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This paper aims to shed light upon the controversial relationship between labour market flexibility and innovation in Italy, paying attention both to inter‐sectoral heterogeneity and to the regional differences.

A set of hypotheses concerning the context‐dependent relationship between labour market flexibility and innovation has been formulated by combining the main results of the theoretical literature concerning this topic. Regional patents are used as a proxy of innovation, while job turnover and wages represent labour market indicators of flexibility. Non‐parametric models and dynamic structural specification of panel data have been estimated to test the aforementioned hypotheses.

The results show that higher job turnover has a significant and negative impact on patent activities in regional sectors of northern Italy, while a positive and significant effect of blue and white collar wages has been generally found in the estimations.

There is a lack of updated information regarding labour market data in the Italian economy.

Knowing in which sectoral and regional context labour flexibility has (or does not have) a positive influence on innovation plays a key role for the decisions of policy makers.

This paper deals with the influence that the heterogeneity of the contexts (at the sectoral and geographical level) exerts on the relationship between the labour market and innovation. Moreover, the endogenous character of this relationship and the cumulative nature of innovative activities have been taken into account by means of a parsimonious dynamic econometric model.

There is increasing interest in the connection between Industry 4.0 (I4.0) and operational excellence approaches; however, studies on the integration between Six Sigma (SS) and I4.0 have been absent from the literature. Integration with I4.0 technologies can maximize the positive effects of SS. The purpose of this study is to understand what types of relationships exist between SS and I4.0 and with I4.0's technologies, as well as the benefits derived from this integration and future directions for this field of study.

A Systematic Literature Review (SLR) was carried out to analyze studies about connections between I4.0 technologies and SS. SLR analyzed 59 articles from 2013 to 2021 extracted from the Web of Science and Scopus databases, including documents from journals and conferences.

The SLR identified relationships between SS and several I4.0 technologies, the most cited and with the greatest possibilities of relationships being Big Data/Big Data Analytics (BDA) and Internet of Things (IoT). Three main types of relationships were identified: (1) support of I4.0 technologies to SS; (2) assistance from the SS to the introduction of I4.0 technologies, and, to a lesser extent; (3) incompatibilities between SS and I4.0 technologies. The benefits are mainly related to availability of large data sets and real-time information, enabling better decision-making in less time.

In addition, the study can help managers to understand the integration relationships, which may encourage companies to adopt SS/Lean Six Sigma (LSS) in conjunction with I4.0 technologies. The results also drew attention to the incompatibilities between SS and I4.0 to anticipate potential barriers to implementation.

The study focuses on three previously unexplored subjects: the connection between SS and I4.0, the existing relationships with different technologies and the benefits resulting from the relationships. In addition, the study compiled and structured different types of relationships for SS and I4.0 and I4.0's technologies, identifying patterns and presenting evidence on how these relationships occur. Finally, exposes current trends and possible research directions.

Modern production facilities produce large amounts of data. The computational framework often referred to as big data analytics has greatly improved the capabilities of analyses of large data sets. Many manufacturing companies can now seize this opportunity to leverage their data to gain competitive advantages for continuous improvement. Six Sigma has been among the most popular approaches for continuous improvement. The data-driven nature of Six Sigma applied in a big data environment can provide competitive advantages. In the traditional Six Sigma implementation – define, measure, analyze, improve and control (DMAIC) problem-solving strategy where a human team defines a project ahead of data collection. This paper aims to propose a new Six Sigma approach that uses massive data generated to identify opportunities for continuous improvement projects in a manufacturing environment in addition to human input in a measure, define, analyze, improve and control (MDAIC) format.

The proposed Six Sigma strategy called MDAIC starts with data collection and process monitoring in a manufacturing environment using system-wide monitoring that standardizes continuous, attribute and profile data into comparable metrics in terms of “traffic lights.” The classifications into green, yellow and red lights are based on pre-control charts depending on how far a measurement is from its target. The proposed method monitors both process parameters and product quality data throughout a hierarchical production system over time. An attribute control chart is used to monitor system performances. As the proposed method is capable of identifying changed variables with both spatial and temporal spaces, Six Sigma teams can easily pinpoint the areas in need to initiate Six Sigma projects.

Based on a simulation study, the proposed method is capable of identifying variables that exhibit the biggest deviations from the target in the Measure step of a Six Sigma project. This provides suggestions of the candidates for the improvement section of the proposed MDAIC methodology.

This paper proposes a new approach for the identifications of projects for continuous improvement in a manufacturing environment. The proposed framework aims to monitor the entire production system that integrates all types of production variables and the product quality characteristics.

This paper explores the connection between agri-food supply chains (AFSCs) and levels of circular economy (CE): supply chain (SCs), firm, and product/materials. We aim to answer the following crucial research questions: (1) What are the specific characteristics of the AFSC that affect the implementation of CE? (2) How can the interplay of AFSC characteristics and CE levels be understood and utilised to close the current knowledge gap in CE implementation? (3) How do the CE levels established (in question 2) correspond to the notion of cascade chains? (4) Can the Triple-Bottom Line (TBL) principles be used to report CE impacts in the agri-food industry? (5) What future research directions need to be explored for sustainable CE configuration in AFSCs? Answering these questions expands the knowledge of the implementation of CE in AFSCs, which is crucial for sustainable configuration based on the TBL principles.

We conducted a comprehensive narrative overview (a type of narrative literature review) followed by a case study guided by the cascade chain theory. The sugar cane industry, a significant source of bioenergy that can contribute to sustainable development, was selected for the case study. To validate our findings from the narrative overview, we interviewed five directors from international sugar companies.

Our research has shown that CE can significantly benefit the sugar cane industry. Based on TBL principles, we have developed a framework to achieve sustainable configurations in AFSCs. The framework starts with regenerative agriculture, material reuse, and energy recovery, where different CE levels intersect. This intersection can guide firms to improve decision-making, promote sustainable practices, and inform policymaking across the sugar cane value chain.

Narrative overview has limitations such as potential subjectivity and bias and may not be suitable for generalisation. To mitigate this limitation, we have included a case study to produce a rounded analysis. We have also gathered information from secondary sources, such as reports and company news articles, to prevent biased results.

This research provides valuable insights to assist companies in aligning their practices with CE levels, leading to sustainable outcomes in AFSCs. The study emphasises the importance of integrating CE levels and AFSC characteristics for policymakers to validate and develop new policies. Through our in-depth analysis of sugar cane SCs, we demonstrate that the various CE levels are interdependent and work in tandem with the unique characteristics of sugar cane, resulting in a sustainable configuration.

Analysing the connectedness between CE levels and AFSC characteristics is crucial to fully understanding the CE sustainable configuration. Unlike other frameworks that only describe the CE concept, the framework presented in this paper clearly explains the implementation of CE in AFSCs. It helps industrial practitioners and policymakers validate current practices and future policies. The paper also highlights future research directions and provides valuable insights.