A Lean Manufacturing Progress Model and Implementation for SMEs in the Metal Products Industry

The Influence of Sample Size on Long-Term Performance of a 6σ Process

There are many criticisms for the association between the Six Sigma concept and the two statistical metrics associated to 6σ processes: 1.5σ shift for maximum deviation and 3.4 PPM non-conformities for the long-term performance. As a result, the paper aims to carry out an analysis of this problem, and the first result obtained is that a stable process can reach a maximum drift, but its value depends on the volume of the sample. It is also highlighted that, using only the criterion “values outside the control limits” for monitoring stability through the Xbar chart, a minimum value can be calculated for the long-term performance of a process depending on the sample size. The main conclusion resulting from the calculations is that, in the case of a 6σ process, the long-term performance is much better than the established value of 3400 PPB: For small volume samples of two pieces it is below 700 PPB, for three pieces it is below 200 PPB, and for samples with a volume greater than or equal to four pieces the performance already reaches values below 100 PPB! So, the long-term performance of 6σ processes is certainly even better than the known value of 3.4 PPM.

Recent Progression Developments on Process Optimization Approach for Inherent Issues in Production Shop Floor Management for Industry 4.0

In the present industry revolution, operations management teams emphasize implementing an efficient process optimization approach with a suitable strategy for achieving operational excellence on the shop floor. Process optimization is used to enhance productivity by eliminating idle activities and non-value-added activities within limited constraints. Various process optimization approaches are used in operations management on the shop floor, including lean manufacturing, smart manufacturing, kaizen, six sigma, total quality management, and computational intelligence. The present study investigates strategies used to implement the process optimization approach provided in the previous research to eliminate problems encountered in shop floor management. Furthermore, the authors suggest an idea to industry individuals, which is to understand the operational conditions faced in shop floor management. The novelty of the present study lies in the fact that a methodology for implementing a process optimization approach with an efficient strategy has been reported for the first time that eliminates problems faced in shop floor management, including industry 4.0. The authors of the present research strongly believe that this research will help researchers and operations management teams select an appropriate strategy and process optimization approach to improve operational performance on the shop floor within limited constraints.

Internet of Things: Development Intelligent Programmable IoT Controller for Emerging Industry Applications

The Internet of Things (IoT) has become critical to the implementation of Industry 4.0. The successful operation of smart manufacturing depends on the ability to connect everything together. In this research, we applied the TOC (Theory of Constraints) to develop a wireless Wi-Fi intelligent programmable IoT controller that can be connected to and easily control PLCs. By applying the TOC-focused thinking steps to break through their original limitations, the development process guides the user to use the powerful and simple flow language process control syntax to efficiently connect to PLCs and realize the full range of IoT applications. Finally, this research uses oil–water mixer equipment as the target of continuous improvement and verification. The verification results meet the requirements of the default function. The IoT controller developed in this research uses a marine boiler to illustrate the application. The successful development of flow control language by TOC in this research will enable academic research on PLC-derivative applications. The results of this research will help more SMEs to move into smart manufacturing and the new realm of Industry 4.0.

Algorithm for Process Innovation by Increasing Ideality

Continual efforts to have better processes lead us to search for new ways to improve and innovate. One of the most powerful approaches to innovating technical systems is the TRIZ (Theory of Inventive Problem Solving). TRIZ is, unfortunately, very hard to learn and adequately use. This paper introduces a new comprehensive algorithm for the innovation of processes in production based on TRIZ principles. The Algorithm for Process Innovation by Increasing Ideality (AP3I) helps search for innovative ways to improve or change the process—either the whole process or its segments. Besides the original TRIZ, AP3I is easier to use and might be applied by engineers in industrial practice. On the other hand, results from AP3I are probably weaker compared to full TRIZ. Still, the AP3I can be very helpful in efforts to improve processes and can provide powerful ideas. The overall algorithm is also demonstrated in case studies on processes of packing and assembly.