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Procedia CIRP 00 (2017) 000–000 Procedia CIRP 72 (2018) 892–897 www.elsevier.com/locate/procedia
51st CIRP Conference on Manufacturing Systems
28th CIRP Design Conference,Customized May 2018, Nantes, France An IoT-based Platform for Automated Shopping in Distributed Environments A new methodology to analyze the functional and physical architecture of a a Mourtzis *, Vlachouoriented Ekaterinia,product Vasilios Zogopoulos existing products forDimitris an assembly family identification a
Laboratory for Manufacturing Systems and Automation (LMS), Department of Mechanical Engineering and Aeronautics, University of Patras, Rio Patras, Greece * Corresponding author. Tel.: +30 2610-910160. E-mail address:
[email protected] École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address:
[email protected]
Abstract
Today’s manufacturing enterprises often face the challenge of manufacturing highly customized products in small lot sizes. One solution to adapt to the ever-changing demands that increases resources flexibility, lies in the digitization of the manufacturing systems. This work Abstract proposes an open and interoperable Internet of Things platform, enhanced by innovative tools and methods that transform them into Cybersupporting smart customized through gathering customers’ requirements, adaptive production, and the logistics of InPhysical today’s Systems, business environment, the trend towardsshopping, more product variety and customization is unbroken. Due to this development, need of vending machines replenishment. platform, exploiting technology capabilities, enables contactless/ cashless paymentproduction solutions, agile and reconfigurable productionThis systems emerged to cope cloud with various products and product families. To design and optimize smart sensing advanced reporting, as well as ubiquitous data access and visualization. systems as welland as control, to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and © 2018 Authors. Published Elsevier nature ofThe components. This fact by impedes anB.V. efficient comparison and choice of appropriate product family combinations for the production Peer-review under responsibility of the scientific committee of the 51st Conference on Manufacturing system. A new methodology is proposed to analyze existing products in CIRP view of their functional and physicalSystems. architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable Keywords: Internet of Things; Customisation; Smart Sensing; Cyber-Physical Systems assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of [4]. As a result, new approaches that will digitalize and 1. Introduction thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. automate production systems, as well as product distribution © 2017 The Authors. Published by Elsevier B.V. andDesign vending machines, will support industries to interact in a Globally operating European companies face the challenge Peer-review under responsibility of the scientific committee of the 28th CIRP Conference 2018.
to efficiently deliver innovative product-services to a globally differing regional requirements, following their needs and preferences [1]. Today’s companies aim to enhance consumer engagement by coming up with new business models and marketing tactics 1.asIntroduction technology continues to evolve at lightning speeds [2]. The advent of the 4th Industrial revolution and the ever-increasing Due toof the fast of development of adoption Internet Things (IoT) in has the and domain will further communication and an ongoing trend of digitization and change existing business models [3]. Implementing sensors digitalization, enterprises are facing and actuators manufacturing in existing products, upgrades them toimportant a Thing, challenges in today’s market environments: a continuing a mechanism that is capable of receiving input from its tendency towards reduction of product developmentwith timesother and environment through sensing or communicating shortened product lifecycles. In addition, there is an increasing devices and adjusting its behavior accordingly, in other words demand of customization, at theThe samedevices time in are a global a Cyber-Physical Systembeing (CPS). also competition with competitors all over the world. This connected to a network that allows remote access to thetrend, data which is inducing thecontrol. development micro collected and remote Over thefrom last 5macro years to there has markets, results in diminished lot sizes due to augmenting been a surge in interest by the vending industry to adopt new product varieties to low-volume production) [1]. technologies and(high-volume especially Internet of Things technologies To cope with this augmenting variety as well as to be able to identify possible optimization potentials in the existing 2212-8271 ©system, 2018 The it Authors. Publishedtobyhave Elsevier B.V. production is important a precise knowledge distributed network ofmethod; customers with strongly Keywords: Assembly; Design Family identification
user- friendly way with the customer, capture the requirements through smart sensing, integrate them into the product through data driven actuation, and produce highlycustomized products quickly and effectively. Especially in the case of SMEs, keeping up with the of the product range andenvironment characteristics and manufactured and/or rapidly evolving technological assembled in this system. In this context, the main challenge in advancements is particularly challenging, as they seldom own modelling and analysis is now not only to cope with single such large investment amounts. However, they are vital for products, limited product range or existing product the EU's aeconomy, since they account for more thanfamilies, 99% of but also to be able to analyze and to compare products to define European businesses, and two thirds of private sector jobs. new product families. It can bemajor observed that classical Thus, their sustainability is of importance [5]. existing product families regrouped in function of clients features. Towards thatareend, this paper proposes an IoTor platform However, assembly oriented product families are hardly to find. which, through smart gathering of customer’s requirements On the product family level, products differ mainly in two and dynamic and adaptive production, will be capable of main characteristics: (i) the number of components and (ii) the supporting customized shopping through automated vendors type of components (e.g. mechanical, electronical).by installed in key locations, so as to beelectrical, easily approachable Classical methodologies considering mainly single products the customers. This platform will be devoted to support and or solitary, already existing product families analyze protect the sustainability and the competitiveness of the all product structure on a physical level (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this
Peer-review under responsibility of the scientific committee of the 51st CIRP Conference on Manufacturing Systems.
2212-8271©©2017 2018The The Authors. Published by Elsevier 2212-8271 Authors. Published by Elsevier B.V. B.V. Peer-review under responsibility of scientific the scientific committee theCIRP 51stDesign CIRP Conference Conference2018. on Manufacturing Systems. Peer-review under responsibility of the committee of the of 28th 10.1016/j.procir.2018.03.199
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enterprises using it and benefitting both the end users and the providers from its services. 2. State of the art Internet of Things has already been adopted by industry in different levels [6]. Evolution of machines to Cyber- Physical Systems by integrating smart sensors, the machines gain awareness of their surroundings and through advanced communication technologies that support high speed data transfer and networks that combine low latency with increased availability, such as tactile internet, may provide meaningful information [6]. In the production level, IoT has emphasized bidirectional information sharing across the global manufacturing value chain from supplier to customer — informed processes lead to a flexible and adaptable supply chain. In the customer level, it has brought together people across all business functions and geographies, and providing them with relevant information in real-time, “informed people” will provide intelligent design, operations and maintenance, as well as higher quality service and safety [7]. In the product level, controls and software applications to obtain and share real-time information as finished goods make their way down the production line combining advanced sensors with existing machinery. One of the greatest benefits of the IoT has been its ability to standardize and commoditize technologies [8]. However, IoT tools, open platforms, as well as communication protocols are slowly adopted and implemented by industry. Recent IoT reference models and protocols can be used and enhanced, acting like live systems [9], where features are gradually developed and integrated in or on top of the IoT network infrastructure, slowly transforming it into an infrastructure for providing global services for interacting with the physical world [10] [11]. One of the levels of IoT application is the customer. Existing solutions in customer integration consider mobile apps and web-based tools for product design and customization [12]. Additionally, smart sensing devices that can be enabled through the mobile apps, exploiting existing sensors have gained great acceptance by the customers and application for product customization have emerged both in literature but also in pilot applications from industrial users but still it is a field that requires further investigation [13]. Nonetheless, there is a lot of potential in exploiting mobile devices and thus there is an increase in the number of embedded sensors. In the field of vending machines, existing vending machines do not include any kind of sensing for product customization, stock availability or products condition, and are based on a standardized set of options and available stock that is manually replenished [4]. Existing vendors that embed sensors that can sense the current condition of the end-user and support product customization based on metrics and preferences are offline without interaction with the vending machines or with the production units and the logistics [14]. Finally, social media until now are used mainly communication and discussion among people, while this concept can be further used also in the industry by enabling customers to discuss on the provided products and services be informed on their condition as well as providing
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useful feedback for product-services re-design and improvement [15]. Automation systems also need to be adapted to the new reality of Industry 4.0. Existing robotic systems uses the fixed control logic (mainly PC based) which needs to be enhanced with more sensors and actuators enabling the IoT-based robotic system while existing sensors in robotic systems are used under fixed sets of rules to achieve certain control cycles and check for individual errors [16]. Autonomous robot learning by providing meaningful and analyzed data is a field of great potential [17] that starts to be integrated into various fields. In the field of vending machines that are consisted of robotic systems, “Balthazar” is the first of its kind for the application of mixing customized skin care products [18]. Balthazar is programmed by typical robotic programming systems and does not use force-torque sensitivity for its processes. Additionally, large manufacturers allow customers to have a unique profile and integrate customer in the product design providing also payment through mobile devices however highly customized products or smart vending machines supported by smart sensors and actuators is a field of great potential and further investigation [19]. However, the adoption of the IoT paradigm generates a great amount of data that should be integrated and analyzed. The amount of data produced and communicated over the Internet has increased significantly over the last few years, thereby creating challenges for the organizations that would like to reap the benefits from analyzing this massive influx of data from various, frequently divergent, sources [20]. Big Data analytics techniques have been already included in the strategic points of today’s companies however, several obstacles and limitations have limited their so far integration in practice [21]. Existing Big Data analytics techniques need to be further enhanced in order to deal with the everincreasing use of sensors and actuators and the increased heterogeneity, velocity and veracity of captured data [22] [23] [24]. Moreover, in order to adapt and exploit the data gathered from the sensors and the information generated from Big Data analytics, it is essential to design new business models that support a high degree of adaptation and integration of data coming from different and heterogeneous sources. Integrating machine sensor data in production monitoring systems was the entry point for data- driven production adaptations [25]. But, although there is a significant effort towards linking the customer sales with the demand profiles of the customers [22], creating a direct link between the supplies, the logistics and the customized production is not covered sufficiently by the current literature. The lack of related approaches and business models, poses an obstacle in the adoption of innovative sensor- based holistic manufacturing approaches that will drive forward the adoption of Industry 4.0 technologies by manufacturing companies [26]. The literature review makes apparent that existing solutions have to be re-designed or enhanced in order to support companies to easily adopt IoT solutions that can be used in different phases. Automated customized shopping is a field of further investigation where IoT, Big Data as well as cloud and wireless technologies can be applied, transforming
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it into a sustainable and competitive field. This environment may offer on the one hand low production cost and increased productivity for the manufacturers and on the other hand increase in customers’ needs satisfaction. Following the above, this research work presents an IoT platform supported by cloud, wireless, and Big Data technologies aiming to provide automated customized shopping in distributed environments. The platform consists of several components capable of sensing the needs integrating the data from the different levels, analyzing it and performing adaptive and effective planning and control of the production, while offering highly customized solutions, following customer’s needs. The data gathered aim not only to support product customization, but also to provide meaningful feedback to production, targeting production to what is required when while reducing waste. 3. The IoT Platform For Customized Shopping The proposed IoT platform will be capable of collecting, integrating, and analyzing the data from the different phases and levels of the product’s lifecycle, aiming to deliver the right information, to the right place, at the right time [22], increasing flexibility and adaptability of the system, while delivering customized /personalized products 24 hours a day, 7 days a week (Fig. 1). The main components of the proposed platform are the following: 1. An interoperable and open IoT platform, supported by industrial standards and protocols, as well as by a cloud-based Big Data environment including Big Data analytics tools and NoSQL databases aiming to support smart and efficient data
collection and integration. Sensors and actuators will be connected to this IoT platform, supporting smart sensing and adaptive control. In the different levels of the IoT platform security protocols and standards will be integrated, enabling secure data exchange, data protection, authentication and prevention against cyber-threats. 2. Advanced and flexible IoT-based production systems (e.g. robotics), adding Industry 4.0 functionalities capable of performing complex handling and assembly tasks (smart grasping, raw component refilling mechanisms, gripping, and mixing systems, quality control, fault-detection mechanisms etc.). The developed systems will include smart sensors and actuators, enabling efficient adaptation to change and reducing delivering time and waste. Advanced robotic systems for product integration and packaging in the production line as well as re-designed vending machines capable of sensing and producing on-spot customized products will increase system’s productivity. 3. Customer integration in the production phase through product customization, customer profile information, as well as feedback gathering on the provided products and services [27]. Smart sensing devices (e.g. UV and skin moisture sensors, RFID, temperature, etc.) and mobile apps will enable automatic product configuration based on the measured metrics and provision of meaningful information related to the customer profile (age, physical condition, etc.). The sensing devices and the mobile apps connected to the IoT platform will sense customers’ needs and preferences, increasing customer’s satisfaction. Data and information from social media will also be considered.
Fig. 1. Architecture of the proposed cloud- based IoT platform .
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4. Intelligent data integration and analysis for the dynamic planning of production, smart logistics supply and replenishment of vending machines. Big Data analytics techniques, including machine learning and statistics, will be used to analyze the heterogeneous data from the various sources (customers, vending machines, distribution center, contactless payment solutions, etc.) mapping and forecasting customer’s needs with production planning, providing the right information, at the right time, to the right place. In the figure below, the workflow of the proposed framework is depicted.
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barcodes) the available stock in the distribution centres will be monitored, reporting back any deficits to the production line. To connect the customers with the proposed approach so as to integrate product personalisation, two entry points are considered. On the one hand, the customer will be able to create a unique profile to the company’s database, through a web browser or a mobile device application. Apart from some personal details, the customer will be able to provide some preferences, while also creating a historical database that will track his preferences, improving customer’s experience and manufacturers’ traceability of preferences and trends. On the other hand, the vending machines will host a series of sensors that will measure the specific needs of every customers and will combine the appropriate product modules. Following future customer requirements and/ or new product modules, new sensors could be considered to measure the current state, redesigning the sales end- point. All and all, the developed framework aims to a holistic approach combining the existing sensor knowledge, cloud repositories and data analysis in a unified platform so as to extract meaningful information that will drive system reaction. The sales end- points as well as the production automations will be enhanced so as to integrate smart behaviour, driven by sensor data and realized through actuators. The smart behaviour will be able to adjust the production to the forecasted customer requirements, based on the input from customer feedback and the current available stock. IoT systems will play a key role in this integration as they will transform the vending machines into smart Things that through the communication network will interact with the production, the distribution and the customer, altering its behaviour and ultimately the final product. The developed platform will result in a product that will best suit the customer requirements while supporting, in an efficient way, the manufacturer in keeping track of the required supplies. 4. IoT Enabling Technologies
Fig. 2. Workflow of the proposed approach
The proposed platform includes the implementation of IoT devices in the production line, the distribution centers and the sales end- points, gathering and sending data to different points of the production line that are integrated under the cloud-based IoT platform. More specifically, in the production line, a set of sensors will be installed and connected to the IoT platform that will allow monitoring its current status. Data from existing systems, such as robots’ PLC, will be considered in the developed platform. To increase the flexibility and responsiveness of the production line, actuators will also be installed so that, based on data and operator input from the IoT platform or the customer feedback based demand forecast, they will be able to dynamically adjust the production. Distribution centres are another part of the existing systems that will be innovated through the implementation of IoT devices. Using sensors (e.g. RFID,
Aiming to develop an IoT platform (Fig. 3) that will place the foundations for future industrial IoT platforms that combine data input from various heterogeneous sources (customers, vending machine sensors, distribution centres and production systems), the developed platform will be based on the exploitation of existing, open-source protocols and platforms. The protocols, like OPC UA, 6LowPAN and SSL/TLS, that may secure the data transmission from the IoT devices to the main platform and vice versa will be open source and in compliance with the industrial demands so as to facilitate both large companies and SMEs to adopt the developed platform. The vending machine in this system will be equipped with sensors that will capture the unique needs of the customer, based on the current state of his skin and a track of previously gathered measurements, as stored in his profile. The metrics gathered from the sensors will be compiled with medical understanding on the needs of the skin, thus providing products that are personalized to the current needs of the customer. Skin sensors that may capture the moisture and other needs of the skin will provide the data that based on analysis compared to other cases, historical data of the customer and relative knowledge will select the appropriate product modules for the customer.
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• • • •
IoT Applications
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Insights Analytics Reporting Decision
SSL/TLS Complex-Event Processing
Machine Learning
Cloud-based IoT-Platform
BigData Analytics Context- and role-aware
IoT provision API
HTTP/MQTT/JSON
FIWARE NGSI-9/10
IoT Gateway
IoT Gateway
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Internet-IPv4/Internet-IPv6/SmartM2M/ OPC UA/ 6LoWPAN/Zigbee IoT - Consumers
Vending Machines
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IoT-enabled
IoT-enabled
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Fig. 3. The proposed Internet of Things platform
Moreover, the customer’s mobile device will be used as personal characteristics input device, for feedback on the product but also, exploiting contactless technologies, like NFC or Bluetooth, the device can be used for customer identification and payment; securing the process is an important goal to address. Moreover, the vending machines will be equipped with touch screens to allow the customer to further interact with the system, even without the mobile device. Additionally, the developed platform will be selfaware, able to monitor the available stock and combined with their location set by GPS, thus creating a map of available stock monitoring for the manufacturer. Based on the available stock, local distribution centres will be notified so as to secure the constant availability of the required modules, while also reporting back to the production, updating the demand profile. Following the above, smart virtualisation of objects (production systems, distribution centres, vending machine, customer) will be achieved. These smart IoT objects enhanced with data and knowledge from the sensor will be selfadjusting and aware.
proposed IoT platform, the company will be capable of providing customization options that reflect customers’ preferences, such as preferred scent, through the customer mobile device and wearables. Vending machines will become a Thing that will host sensors, interfaces and internet connection so that it will interact with the IoT platform while providing services for the customers. The vending machine will also host skin and UV sensors so as to capture metrics that will affect the synthesis of the final product. These sensors will be taken into advantage to automatically detect the needs of the customer, based on the data collected. The data will also be sent to customer’s mobile device, allowing him to keep track of his preferences, while also they will be stored in the manufacturer’s database, so as to keep track of historical data that through Big Data analytics could provide predictions about future demand. Based on the input parameters, the vending machine will run a decision support algorithm so as to select the optimal solution from the available product modules, as depicted in Fig 4 below.
5. Case Study The proposed IoT platform is applied in a case from the cosmetics industry. In this case, the company wants to upgrade their existing vending machines to create an autonomous vending machine, capable of creating the final product on-spot, based on customer needs and requirements, while also dynamically reporting their current stock. The vending machine will need to have a way to receive personalized input from the customer on the products specifications, an autonomous production system and a stock of the required materials. Through the integration of the
Fig. 4. Module selection decision making to meet customer requirements
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Based on the customers and sensors input, the vending machine is equipped with automations that allow it to create the final product on- demand. They are equipped with proper actuators that are capable of handling and mixing the ingredients/ product modules required to create the personalized product. In addition, the vending machine is equipped with sensors that measure and maintain the required stock in adequate levels and it will also be connected to the IoT platform, retrieving feedback back to the manufacturer, adjusting the volumes of production to the customer demands, creating a sustainable and self-adjusting system. The application of such a system in the real case, bridging the gap between the manufacturer and the customer. The use case provider that is marching towards Industry 4.0, found merit in the proposed framework. The cosmetics industry integrated vending machines with standardized products that do not report back to the OEM about the available stock, requiring manual check. Integrating the proposed framework, the manufacturer will be able to move from mass to personalized products while also connecting the customer demand with the supply of input. Applying the proposed system, the manufacturer estimates that it will bring an increase in sales, as the customers will prefer the personalized products, reducing the cost of production, while also having an improved insight about the required supplies and the replenishment of the vending machines, thus improving the design of the supply networks.
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6. Conclusions In this work, an innovative IoT platform has been presented aiming to support automated customized shopping. The IoT platform consists of different components capable of sensing the customers’ requirements, integrating data from different levels analyzing it and performing adaptive and effective production of customized products. Among the main benefits of the proposed IoT platform are the reduction of waste (delivery time, material), the increased awareness on the condition of the IoT things (machine, customer, sensors, etc.), as well as the increased product customization level. Future work will be focused on further applying the proposed platform in the cosmetics case and also apply it to new industrial cases such as food industry. Implementations of IoT platforms should be enhanced and expanded to more fields of interest and thus generalize the integration of Industry 4.0 in the product personalization and manufacturing. References [1] [2] [3] [4]
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