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This paper presents a novel lean tool called Project Time Deployment (PTD) whose objectives are to classify, analyse, and eliminate losses in order to reduce production lead time in Engineer-to-Order (ETO) environments. By combining two already known approaches, i.e. the Manufacturing Critical-path Time and the Manufacturing Cost Deployment, PTD identifies the critical losses affecting the project, focusing on the business processes where causal losses occur, and providing opportunities for greater efficiency and effectiveness by reducing or even eliminating them. In ETO projects, the lead time and respecting deadlines are of paramount importance and they are often threatened by several different losses that are difficult to compare. Companies thus need a tool to identify the losses and the tasks where they occur, quantifying them in terms of a single dimension: the time. PTD was designed using a methodology based on four steps: analysis of current solutions, concept design and prototype, proof of concept and validation, and definition of future researches. It was also validated in an industrial implementation concerning an Engineering, Procurement & Construction company operating in the steel industry, and led to an approximate 24% reduction in lead time.
Massimo Bertolini; Marcello Braglia; Leonardo Marrazzini; Mattia Neroni. Project Time Deployment: a new lean tool for losses analysis in Engineer-to-Order production environments. International Journal of Production Research 2021, 1 -18.
AMA StyleMassimo Bertolini, Marcello Braglia, Leonardo Marrazzini, Mattia Neroni. Project Time Deployment: a new lean tool for losses analysis in Engineer-to-Order production environments. International Journal of Production Research. 2021; ():1-18.
Chicago/Turabian StyleMassimo Bertolini; Marcello Braglia; Leonardo Marrazzini; Mattia Neroni. 2021. "Project Time Deployment: a new lean tool for losses analysis in Engineer-to-Order production environments." International Journal of Production Research , no. : 1-18.
Machine Learning (ML) is a branch of artificial intelligence that studies algorithms able to learn autonomously, directly from the input data. Over the last decade, ML techniques have made a huge leap forward, as demonstrated by Deep Learning (DL) algorithms implemented by autonomous driving cars, or by electronic strategy games. Hence, researchers have started to consider ML also for applications within the industrial field, and many works indicate ML as one the main enablers to evolve a traditional manufacturing system up to the Industry 4.0 level. Nonetheless, industrial applications are still few and limited to a small cluster of international companies. This paper deals with these topics, intending to clarify the real potentialities, as well as potential flaws, of ML algorithms applied to operation management. A comprehensive review is presented and organized in a way that should facilitate the orientation of practitioners in this field. To this aim, papers from 2000 to date are categorized in terms of the applied algorithm and application domain, and a keyword analysis is also performed, to details the most promising topics in the field. What emerges is a consistent upward trend in the number of publications, with a spike of interest for unsupervised and especially deep learning techniques, which recorded a very high number of publications in the last five years. Concerning trends, along with consolidated research areas, recent topics that are growing in popularity were also discovered. Among these, the main ones are production planning and control and defect analysis, thus suggesting that in the years to come ML will become pervasive in many fields of operation management.
Massimo Bertolini; Davide Mezzogori; Mattia Neroni; Francesco Zammori. Machine Learning for industrial applications: A comprehensive literature review. Expert Systems with Applications 2021, 175, 114820 .
AMA StyleMassimo Bertolini, Davide Mezzogori, Mattia Neroni, Francesco Zammori. Machine Learning for industrial applications: A comprehensive literature review. Expert Systems with Applications. 2021; 175 ():114820.
Chicago/Turabian StyleMassimo Bertolini; Davide Mezzogori; Mattia Neroni; Francesco Zammori. 2021. "Machine Learning for industrial applications: A comprehensive literature review." Expert Systems with Applications 175, no. : 114820.
The paper deals with cycle time calculation of Automated Storage and Retrieval Systems (AS/RS). Cycle time has a high impact on the operating performance of an AS/RS, and its knowledge is essential, both at the operational and design level. The novelty of the paper concerns the peculiar kind of system that is considered, as the focus is on the Shuttle-Lift-Crane AS/RS. This solution, common in the steel sector, is used to store bundles of long metal bars, which are automatically handled by cranes, lifts, and shuttles. The functioning of these machines, which can operate in parallel and independently, is stochastically modeled, and the probability distribution function of the cycle time is computed, both for single and dual command cycles. The model, assessed via discrete event simulation, assures a high average accuracy of 96% and 98%, under single and dual command cycles, respectively.
Francesco Zammori; Mattia Neroni; Davide Mezzogori. Cycle time calculation of shuttle-lift-crane automated storage and retrieval system. IISE Transactions 2021, 1 -31.
AMA StyleFrancesco Zammori, Mattia Neroni, Davide Mezzogori. Cycle time calculation of shuttle-lift-crane automated storage and retrieval system. IISE Transactions. 2021; ():1-31.
Chicago/Turabian StyleFrancesco Zammori; Mattia Neroni; Davide Mezzogori. 2021. "Cycle time calculation of shuttle-lift-crane automated storage and retrieval system." IISE Transactions , no. : 1-31.
In recent years, the diffusion of automated warehouses in different industrial sectors has fostered the design of more complex automated storages and handling solutions. These circumstances, from a technological point of view, have led to the development of automated warehouses that are very different from the classic pallet Automated Storage and Retrieval Systems (AS/RS), both in terms of design and operating logic. A context in which these solutions have spread is the steel sector. Warehouses with innovative layouts and operating logics have been designed to move metal bundles of different sizes, weights and quality levels, instead of standard, interchangeable stock keeping units. Moreover, picking is often not allowed in these warehouses, due to the configuration of the loading units. In this work we propose a meta-heuristic algorithm based on the Simulated Annealing (SA) procedure, which aims to optimize performance during the retrieving phase of an automated warehouse for metal bundles. The algorithm translates the customers’ requests, expressed in terms of item code, quality and weight into a list of jobs. The goal is to optimize the retrieving performance, measured in missions per hour, minimizing the deviations in quality and weight between customer request and the material retrieved. For the validation, a simulation model of an existing warehouse has been created and the performance of the algorithm tested on the simulation model has been compared with the current performance of the warehouse.
Massimo Bertolini; Giovanni Esposito; Davide Mezzogori; Mattia Neroni. Optimizing Retrieving Performance of an Automated Warehouse for Unconventional Stock Keeping Units. Procedia Manufacturing 2019, 39, 1681 -1690.
AMA StyleMassimo Bertolini, Giovanni Esposito, Davide Mezzogori, Mattia Neroni. Optimizing Retrieving Performance of an Automated Warehouse for Unconventional Stock Keeping Units. Procedia Manufacturing. 2019; 39 ():1681-1690.
Chicago/Turabian StyleMassimo Bertolini; Giovanni Esposito; Davide Mezzogori; Mattia Neroni. 2019. "Optimizing Retrieving Performance of an Automated Warehouse for Unconventional Stock Keeping Units." Procedia Manufacturing 39, no. : 1681-1690.