why´s analysis. They made a graphic Ishikawa diagram in which the presented all the potential causes of a failed weld (Figure 26), There are many possible welding defects such as welding angles, overlap, lack of fusion just to name a few, the team found that the main ones were on the welding technique and the proper set up of welding shields, Improve For this project four solution were implemented C) Inspect windshield and train welders to use the windshield properly and mitigate wind impact on the weld E) Welder´s University; properly train welders to keep the related to modern topics and let them practice on real parts. F) Standard welding training. H) Vision test: It requires welders to have 20/30 vision confirmed by eye test, if this failed, they had optical insurance and loans to get the proper visual help. The results of the project were the following: “by implementing windshield standards, training welders through Welder University, and instituting eyesight tests for welders company-wide, the weld repair rate decreased by more than 25%, which translated into a savings of $90,000 for this company.” Control To control the constant register of defects the team implemented a software for inspections in which you don’t only report the defect, but you have to input defect investigation and also the welder´s id to pass to the next step of the rerouting assuring that every defect is precisely recorded. Also, the welder´s university has a minimum passing grade of 6, grades are monitored, and welders should constantly prove their knowledge on it, this allows the corporate to maintain control of the welders and if by any cause the grades come down take immediate action and start an investigation. Utilizing six sigma to improve the processing time: a simulation study at an emergency department Emergency rooms play a crucial role on the patient’s treatment cycle as in there the patient should be transferred to the appropriate clinic or room, given this situation and that some patients might have severe complications time is key for nurses at the receiving desk to have the optimal route to follow and to get the person with the correct physician as soon as possible. Background This case study takes places at “Dr. Georges-L-Dumont Hospital” in Moncton Canada, this clinic strives to apply a project in which the waiting time (WT) and length of visit (LOS) are reduced and fitted depending on the patient’s complication status. For this project an Arena software is used to gather data as WT and LOS, simulations are critical for these kind of departments as the demand is always variable and the number of critical cases are not easily predictable. “The Society for Academic Emergency Medicine Simulation Task Force” was created to share key findings from emergency rooms such as successful practices and complications that these rooms find, one of the objectives of the task force was to create a research agenda for the use of simulation in emergency medical education and its applications. The hospital implemented a DMAIC analysis in which they obtained the necessary data and implemented a “triage model” the sorting of injured or sick people according to their need for emergency medical attention (Torrey 2021). DMAIC Analysis Define For the define phase the current variables were analyzed, they defined key x’s an y’s such as WT and LOS which were presented in the background these variables helped defined the Critical to quality (CTQ) factors that were involved on an emergency room, they developed a charter that was approved by the hospital’s management. Measure As previously stated, WT and LOS were identified as the relevant variables these had to be measured in order to develop certain simulations to improve the process, the results were the following. The measured mean WT was 33.21 min, with a standard deviation of 15.77 min The measured mean LOS was 84.49 min, and the standard deviation was 26.66 min. Additional data as human resources, customer satisfaction and CTQ factors were analyzed to be considered. Analyze In this phase the results from the measured data were converted into information which was followed by the identification of critical bottlenecks and root cause problem solving. 81% of the respondents consider the current WT quite long. In this analysis the third-party company noticed that they were using a “First in first out” (FIFO) technique in which there were only two types of patients cold cases and noncold ones (cold ones could wait longer), which did not present many room for improvement and often resulted on a diagnosis error. To add to this problem there was only one nurse registering cold and non-cold cases and this was done by vital sign detection and a quick form which described the state of the patient leaving the person’s first diagnose to the criteria of the nurse and his/her interpretation of the situation. After analyzing the information, the main results were: “Reducing WT received the highest grade, followed by the LOS. The triage process received a grade of 99 as shown in Figure 2, which indicates that it is the most critical to achieving patients’ VOC.” Giving this analysis a triage system was implemented with the following criteria Color tag Wait time (min) Red Immediate Orange Within 15 min Yellow Within 30 min Green Within 90 min Blue Within 120 min This new model added 3 more considerations to speed up the process on each particular case of the patient. Improve Implementing the flowchart process from FIGURE # threw positive results in the simulation, “The results for 96 replications, after the warm up period, show that under the triaged simulation model the WT has reduced by 61%, where it reaches on the average 12.93 min compared to 33.21 min. Furthermore, LOS has reduced by 34%, where it reached 55.5 min compared to 84.49 min” (REFERENVIA) Control Constant model investigation and high demand simulations are constantly made to hold the gains from this project, as conclusion they took a six-sigma level approach in which they guided by the results of the triage simulation model revealed that the WT sigma level is improved from 0.66 to 5.18, and the LOS sigma level is improved from 0.58 to 3.09 the company is striving to maintain this sigma level by qualifying their nurses on accurate first diagnoses and constantly evaluating the questions on the receiving form. Conclusion By doing this project we have put in practice all our theorical and practical knowledge due to the several tools and concepts that each article manages. Reading these scholar papers can give us a preview of what will be doing in the future, right now we have developed statistical analysis to short problems, but we are aiming to have the capability to take actions in the industry based on the result of those calculations. After analyzing these case studies, I have a broader vision about the impact that six sigma and lean manufacturing could have on any company that intends to improve, tangible results from a 9-month long project saved 90,000 dollars for a company which motivates me to find useful but also creative solutions for industry problems. I enjoyed reading the case studies and learning about how the industry uses industrial engineering terminology and tools to analyze data in order to make decisions and thanks to the control phase of the DMAIC retain all those gains that were made in the process of the project because quick fixes that only improve the process and does not give longevity to it will eventually disappear, and all the efforts will be wasted Table of Images Figure 1 & 2 Figure 3 & 4 Figure 5 Figure 6 Figure 7 & 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 & 16 Figure 17 Figure 18 Figure 19 & 20 Figure 21 Figure 22 Figure 23. Figure 24 Figure 25 Figure 26 Figure 26 References Anderson, N., & Kovach, J. (2014). Reducing Welding Defects in Turnaround Projects: A Lean Six Sigma Case Study. Quality Engineering, 26(2), 168–181. https://ebiblio.cetys.mx:4083/10.1080/08982112.2013.801492 Mandahawi, N., Shurrab, M., Al-Shihabi, S., Abdallah, A. A., & Alfarah, Y. M. (2017). Utilizing six sigma to improve the processing time: a simulation study at an emergency department. Journal of Industrial & Production Engineering, 34(7), 495–503. https://ebiblio.cetys.mx:4083/10.1080/21681015.2017.1367728 Business Jargons (2021) Turnaround Strategy https://businessjargons.com/turnaround-strategy.html Torrey, T (2021) What medical Triage is in a Hospital What Medical Triage Is in a Hospital (verywellhealth.com) Krovvidi, V. B., Pinninti, V. R. R., & Dwivedula, R. (2019). A Six Sigma Case for Process Optimization in Water Treatment Plant. IUP Journal of Operations Management, 18(3), 37–48. Prasad, A. G., Saravanan, S., Gijo, E. V., Dasari, S. M., Tatachar, R., & Suratkar, P. (2016). Six Sigma-based approach to optimise the diffusion process of crystalline silicon solar cell manufacturing. International Journal of Sustainable Energy, 35(2), 190–204. https://ebiblio.cetys.mx:4083/10.1080/14786451.2013.861463