Analisi delle preferenze per le diverse categorie di pomodoro trasformato nel food service

Il pomodoro è una delle colture principali del panorama agro-alimentare italiano e rappresenta un ingrediente base della tradizione culinaria nazionale. Il pomodoro lavorato dall’industria conserviera può essere trasformato in diverse tipologie merceologiche, che si differenziano in base alla tecniche di lavorazione impiegate ed alle caratteristiche del prodotto finito. la percentuale di spesa totale destinata all’acquisto di cibo fuori casa è in aumento a livello globale e l’interesse dell’industria alimentare nei confronti di questo canale di vendita è quindi crescente. Mentre sono numerose le indagine in letteratura che studiano i processi di acquisto dei consumatori finali, non ci sono evidenze di studi simili condotti sugli operatori del Food Service. Obiettivo principale della ricerca è quello di valutare le preferenze dei responsabili acquisti del settore Food Service per diverse tipologie di pomodoro trasformato, in relazione ad una gamma di attributi rilevanti del prodotto e di caratteristiche del cliente. La raccolta dei dati è avvenuta attraverso un esperimento di scelta ipotetico realizzato in Italia e alcuni mercati esteri. Dai risultati ottenuti dall’indagine emerge che i Pelati sono la categoria di pomodoro trasformato preferita dai responsabili degli acquisti del settore Food Service intervistati, con il 35% delle preferenze dichiarate nell'insieme dei contesti di scelta proposti, seguita dalla Polpa (25%), dalla Passata (20%) e dal Concentrato (15%). Dai risultati ottenuti dalla stima del modello econometrico Logit a parametri randomizzati è emerso che alcuni attributi qualitativi di fiducia (credence), spesso impiegati nelle strategie di differenziazione e posizionamento da parte dell’industria alimentare nel mercato Retail, possono rivestire un ruolo importante anche nell’influenzare le preferenze degli operatori del Food Service. Questo potrebbe quindi essere un interessante filone di ricerca da sviluppare nel futuro, possibilmente con l'impiego congiunto di metodologie di analisi basate su esperimenti di scelta ipotetici e non ipotetici.

Edge and Big Data technologies for Industry 4.0 to create an integrated pre-sale and after-sale environment

The fourth industrial revolution, also known as Industry 4.0, has rapidly gained traction in businesses across Europe and the world, becoming a central theme in small, medium, and large enterprises alike. This new paradigm shifts the focus from locally-based and barely automated firms to a globally interconnected industrial sector, stimulating economic growth and productivity, and supporting the upskilling and reskilling of employees. However, despite the maturity and scalability of information and cloud technologies, the support systems already present in the machine field are often outdated and lack the necessary security, access control, and advanced communication capabilities. This dissertation proposes architectures and technologies designed to bridge the gap between Operational and Information Technology, in a manner that is non-disruptive, efficient, and scalable. The proposal presents cloud-enabled data-gathering architectures that make use of the newest IT and networking technologies to achieve the desired quality of service and non-functional properties. By harnessing industrial and business data, processes can be optimized even before product sale, while the integrated environment enhances data exchange for post-sale support. The architectures have been tested and have shown encouraging performance results, providing a promising solution for companies looking to embrace Industry 4.0, enhance their operational capabilities, and prepare themselves for the upcoming fifth human-centric revolution.

An architecture for network acceleration as a service in the cloud continuum

The pervasive availability of connected devices in any industrial and societal sector is pushing for an evolution of the well-established cloud computing model. The emerging paradigm of the cloud continuum embraces this decentralization trend and envisions virtualized computing resources physically located between traditional datacenters and data sources. By totally or partially executing closer to the network edge, applications can have quicker reactions to events, thus enabling advanced forms of automation and intelligence. However, these applications also induce new data-intensive workloads with low-latency constraints that require the adoption of specialized resources, such as high-performance communication options (e.g., RDMA, DPDK, XDP, etc.). Unfortunately, cloud providers still struggle to integrate these options into their infrastructures. That risks undermining the principle of generality that underlies the cloud computing scale economy by forcing developers to tailor their code to low-level APIs, non-standard programming models, and static execution environments. This thesis proposes a novel system architecture to empower cloud platforms across the whole cloud continuum with Network Acceleration as a Service (NAaaS). To provide commodity yet efficient access to acceleration, this architecture defines a layer of agnostic high-performance I/O APIs, exposed to applications and clearly separated from the heterogeneous protocols, interfaces, and hardware devices that implement it. A novel system component embodies this decoupling by offering a set of agnostic OS features to applications: memory management for zero-copy transfers, asynchronous I/O processing, and efficient packet scheduling. This thesis also explores the design space of the possible implementations of this architecture by proposing two reference middleware systems and by adopting them to support interactive use cases in the cloud continuum: a serverless platform and an Industry 4.0 scenario. A detailed discussion and a thorough performance evaluation demonstrate that the proposed architecture is suitable to enable the easy-to-use, flexible integration of modern network acceleration into next-generation cloud platforms.