High business competition demands business players to improve quality. The Six Sigma

with DMAIC phases is a strategy that has proven effective in improving product and service quality. This study aims to find the consistency of DMAIC phases implementation and

analyze the objective value in Six Sigma research. By using a number of trusted article

sources during 2005 until 2019, this research finds that 72% research in manufacturing industry consistently implemented DMAIC roadmap especially in case study research type

for problem-solving, while service industry pointed out the fewer number (60%). The causes

of variations and defective products in the manufacturing industry are largely caused by

a 4M 1E factor, while in service industry are caused by human behavior, and it’s system

poorness. Both manufacturing & service industry emphasized standardization & monitoring to control the process which aimed at enhancing process capability and organization

performance to increase customer satisfaction.

Higher education institutions (HEIs) typically generate income from two main sources; student

fees and research income. In contrast, the predominant waste streams in HEIs tend

to include; (1) assignment/examination mark submission process, (2) photocopying process

and (3) the funding application process. Unintended internal process complexities and barriers

typically aggravate the challenges already inherent in the research grant application

process. Although Lean Six Sigma (LSS) has been adopted by a number of HEIs in Ireland,

very few have adopted an integrated LSS approach for waste reduction in the research grant

application process. To identify barriers and waste in the research grant application process

within an Irish HEI in an EU environment, the authors used an online survey deployed to

240 academics and researchers. The survey response rate was 13%. The participating HEI

in this pilot study generated an annual income (including student fees and research income)

exceeding e240 million for the academic year 2017/2018. Using an LSS lens, this paper identified

the primary waste in the research grant application process from an academic and

researcher perspective to be; editing and revising applications, liaising and communicating

with collaborators and waiting for information. Organised thematically, the main barriers

were strategic thinking, collaborator identification and co-ordination, eligibility, process,

time and support & mentoring. The results from this study can be used to inform the next

stage of the research where empirical studies will be carried out in other HEIs to develop a

practical roadmap for the implementation of LSS as an operational excellence improvement

methodology in the research grant application process.

This paper presents a new welding quality evaluation approach depending on the analysis

by the fuzzy logic and controlling the process capability of the friction stir welding of

pipes (FSWoP). This technique has been applied in an experimental work developed by

alternating the FSW of pipes process major parameters: rotation speed, pipe wall thickness

and travel speed. variable samples were friction stir welded of pipes using from 485 to 1800

rpm, 4–10 mm/min and 2–4 mm for the rotation speed, the travel speed, and the pipe wall

thickness respectively. DMAIC methodology (Defining, Measuring, Analyzing, Improving,

Control) has been used as an approach to analyze the FSW of pipes, it depends on the

attachment potency and technical commonplace demand of the FSW of pipes process.

The analysis controlled the Al 6061 friction stir welded joints’ tensile strength. To obtain

the best tensile strength, the study determined the optimum values for the parameters from

the corresponding range.

The objective of this research is to minimize product defects based on labor performance and prove the hypothesis on how labor performance affects the quality of a product through a scientific calculation using Overall Labor Effectiveness (OLE). The primary data is obtained by interviewing the supervisor and labor directly. For secondary data is obtained from the company, such as labor working time, machine scheduled downtime, total production, and defective products. The approach to extract the data is using OLE and the continued regression method. Furthermore, it proceeds to Six Sigma using the DMAIC approach since the results show a significant correlation. The result from Failure Mode and Effects Analysis (FMEA) shows four of six potential failures caused by product defects are coming from labor. To prevent failure mode, it is recommended to have the regular machine checked by labor, check the temperature of the machine, and provide Standard Operating Procedures.

Nowaday, many manufacturing companies are integrating Industry 4.0 technology into their operational processes, particularly those aiming to enhance production operations. However, business decision-makers must remain vigilant about potential risks associated with adopting this technology. These risks include initial financial investments for testing and system installation, managing human resources to operate the new system, and concerns regarding data security. This study proposes designing an Industry 4.0 technology system to augment machining machine operations, leveraging Internet of Things (IoT) devices to facilitate connectivity and data transmission. Additionally, it aims to improve production process monitoring through visual management techniques. The machines under study are semi-automatic and lack operational digitization or expansion capacity. Through research on integrating low-cost Industry 4.0 technology into the production process, this study has achieved an annual reduction in production costs by $9593. Moreover, the defect rate for product length dimensions has plummeted from 54.90% per month to zero defects. The study employs the DMAIC method (Define-Measure-Analysis-Improve-Control) cycle within the Six Sigma methodology to investigate and apply low-cost Industry 4.0 technology to production process enhancement. This combined approach can be customized and applied to various business process improvement models, further enhancing the operation of machining machines originally equipped with Industry 3.0 technology.

This study was conducted in a company that produces palm oil-based products such as cooking oil and margarine. The study aimed to encounter defects in packaging pouches. This study integrated the overall equipment effectiveness (OEE) with the six sigma DMAIC method. The OEE was performed to measure the efficiency of the machine. Three factors were measured in OEE: availability, performance, and quality. These factors were calculated and compared to the OEE world-class value. Then, the Multiple Linear Regression was performed using SPSS to determine the correlation between measurement variables toward the OEE value. Lastly, the six sigma method was implemented through the DMAIC approach to find the solution and improve the packaging quality. Supposing the recommendations are implemented, the OEE is expected to increase from 82% to 85%, with availability ratio, performance ratio, and quality ratio at, 99%, 86%, and 99.8%, respectively.