Integration of Industry 4.0 technologies into Lean Six Sigma DMAIC: a systematic review

This review examines which Industry 4.0 (I4.0) technologies are suitable for improving Lean Six Sigma (LSS) tasks and the benefits of integrating these technologies into improvement projects. Also, it explores existing integration frameworks and discusses their relevance. A quantitative analysis of 692 papers and an in-depth analysis of 41 papers revealed that “Analyse” is by far the best-supported DMAICs phase through techniques such as Data Mining, Machine Learning, Big Data Analytics, Internet of Things, and Process Mining. This paper also proposes a DMAIC 4.0 framework based on multiple technologies. The mapping of I4.0 related techniques to DMAIC phases and tools is a novelty compared to previous studies regarding the diversity of digital technologies applied. LSS practitioners facing the challenges of increasing complexity and data volumes can benefit from understanding how I4.0 technology can support their DMAIC projects and which of the suggested approaches they can adopt for their context

Process Mining for Six Sigma

Process mining offers a set of techniques for gaining data-based insights into business processes from event logs. The literature acknowledges the potential benefits of using process mining techniques in Six Sigma-based process improvement initiatives. However, a guideline that is explicitly dedicated on how process mining can be systematically used in Six Sigma initiatives is lacking. To address this gap, the Process Mining for Six Sigma (PMSS) guideline has been developed to support organizations in systematically using process mining techniques aligned with the DMAIC (Define-Measure-Analyze-Improve-Control) model of Six Sigma. Following a design science research methodology, PMSS and its tool support have been developed iteratively in close collaboration with experts in Six Sigma and process mining, and evaluated by means of focus groups, demonstrations and interviews with industry experts. The results of the evaluations indicate that PMSS is useful as a guideline to support Six Sigma-based process improvement activities. It offers a structured guideline for practitioners by extending the DMAIC-based standard operating procedure. PMSS can help increasing the efficiency and effectiveness of Six Sigma-based process improving efforts. This work extends the body of knowledge in the fields of process mining and Six Sigma, and helps closing the gap between them. Hence, it contributes to the broad field of quality management

Entropy-based approach for semi-structured processes enhancement

The paper analyses traditional quality control methods for business processes and their efficiency in respect to semi-structured process. We update a classical methodology to handle semi-structured processes: improve its execution and operational efficiency in a company. We propose Some new methods: under the Define step – a new method for describing business processes with the help of semantic maps; under the Measure step – a new method for the automated detection of non-random deviations (bottlenecks, errors); under the Analyse step – a new method for automated experts search to identify the root causes of business process problems based on an analysis of the information field

A Strategic Roadmap for the Manufacturing Industry to Implement Industry 4.0

Industry 4.0 (also referred to as digitization of manufacturing) is characterized by cyber physical systems, automation, and data exchange. It is no longer a future trend and is being employed worldwide by manufacturing organizations, to gain benefits of improved performance, reduced inefficiencies, and lower costs, while improving flexibility. However, the implementation of Industry 4.0 enabling technologies is a difficult task and becomes even more challenging without any standardized approach. The barriers include, but are not limited to, lack of knowledge, inability to realistically quantify the return on investment, and lack of a skilled workforce. This study presents a systematic and content-centric literature review of Industry 4.0 enabling technologies, to highlight their impact on the manufacturing industry. It also provides a strategic roadmap for the implementation of Industry 4.0, based on lean six sigma approaches. The basis of the roadmap is the design for six sigma approach for the development of a new process chain, followed by a continuous improvement plan. The reason for choosing lean six sigma is to provide manufacturers with a sense of familiarity, as they have been employing these principles for removing waste and reducing variability. Major reasons for the rejection of Industry 4.0 implementation methodologies by manufactures are fear of the unknown and resistance to change, whereas the use of lean six sigma can mitigate them. The strategic roadmap presented in this paper can offer a holistic view of phases that manufacturers should undertake and the challenges they might face in their journey toward Industry 4.0 transition

How to Reduce Information Silos While Blockchain-ifying Recycling Focused Supply Chain Solutions?

Blockchain has already found applications in the supply chain domain to ensure transparency. Recently, blockchain has further been extended to support the circular economy. Existing literature can broadly be divided into product tracing (or track-n-trace) and anti-counterfeiting. Unfortunately, the information generated in existing supply chain applications has stayed in silos. The existence of information silos reduces the value of “blockchain-ifying” the supply chain. Proper data curation via blockchain secures the information and eases the information flow in the supply chain ecosystem, which can accelerate the implementation of the circular economy. In this paper, a blockchain-IoT-based supply chain management framework has been proposed that offers two primary features. They are i) reducing data sitting in silos while opening doors to circular economy-focused services (particularly recycling), ii) documenting suppliers’ performances while delivering quality products focusing on sustainability. Thanks to such unification, relevant supply chain stakeholders will also have access to important events (ranging from the initial stage to the end of the product’s life cycle)

Agile Six Sigma – A Descriptive Approach

Organizations are more dynamic, competitive and uncertain than in the past; therefore, they must be highly flexible in order to provide an agile condition for responsiveness to customer changes. This paper aims to explain how being Agile can improve the Six-Sigma methodology and explore how Agile and Lean Six Sigma (LSS) principles work together. We will outline the benefits of their relation with each other

Evaluating Lean Healthcare implementation with data mining: opportunities and improvements in emergency services

The waiting time for care in emergency services impacts overcrowding. The Fast Track method contributes to reducing this waiting time. The objective of this study was to evaluate the problems identified and improvements made in emergency services through the execution of the Lean Healthcare project. With the qualitative research approach, data mining allowed reaching results that demonstrated similar problems in emergency services in eight federative units. The improvements implemented contributed to the reduction of patient waiting time. Data mining allowed evaluating groups with similar characteristics, which showed the correlation between the problems encountered and the improvements made in the emergency services

An Integrated Six-Sigma and CMMI framework for software process improvement

A process improvement framework such as Capability Maturity Model (CMM) can help develop the maturity of a software development organization over time to achieve predictable and repeatable process performance. However, in the absence of a methodology for process performance measurement, ongoing data-oriented process improvement is hard to institutionalize. For organizations following CMMI, this makes navigating their way through higher-level process management and optimization activities called forth in CMMI Level 4 and Level 5 especially challenging. Altogether, this constitutes a major stumbling block for software organizations striving for higher process maturity as Level 4 and Level 5 Process Areas are essential to institutionalizing process improvement in an organization. Six-Sigma introduces tremendous process measurability through its statistical error-control focus and offers compelling tools and techniques that have strong applicability to software development. Six-Sigma focus on data and metrics married with the CMMI coverage of all aspects of software development through its Process Areas can together provide a powerful process control and improvement framework. A CMMI and Six-Sigma hybrid framework has been presented as a means of achieving software development performance and productivity improvements through statistical error control. Such a hybrid CMMI and Six Sigma framework provides not just greater guidance and rigor in certain areas than CMMI alone but also an inherent flexibility by making an extensive toolset available for use in a wide variety of scenarios. This integrated framework demonstrates that CMMI and Six Sigma are highly complementary and are capable of adding greater value when used in conjunction with each other. This is partly because together they address the weaknesses that may become apparent when either framework is used alone. Six Sigma answers the \u27how\u27 for areas where CMMI only provides the \u27what\u27. Conversely, CMMI provides the overall vision and roadmap that is lacking from individual Six Sigma improvements. It is hoped that this will serve as a blueprint for an implementation of CMMI that makes use of relevant Six Sigma tools and techniques

Data Enabled Failure Management Process (DEFMP) across the Product Value Chain

The continuously increasing amount of production data and the advancing development of digitization solutions promote advanced data analytics as a promising approach for failure management. Beyond the consideration of single units, examining the end-to-end value chain, including development, production, and usage, offers potential for failure in management-related investigations. Nonetheless, challenges regarding data integration from different entities along the value creation process, data volume and formats handling, effective analytics, and decision support arise. The CRISP-DM approach has become a widely established reference as a conceptual framework for data-driven solutions. However, the linkage between existing failure management procedures and the subsequent development of data-driven solutions needs to be specified. Accordingly, this paper presents a cross-value chain Data Enabled Failure Management Process (DEFMP). The central element is a process model to implement a cross-value chain data-enabled failure management, considering established quality management and data analytics approaches. Based on available failure, product, and process knowledge along the value chain, a path towards developing a comprehensive decision support system is shown. DEFMP combines a reactive failure process with a data-driven approach to incorporate data analytics for proactive improvements. Using DEFMP, the failure management process of a commercial vehicle manufacturer is adapted. With this, partial automation of failure management is made possible. In addition, the potential for improvements is identified and prioritized

An empirical study of how the deployment of lean sigma can reduce its enemies: waste, overburden and defect

Lean thinking focuses on shortening cycle time by eliminating non-value-added activities in every process so that the production can flow efficiently. Six sigma focuses on increasing process capability by eliminating process variation to reduce defects. Both concepts are integrated into Lean Sigma methodology and offered to companies in a form of DMAIC framework to achieve two benefits: quality and speed. In terms of lean implementation, several empirical studies have used the DMAIC framework to emphasis on defining and analysing muda (waste), measuring the level of process cycle efficiency, and making the improvement initiatives to remove waste. This research proposed an additional analysis to the existing DMAIC framework on identifying and evaluating muri (overburden), measuring the muri score, and formulating the improvement plans by identifying less ergonomic work conditions that create overburden activities. The proposed DMAIC framework has been deployed in an Indonesian-footwear manufacturer and the implementation indicated that muri analysis can be aligned with muda analysis to create better process improvement. The research also showed that the proposed framework was able to give significant impacts on reducing the two enemies of lean: muda and muri