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The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.

Assets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).

A case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.

The novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

The purpose of this paper is to analyze relationships between operational characteristics and business growth in Italian manufacturing companies comparing high lean performers (HLPs), which are companies with a high degree of application of lean practices, and low lean performers (LLPs). This analysis aims at highlighting the effect of the implementation level of lean practices on operational characteristics of Italian companies and, as a consequence, on business growth.

A classification in “LLPs” and “HLPs” of Italian manufacturing industries has been developed in order to measure the company’s degree of application of lean practices. A survey was carried out, and a combination of cluster analysis and multi-group structural equation path model was developed in order to answer research hypotheses.

Results suggest that HLPs are characterized by a greater number of employees and a larger turnover. The operational characteristics that contribute to the business growth owing to the introduction of lean practices are as follows: the range of different finished products managed, the delivery reliability, and speedy response to warranty claim. No differences between HLPs and LLPs in terms of business growth have been highlighted when companies vary the batch size, reduce the medium production time, reduce the percentage of finished products which are subject to claim, and increase the percentage (of turnover) of R&D investment. The study highlights that “LLPs” companies are apparently capable of changing their production schemes and adjusting themselves better for customizing their products.

Companies that aim at implementing lean practices should focus their attention on practices that affect the operational characteristics that contribute to the business growth. Moreover, because of the complexity and effort for the implementation of certain lean practices connected to supplier, workforce, quality, and production efficiency, these practices are more suitable only for large firms with enough resources.

As an attempt to offer strategic and operational perspective on Italian lean practices implementation, this study examined how the degree of application of the different lean practices has an impact on operational and companies’ performance. Hence, even if the present study is limited to the Italian manufacturing companies, it could be a representative of companies that are operating in developed countries that are facing the so-called “anemic growth.”

The purpose of this paper is to investigate the perceptions toward issues and benefits of ISO 9000 certification in seven different productive firms of a company leader in the production and commercialization of heating systems for water and environment, for industrial and domestic use.

An innovative vertical analysis on ISO 9000 was conducted by an empirical study on a representative sample of the supply chain of a company in the heating sector. Questionnaires were used which covered several topics related to the reasons for pursuing ISO certification, to the installation phase and the identified benefits and issues.

The results of the questionnaire outline that the reasons behind the implementation of ISO 9000 were mainly externally driven (customer request, company image and competitive advantages). The vertical analysis carried out demonstrated a shared vision between the main company and the seven selected productive plants involved in the study on the quality system based on the ISO 9000 standard.

This study represents the first step in analyzing the relationship between ISO certification and company performance.

The purpose of this paper is to present the application of a procedure for the quality control of stainless steel tubes produced for automotive exhaust systems from a leading company in the steel sector, based on the Delphi method in accordance with the ISO/TS 16949:2009 and the ISO 9000:2008. Using Delphi methodology, it was possible to identify the main problems in the production lines object of the study, the main defects and their causes. Statistical methods were used to monitor process compliance and capacity. The panel of experts involved in Delphi method was able to identify causes of non‐compliance and suggest corrective actions.

The quality procedure implemented involves the application of the Delphi method and the ISO/TS 16949:2009 standard in conjunction with ISO 9000:2008 to the production line of welded tubes for exhaust systems. The statistical methods used to monitor the process were mainly control charts. Capability index, Cp and Cpk, were used to measure the process attitude to produce compliant outputs. Dimensional data were acquired by non‐destructive testing on diameters and X‐R charts were used to graphically represent the process state of control. Destructive tests were performed to monitor the welding quality and P‐chart were used to assess the proportion of nonconforming units.

In this work, a procedure was developed in order to characterize the production process of TXM tubes realized in the line 31 of the leader company plant. The use of Delphi methodology, in order to incorporate experts opinions in the quality control of stainless steel tubes, was one of the main points of this work. The panel of experts worked together to identify process issues, define their causes and propose corrective actions. The paper provides an overview about the quality approach of one of the world's largest companies in the production of steel and shows also how the statistical tools are used in order to manage process behavior.

The value of this paper is to illustrate an innovative approach to a real life quality problem; it demonstrates how the application of qualitative and quantitative quality instruments in accordance with technical specification can help in increasing and maintaining product compliance and in optimizing the management of resources.

The purpose of this paper is to investigate the influence of technical and management practices of a new product development (NPD) project on the project success in terms of observed value-added time. The authors address the research questions: “How do the product innovation aspects, information process, suppliers integration and project team aspects influence the success of NPD projects in terms of observed value-added time?”

Specific hypotheses are posed based on literature research and semi-structured interviews with project managers used for the pilot study. In total, 40 projects carried out by multinational corporation, concerning NPD, are analysed, comparing product and project features with results obtained in terms of performance indexes usually used in “Lean Production” sector: “waste time”; and “type of wastes”.

This research highlighted that “over-processing” is the most important waste made in the projects when the product concept is Completely New. Great support has been found for the hypothesis that a clear definition of products’ concept can reduce waste time. Unexpected result has been obtained analysing “Information process aspects” category where the more frequent use of support systems (i.e. quality function deployment, computer-integrated manufacturing, computer-aided design, etc.) tended to increase waste time. Important connections have also been found between project team aspects and low waste time.

The identification of sources of waste can help managers to assess their current innovation practices, identify gaps between their current practice and best practice, and define action plans to close those gaps.

In literature, researches exploring the effects that both organizational and managerial factors have on value-added has received only scant attention. This study attempts to bridge this gap, and takes a first step to investigate the role of product innovation aspects, information process, supplier integration and project team aspects in “value-added time” in a NPD project.

The purpose of this paper is to contribute to the existing knowledge system of lean supply chain management (LSCM), i.e. by using main path analysis, and the authors extract the main development track of LSCM. It is advantageous for relevant scholars to deepen their understanding of this academic field from a bibliometrics view to grasp the future directions better.

Structuring a citation network with the processed data set containing 866 papers and relevant information collected from Web of Science (WoS). Conducting review analyses aiming at the main paths that are extracted from the above citation network.

There are two different evolution paths in LSCM field, i.e. improving corporate sustainability performance through combining lean and green practices, and seeking the balance between lean and agility to structure leagile supply chain for specific industries. LSCM research studies mainly focus on five aspects: (1) establishment and development of LSCM theory; (2) structuration of lean supply chain; (3) research studies of the relationship between LSCM and corporate performance; (4) supply chain evaluation system; and (5) review and vista of LSCM field. The intersection of two knowledge evolute routes would be Industry 4.0, which is an integrated theory system combining lean, agility, green and other supply chain thinking.

The data set collected from WoS cannot contain all the research studies about LSCM is the main research limitation. Sustainability, as represented by environmental performance, will continue to be a major pursuit of this field. Integrating LSCM in Industry 4.0 will be the next hotspot in LSCM field.

Providing the main research contents and common methods of LSCM field. It is conducive to deepening the understanding of relevant practitioners and scholars to LSCM field from a dynamic perspective.

To the best of the authors’ knowledge, it is the first time to reveal the knowledge diffuse trajectories of LSCM under different view with main path analysis. This study is unique that it provides a new view to understand the field of LSCM.