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Prof. Riccardo Manzini
Department of Industrial Engineering, University of Bologna—Alma Mater Studiorum, Viale Risorgimento, 2, 40136 Bologna, Italy

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0 Management
0 Packaging
0 control
0 food supply chain

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Journal article
Published: 30 June 2020 in Sustainable Production and Consumption
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The current public and private policies pursuing environmental sustainability targets mandate incisive management of packaging waste, starting with those sectors that use virgin materials most. Food industries and food supply chains adopt huge volumes of plastic crates, cardboard boxes, and wooden boxes as transport packaging, thereby representing a hotspot and an urgent call for scholars and practitioners to address. Whilst wooden and cardboard boxes are disposable solutions, plastic containers can be employed as infinitely reusable and recyclable packages but require complex logistic systems to manage their life cycle. Optimization techniques can be exploited to aid the design and profitability of such complex packaging networks. This paper falls within the scarce literature on the design of pooling networks for reusable containers in the food industry. It proposes a strategic mixed-integer linear programming model to design a closed-loop system from the perspective of the packaging maker responsible for serving a food supply chain. The container's lifespan, i.e. the number of cycles a package can be reused before recycling, represents a crucial aspect to consider when modeling such networks. Incorporating lifespan constraints within the proposed closed-loop network design model is the main novel contribution we provide to the literature. This model is applied to a real-world instance of an Italian package pooler operating with a consortium of large-scale retailers for the distribution of fruits, vegetables, bakery, and meat products. A multi-scenario what-if analysis showcases how the optimal network evolves according to potential variations in the packaging demand, as well as in the container lifespan, demonstrating how to lead packaging makers to the profitability and the long-term sustainability of the closed-loop network.

ACS Style

Riccardo Accorsi; Giulia Baruffaldi; Riccardo Manzini. A closed-loop packaging network design model to foster infinitely reusable and recyclable containers in food industry. Sustainable Production and Consumption 2020, 24, 48 -61.

AMA Style

Riccardo Accorsi, Giulia Baruffaldi, Riccardo Manzini. A closed-loop packaging network design model to foster infinitely reusable and recyclable containers in food industry. Sustainable Production and Consumption. 2020; 24 ():48-61.

Chicago/Turabian Style

Riccardo Accorsi; Giulia Baruffaldi; Riccardo Manzini. 2020. "A closed-loop packaging network design model to foster infinitely reusable and recyclable containers in food industry." Sustainable Production and Consumption 24, no. : 48-61.

Earlycite article
Published: 28 April 2020 in British Food Journal
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Purpose This paper addresses the trade-off between asset investment and food safety in the design of a food catering production plant. It analyses the relationship between the quality decay of cook-warm products, the logistics of the processes and the economic investment in production machines. Design/methodology/approach A weekly cook-warm production plan has been monitored on-field using temperature sensors to estimate the quality decay profile of each product. A multi-objective optimisation model is proposed to (1) minimise the number of resources necessary to perform cooking and packing operations or (2) to maximise the food quality of the products. A metaheuristic simulated annealing algorithm is introduced to solve the model and to identify the Pareto frontier of the problem. Findings The packaging buffers are identified as the bottleneck of the processes. The outcome of the algorithms highlights that a small investment to design bigger buffers results in a significant increase in the quality with a smaller food loss. Practical implications This study models the production tasks of a food catering facility to evaluate their criticality from a food safety perspective. It investigates the tradeoff between the investment cost of resources processing critical tasks and food safety of finished products. Social implications The methodology applies to the design of cook-warm production. Catering companies use cook-warm production to serve school, hospitals and companies. For this reason, the application of this methodology leads to the improvement of the quality of daily meals for a large number of people. Originality/value The paper introduces a new multi-objective function (asset investment vs food quality) proposing an original metaheuristic to address this tradeoff in the food catering industry. Also, the methodology is applied and validated in the design of a new food production facility.

ACS Style

Alessandro Tufano; Riccardo Accorsi; Riccardo Manzini. A simulated annealing algorithm for the allocation of production resources in the food catering industry. British Food Journal 2020, 122, 2139 -2158.

AMA Style

Alessandro Tufano, Riccardo Accorsi, Riccardo Manzini. A simulated annealing algorithm for the allocation of production resources in the food catering industry. British Food Journal. 2020; 122 (7):2139-2158.

Chicago/Turabian Style

Alessandro Tufano; Riccardo Accorsi; Riccardo Manzini. 2020. "A simulated annealing algorithm for the allocation of production resources in the food catering industry." British Food Journal 122, no. 7: 2139-2158.

Journal article
Published: 06 April 2020 in Procedia Manufacturing
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Nowadays, the variety in the product mix, unpredictable customer demand and the need for a high level of service are crucial challenges in the management of a supply chain. Flexible processes are needed to gain competitive advantage and economic edges. This paper presents a data-driven application of unsupervised machine learning clustering algorithms to a real-world case study in the automotive industry. The clustering input dataset collects the data available to a third-party logistics (3PL) provider. Clustering algorithms are used to define product families for the assignment of the workload to the processing resources. Several clustering algorithms (k-means, Gaussian mixture models and hierarchical clustering) define different product families scenarios using different tuning parameters. The impact of each clustering scenario on the operations is assessed via a dashboard of logistics KPIs to identify the best performing clustering algorithm. The performance of each clustering is, then, compared to a logistic benchmark given by a capacitated clustering to identify the best compromise between a logistic-constrained algorithm with a long runtime and fast data-driven uncapacitated algorithm.

ACS Style

Alessandro Tufano; Riccardo Accorsi; Riccardo Manzini. Machine learning methods to improve the operations of 3PL logistics. Procedia Manufacturing 2020, 42, 62 -69.

AMA Style

Alessandro Tufano, Riccardo Accorsi, Riccardo Manzini. Machine learning methods to improve the operations of 3PL logistics. Procedia Manufacturing. 2020; 42 ():62-69.

Chicago/Turabian Style

Alessandro Tufano; Riccardo Accorsi; Riccardo Manzini. 2020. "Machine learning methods to improve the operations of 3PL logistics." Procedia Manufacturing 42, no. : 62-69.

Earlycite article
Published: 09 February 2020 in Business Process Management Journal
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PurposeThird-party logistic providers (3PLs) continuously strive for controlling and improving their performances to gain a competitive advantage. The challenging environment where they operate is affected by high variety in type and number of clients, the inventory mix and the demand profiles they have to meet. Consequently, better understanding the dynamics of warehousing operations and the characteristics of the inventory mix is critical to handle such a complexity.Design/Methodology/approachThis paper proposes a decision-support framework, suited for 3PL warehouse practitioners, that aids to design and implement effective and affordable activities for measuring and improving the warehousing performances. Such goal is pursued by the framework by leading the managers through an initial mapping and diagnosis of the system, then by developing a tailored measurement system to track the performance, paving the way to the identification of the criticalities and the potential improvement scenarios.FindingsThis paper presents a case study on the implementation of the proposed framework at a warehouse of an Italian 3PL provider to introduce a new storage assignment policy and reduce the travelling time for order picking. Furthermore, the paper exemplifies how the framework contributes to enhance the awareness of managers on warehousing operations and the involvement of the personnel throughout the improvement process.Practical implicationThe proposed framework can be implemented by operations managers of 3PL warehouses who want to pursue general performance improvement projects. With respect to the case study, this framework contributes to identify the storage assignment policy that reduces the travelling for order picking in the observed warehouse of 8 percent in a month but is intended to address to even other areas of improvement in 3PL warehousing environments.Originality/valueInstead of focusing on the proper methods and models that optimize a specific task or performance indicator, it provides a general framework that leads the managers through the decisional process, from the preliminary diagnosis of the system, to its benchmarking, towards the implementation of corrective and improving solutions.

ACS Style

Giulia Baruffaldi; Riccardo Accorsi; Riccardo Manzini; Emilio Ferrari. Warehousing process performance improvement: a tailored framework for 3PL. Business Process Management Journal 2020, 26, 1619 -1641.

AMA Style

Giulia Baruffaldi, Riccardo Accorsi, Riccardo Manzini, Emilio Ferrari. Warehousing process performance improvement: a tailored framework for 3PL. Business Process Management Journal. 2020; 26 (6):1619-1641.

Chicago/Turabian Style

Giulia Baruffaldi; Riccardo Accorsi; Riccardo Manzini; Emilio Ferrari. 2020. "Warehousing process performance improvement: a tailored framework for 3PL." Business Process Management Journal 26, no. 6: 1619-1641.

Journal article
Published: 30 June 2019 in Journal of Cleaner Production
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As urbanization gradually modifies natural ecosystems and affects environmental sustainability, urban spatial planning can be used as a tool to address to Urban Metabolism and meet sustainable development targets. The concentration of people in urban areas makes these increasingly requiring for primary products and services as food and energy, and the fulfilment of such needs result in significant carbon emissions. The inclusion of spatial functions as agriculture and renewables in the urban planning can address to this environmental impact, but would require support-planning tools able to explore new land-use allocation strategies within an integrated urban-rural ecosystem. In this paper, we propose an optimization framework for the planning of low carbon urban-rural ecosystems that integrates transport and land-use planning and cope with urban metabolism, involving urban mobility, food transportation, energy supplies. This framework contributes to the literature as it formulates a network between urban, agricultural, energy, and carbon mitigation land-covers and optimizes the horizontal carbon fluxes within an integrated urban-rural environment. In order to minimize carbon emissions by mobility and resources (i.e. food) transportation, the framework aids identifying trade-offs between accessibility and density over the spatial distribution of resource-generating and resource-consuming land-covers. Proof of concept is provided with a realistic numerical example, propelled by real-world data from an Italian region. The land-use allocation solution makes the exemplifying urban-rural ecosystem behaving as carbon sink due to the established green areas and the configuration of the spatial uses. A sensitivity analysis is finally carried out to assess the impacts of mobility and resources transportation on the spatial urban-rural structure and associated carbon emissions. It comes out that the optimal urban configuration to mitigate carbon emissions from transportation integrates urban and rural uses and guarantees accessibility to several functions as cultivated areas, renewables and green covers, responsible to provide food, energy and air cleaning respectively to dwellers.

ACS Style

Stefano Penazzi; Riccardo Accorsi; Riccardo Manzini. Planning low carbon urban-rural ecosystems: An integrated transport land-use model. Journal of Cleaner Production 2019, 235, 96 -111.

AMA Style

Stefano Penazzi, Riccardo Accorsi, Riccardo Manzini. Planning low carbon urban-rural ecosystems: An integrated transport land-use model. Journal of Cleaner Production. 2019; 235 ():96-111.

Chicago/Turabian Style

Stefano Penazzi; Riccardo Accorsi; Riccardo Manzini. 2019. "Planning low carbon urban-rural ecosystems: An integrated transport land-use model." Journal of Cleaner Production 235, no. : 96-111.

Journal article
Published: 04 June 2019 in Sustainability
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Manufacturing, storage, and transportation processes are typically facilitated by pallets, containers, and other reusable transport items (RTIs) designed to guarantee many cycles along a lifespan of several years. As a consequence, both supply and reverse transportation of RTIs need to be managed to avoid stockout along the supply chain and the unsustainable production of new tools from virgin materials. This paper focuses on the business of pallet management by analyzing the transport operations of a pallet pooling network serving a large-scale nationwide retailer. The pooler is responsible for supplying, collecting, and refurbishing pallets. The combination of the pooler’s management strategies with different retailer network configurations results in different pooling scenarios, which are assessed and compared in this paper through a what-if analysis. The logistical and environmental impacts generated by the pallet distribution activities are quantified per each scenario through a tailored software incorporating Geographic Information System (GIS) and routing functionalities. Findings from this analysis suggest how to reduce vehicle distance traveled (vehicles-km) by 65% and pollutant emissions by 60% by combining network infrastructures and pooling management strategies—identifying an empirical best practice for managers of pallet businesses.

ACS Style

Riccardo Accorsi; Giulia Baruffaldi; Riccardo Manzini; Chiara Pini. Environmental Impacts of Reusable Transport Items: A Case Study of Pallet Pooling in a Retailer Supply Chain. Sustainability 2019, 11, 3147 .

AMA Style

Riccardo Accorsi, Giulia Baruffaldi, Riccardo Manzini, Chiara Pini. Environmental Impacts of Reusable Transport Items: A Case Study of Pallet Pooling in a Retailer Supply Chain. Sustainability. 2019; 11 (11):3147.

Chicago/Turabian Style

Riccardo Accorsi; Giulia Baruffaldi; Riccardo Manzini; Chiara Pini. 2019. "Environmental Impacts of Reusable Transport Items: A Case Study of Pallet Pooling in a Retailer Supply Chain." Sustainability 11, no. 11: 3147.

Journal article
Published: 11 March 2019 in Industrial Management & Data Systems
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PurposeThe purpose of this paper is to illustrate an original decision-support tool (DST) that aids 3PL managers to decide on the proper warehouse management system (WMS) customization. The aim of this tool is to address to the three main issues affecting such decision: the cost of the information sharing, the scarce visibility of the client’s data and the uncertainty of quantifying the return from investing into a WMS feature.Design/methodology/approachThe tool behaves as a digital twin of a WMS. In addition, it incorporates a set of WMS’s features based both on heuristics and optimization techniques and uses simulation to perform what-if multi-scenario analyses of alternative management scenarios. In order to validate the effectiveness of the tool, its application to a real-world 3PL warehouse operating in the sector of biomedical products is illustrated.FindingsThe results of a simulation campaign along an observation horizon of ten months demonstrate how the tool supports the comparison of alternative scenarios with theas-is, thereby suggesting the most suitable WMS customization to adopt.Practical implicationsThe tool supports 3PL managers in enhancing the efficiency of the operations and the fulfilling of the required service level, which is increasingly challenging given the large inventory mix and the variable clients portfolio that 3PLs have to manage. Particularly, the choice of the WMS customization that better perform with each business can be problematic, given the scarce information visibility of the provider on the client’s processes.Originality/valueTo the author’s knowledge, this paper is among the first to address a still uncovered gap of the warehousing literature by illustrating a DST that exploits optimization and simulation techniques to quantify the impacts of the information availability on the warehousing operations performance. As a second novel contribution, this tool enables to create a digital twin of a WMS and foresee the evolution of the warehouse’s performance over time.

ACS Style

Giulia Baruffaldi; Riccardo Accorsi; Riccardo Manzini. Warehouse management system customization and information availability in 3pl companies. Industrial Management & Data Systems 2019, 119, 251 -273.

AMA Style

Giulia Baruffaldi, Riccardo Accorsi, Riccardo Manzini. Warehouse management system customization and information availability in 3pl companies. Industrial Management & Data Systems. 2019; 119 (2):251-273.

Chicago/Turabian Style

Giulia Baruffaldi; Riccardo Accorsi; Riccardo Manzini. 2019. "Warehouse management system customization and information availability in 3pl companies." Industrial Management & Data Systems 119, no. 2: 251-273.

Journal article
Published: 01 January 2019 in Procedia Manufacturing
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ACS Style

Francesca Calabrese; Mauro Gamberi; Giovanni Lelli; Riccardo Manzini; Francesco Pilati; Alberto Regattieri. Optimal Operations Management of Hybrid Energy Systems Through Short-Term Atmospheric and Demand Forecasts. Procedia Manufacturing 2019, 39, 702 -711.

AMA Style

Francesca Calabrese, Mauro Gamberi, Giovanni Lelli, Riccardo Manzini, Francesco Pilati, Alberto Regattieri. Optimal Operations Management of Hybrid Energy Systems Through Short-Term Atmospheric and Demand Forecasts. Procedia Manufacturing. 2019; 39 ():702-711.

Chicago/Turabian Style

Francesca Calabrese; Mauro Gamberi; Giovanni Lelli; Riccardo Manzini; Francesco Pilati; Alberto Regattieri. 2019. "Optimal Operations Management of Hybrid Energy Systems Through Short-Term Atmospheric and Demand Forecasts." Procedia Manufacturing 39, no. : 702-711.

Original article
Published: 27 November 2018 in The International Journal of Advanced Manufacturing Technology
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The aim of this paper is to introduce a practice-ready systematic methodology for the management of storage assignments and allocation decisions as well as an assessment of the resulting performance in an order picking system (OPS). Built on extant and well-known metrics of performance this method implements a double cross-analysis through an original visual tool that is easy to understand by warehousing managers and practitioners. This tool is organized in two main steps. The first step is a cross-analysis that combines multiple performance indicators to help the decision-maker understand whether an OPS provides the scope for performance improvement. A comparison with potential storage configurations is then conducted in the second step through a tailored multi-scenario cross-analysis, which attempts to identify the best combination of allocation and assignment policies capable of minimizing the overall traveling performance. The proposed methodology is applied to a significant real-world OPS. The selected case study represents a reference framework for decision-makers and practitioners.

ACS Style

Riccardo Manzini; Riccardo Accorsi; Giulia Baruffaldi; Daniele Santi; Alessandro Tufano. Performance assessment in order picking systems: a visual double cross-analysis. The International Journal of Advanced Manufacturing Technology 2018, 101, 1927 -1938.

AMA Style

Riccardo Manzini, Riccardo Accorsi, Giulia Baruffaldi, Daniele Santi, Alessandro Tufano. Performance assessment in order picking systems: a visual double cross-analysis. The International Journal of Advanced Manufacturing Technology. 2018; 101 (5-8):1927-1938.

Chicago/Turabian Style

Riccardo Manzini; Riccardo Accorsi; Giulia Baruffaldi; Daniele Santi; Alessandro Tufano. 2018. "Performance assessment in order picking systems: a visual double cross-analysis." The International Journal of Advanced Manufacturing Technology 101, no. 5-8: 1927-1938.

Journal article
Published: 27 September 2018 in Computers in Industry
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Every day, thousands of pupils, students, employees, and hospital patients eat food outside their homes that is cooked far from the place of consumption. The food service industry is responsible for supplying this food to schools, hospitals, nurseries, as well as to company canteens. The design, control, and management of food service operations is challenging given the complexity of such multiple facility production networks and entails multidisciplinary perspectives and competences. Both production and logistics operations play crucial roles and significantly affect the service performance as long as food products are prepared within a facility, and as long as they are distributed to multiple consumption sites. Hence, there are many planning decisions (e.g. the definition of the production facility location, the allocation of task to resources and the scheduling of production jobs), that are handled at different stages by different actors, who often decide based on their own practical experience and barely adopt integrated decision-support systems. A review of the literature shows that there is no integrated approach to support the design of food service production facilities, known as centralized kitchens (CEKIs). To facilitate such integration and assist food service managers to adopt quantitative and data-driven design approaches, this study proposes an original computer-based multidisciplinary decision-support tool for the design and configuration of a CEKI. The proposed tool aids decisions taken by multiple actors simultaneously through a set of interfaces driven by quantitative data that follow the logistical flow of materials throughout the CEKI (1), assesses performance indicators in a multidisciplinary dashboard (2), and implements what-if, multiple scenario analyses based on simulations (3). Graphical interfaces are designed to facilitate communication between the decision makers and the integration of data-driven analyses. The design of a new CEKI is used as a testbed for the decision-support tool. The real-world example highlights the interdependencies between issues and decisions and showcases how computer applications facilitate decision-making and improve communication between managers.

ACS Style

Alessandro Tufano; Riccardo Accorsi; Federica Garbellini; Riccardo Manzini. Plant design and control in food service industry. A multi-disciplinary decision-support system. Computers in Industry 2018, 103, 72 -85.

AMA Style

Alessandro Tufano, Riccardo Accorsi, Federica Garbellini, Riccardo Manzini. Plant design and control in food service industry. A multi-disciplinary decision-support system. Computers in Industry. 2018; 103 ():72-85.

Chicago/Turabian Style

Alessandro Tufano; Riccardo Accorsi; Federica Garbellini; Riccardo Manzini. 2018. "Plant design and control in food service industry. A multi-disciplinary decision-support system." Computers in Industry 103, no. : 72-85.

Journal article
Published: 29 August 2018 in Journal of Cleaner Production
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The agro-food industry is one of the largest parts of the European Union's economy and faces economic and environmental stresses. While food traceability systems (FTSs) inform supply chain actors of product and logistical attributes, large scale implementations are scarce and are do not support active decision making. We present a framework developed for FUTUREMED project used to perform a data-driven analysis that considers both micro and macro aspects of a food supply chain (FSC). With its comprehensive multiple-depth data architecture incorporated within a tailored decision-support platform, this framework and the resulting decision-support tool is the first to move beyond simple traceability implementation to the sustainable planning of food logistics, bridging the gap between research techniques and real-world data availability. We define KPIs that measure a subset of economic and environmental factors to quantify the impact of logistical decisions. We validate the framework with the case study of an Italian fruit trader that is considering opening a new warehouse. We conclude by suggesting that this framework be applied to more complex case studies and be enhanced through including more dimensions of sustainability.

ACS Style

Riccardo Accorsi; Susan Cholette; Riccardo Manzini; Alessandro Tufano. A hierarchical data architecture for sustainable food supply chain management and planning. Journal of Cleaner Production 2018, 203, 1039 -1054.

AMA Style

Riccardo Accorsi, Susan Cholette, Riccardo Manzini, Alessandro Tufano. A hierarchical data architecture for sustainable food supply chain management and planning. Journal of Cleaner Production. 2018; 203 ():1039-1054.

Chicago/Turabian Style

Riccardo Accorsi; Susan Cholette; Riccardo Manzini; Alessandro Tufano. 2018. "A hierarchical data architecture for sustainable food supply chain management and planning." Journal of Cleaner Production 203, no. : 1039-1054.

Journal article
Published: 08 November 2017 in Sustainability
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Modern food production-distribution processes represent a critical stressor for the environment and for natural ecosystems. The rising flows of food across growing and consumption areas couple with the higher expectations of consumers for the quality of products and compel the intensive use of refrigerated rooms and transport means throughout the food supply chain. In order to aid the design of sustainable cold chains that incorporate such aspects, this paper proposes a mixed integer linear programming model to minimize the total energy consumption associated with the cold operations experienced by perishable products. This model is intended for food traders, logistics practitioners, retail managers, and importers collaboratively called to design and plan a cost and environmentally effective supply strategy, physical channels, and infrastructures for cold chains. The proposed model is validated with a case study inspired by the distribution of two example food products, namely fresh apples and ice cream, along the New Silk Road connecting Europe and China. The illustrated analysis investigates the effect of alternative routes and transport modes on the sustainability of the cold chain. It is found that the most energy-efficient route for ice cream is via rail over a northern route and, for apples, is via a southern maritime route, and, for these two routes, the ratios of the total energy consumed to the energy content of the food are 760 and 913, respectively. By incorporating the energy lost due to the food quality decay, the model identifies the optimal route to adopt in accordance with the shelf life and the conservation temperature of each product.

ACS Style

Andrea Gallo; Riccardo Accorsi; Giulia Baruffaldi; Riccardo Manzini. Designing Sustainable Cold Chains for Long-Range Food Distribution: Energy-Effective Corridors on the Silk Road Belt. Sustainability 2017, 9, 2044 .

AMA Style

Andrea Gallo, Riccardo Accorsi, Giulia Baruffaldi, Riccardo Manzini. Designing Sustainable Cold Chains for Long-Range Food Distribution: Energy-Effective Corridors on the Silk Road Belt. Sustainability. 2017; 9 (11):2044.

Chicago/Turabian Style

Andrea Gallo; Riccardo Accorsi; Giulia Baruffaldi; Riccardo Manzini. 2017. "Designing Sustainable Cold Chains for Long-Range Food Distribution: Energy-Effective Corridors on the Silk Road Belt." Sustainability 9, no. 11: 2044.

Journal article
Published: 01 November 2017 in Journal of Cleaner Production
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ACS Style

Riccardo Accorsi; Andrea Gallo; Riccardo Manzini. A climate driven decision-support model for the distribution of perishable products. Journal of Cleaner Production 2017, 165, 917 -929.

AMA Style

Riccardo Accorsi, Andrea Gallo, Riccardo Manzini. A climate driven decision-support model for the distribution of perishable products. Journal of Cleaner Production. 2017; 165 ():917-929.

Chicago/Turabian Style

Riccardo Accorsi; Andrea Gallo; Riccardo Manzini. 2017. "A climate driven decision-support model for the distribution of perishable products." Journal of Cleaner Production 165, no. : 917-929.

Journal article
Published: 18 September 2017 in Procedia Manufacturing
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Complex production systems may count thousands of parts and components, subjected to multiple physical and logical connections and interdependencies. This level of complexity inhibits the traditional and statistically-based approach to reliability engineering, failure prediction and maintenance planning. The existing ICT solutions simplify the on-field collection of large amount of data, but require models and tools able to create knowledge from these data. Key questions on how to predict in advance the performance of the production system and the associated failure events could be finally addressed. This paper introduces a set of data analytics models and methods that can be profitably used for decision making in general, and, specifically, in maintenance engineering. These classification models, specifically decision trees, random forests, and neural networks, are applied to a real-world case study, and the resulting accuracy on predicting faults is quantified and compared. We used the historical profiles of the energy variables of an high-speed packaging machine to find out some strategies for the prediction of a given failure. The conducted experiments demonstrate that the accuracy of the random forest is slightly better than the other methods, but even increases the probability of false alarm, which would result in unwanted production break-down. Even though the obtained results are promising, they leave room for further experiments based on the application of other classifiers, rather than the definition of customized methods able to embrace such complexity.

ACS Style

Riccardo Accorsi; Riccardo Manzini; Pietro Pascarella; Marco Patella; Simone Sassi. Data Mining and Machine Learning for Condition-based Maintenance. Procedia Manufacturing 2017, 11, 1153 -1161.

AMA Style

Riccardo Accorsi, Riccardo Manzini, Pietro Pascarella, Marco Patella, Simone Sassi. Data Mining and Machine Learning for Condition-based Maintenance. Procedia Manufacturing. 2017; 11 ():1153-1161.

Chicago/Turabian Style

Riccardo Accorsi; Riccardo Manzini; Pietro Pascarella; Marco Patella; Simone Sassi. 2017. "Data Mining and Machine Learning for Condition-based Maintenance." Procedia Manufacturing 11, no. : 1153-1161.

Journal article
Published: 14 August 2017 in The International Journal of Logistics Management
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PurposeThe food processing industry is growing with retail and catering supply chains. With the rising complexity of food products and the need to address food customization expectations, food processing systems are progressively shifting from production line to job-shops that are characterized by high flexibility and high complexity. A food job-shop system processes multiple items (i.e., raw ingredients, toppings, dressings) according to their working cycles in a typical resource and capacity constrained environment. Given the complexity of such systems, there are divergent goals of process cost optimization and of food quality and safety preservation. These goals deserve integration at both an operational and a strategic decisional perspective. The twofold aim of this paper is to design a simulation model for food job-shop processing and to build the understanding of the extant relationships between food flows and processing equipment through a real case study from the catering industry.Design/methodology/approachWe designed a simulation tool enabling the analysis of food job-shop processing systems. A methodology based on discrete event simulation (DES) is developed to study the dynamics and behaviour of the processing systems according to an event-driven approach. The proposed conceptual model builds upon a comprehensive set of variables and key performance indicators (KPIs) that describe and measure the dynamics of the food job-shop according to a multi-disciplinary perspective.FindingsThis simulation identifies the job-shop bottlenecks and investigates the utilization of the working centres and product queuing through the system. This approach helps to characterize how costs are allocated in a flow-driven approach and identifies the trade-off between investments in equipment and operative costs.Originality/valueThe primary purpose of the proposed model relies on the definition of standard resources and operating patterns that can meet the behaviour of a wide variety of food processing equipment and tasks, thereby addressing the complexity of a food job-shop. The proposed methodology enables the integration of strategic and operative decisions between several company departments. The KPIs enable identification of the benchmark system, tracking the system performance via multi-scenario what-if simulations, and suggesting improvements through short-term (e.g., tasks scheduling, dispatching rules), mid-term (e.g., recipes review), or long-term (e.g., re-layout, working centres number) levers.

ACS Style

Stefano Penazzi; Riccardo Accorsi; Emilio Ferrari; Riccardo Manzini; Simon Dunstall. Design and control of food job-shop processing systems. The International Journal of Logistics Management 2017, 28, 782 -797.

AMA Style

Stefano Penazzi, Riccardo Accorsi, Emilio Ferrari, Riccardo Manzini, Simon Dunstall. Design and control of food job-shop processing systems. The International Journal of Logistics Management. 2017; 28 (3):782-797.

Chicago/Turabian Style

Stefano Penazzi; Riccardo Accorsi; Emilio Ferrari; Riccardo Manzini; Simon Dunstall. 2017. "Design and control of food job-shop processing systems." The International Journal of Logistics Management 28, no. 3: 782-797.

Journal article
Published: 27 July 2017 in International Journal of Logistics Research and Applications
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ACS Style

Riccardo Accorsi; Giulia Baruffaldi; Riccardo Manzini; Alessandro Tufano. On the design of cooperative vendors’ networks in retail food supply chains: a logistics-driven approach. International Journal of Logistics Research and Applications 2017, 21, 35 -52.

AMA Style

Riccardo Accorsi, Giulia Baruffaldi, Riccardo Manzini, Alessandro Tufano. On the design of cooperative vendors’ networks in retail food supply chains: a logistics-driven approach. International Journal of Logistics Research and Applications. 2017; 21 (1):35-52.

Chicago/Turabian Style

Riccardo Accorsi; Giulia Baruffaldi; Riccardo Manzini; Alessandro Tufano. 2017. "On the design of cooperative vendors’ networks in retail food supply chains: a logistics-driven approach." International Journal of Logistics Research and Applications 21, no. 1: 35-52.

Journal article
Published: 01 June 2017 in Food Packaging and Shelf Life
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ACS Style

Riccardo Manzini; Riccardo Accorsi; Francesco Piana; Alberto Regattieri. Accelerated life testing for packaging decisions in the edible oils distribution. Food Packaging and Shelf Life 2017, 12, 114 -127.

AMA Style

Riccardo Manzini, Riccardo Accorsi, Francesco Piana, Alberto Regattieri. Accelerated life testing for packaging decisions in the edible oils distribution. Food Packaging and Shelf Life. 2017; 12 ():114-127.

Chicago/Turabian Style

Riccardo Manzini; Riccardo Accorsi; Francesco Piana; Alberto Regattieri. 2017. "Accelerated life testing for packaging decisions in the edible oils distribution." Food Packaging and Shelf Life 12, no. : 114-127.

Original article
Published: 06 March 2017 in The International Journal of Advanced Manufacturing Technology
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Warehouse building design tackles the industrial issue of best studying the overall configuration and dimensions of the storage systems reaching one or more predefined target of performance. This paper proposes a multi-objective model for the warehouse building design to minimize the cycle time, the total cost, and the carbon footprint of the storage system over its lifetime. The goal is to define the overall building dimensions, addressing the target storage capacity and the handling performances, balancing the aforementioned three objective functions. The cycle time computes the average duration of the pickup and drop-off activities, while the system total cost and carbon footprint rise over the entire warehouse lifetime, including the installation and operating phases. The developed model is applied to design the warehouse for an Italian food and beverage company. Results highlight that the total cost and the carbon footprint functions lead to similar warehouse configurations distinguished by a compact vertical structure. On the contrary, the cycle time function takes advantage of a flatter and wider building even if a dramatic increase of the environmental (+40%) and cost (+10%) objective functions occurs. The proposed best balance solution limits the total cost and carbon footprint increment below 1% compared to their single-objective optima, while the cycle time worsening is limited to 4% compared to the optimal cycle time solution.

ACS Style

Riccardo Accorsi; Marco Bortolini; Mauro Gamberi; Riccardo Manzini; Francesco Pilati. Multi-objective warehouse building design to optimize the cycle time, total cost, and carbon footprint. The International Journal of Advanced Manufacturing Technology 2017, 92, 839 -854.

AMA Style

Riccardo Accorsi, Marco Bortolini, Mauro Gamberi, Riccardo Manzini, Francesco Pilati. Multi-objective warehouse building design to optimize the cycle time, total cost, and carbon footprint. The International Journal of Advanced Manufacturing Technology. 2017; 92 (1-4):839-854.

Chicago/Turabian Style

Riccardo Accorsi; Marco Bortolini; Mauro Gamberi; Riccardo Manzini; Francesco Pilati. 2017. "Multi-objective warehouse building design to optimize the cycle time, total cost, and carbon footprint." The International Journal of Advanced Manufacturing Technology 92, no. 1-4: 839-854.

Journal article
Published: 01 March 2017 in Applied Energy
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ACS Style

Matteo M. Savino; Riccardo Manzini; Vincenzo Della Selva; Riccardo Accorsi. A new model for environmental and economic evaluation of renewable energy systems: The case of wind turbines. Applied Energy 2017, 189, 739 -752.

AMA Style

Matteo M. Savino, Riccardo Manzini, Vincenzo Della Selva, Riccardo Accorsi. A new model for environmental and economic evaluation of renewable energy systems: The case of wind turbines. Applied Energy. 2017; 189 ():739-752.

Chicago/Turabian Style

Matteo M. Savino; Riccardo Manzini; Vincenzo Della Selva; Riccardo Accorsi. 2017. "A new model for environmental and economic evaluation of renewable energy systems: The case of wind turbines." Applied Energy 189, no. : 739-752.

Book chapter
Published: 17 February 2017 in Sustainability Challenges in the Agrofood Sector
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The food industry is facing new challenges merging the need to scale up the business to a global scale, and address the requirements in terms of quality and environmental sustainability of the products. Measuring the carbon emissions associated with the modern agrofood supply chains is a best practice to find out solutions or operations improvements against climate change. This chapter assesses the environmental impacts associated with the food supply chain of an Italian large-scale retailer, considering two alternative configurations of the logistics and distribution network. The results illustrate the benefits of locating three distribution centres, respectively for frozen, fresh and dry products, between the suppliers and the regional warehouses that supply the markets. The chapter illustrates how the establishment of these intermediate hubs allows the retailer to reduce the carbon emission from distribution activities to 27% of what they originally were, coupling the cost reduction and the service level improvement with relevant environmental savings.

ACS Style

Riccardo Accorsi; Riccardo Manzini; Chiara Pini. How Logistics Decisions Affect the Environmental Sustainability of Modern Food Supply Chains. Sustainability Challenges in the Agrofood Sector 2017, 175 -196.

AMA Style

Riccardo Accorsi, Riccardo Manzini, Chiara Pini. How Logistics Decisions Affect the Environmental Sustainability of Modern Food Supply Chains. Sustainability Challenges in the Agrofood Sector. 2017; ():175-196.

Chicago/Turabian Style

Riccardo Accorsi; Riccardo Manzini; Chiara Pini. 2017. "How Logistics Decisions Affect the Environmental Sustainability of Modern Food Supply Chains." Sustainability Challenges in the Agrofood Sector , no. : 175-196.