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The objective of this study is to analyse and propose alternatives to the lack of integration and cooperation of industrial firms in a Brazilian cluster of the jewelry sector. The relationships between the functional factors and the surrounding factors of the cluster are examined.

This research is based on a face‐to‐face survey that established relationships between the internal factor/variables of the companies, the cluster and the external environment's factor/variables.

It was found that integration and cooperation are essential for the development of a cluster since it stimulates the use of local resources. High levels of clusters' inter‐firm integration may represent high levels of operational efficiency.

This study has some limitations related to generalizations, but it can be said that the statistical tools applied to characterize the companies' subgroups in a cluster proved valid. In view of this, further research on other local productive systems is suggested in order to strengthen the propositions making use of the same tools described in this study.

The findings indicate the need for strategic actions by the companies in order to seize opportunities to promote local innovation.

The paper indicates an enterprise's classification according to the characteristics of the company's competitive strategy.

The objective of this article is to identify and analyze critical factors in the development of continuous improvement (CI) activities in Brazilian companies.

A conceptual model of the relationship between practices and results was tested to identify the critical factors using a survey conducted in 46 industrial companies. Non‐parametric tests were used to test some hypotheses developed based on the literature.

The results indicate the importance of staff training in problem solution tools, incentives for suggestions, face‐to‐face communication and regular shop floor visits such as critical factors for the success of continuous improvement activities (CI). Operational practices of CI contribute to company performance in relation to improvements in productivity, quality, lead time, cost, customer satisfaction and development of employees’ skills to solve problems.

Although the detected constructs are fairly accurate, they are still subject to improvements and new dimensions can be incorporated to them.

These critical factors are related to actions that encourage employees to participate in CI activities and incentive mechanisms to be able to apply identification techniques and tools successfully, as well as find solution to problems.

The results of this work provide a thorough understanding of the success drivers when conducting CI activities.

The purpose of this paper is to find the configuration of the number (m) and size (n) of the sample in Phase I that would make it possible to detect the out-of-control (OOC) state of the process with the smallest number of samples and ensure a capability index (C_pk) that would meet the customer’s requirements.

The suggested approach addresses this problem using simulation techniques and design of experiments (DOE). The simulation techniques made it possible to reproduce the normal operating conditions of the process. The DOE was used to construct a predictive model for control chart performance and thus to determine combinations of m and n in Phase I that would meet the capability objectives of the process. A numerical example and a simulation study were conducted to illustrate the proposed method.

Using simulation techniques and DOE, the authors can find the number (m) and size (n) of the sample in Phase I that would make it possible to detect the OOC state of the process with the smallest number of samples and ensure a C_pk that would meet the customer’s requirements.

In the real situations of many companies, choosing the numbers and sizes of samples (m and n) in Phases I and II is a crucial decision in relation to implementing a control chart. The paper shows that the simulation method and use of linear regression are effective alternatives because they are better known and more easily applied in industrial settings. Therefore, the need for alternatives to the X control chart comes into play.

The traditional Shewhart control chart, the X-bar and R/S chart, cannot give support to decide when it is not economically feasible to stop the process in order to remove special causes. Therefore, the purpose of this paper is to propose a new control chart design – a modified acceptance control chart, which provides a supportive method for decision making in economic terms, especially when the process has high capability indices.

The authors made a modeling expectation average run length (ARL), which incorporates the probability density function of the sampling distribution of C_pk, to compare and analyze the efficiency of the proposed design.

This study suggested a new procedure to calculate the control limits (CL) of the X-bar chart, which allows economic decisions about the process to be made. By introducing a permissible average variation and defining three regions for statistical CL in the traditional X-bar control chart, a new design is proposed.

A framework is presented to help practitioners in the use of the proposed control chart. Two new parameters (C_p and C_pk) in addition to m and n were introduced in the expected ARL equation. The C_pk is a random variable and its probability function is known. Therefore, by using a preliminary sample of a process under control, the authors can test whether the process is capable or not.

Competitive pressures force companies to seek solutions to eliminate wastes while improving product quality. Lean Six Sigma (LSS) has been considered one of the most effective approaches for business transformation. This article aims to present an empirical case study where LSS and Define, Measure–Analyze–Improve–Control (DMAIC) methodologies are applied to reduce defects in a car parts manufacturer.

The study follows the DMAIC methodology. Design of experiments and hypothesis testing were applied in a single case study.

The main defects and the main factors that cause defective parts were indicated for die-casting and machining processes. Solutions implemented reduced the defect incidence from a chronically high level to an acceptable one. The sigma level rose from 3.4 s to 4 s sustainably.

The study is limited to a single case study, without intention of generalizing the results to other types of industries.

This paper can be a useful guide of how to use DMAIC Six Sigma approach to defect reduction and can be applied in many sectors.

This study offers the knowledge on how to apply the Six Sigma DMAIC methodology, reducing the dependence on specialization courses.

This study describes in detail the process used in a structured improvement exercise including sigma-level calculation, factorial experiments and hypothesis tests – a set of techniques still poorly combined in the literature.

This study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.

The research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.

Validation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.

The main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.

The purpose of this paper is to develop a monitoring multiple-stream processes control chart with a finite mixture of probability distributions in the manufacture industry.

Data were collected during production of a wheat-based dough in a food industry and the control charts were developed with these steps: to collect the master sample from different production batches; to verify, by graphical methods, the quantity and the characterization of the number of mixing probability distributions in the production batch; to adjust the theoretical model of probability distribution of each subpopulation in the production batch; to make a statistical model considering the mixture distribution of probability and assuming that the statistical parameters are unknown; to determine control limits; and to compare the mixture chart with traditional control chart.

A graph was developed for monitoring a multi-stream process composed by some parameters considered in its calculation with similar efficiency to the traditional control chart.

The control chart can be an efficient tool for customers that receive product batches continuously from a supplier and need to monitor statistically the critical quality parameters.

The present paper aims to demonstrate the potential of integration between industrial robotics and Lean Manufacturing (LM) approach to increase the efficiency of an assembly line.

Based on a case study performed in an Italian company, this paper reports a comparative analysis of the results produced on a line balancing study involving a semi-automated production line, aided by an industrial robot.

The results suggest the possibility of implementing industrial robotics in line balancing studies highlighting efficiency gains and idle reduction. Further, it also addresses some concepts directly related to industry 4.0, such as collaborative robotics, artificial intelligence, and lean automation.

Line balancing studies may include advanced robotics in order to extend traditional lean practices toward Digital LM.

This study adds contributions to the operational excellence literature, demonstrating the symbiosis between industrial robotics and LM practices.

The aim of this paper is to identify some specificities of production planning and control (PPC) activities in the one-of-a-kind-production (OKP) process through an extensive literature review. Relevant aspects related to systems and PPC activities in the context of OKP environment are discussed, and six opportunities for future research are highlighted.

The following research is based on a review of 53 articles published in peer-reviewed journals over the past three decades. After an initial descriptive analysis based on bibliometric indicators, a cluster analysis of 15 most cited articles was carried out using multivariate data analysis techniques and in-depth analysis.

The results reveal some specificities inherent to the clusters featured in the research, including aspects of planning, control and systems for OKP process. This cluster addresses information regarding next-generation manufacturing systems, scheduling and design science, computer simulation and project approach. On the other hand, the authors point out six topics for future research regarding contemporary issues associated with PPC in the context of OKP.

This paper fills an important gap regarding OKP production planning and control practices. The results provide a theoretical overview of different PPC practices suitable for the OKP environment. Furthermore, it can provide insights for scientific developments in order to manage the complexity inherent in the OKP process.

The purpose of this paper is to identify the risks to which agrifood supply chains are exposed and to analyze how these risks impact the degree of coordination of the chain.

The present investigation was executed in two steps. Initially, a literature review and interviews with slaughterhouse managers were carried out to identify the main risks to which agrifood supply chains are exposed. The second step consisted of a survey involving 66 Brazilian slaughterhouses to identify how the perception of risks influences the degree of coordination in the examined chains.

The study revealed that risks, transaction costs and creation of collaborative advantages are determining factors in defining the degree of coordination in the analyzed agrifood supply chains.

The results allow slaughterhouse managers to more easily recognize the risks to which the supply chains are exposed and evaluate in more detail strategies for relationships with their suppliers. These strategies may be able to avoid conflict and create value for the supplier by stimulating longer relationships and facilitating animal purchase transactions for slaughter. This can lead to quality improvements, lower costs and reduced risk.

Studies of risks in agrifood supply chains are rare in comparison with those developed in other sectors. The present investigation is innovative in identifying the main risks specific to agrifood supply chains and associating those risks with a degree of coordination that minimizes them.