University of Bergamo
  • Bergamo, Italy
Recent publications
We investigate a firm's dynamic pricing policy in a storable good market where the cost of production varies over time. In anticipation of a cost increase, the firm selects its prices to affect consumer storage. Price dynamics hinge upon the curvature of demand and the magnitude of the consumer storage cost. When demand is not too convex, the consumers' reluctance to store leads the firm to reduce prices to stimulate consumer storage. This shapes the firm's cost pass‐through and the price commitment effects. Our analysis provides a novel explanation for the well‐documented puzzling patterns of incomplete and negative cost pass‐through. This article is protected by copyright. All rights reserved
The present research investigates the possible causes of resistance to vaccination against the COVID-19 virus. A significant percentage of different countries' populations is refractory to being vaccinated (i.e., in October 2021, in Italy, 20% aged 40–50 years old). A 92-item questionnaire was filled in by a sample of 613 subjects, of which 50.4% said they were against COVID-19 vaccines (63.1% female). Guided by the hypothesis that emotionality constitutes a basis of pre-reflective judgment, items relating to fear, anger and anguish were introduced in the survey. The subjects compiled the Difficulties in Emotional Regulation Scale. The differences between the means of the two samples evaluated with the Student test show that it is, above all, the underlying anguish that constitutes the primary discriminant between the two samples. No Vax mainly considers external the sources of anguish, while Yes Vax sources of anguish appear more internal. From this result an interpretation is advanced: it seems more difficult for No Vax to trust authority recommendations/obligations to get vaccinated because anguish is located just outside the one's body, where Authority dominates.
Film-cooling approach is widely used in advanced gas turbines to protect the turbine components from the hot mainstream flow. The geometry of the cooling holes plays an important role in film cooling performance. In the current study, a large eddy simulation (LES) approach was performed to investigate the effects of various geometrical parameters of shaped cooling holes on the film-cooling effectiveness and turbulent flow characteristics. The reference cooling hole was located on a flat plate with a 35-degree injection angle at a constant density ratio of 1.5. The film-cooling effectiveness calculated by the LES approach was validated compared to that of the experimental data measured by PSP technique at different blowing ratios of M= 1.0, 1.5, and 2.0. Four important geometrical parameters, injection angle, metering length, forward expansion angle, and lateral expansion angle, were considered as design variables, and six different cases were designed and compared with the reference cooling hole. The computational data revealed that the cooling hole geometrical parameters significantly influence film-cooling performance. The designed shaped cooling hole showed an improvement in cooling performance of about 32% for the best case, compared to the reference case. In addition, the time/space analysis of the flow-field inside the cooling hole showed greater velocity fluctuations and more flow unsteadiness for the cooling holes with larger area ratios.
Mathematical models are critical to understand the spread of pathogens in a population and evaluate the effectiveness of non-pharmaceutical interventions (NPIs). A plethora of optimal strategies has been recently developed to minimize either the infected peak prevalence (IPP) or the epidemic final size (EFS). While most of them optimize a simple cost function along a fixed finite-time horizon, no consensus has been reached about how to simultaneously handle the IPP and the EFS, while minimizing the intervention’s side effects. In this work, based on a new characterization of the dynamical behaviour of SIR-type models under control actions (including the stability of equilibrium sets in terms of herd immunity), we study how to minimize the EFS while keeping the IPP controlled at any time. A procedure is proposed to tailor NPIs by separating transient from stationary control objectives: the potential benefits of the strategy are illustrated by a detailed analysis and simulation results related to the COVID-19 pandemic.
Since the introduction of the industry 4.0 paradigm, manufacturing companies are investing in the development of algorithmic diagnostic solutions for their industrial equipment, relying on measured data and process models. However, process and fault models are not usually available for complex productions plants and production data are usually unlabeled. Thus, to classify machine status, unsupervised approaches such as anomaly detection and signal processing strategies have to be employed. Due to the unsupervised nature of the problem, it is meaningful to apply several diagnostic algorithms to cover most of the process anomalous behaviors. Additionally, in some contexts, the experience of process operators in grasping the correct functioning of machines as well as their ability in understanding early signs of deterioration is relevant for the diagnosis of incoming failures. However, seldom these information can be included in failure diagnosis algorithms. In this paper, we propose a diagnostic scheme for condition monitoring of mechanical components. The proposed scheme combines anomaly detection algorithms, envelope analysis of vibration data, and eventually additional qualitative information on machine functioning. The combination of all the fault indicators is obtained leveraging on a fuzzy inference system. The proposed scheme is experimentally validated on a steel making plant with real process data, making use of heuristic information such monitoring reports of machine health status.
The humidity role in the partial discharge (PD) inception mechanism is quite challenging, especially when considering the environmental temperature. Indeed, there is no general rule to explain the humidity effect on the PD phenomenon. In this paper, the PD activity in inter‐turn insulation is experimentally investigated for different relative humidity (RH) conditions at three different ambient temperatures, that is, 30°C, 60°C, and 90°C. Partial discharge inception voltage (PDIV) is directly measured through a photomultiplier tube (PMT), whereas the tip‐up tests are performed aiming at monitoring both dissipation factor (tanδ) and insulation capacitance (IC). These extra measurements (diagnostic dielectric markers) allow better assessing the insulation status. The adoption of the tip‐up test enables the insulation properties measurement. Based on the tip‐up tests’ findings, the interfacial polarization process starts at 75% RH under 60°C, while the high conductivity area is already formed at 75% RH when the ambient temperature is 90°C. The water film formation deduced from the tip‐up test is then used to explain the trend of PDIV, and the validity is further proved by finite element analysis (FEA).
Hydrogen peroxide (H2O2) is a strong oxidizing agent often used in hair coloring and as a component in disinfecting and bleaching processes. Exposures to H2O2 generate reactive oxygen species (ROS) that can cause significant airway irritation and inflammation. Even though workers have reported symptoms associated with sensitivity and irritation from acute exposures below the H2O2 occupational exposure levels (OELs), a lack of sensitive analytical methods for measuring airborne concentrations currently prevents evaluating low or peak H2O2 exposures. To fill these gaps, we propose two different sensitive approaches: (i) luminol chemiluminescence (CL) to specifically measure H2O2; and (ii) photonic sensor method based on the ferrous-xylenol orange assay to evaluate total oxidative potential (OP), a measure of ROS in sampled air. We chose two exposure scenarios: hairdressers preparing and applying hair color to clients (both in simulated and field environments) and workers operating disinfecting cycles at a bottling company. Hair coloring took about 1 h for each client, and the application of the coloring product generated the highest H2O2 concentrations. OP values were highly correlated with H2O2 concentrations (CL measurement) and allowed peak measurements as low as 6 µg m-3 of H2O2 concentrations. The bottling company used a disinfectant containing H2O2, acetic acid and peracetic acid (PAA) in an enclosed process. The photonic sensor was immediately saturated. The CL results showed that the process operator had the highest exposures during a 15-min cycle. There is still a need to develop these direct reading methods for operating in the field, but we believe that in the future an OEL for OP could protect workers from developing airway irritation and inflammation by reducing exposures to oxidizing chemicals.
[BEST PAPER AWARD] The importance of Visual Management (or "Mieruka" as it is called in Japanese) has been largely demonstrated over the last few years, especially when it comes to the creation and management of data-rich environments for effective and efficient data-driven decision-making, such as digital lean smart factories. Although the different functions of visual systems are already known by the scientific community, further analysis of the capabilities and benefits provided by these tools, especially when enhanced with modern digital technologies, has yet to be provided. Therefore, this paper aims to frame a list of capabilities of the current physical visual systems, and their cyber/digital equivalents, according to a reference framework called the "7Is", which was extracted from a review of the current literature available. This may serve as a valid common reference for future research on this topic.
Poka-Yoke devices have always been regarded by lean manufacturing companies as essential quality control and assurance tools to support efficient and effective manufacturing processes and procedures. Thanks to their ease of use and low cost, these devices help maintain high-quality standards and also encourage organisations to undertake Kaizen continuous improvement activities. With the advent of new digital and analytical technologies, these devices have undergone significant transformations. Based on a study of the scientific literature and the results of brainstorming sessions conducted with factory managers and lean experts, this paper analyzes how and to what extent digitalization changes the definitions, functions, approaches, and perspectives of traditional Poka-Yokes. Furthermore, it examines how the change in data collection, sharing, analysis, processing, and feedback (interpretation) approaches brought by the digitalization and smartification of Poka-Yoke devices affects the operational performance of modern Digital Lean Cyber-Physical Production Systems.
In the contention of the current industrial landscape, an increasing number of manufacturing firms are experimenting with the transition from product-centric offerings to service-based value concepts and product-service bundles as high-value integrated customer solutions to increase their revenues and build sustainable competitive advantages; a phenomenon known as the “servitization” of manufacturing. Nowadays, consistently with the Industry 4.0 paradigm, these companies have therefore started a process of integrating their traditional value offerings with digital services. This recent strategy is known as “Digital Servitization” and consists of developing new services and/or improving existing ones through digital technologies. However, this transformation is challenging, and companies often struggle to achieve their expectations. Thus, this study aims to shed light on the current state of Digital Servitization strategies in the manufacturing sector based on a survey addressed to the top and middle management. The results obtained by the analysis of the data collected from the survey show an increasing trend towards the adoption of digital technologies for enabling innovation and differentiation in service delivery processes.
This paper aims to understand why firms engage with their suppliers to collaborate for sustainability. For this purpose, we use the Carbon Disclosure Project (CDP) Supply Chain dataset and apply the Structural Topic Model to: 1) identify the topics discussed in an open-ended question related to climate-related supplier engagement and, 2) estimate the differences in the discussion of such topics between CDP members and non-members, respectively focal firms and first-tier suppliers. The analysis highlights that the two prominent reasons why firms engage with their suppliers relate to several aspects of the supply chain management, and the services and good transportation efficiency. It is further noted that first-tier suppliers do not possess established capabilities and, therefore, are still improving their processes. On the contrary, focal firms have more structured capabilities so to manage supplier engagement for information collection. This study demonstrates how big data and machine learning methods can be applied to analyse unstructured textual data from traditional surveys.
During the last years, many manufacturing processes have been developed combining the typical flexibility of polymeric processes and the resistance of metal components. Among them, metal injection moulding (MIM) is of considerable importance, but this technique is considered very expensive in terms of energy and investments. For this reason, new solutions are being implemented. In particular, the use of a polymeric material extrusion machine to print a metal filament (i.e. a polymeric filament loaded with metal powder) can be an alternative solution characterized by low investment and a more flexible process chain with respect to the MIM ones. In this context, it is necessary to investigate the economic aspects to select the best process according to the component requirements in terms of specifications, level of customization, and production volume. The presented developed cost model and its application are, identifying the different trends of cost and productivity, underlining the weaknesses and the strengths of the two technologies, and showing the convenience of the process according to the considered production volume.
This study theoretically articulates and empirically validates a model of relationships between a firm’s manufacturing strategy – proxied through competitive priorities – and servitization orientation. In addition, it analyzed the moderating effect of firm size on this relationship. The model was developed and tested through hierarchical regression analysis on the Sixth International Manufacturing Strategy Survey (IMSS-VI) data. The results indicate that different manufacturing strategies positively affect servitization orientation. In addition, firm size positively moderates the effect of exploitative manufacturing strategy. This research is one of the first quantitative studies that examine how different manufacturing strategies influence servitization orientation according to firm size.KeywordsManufacturing Strategy (MS)Servitization orientationSmall-medium sized enterprises (SMEs)Competitive prioritiesInternational manufacturing strategy survey (IMSS)
The establishment of valuable collaborations among supply chain partners is essential for the success of a circular economy. However, there are still many doubts about the most helpful collaboration practices that could support sustainable development. The analysis deals with deepening links between the circular economy and supply chain collaborations that could favor its development. Specifically, the study focuses on recognizing the most diffused collaboration practices among the actors that successfully implemented circular systems. A multiple case study from the Italian textile industry is the methodology chosen to carry out the research. The analysis of five selected companies confirms the creation of valuable collaborations is essential to successfully implement circular practices, highlighting that a holistic approach is needed to establish this new economic paradigm effectively.KeywordsCircular economySupply chain collaborationMultiple case study
Closed-loop glycemic control algorithms have demonstrated the ability to improve glucose regulation in patients with type 1 diabetes mellitus (T1D), both in silico and clinical trials. Many of the proposed control strategies have been developed, based on time-invariant linear models, without considering the parametric variations of T1DM subjects. In this work, a pulsatile Zone Model Predictive Control (pZMPC) is proposed, which explicitly considers patterns of intra-day insulin sensitivity (SI), according to the latest updates of the FDA-approved UVA/Padova simulator. Results show a significant improvement in the performance, which a-priori justifies the increment in the controller complexity.
Although modeling studies are focused on the control of SIR-based systems describing epidemic data sets, few of them present a formal dynamic characterization in terms of the two main indexes: the infected peak prevalence (IPP) and the final epidemic size (EFS). These indices are directly related to equilibrium sets and stability, which are crucial concepts to understand what kind of non-pharmaceutical interventions (social distancing, isolation measures, mask-wearing, etc.) can be implemented to handle an epidemic. The objective of this work is to provide a theoretical single-interval control strategy that simultaneously minimizes the EFS while maintaining the IPP arbitrary low, according to health system capacity limitations. Several simulations illustrate the true role of the herd immunity threshold and provide new insight into the way authorities may act.
High-growth firms contribute disproportionately to the creation of employment, wealth and economic development on a global basis. Yet, knowledge of the circumstances under which such growth patterns occur is limited, and the findings with regard to small and medium sized enterprises (SMEs) are inconclusive. Adopting the behavioural agency model, we analyse the effect of family control and related nuances (i.e. degree of family ownership and presence of a family chief executive officer (CEO)) on SME growth. Furthermore, we argue that the type of slack resources and their availability are a crucial organisational contingency when investigating high growth in SMEs. Using a sample of 39,631 European SMEs over a 13-year period, we find that family firms are less likely to achieve high growth compared to non-family firms; having a family CEO further reduces this likelihood. Instead, at higher (vs lower) levels of family ownership, the probability of family firms achieving high growth increases. Furthermore, the availability of high- and low-discretion slack resources influences these relationships. Our study advances current understanding of high growth in general, and family firms in particular.
Although the benefits and advantages are emerging more clearly, the transition of traditional manufacturing and logistics systems to Industry and Logistics 4.0 paradigms is still challenging for companies. In particular, the change management strategies suggested and employed so far lack consideration of the role of human factors to support a successful transition. Starting from the analysis of a case study conducted in a medium-sized Italian manufacturing company, this article aims to show how the critical consideration of the human factors involved in a change of operational processes in a logistics 4.0 perspective is crucial to achieve the objectives set. The article discusses the main strategies to consider from the beginning and the overall impacts on all the job profiles, tasks, and human factors involved to prevent potential resistance and inefficiencies in the implementation phases.KeywordsLogistics 4.0Human factorsChange managementCase study
The concepts of Industry 4.0 trigger the transformation of manufacturing firms. Digital technologies upgrade traditional products and services to increase the satisfaction of customers. In this paper, the authors investigate digital products and services in manufacturing firms. Additionally, the authors challenge relations between digital products and services and their share in the gross annual turnover of manufacturing firms. The data for this research are obtained through the Digital Servitization Survey coordinated by the IFIP WG5.7 Special Interest group on Service Systems Design, Engineering, and Management. We used the Serbian dataset from 136 manufacturing firms. The results show that 68% and 42% of manufacturing firms use digital technologies for product creation and digital services, respectively. Moreover, results demonstrate products have the 90% of the share in gross annual turnover in manufacturing firms. However, the prediction of the production managers for the next two years shows that services will reach a 30% share in gross annual turnover of firms. KeywordsProduct-service systemsServitizationDigital servicesDigital productsSurvey
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4,581 members
Michele Caponigro
  • ISHTAR Indeterministic Sciences and Historico-philosophical Transdisciplinar Advanced Research Centre
Andrea Greco
  • Department of Human and Social Sciences
Francesca Morganti
  • Department of Human and Social Sciences
Michela Cameletti
  • Department of Management, Economics and Quantitative Methods
via Salvecchio 19, 24129, Bergamo, Italy
Head of institution
Prof. Remo Morzenti Pellegrini
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