Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
Session Overview
Session
SES 7.5: Smart Factories and Industrial IoT
Time:
Wednesday, 28/Jun/2017:
4:30pm - 5:50pm

Session Chair: Americo Lopes Azevedo
Location: Aula Q (first floor)

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Presentations

319. Development of IOT-based Reconfigurable Manufacturing System to solve Reconfiguration Planning Problem

Kezia Amanda Kurniadi, Kwangyeol Ryu

Pusan National University, Korea, Republic of (South Korea)

Reconfigurable Manufacturing System (RMS) appeared as a solution to high variation in customer demands allowing manufacturers to satisfy different amount of demands in each single period. In RMS, the system satisfies demands by reconfiguring the machines exactly when and where needed by adding and removing machines whose number depends on the demand of every single period. The reconfiguration process brings a critical issue within the RMS that is called as reconfiguration planning problem (RPP) in this paper. However, with the rise of Internet of Things (IoT) that has been a global issue, many companies and manufacturers are trying to integrate it into their smart systems. RMS as well needs to apply IoT in order to establish the internetworking between machines and the logic, so that RPP can be solved, automated, and controlled. This paper addresses the importance of the integration of IoT into RMS and presents the development of mathematical model to solve RPP in order to save reconfiguration time, cost, and effort. The result of the proposed idea is validated by using simulation software.


17. Benchmarking of tools for User eXperience analysis in Industry 4.0

Margherita Peruzzini, Fabio Grandi, Marcello Pellicciari

University of Modena and Reggio Emilia, Italy

Industry 4.0 paradigm is based on systems communication and cooperation with each other and with humans in real time to improve process performances in terms of productivity, security, energy efficiency, and cost. Although industrial processes are more and more automated, human performance is still the main responsible for product quality and factory productivity. In this context, understanding how workers interact with production systems and how they experience the factory environment is fundamental to properly model the human interaction and optimize the processes. This research investigates the available technologies to monitor the user experience (UX) and defines a set of tools to be applied in the Industry 4.0 scenario to assure the workers’ wellbeing, safety and satisfaction and improve the overall factory performance.


306. An application of Industry 4.0 to the production of packaging films

Pierpaolo Caricato, Antonio Grieco

Università del Salento, Italy

The “Piano Nazionale Industria 4.0”, the Italian plan for the adoption of the Industry 4.0 paradigm by the Italian manufacturing system, indicates a set of enabling technologies that must be used to be able to achieve the rewards that such paradigm promises. Advanced manufacturing solutions and Big Data and analytics are among them. 

We present an application of these enabling technologies to the production of packing films, showing the results of the application of such techniques to a real case in this sector.

The production planning issues that are addressed often include contrasting objectives and strategies: on one hand the legitimate requirement to provide the customers with an effective service, on the other hand the need to efficiently use the production capacity. These two drivers often lead to opposite directions when a decision must be taken.

The usage of the presented Advanced Planning and Scheduling (APS) tool allows the decision maker to rapidly generate a wide range of different scenarios for the production planning problem at hand, that are obtained automatically varying the weight of the different drivers defined by the user. The vast amount of different results is then analyzed and presented to the decision maker, using advanced data analytics techniques in order to put him/her in the condition to rapidly take an aware and solidly supported decision.

We introduce the main aspects of the production planning addressed by the presented tool, with an insight in the artificial intelligence techniques used to represent its constraints and its objectives. We then show how different scenarios can be built for the same problem by varying the importance given to the main defined strategies, namely: meeting the customers’ deadlines, efficiently using the available production capacity, minimizing the stock costs. Finally, we illustrate how the usage of an effective and reasonably compact representation of the results can rapidly allow the user to take conscious decisions that lead to a well-balanced trade-off between the pursued contrasting objectives.


203. An Industry 4.0 case study in fashion manufacturing

Antonio Grieco1, Pierpaolo Caricato1, Doriana Gianfreda1, Matteo Pesce2, Valeria Rigon2, Luca Tregnaghi2, Adriano Voglino2

1Università del Salento, Italy; 2Bottega Veneta srl, Montebello Vicentino (VI), Italy

The “Piano Nazionale Industria 4.0”, the Italian plan for the adoption of the Industry 4.0 paradigm by the Italian manufacturing system, indicates a set of enabling technologies that must be used to be able to achieve the rewards that such paradigm promises. Advanced manufacturing solutions, simulation, horizontal/vertical integration and Big Data and analytics are among them.

We present an application of these enabling technologies to Bottega Veneta, an Italian luxury goods house renowned in the world for its leather goods. In particular, we address the production process, which is distributed across several elements of the supply chain and the relative the management issues.

We show how the integration among the different entities in the supply chain and the interoperability of systems within each entity leads to the availability of a large set of data and information, that can be effectively used to feed data analytics systems such as decision support systems. These data are hence processed with advanced tools (analytics and algorithms) to generate meaningful information.

The Bottega Veneta supply chain includes: the main firm, controlled factories and several independent producers, which provide the ability to perform specific parts of the production process. The “as is” production management is conducted using different systems: an ERP system for the main firm, a vertical ERP solution tailored for fashion companies in the factories, an APS (Advanced Planning and Scheduling) tool to provide plans for the factories. The integration among the systems is achieved through traditional data exchange tools. The traditional ERP processes customers’ orders data to feed the APS, which provides its processed results in terms of due dates and production orders to the factories’ vertical ERPs. Independent producers are individually managed outside these systems.

We propose an Industry 4.0 inspired framework as an evolution of the current situation, introducing a uniform data model, used by all the actors involved in the production process. This model is used to collect and represent the large amount of data that are involved in the production process, including logistic information such as due dates and customers’ data, production details such as production cycles, technological constraints and feedback data from the floor shop.

The continuously collected data are both used to effectively coordinate the different actors in the supply-chain as well as within each factory and to feed a complex analytics system that includes, among the other, visual representations of the data that are meaningful to the proper user and a DSS (Decision Support System) that allows production planner at different levels to focus on different automatically generated and locally optimized scenarios to support them in taking better decisions. A specific insight in the algorithms and mathematical models used by the DSS is also presented.



 
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