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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…
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.
Modern production facilities produce large amounts of data. The computational framework often referred to as big data analytics has greatly improved the capabilities of…
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.