POLITECNICO DI MILANO
School of Industrial and Information Engineering Master of Science in Management, Economics and Industrial EngineeringAn overview of startups belonging to Industry 4.0: IT
solutions integration for the Smart Manufacturing
Supervisor: Prof. Giovanni Miragliotta Co-supervisor: Ing. Gianluca Tedaldi Thesis by: Eleonore Josephine Valentine Ninove 876678INDEX
ABSTRACT……….…4
1. INTRODUCTION.……….…6
1.1 The Origins………7
1.2 The First Three Industrial Revolutions………..8
1.3 The Fourth Industrial Revolution………..………….…12
1.4 The Smart Technologies………16
1.4.1 Industrial Internet of Things…………..………..17
1.4.2 Cloud Manufacturing……….21
1.4.3 Industrial Analytics………..27
1.4.4 Additive Manufacturing………...33
1.4.5 Advanced Automation………..39
1.4.6 Advanced Human-Machine Interfac………44
2. METHODOLOGY AND OBJECTIVES……….…49
2.1 The Thesis Work………..……….…..49
3. START-UP ANALYSIS………..………..…53
3.1 General Overview………53
3.2 Technological Cluster Analysis………....…….59
3.2.1 IT Technologies……….…….59
3.2.2 Industrial Internet of Things……….60
3.2.3 Cloud Manufacturing……….…………62
3.2.4 Industrial Analytics……….…….64
3.2.5 OT Technologies……….………66
3.2.6 Additive Manufacturing……….….….67
3.2.7 Advanced Automation……….….…69
3.2.8 Advanced Human-Machine Interface………..…..70
3.3 Start-up Interviews………..…………..…71
3.3.1 Start-up Description………...……….………72
Cohaerentia……….……….…….72
Cubbit……….……73
Exolvia……….…..……75
Nextome………..……..….76
OL3 Solutions………..…..…………..79
SmartFab……….….80
Smart Factory……….………….….81
3.3.2 The Results……….……….….84
4. CONCLUSIONS……….……….……90
BIBLIOGRAFY……….…..………92
SITOGRAFY……….…….………….97
EXTRACTS……….….………98
FIGURES INDEX
Figure 1 The four Industrial Revolutions………..………….………...…..…9 Figure 2 Applications of the Industrial Internet………11 Figure 3 “Internet of Things” paradigm as a result of the convergence of different vision………..…..…18 Figure 4 IoT Architecture………...19 Figure 5 Layered architecture of a cloud manufacturing system……….…23 Figure 6 Genesis of big data applications……….……....29 Figure 7 Big Data Management Process……….30 Figure 8 Framework for AM implementation ……….………..37 Figure 9 Factors influencing Automation implementation……….………....43 Figure 10 Key areas of application of AR and VR technologies……….…..…….……47 Figure 11 Start-up number according to the technological cluster………..….……54 Figure 12 Number of start-up according to the Industry 4.0 framework field…….…….55 Figure 13 Number of start-up per geographical area ……….…………56 Figure 14 IT start-up distribution ……….……….59 Figure 15 Total and Average Funding per (IT) Cluster in mln$………..…………60 Figure 16 Industrial Internet of Things start-up geographical distribution……….…….…61 Figure 17 Cloud Manufacturing start-up geographical distribution………..…63 Figure 18 Industrial Analytics start-up geographical distribution………..……….64 Figure 19 OT start-up distribution……….……….66 Figure 20 Total and Average Funding per (OT) Cluster in mln$……….67 Figure 21 Additive Manufacturing start-up geographical distribution……….67 Figure 22 The results of the Interviews……….……….……….84 Table 1 Key technologies on A/V reality……….…48ABSTRACT
During the last years the manufacturing world has kept on changing following the lead of new digital technologies development. This incredible and simultaneous transformation of manufacturing technologies signed the beginning of the Fourth Industrial Revolution, also called Industry 4.0.
The implementation of Industry 4.0 technologies – the so-called Smart Technologies, such as additive manufacturing, industrial analytics, internet of things, cloud manufacturing, advanced human-machine interface and advanced automation became increasingly important. Nowadays companies, especially of western countries where the workforce is more expansive, need to adopt these new manufacturing solutions to stay competitive in a more and more challenging market.
Start-up are among the most important Smart Technologies providers but, differently from others, their incredible capability to think outside the box makes them the seed of innovation.
This is why this thesis aims, analyzing the main fields where start-up are concentrated, at understanding the future directions of innovation.
After the creation of a database constituted by 215 start-up from all over the world, a general analysis of the start-up trends is made, giving a particular focus to the start-up distribution within the different Smart Technologies groups, geographical areas and funding characteristics. The analysis output is then supported and integrated with interviews made to Italian start-up operating in the Industry 4.0. The interviews, made to start-up offering IT solutions, aimed at deepening the results of the database analysis paying particular attention to the motivations behind the integration between IT solutions, the barriers hindering start-up development and the Italian start-up funding situation.
A suggestion for future researches could be understanding why and which of the nowadays new-born start-up actually had success and could develop and why others couldn’t exit from the start-up phase and declined. This could lead to the creation of a general framework for Industry 4.0 start-up success and also give practical indications on Industry 4.0 technologies demand. Keywords: Industry 4.0, Startup, Digital Innovation, Smart Technology
1. INTRODUCTION
The objective of this chapter is to introduce the reader to the world of Industry 4.0.
A first glance at the origins of the name will be given, discussing how the German government launched the first Industry 4.0 program and how it was later followed by other nations around the world.
The second part will be dedicated to the first three industrial revolutions that put the basis of the current one and also gave the hint for further improvements in the manufacturing industry.
The third part of this chapter will be focused on the potentials of the fourth industrial revolution together with the key areas on which efforts should be put to better exploit potentials.
The last chapter will focus on the six main technologies constituting the Industry 4.0 according to the studies made by the “Osservatorio Industry 4.0” of the Politecnico di Milano.
1.1 THE ORIGINS
The term Industry 4.0 is derived from an initiative - “Industrie 4.0”- launched by the German government in 2011 for safeguarding the long-term competitiveness of the manufacturing industry. This program was part of the High-Tech Strategy 2020 Action Plan, a broad national concept in which the key stakeholders involved in innovation shared a joint vision about how Germany should face global challenges such as climate change, demographic development and finiteness of energy sources (Federal Ministry of Education and Research, 2011). The first recommendations contained in the “strategic initiative Industrie 4.0” were formulated by the Industrie 4.0 Working Group between January and October 2012, while further implementations were made by specialized working groups under the management of the Industrie 4.0 Platform, made up of three professional associations: BITKOM, VDMA and ZVEI1. The final report “Recommendations for implementing the strategic initiative Industrie 4.0” was published in April 2013, under the supervision of Prof. Kagermann, President of Acatech.
Following the German lead, also Europe and the US instituted national associations to promote the contents of Industry 4.0, respectively the Public-Private Partnership (PPP) for Factories of the Future (FoF) in the United States and the Industrial Internet Consortium(ICC) in Europe (Stock and Seliger, 2016). In China, the Internet of Manufacturing Things has been investigating the use of IoT in manufacturing. Actually, Smart Manufacturing is recognized as a key topic in the 10-year national plan “Made in China 2025” (Yao et al., 2017). Concerning the link between the two aforementioned terms “Industry 4.0” and “Smart Manufacturing”, experts have opposite opinions: some consider Smart Manufacturing as a broader concept than Industry 4.0, that just refers to the 1 BITKOM, VDMA AND ZVEI are respectevely: Federal Association for Information Technology,
national German project, others consider Smart Manufacturing as part of the Industry 4.0 (Yao et al., 2017). Anyway, all the scholars agree in accounting CPSs (Cyber-Physical Systems) as the central concept on which the Industry 4.0 puts
its basis (Hermann et al., 2015).
CPSs can be defined, in general, as systems of collaborating computational elements controlling physical entities. Zheng et al. (Zheng et al., 2018) point out the difference between this term, considered as general, and CPPSs, considered as the materialization of CPSs in the manufacturing environment. CPPSs would then comprise smart machines, warehousing systems and production facilities that have been developed digitally and feature end-to-end ICT based integration comprehending inbound logistics, production, marketing, outbound logistics and service.
1.2 THE FIRST THREE INDUSTRIAL
REVOLUTIONS
History is not a scientific discipline, indeed historians disagree on the number of industrial revolutions there have been and the period when they took place. In this situation, we have decided to adopt the most used classification, that is also the one considered by Prof. Kagermann in his Industry 4.0 report where Fig.1 is taken from.Fig. 1 The four Industrial Revolutions
The First Industrial Revolution started in the second half of the eighteenth century and it was mainly driven by the commercialization of steam engines, the birth of new chemical manufacturing and iron processes and the development of novel machine tools. It was born in Great Britain - the world’s leading commercial nation at that time - and soon spread in the United States and the rest of Europe.
The Industrial Revolution marked a major turning point in history: the world passed from hand production to machines, the factory system was born and for the first time in history the standard of living started to grow consistently.
The Second Industrial Revolution, also called Technological Revolution, began in the final third of the nineteenth century and ended with the First World War. The beginning of this period was marked by important improvements in manufacturing that brought the innovations of the first revolution to be widespread around the world: railroad networks, telegraphs, gas and water supply and sewage systems, which earlier had been concentrated just in few places. The development of railways made it simpler to move from place to place
in a way that this unprecedented motion of people and ideas brought to the first wave of globalization. This is why the technological innovation pace started to increase, quickly leading to new technological systems like electric power and telephones. The final result of 150 years of technological innovation was an incredible change in the working organization: the division of labor and the production line, mainly promoted by Taylor and Ford, brought to unbelievable productivity improvements, boosting economies of scale and decreasing costs.
Despite all the gains reaped by the economy and society during those years, new problems occurred: the global economic system became much more resource-intensive causing significant impacts on the external environment and the new working arrangement started to show the first weaknesses and be counterproductive.
Actually, it was the will to resolve these problems one of the main hints that brought to the birth of the Fourth Industrial Revolution. But, before, there is a last step that has to be mentioned: the so called Internet Revolution. The Internet Revolution, the third in time, took place in the second half of the twentieth century but can actually be furtherly divided into two sub periods: the first one started in the Fifties with the creation of large main frame computers and the second one, during the Seventies, marked by the birth of the Internet, a huge infrastructure made up of a network of computers.
The Internet had the special characteristic of linking different machines all around the world. This feature brought huge changes to the commercial sector: - Easing and fastening the way to exchange ideas and opinions, it created new
ways to make value, fostering distributed intelligence;
- It gave the possibility for new transaction and commercial methods to arise, revolting the traditional business models;
- It permitted new ways of communicating inside companies, this contributing to a general decentralization of power and horizontalization of companies structures. All these changes led, little by little, to the birth of a new type of company: the start-up, characterized by innovative business models, horizontal structures and knowledge intensity.
Actually, if the first and the second industrial revolutions can be defined as resource-intensive, the third one is definitely knowledge-intensive.
So, now, the question that arises is: where will the fourth one be placed?
The Fourth Industrial Revolution, as highlighted by its name “Industrial Internet Revolution”, can be seen as a bridge between the first twos and the third one: on one side, there are enormous technological improvements regarding machinery, equipment, plants and resource exploitation; on the other hand the increase in the ICT (Information and Communication Technologies) capabilities follows the lead traced by the third revolution.
These two aspects are linked together by the CPS, the main concept born with the current industrial revolution whose aim is networking resources, information, objects and people.
The figure above well explains the meeting between the real world, constituted by networks, fleets, facilities and machines and the digital one, made up of intelligent devices, intelligent systems and intelligent decisioning. The contemporaneous optimization of the industrial world exploiting the digital one opens up new frontiers to accelerate productivity, reduce inefficiency and waste,
and enhance the human work experience.
In particular, intelligent devices and systems gather data that are then stored, analyzed and visualized using big data and analytics tools. The resulting “intelligent information” can then be used by decision makers in real time, if necessary, or to make broader optimization and strategic plans (Evans and Annunziata, 2012).
1.3 THE FOURTH INDUSTRIAL REVOLUTION
As previously said, the main technological concept on which the last industrial revolution is focused is the CPS.Thanks to CPS, companies can develop vertically by integrating business processes, and horizontally by creating value networks between different enterprises; both directions require and enable end-to-end engineering across the entire value chain.
Vertical integration is harnessed by the gathering of data by smart devices and
smart machines at each stage of a company process, after this, data are analyzed at a central level and transformed in “intelligent information” in order to synchronize every stage of the process and increase efficiency.
Vertical integration through CPS is catching up with the current trend of Mass Customization (Brettel et al., 2014): markets nowadays expect cheap products for low prices, so companies must be able to realize scale economies
guaranteeing product individualization to customers. This aim can be reached through flexible processes and modularized product design.
Horizontal integration is one of the best solutions to face the contemporaneous
decrease of added-value depth and the increase of product complexity. Thanks to collaborative networks, risk can be balanced and combined resources can expand the range of perceivable market opportunities. The organization in networks multiplies the available capabilities without further investments, this helps companies to adapt to volatile markets and to shorten product lifecycles. Data sharing between companies is the key concept of horizontal integration but also the main source of problems. Information sharing can trigger innovation but can also lead to asymmetric learning caused by opportunistic behavior. Particularly in global networks, different mentalities towards information and cost-sharing can result in high coordination costs.
Coordination is actually the second relevant problem of horizontal integration: the decoupling and spatial separation of production processes while merging data can bring to big coordination issues (Stock and Seliger, 2016).
End-to-end engineering gives differentiation opportunities by offering
value-added services that cover the whole product value chain. After-sale services and long-term contracts in general also help to leverage high demand volatility risks, as the actual product serves as a platform for further service sales over time utilization. In his “Recommendations for implementing the strategic initiative Industrie 4.0”, Prof. Kagermann underlines the potentials of this revolution together with the key areas on which technological innovations should be concentrated to better exploit the potentials. Thanks to new technologies, meeting individual customer requirements will be possible: customer specific criteria will be included in the design, configuration,
ordering, planning, manufacture and operation phases; manufacture of one-off items and making profit at the same time will become easier and easier. A new level of flexibility is reached thanks to the CPS-based ad hoc networking that enables dynamic configuration of different aspects of business processes, such as quality, time, risk, robustness, price and eco-friendliness. The end-to-end transparency allows companies to take optimized decisions in a short notice allowing early verification of design decisions and flexible responses to disruption. Decision optimization together with CPS flexibility has important influences on the resource productivity and efficiency: the first makes it possible to take better decisions at a global level, the second helps the decisions to be implemented easily.
End-to-end management gives also new opportunities to create value through
new services: as previously said, companies will start to offer more and more
downstream services, this creating new forms of employment. Also the increasing collaboration between human beings and technological systems will create new forms of employment: the switch from manual to knowledge intense jobs will permit industry 4.0 to adapt to the demographic change allowing longer working times but also enabling diverse and flexible career paths.
Benefits from the working point of view are not finished yet: flexible work organization models of companies using CPS mean that they are well placed to strike a better work-life-balance and also to guarantee a better trade-off between personal development and continuing professional development. Finally, Industry 4.0 offers to high-wage economies the opportunity to stay
competitive developing a leading supplier position and a leading market
position, the so-called “dual strategy”, that will be better explained later in this chapter.
As said before, to better exploit all the potentials of Industry 4.0, the stakeholders of the Fourth Industrial Revolution should work together in the following key areas:
- Standardization and reference architecture: to facilitate technological implementation, a set of common standards and a reference architecture should be developed;
- Managing complex systems: growing complexity of products and manufacturing system should be managed with appropriate planning and explanatory models;
- A comprehensive broadband infrastructure for industry: reliable, comprehensive and high quality communication networks are a key requirement for Industry 4.0, therefore broadband internet infrastructure has to be expanded;
- Safety and security: production facilities and products themselves should not pose a danger either to people or environment and should be protected against misuse and unauthorized access;
- Work organization and design: since work content, work processes and working environment will change a lot, an appropriate work organization and design should be implemented. It will be important to deploy lifelong learning measures and to launch model reference projects;
- Training and continuing professional development: still linked to the transformation of jobs and competence profiles, the implementation of training strategies will be mandatory. To achieve this, “best practice networks” should be promoted and digital learning techniques should be investigated;
- Regulatory framework: existing legislation will need to be renewed to adapt to innovations. The challenges include the protection of corporate data, liability issues, handling of personal data and trade restrictions;
- Resource efficiency: as said in the previous paragraph, resource efficiency is one of the potentials of the Industry 4.0. Anyway, it will be necessary to study the trade-off between resource efficiency and costs. To better exploit the opportunities of the Industry 4.0, every country should try to develop the key areas mentioned above; this would lead also to a wider and faster integration between companies of different nations. Each country should also develop a double strategy – “dual strategy”: by pushing the adoption of the Industry 4.0 technologies inside manufacturing companies (market strategy) and by fostering the birth of new enterprises supplying innovative technologies (supply strategy) (Federal Ministry of Education and Research, 2011).
1.4 THE SMART TECHNOLOGIES
Technologies are always the trigger of industrial revolutions, for this reason it is important to give an appropriate overview of the innovative technologies that characterize Industry 4.0.According to the classification made by the Osservatorio Industry 4.0 of the Politecnico di Milano, the so-called “Smart Technologies” are six: Industrial Internet of Things, Cloud Manufacturing and Industrial Analytics can be grouped in the set of Information Technologies(IT), while Additive Manufacturing, Advanced Automation and Advanced Human Machine Interface are grouped in the more heterogeneous set of Operation Technologies (OT). The link between the OT technologies is mainly represented by the strong interconnection between the resources used during operational processes.
Hereafter a description of the Smart Technologies will be given.
1.4.1 Industrial Internet of Things
“A world-wide network of interconnected objects uniquely addressable, based on standard communication protocols” (INFSO D.4 Networked Enterprise & RIFID, 2008) Among the different definitions of IoT given during the years, the one above is probably the most used one since it easily explains the relationship between the internet – that is the network- and the things – the objects: each object is uniquely identified by a code, these objects can communicate between each other thanks to a network based on standard communication protocols.
Even though the Internet of Things was put among the “Six Disruptive Civil Technologies with Potential Impacts on the US Interests Out to 2025” by the National Intelligence Council in 2008, its concept already came to existence in 1998 and the term Internet of Things was introduced by Kevin Ashton in 1999 (Ashton, 1999).
This term, actually, gives space to two visions of the IoT technology: an “Internet oriented” and a “Things Oriented” perspective. Differences in the IoT visions raise from the fact that stakeholders, business alliances, research and standardization bodies started approaching one between the two perspectives depending on their specific interests, backgrounds and finalities. The “Things Oriented” vision was probably the first one to take place along with the RFID technology. The trait d’union between the “Internet Vision” and the “Things” was possible thanks to the focus of the CASAGRAS2 consortium on “a world where things can automatically communicate to computers and each other providing services to the benefit of the humankind”. The consortium proposes the vision of a global 2
network that connects the real and the virtual world and highlights the importance of including evolving and existing internet in this vision.
Indeed, the “Internet Vision” sees the various physical devices interacting with each other and considers these smart embedded objects as microcomputers with computing resources. The latter vision was developed by the IPSO (IP for Smart Objects) Alliance in 2008.
Since the object unique addressing and the representation and storing of the exchanged information became the most challenging issue, a third perspective of the IoT was developed: the so-called “Semantic Vision”. This perspective states that the amount of data gathered by smart objects is huge and not always useful. Then, semantic technologies able to manage effectively these data play a key role. The three visions of IoT are shown in Fig.3 (Atzori et al., 2010) together with the enabling technologies of each vision. Fig. 3"Internet of Things" paradigm as a result of the convergence of different visions Even if a lot of different architectures for IoT can be found in literature, the one proposed by Mehta et al. in their 2018 “Internet of Things: Vision, Application and Challenges” (Mehta et al., 2018) well summarizes the crucial parts. Actually,
this architecture joints the ones proposed respectively by Wu et al. 2010 (Wu et al., 2010) and Tan et al. 2010 (Tan et al., 2010).
The suggested architecture (Fig. 4) (Mehta et al., 2018) is made by five layers: perception layer, network layer, middleware layer, application layer, business layer. Fig. 4 IoT Architecture The first layer, also called “Device Layer”, is made up of objects and sensors. At this stage, data are identified and collected via sensor devices and then sent to the next layer for its secure transmission and processing. The second layer, the network one, also called “Transmission Layer”, is responsible for the transmission of information from the previous layer to the next one. The transmission can be wired or wireless and the technology used (3G, UMTS, Wi-Fi, Bluetooth, infrared, ZigBee, etc) depend on the sensors that gathered data. The middle layer main activity is to store data received from the Network layer into databases, it also processes information and gives solutions based on data analysis.
The “Application Layer” is responsible for managing the information globally depending on the processing of objects’ information in the previous layer.
The final one, the “Business Layer” manages the complete IoT system in terms of services and applications. The information received from the application layer a re-analyzed and managed in order to give a complete knowledge about the IoT system and permitting to predict future actions. Thanks to their particular architecture, IoT systems have important utilities, as underlined by Sebastian and Ray, 2015 (Sebastian and Ray, 2015). They are: - Dynamic and self-adapting: IoT systems can adapt to changing conditions without any need for human help; - Self-configuring: IoT systems don’t need or poorly need human help also for configurations and updates;
- Interoperable communication protocols: IoT systems are able to communicate within different protocols;
- Unique identity: each Smart Object has its own identity and is uniquely addressable;
- Integrated into information network: IoT devices are integrated into an information network in which they can be dynamically discovered by other devices; they can also describe them-selves and their characteristics to other devices or user applications;
- Context awareness: the decisions taken by the sensor nodes take into account the surrounding environment thanks to the data gathered by Smart Objects; - Intelligent decision-making capability: thanks to the collaboration of sensor nodes, complex decisions can be taken. Even though IoT systems already have incredible utilities, there’s still space for improvement. Mehta et al. (Mehta et al., 2018) underline the key challenges that IoT has to face:
1) Unique identity management: the number of Smart Objects will keep on increasing during the years, a proper identity management is then needed
to dynamically assign and manage unique names for a wide range of physical devices;
2) Standardization and interoperability: standardization of communication protocols should be improved in order to ease communication between different networks;
3) Privacy of information: the exchange of information is the basis of the IoT systems, so its security has always to be considered. Moreover, the increasing number of Smart Objects implies an augmented difficulty in information security management;
4) Safety of physical devices: it will be important to guarantee physical safety to Smart Objects;
5) Confidentiality of information: at the information processing system, sensors should follow encryption mechanism to ensure data integrity; 6) Network security: when sensors transmit data over wired or wireless
systems, any loss of information should be avoided as well as no external intervention should occur.
1.4.2 Cloud Manufacturing
“Cloud Manufacturing (CM) is a customer-centric manufacturing model that exploits on-demand access to a shared collection of diversified and distributed manufacturing resources to form temporary, reconfigurable production lines which enhance efficiency, reduce product lifecycle costs, and allow for optimal resource loading in response to variable-demand customer generated tasking” (Wu et al. 2013)
The above definition of Cloud Manufacturing well summarizes its main characteristics - customer-centric, on demand, distributed- and its main benefits: efficiency, reduced product lifecycle costs, and optimal resource loading, that will be furtherly analyzed later in this chapter. Moreover, it mirrors
the definition of Cloud Computing (CC) given by the National Institute of Standards and Technology (NIST) in 2009: “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction”. Actually, Cloud manufacturing can be seen as the manufacturing version of cloud computing, where, instead of “configurable computing resources”, “distributed manufacturing resources” are used. The main characteristic of cloud computing is treating everything as a service: software (SaaS), platforms (PaaS) and infrastructures (IaaS) and these services define a layered architecture for cloud computing. The transformation of buyable goods into services is also one of the basic concepts of Cloud Manufacturing; where machines, equipment and also knowledge can be accessed by anyone entering the cloud platform without any need of physical possession.
As it has been underlined by Xu in 2011 (Xu, 2011) the first connection between these two worlds came with the direct adoption of cloud computing in the manufacturing sector. At first cloud computing was implemented around IT and new business models (ex. pay-as-you-go), in particular it was centered on BPM applications such as HR, CRM and ERP.
The adoption of CC in manufacturing brought several benefits to the manufacturing world: cost benefits coming from the elimination of some IT functions, efficiency due to a better integration of processes and collaboration at scale. This last feature was highlighted as an emerging trend by McKinsey (McKinsey, 2018) putting demand planning and supply chain organization on a cloud-based system different parts of the organization are able to take a look into the opportunities their sales teams are working on, thus providing a collaborative environment that can give people more transparency, agility and empowerment through more effective collaborations.
The following step was to move from product-oriented manufacturing to service-oriented manufacturing, and that’s when Cloud Manufacturing solutions started to catch on. In CM, distributed resources are tied altogether into cloud services and centrally managed. Clients can request a large number of services - such as design, testing, manufacturing and all other stages of a product lifecycle – that are performed by the cloud platform exploiting the manufacturing resources. To better understand how Cloud Manufacturing works, a cloud manufacturing system framework is hereafter explained (Fig. 5) (Xu, 2011). Fig. 5 Layered architecture of a cloud manufacturing system - Manufacturing Resource Layer This layer comprehends all the resources that are needed during the lifecycle of a product, it considers manufacturing physical resources and manufacturing
consists of machining, equipment, computers, servers and raw materials, while software takes into account simulation software, employees, analysis tools and know how. The latter includes the capability of an organization of undertaking a particular task with competence. - Virtual Service Layer
At this level, the main activities undertaken are the identification of manufacturing resources, their virtualization and their packaging as a cloud manufacturing system.
To identify resources, IoT technologies can be used.
The virtualization of computational resources and knowledge is made following the techniques already used in cloud computing while manufacturing hardware is usually mapped to become virtual machines that are system-independent. Three mapping methods can be used: one-to-one mapping, many-to-one and one-to-many.
The last step, the packaging, is completed thanks to resource description protocols and service description languages.
- Global Resource Layer
At this level, the resources are managed inside the enterprises in the cloud platform. Two types of cloud manufacturing service modes can take place: the complete service mode and the partial service mode. With the first one, the global resources layer takes full responsibility of the entire cloud operational activities, while with the second one just some activities are handed over to the cloud manufacturing service.
- Application Layer
This level is the interface between the user and the manufacturing cloud resources.
The main issue in this layer is data security: through the interface, the client puts on the cloud personal information or sensitive information about the company and its suppliers. Some technology can enhance data integrity, security and confidentiality; for example: data compressing and encrypting at the storage level, virtual LANs, that can offer remote communications and network middleboxes, for fail-safe communications. Wu et al. in their article of 2013 (Wu et al., 2013) identify the three main actors of a cloud manufacturing system. In this case, the classification is no more based on the activities undertaken by each layer as in Xu’s architecture, but on the basis of who actually makes the considered activity. Hereafter, the three actors are analyzed: - Users Users are the consumers of CM, they generate engineering requirements that describe the desired product and its final conditions. These requirements are then sent to the application providers for interpretation. Users can either be individuals or large OEMs, anyone who needs to manufacture something and doesn’t have the means to do so. - Application Providers The application providers are the ones who control and manage the application layer. They offer their services as an intermediary between users and resource providers for a portion of the product profit. The main objective of this layer is to transform the requirements received from users into data that the following layer can use to manufacture the final product. - Physical Resources Providers
PRPs physically own and operate manufacturing equipment, that includes: machining, finishing, inspection, packaging technologies and testing resources. Most important, they have the knowledge and the expertise to run the machines
they own effectively and efficiently. The final aim of CM is to create a huge network of PRPs, so that any manufacturing capability could be accessible in the marketplace by accessing the cloud platform. The fast adoption of Cloud Manufacturing is motivated by the several benefits that it brings. Some of them –efficiency, reduced costs and load balancing- were already mentioned in Wu’s definition at the beginning of the chapter, others – such as customer centricity, flexibility and collaboration - were highlighted in his following paper in collaboration with Greer, Rosen and Schaefer. Flexibility is definitely the most important feature of CM systems; it is assured by the reconfigurability and the repurposing of machining but also by the large number of different resources that the system can exploit to produce the same good. Flexibility makes it possible to perfectly follow the demand without any need for forecasting: demand is not just instantly fulfilled, but it also completely satisfies customer needs (customer centricity). The wide number of available resources gives customers the possibility to prioritize their objectives (price, quality, time to deliver, ..) and find the solution that better suits their requirements. Flexibility also allows load balancing: the production of a certain good can be split on different machines in order to guarantee a balanced machine load and avoid big differences in capacity saturation. Reconfigurability and temporality of production lines made CM systems flexible also from the point of view of production schedule: small lots of very different products can be produced one after the other, but also longer production runs are guaranteed. This assures a reduction in downtimes, instant demand response and higher efficiency. The last but not least important advantage brought by flexibility is the possibility to release any job because economic issues are no more a problem. In particular, this “turn no job away” is the result of the wide alternative choice offered to users and the possibility to produce small batches.
Collaboration is one of the crucial advantages that comes with the implementation of CM systems: collaboration is intended both between physical resources providers and application providers and between PRPs and users during the process of product design (co-creation). The main issue regarding the collaboration between enterprises concerns the division of the value added of the product given by their cooperation and the difficulty of good value addressing method. While collaboration is a future trend of CM implementation, application-specific issues and integration with other technologies are hot research topics in the CM field. Examples of application specific issues are field-level SOA approaches, multi-cloud integration and 3D printing. According to Y. Liu et al. (Y. Liu et al., 2018), the main innovative technologies that are being integrated with CM are 3D printing, big data and industrial internet, 3 out of the other 5 smart technologies that characterize Industry 4.0.
In Liu’s article also key research areas are highlighted: concept, connotation and definition of Cloud Manufacturing, standardization of enabling technologies and procedures for the management of CM systems, service scheduling and information security.
It is also interesting to mention F. Liu et al. study (F. Liu et al., 2017) about the integration between cloud manufacturing and cyber-physical systems: integrating these two technologies machining tools can be directly monitored and operated over the internet from clouds.
1.4.3 Industrial Analytics
“Over the last few years, the volume of data worldwide has exploded with the amplified use of various digital services that continuously generate massiveamounts of heterogeneous, structured or unstructured, data resulting in what is now called big data”
(Kambatla et al., 2014)
Big Data (BD), as the name suggests, is the name given to the incredible amount of data that has been produced during the last years and keeps on being produced every day. Kambatla’s definition highlights the two main aspects related to Big Data: the increasing number of available information permits optimal decision making, but, on the other hand, creates huge difficulties to traditional data processing methods.
The definition above also mentions the two concepts of structured and unstructured data: among the difficulties brought by BD management, the fact of dealing with unstructured data is crucial.
While structured data are organized into a formatted repository, usually a database, so that its elements can be easily retrieved for processing and analysis, unstructured data are not pre-organized and do not have a data model; they can contain any type of data so that these irregularities and ambiguities make it difficult to use traditional programs.
Most data scientists and experts summarize Big Data characteristics with 3 Vs: volume, velocity, variety (Furht and Villanustre, 2016). Volume refers to the enormous amount of data generated continuously from millions of devices and applications, Velocity takes into account the speed at which data are generated and Variety indicates that data come from different sources and so they are different as well. Emani et al.( Emani et al., 2015) and Gandomi and Haider (Gandomi and Haider, 2015) added some more characteristic to Big Data in order to better define them: Verification (processed data conform to some specification), Value (pertinent information can be extracted for many sectors), Complexity (it is difficult to analyze big data because of evolving relationship) and Immutability (collected and stored Big Data can be permanent if well managed) (Oussous et al.,2017).
To better understand why Big Data have the characteristics aforementioned, it can be useful to make a little digression on the genesis of BD application.
Fig.6 illustrates the main technologies that contributed to the creation of BD applications starting from the Sixties. In particular, the top portion of the figure shows the technologies, while the bottom one summarizes the requirements of every era. In particular, the requirements are: storage architecture, that offers criteria to manage flows of data in the system, computing distribution, that refers to the different physical components that work as a unified system, storage technology, that refers to the technology used to store data, analytics technology, that refers to the technology utilized to transform data into information and user experience that refers to the overall user’s interaction with the system (Yaqoob et al., 2016). Fig. 6 Genesis of big data applications, including the gradual development of the architecture of candidate applications from early desktop to recent versions (Abolfazlietal., 2014) While requirements addressed to technologies become always more difficult to accomplish, the volume of data continues to increase.
The last two paragraphs make it easier to understand why the Big Data Management is such a hot issue: the high standards of customers together with the huge and complex amount of data makes it difficult to find good storage and processing data methods while also assuring data security.
Siddiqa et al. (Siddiqa et al., 2016) studied in 2016 a taxonomy of Big Data management process highlighting its stages and sub-stages, the available technologies for each stage, their pros and cons and an overview on the security issue. Fig. 7 Big Data Management Process The figure above summarizes the big data management process: data coming from big data sources are firstly stored following three sub-steps (clustering, replication and indexing). Data are then pre-processed: this stage comprises data cleansing, transformation, integration, transmission, reduction and discretization. The following stage is the process one: data here are classified, integrated and transformed. The last stage, harnessing decision-making, is prediction.
Security management is invoked in parallel to all activities.
Starting from Big Data sources, these can be grouped into Internet of Things, self-quantified, multimedia and social media data. IoT data are generated by any smart device that has sensors included, self-quantified data come from
images, audios and graphics objects while social media data, as the name suggests, are derived from Facebook, Twitter, Linked-In, Flickr, Instagram and all the other social networks.
Data are then transmitted to storage devices, usually exploiting cloud computing technology. In particular, data are firstly summarized into groups on the basis of similar features in the clustering stage. In this way, the storage required is reduced since results are grouped and presented concisely. During the following stage, the replication, data are made available and accessible to user sites by copying them. The main issues here are related to the consistency and trustworthiness of the copies and their propagation time.
The last sub-stage of Data Storage is indexing: obtaining optimized query execution results is one of the biggest challenges of big data storage; indexing improves performance and future efficient retrieval of stored data while preserving space resources.
Pre-processing is the stage following data storage, here raw data, that can be inconsistent or incomplete and have many errors or unusable parts, is transformed into an understandable format ready for a more efficient analysis process.
The sub-stages of pre-processing can be described as follows:
- Data cleansing: is the process of identifying and removing inaccuracies, incompleteness and inconsistencies of data;
- Data transformation: consists of normalization and aggregation;
- Data integration: combines the data views in order to provide a single view of the data distributed over different sources;
- Data transmission: transfers data to a data storage infrastructure, commonly a data center, object storage system or distributed cloud storage for subsequent processing;
- Data reduction: reduces large datasets in size for use by nearly all real-time applications;
- Data discretization: is an essential pre-processing step of decision tree learning, it refers to increasing the continuous attribute intervals such that the obtained values will be reduced.
The central stage of the big data management process, the data processing, has two main goals: to develop effective data mining methods that can accurately predict future observations and to gain insight into the relationship between features. To achieve these goals, the classification sub-stage is crucial: it classifies objects into different groups or classes to find unknown and homogenous patterns for analysis. The main model used is the MapReduce programming model comprehended in the Apache Hadoop technology.
As anticipated, the last step, the prediction, is aimed to help in the decision-making process: thanks to prediction methods, relationships between dependent and independent variables can best be done.
Security management is an issue that touches all the stages of the big data management process since the possibilities to nick security are a lot. In particular, security problems can be divided into privacy, integrity, confidentiality and availability.
Privacy problems regard the risk of data leakage. Integrity of data is critical for collaborative activities in which organizations share information with each other to help managers in the decision-making process. If integrity within the companies’ network is not guaranteed, there would be a lack of trust such that all the efforts in sharing information would be useless. Therefore, we can conclude that the main aim of integrity management is preventing illegal or unauthorized changes in data.
Confidentiality can be achieved by protecting data from unauthorized and unintended users. Instead, availability is important to guarantee quality of service to customers.
To conclude this overview of big data, it could be interesting to report some research areas which need to be explored in the future:
- Graph processing: graphs are useful to visualize data, having a huge amount of data, this capability is even more important. The downside is that the higher the amount of data is, the more difficult is to organize them into graphs. That’s why improvements in this field are needed; - Heterogeneous computing: given the completely different data sources that generate BD, big data are usually unstructured, this gives lots of problems in their processing. So, extensive research and field expertise are required to enable heterogeneity support in existing processing technology; - Hybrid computing: also different processing technologies put together could improve big data management, further studies in this field are then needed; - In memory processing: storing data on systems based on disk and relational databases causes delay in query response time, that’s why the processing of large amounts of data stored in an in-memory database is a future research area that needs to be explored.
1.4.4 Additive Manufacturing
“Additive Manufacturing is defined as the process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies, such as traditional machining” (ASTM Standard) The technologies included in the wider term Additive Manufacturing (AM) are stereolithography (SL), fused deposition modeling (FDM), selective laser sintering (SLS) and 3D printing (3DP). All these technologies were born, more or less, in the same period, therefore they all are very similar and this is the reason why we can include all of them in the concept of AM.Among the aforementioned technologies, the first one to be commercialized was SL: a concentrated beam of ultraviolet lamp is used to solidify a liquid
photopolymer by drawing a two dimensional (2D) levels in the form of a contour and then an infill. Once the beam has completed a single level, the production platform will then move downward in the z-axis, another level of photopolymer is put and everything is repeated until the final level is done (Mellor et al., 2013). Laser sintering and laser melting, FDM and 3DP all adopt similar production processes.
Additive Manufacturing was born around 20 years ago mainly harnessed as a Rapid Prototyping (RP) technology, that still is its main application. As AM machines improved, RT (Rapid Tooling) became established and, more recently, AM has started to be used to produce end-use parts or whole products (Rapid Manufacturing-RM).
Even though the potentialities of Additive Manufacturing in the RM are huge, the overhyping of AM machines as “just press and print” have disfranchised industrial clients to use them outside the RP field. Actually, even if 3D printing technologies were born two decades ago, researches along these years mainly focused on the capabilities of machines, without exploring how to insert them in a manufacturing system. In particular, according to Mellor et al., the areas that still need an in-depth analysis are: - Manufacturing processes and materials; - Design; - Management, organization and implementation.
As Holmstrom et al. (Holmstrom et al., 2010) underlined, AM offers a lot of benefits: small batches are feasible, design can be changed quickly, products can be optimized by function, custom products can still be economical, waste is reduced, design customization and shorter lead times are possible. These benefits are among the main reasons why the implementation of Rapid Manufacturing should be pursued. Moreover, since AM is still underused in industrial applications, its adoption could become an important competitive
weapon. As Porter pointed out in his influential work on competitive strategy of 1985, technology innovation is probably the most important factor that changes market shares and that causes the demise of dominant firms.
The 2013 paper by Mellor et al. indicates the five internal factors that most influence the implementation of AM: strategic, technological, organizational, operational and supply chain. Also external factors such as competitive pressures, environmental, legislation and customer requirements have to be considered (Fig. 8).
- Strategic factors: before implementing AM technologies, a check on the consistency between the company’s strategy and the benefits offered by AM has to be done.
In particular, it could be useful to adopt Additive Manufacturing if the companies’ products satisfy the following requirements (market-driven approach)
è High degree of customization
è Increased functionality through design optimization è Low volumes
Another approach is also possible: the technology-push strategy. In this case, the company decides to buy 3D printing machines and to adapt its production to the AM characteristics. - Technological factors: when considering whether or not using AM machines, technology constraints have to be analyzed. In general, the material range for AM processes remains low while machine and material costs are high, technical standards are lacking causing additional barriers to adoption.
- Organizational factors: organizational factors take into account both changes of jobs and tasks inside the company and cultural barriers that changes always bring. Cultural barriers can have different motivations: for example, the change in jobs and skills mentioned above, the fact that the
company may be a RP one and shifting into RM and the rethink of design for manufacturing by designers and engineers.
- Operational factors: AM offers an incredible increase in design freedom, but some constraint still has to be considered. Designers and engineers have to study and get skilled in these new designing way. Also preprocessing and post-processing have to be taken into account: it is generally thought that producing with AM technologies doesn’t require tools nor any production support activity; this is false. No technology is currently able to create net shape parts.
- Supply chain factors: AM implementation is at the intersection of two supply chains; the first comprehends the supply chain from the machine sellers to the purchaser of the technology, the second involves the purchaser and his
suppliers and clients.
Therefore, the integration between the two supply chains is crucial. Particularly, the help of the vendor during the technology installation is
decisive for the implementation.
Other two issues related to the supply chain are the tendency of machine suppliers of being also raw materials suppliers and decisions regarding the production location. The first issue means that suppliers have the bargaining power, while the second is related to the fact that normally people think to easily distribute 3D printers not considering that this implies big investments.
Fig. 8 Framework for AM implementation While the aforementioned article of Mellor et al. focuses on the way and the factors that most influence the implementation of AM in a company, the 2017 paper by Eyers and Potter (Eyers and Potter, 2017) underlines the importance of considering the whole industrial system while implementing AM machines. Since usually the focus of researchers and scholars is mostly on single machines, some aspect they generally consider as benefit is instead a disadvantage when considering the system as a whole. For example, cost is usually associated just to the cost of the machines and of raw materials, while also design cost and pre and post processing costs have to be considered. Competitiveness can significantly improve if the full-system costs are targeted. For quality the situation is very similar: it doesn’t depend just on the capability of the single machine but on the whole system. Preprocessing and post-processing are crucial activities such as the integration between design and production.
Usually speed is considered as one of the major advantages of Additive Manufacturing, but actually the lead time to produce a good depends on the time spent to undertake every activity of the process. Indeed, speed can still be a benefit if all the activities are well integrated, otherwise the speed of the AM machine doesn’t coincide with the speed of the whole process. The dependability of an operation is the ability to deliver products on time and of the correct quality. Dependability takes into account all the parts of a system, if a part fails the overall performance is degraded. Dependability is a trade-off between delivery time and quality: this trade-off is particularly crucial since AM technologies were born and still are massively applied for Rapid Prototyping, where delivery time is the most value-adding characteristic. Companies that have a RP background have to notably focus on products quality if wanting to enter in the Rapid Manufacturing field.
As speed, also flexibility is considered as one of the main benefits offered by AM. Flexibility is generally defined as the capability to react to changes within a short period of time. While speaking of 3D printers, flexibility is identified with their capability to produce on-demand, to create a wide range of products or to produce complex geometry parts. Actually, flexibility, in a manufacturing system, concerns how the resources can be leveraged to ensure that the whole system can adapt to demand properly.
In conclusion, AM offers great opportunities but, while researches and applications on AM machines are well developed, studies of production systems using AM are lacking. This is why a company willing to implement AM has to consider all the trade-offs and sustain an in-depth investment investigation.
1.4.5 Advanced Automation
“Advanced automation refers to sophisticated automated systems, ideally with the additional capability for self-maintenance and repair, mostly requiring little or no human interaction to operate, apart from top-level guidance”. (adciv.org, 2018) The definition above stresses that Advanced Automation (AA) is different from industrial automation because it’s “sophisticated”. As traditional industrial automation, AA requires poor human help, just at the top-level to engineer the automated system and to make seldom controls. In fact, Advanced Automated systems are able to self-maintain and repair, this decreasing a lot human need. Actually, this is the main reason why, during the last years, automation has become more and more important in the companies of US and Europe: to compete against low-cost countries where the cost of labor is way lower, western countries have found in automation a means for staying competitive by replacing manual work with machines.But still, in manufacturing companies, both human and technological resources are needed. In modern automated systems, the balance between these two resources has changed causing an increase in the use of technology together with an improvement of technological resources’ capabilities and a change in human resources skills.
Actually, the implementation of automation doesn’t affect only operational performances but also has huge impact on workers. Achraya et al. (Achraya et al., 2017) divide literature on automation into two categories, following the two main fields in which automation has big influences: industrial engineering and operations management literature that studies the effects on the operational performances and industrial sociology literature focusing on the effects that automation implementation has on workers.
The impacts of AA on operations management and workers are incredibly important considering the huge changes that it brought in the companies where it was implemented. Actually, AA solutions are characterized by high cognitive ability, adaptation to the context, collaboration and reconfigurability. AA solutions are different from the typical industrial robots characterized only by mechanical and repetitive operations. The distinctive features of these new technologies are, therefore, the ability and ease of integration with the environment, self-learning, automatic self-guidance, the use of vision and pattern recognition techniques and, last but not least, the capacity to interact with other machines and work side-by-side with the operators, not in a separate but in a complementary way. The term "advanced" therefore underlines the fact that this cluster should not include robots and automation solutions that are classic and already in use since more than ten years, but something more innovative. Modern mass production (cars, appliances, commodities), which require more and more personalization and therefore a certain flexibility, would be unthinkable today without the support of automation.
Even though automation is meant to replace human work, this is not always feasible. Lindstrom et al. (Lindstrom et al., 2006) individuated the activities that are better to be held manually, together with those for which automation is suggested; their paper also provides an overview on the benefits and the disadvantages of automation implementation.
Assembly, packaging and maintenance are those activities that companies prefer to do manually, together with tasks related to the production of occasional goods, small batches and products with a short lifecycle. The motivation that leads companies to avoid automation implementation for the aforementioned activities is that the related complexity would be way larger than the benefits brought.
For activities such as machining and manufacturing, automation would instead bring a large range of advantages: cost savings within the production, higher efficiency, increased competitiveness and productivity, improvement of the working environment and enablement of production exploiting a minimum number of employees. Improvement of working environment is mainly connected to the use of automation in bad ergonomic conditions and to the change in the jobs needed that switch from repetitive to knowledge-intense ones.
Even if automation can create great opportunities when adopted within the right activities, it also has downsides: since automation brings more complexity inside the company, the system could result more difficult to handle, especially if customized products are required. Other issues identified by companies are related to the adaptation of products for automated production and the lack of time for planning the usage of automation or for training of the employees. Others indicate the difficulty to get payback on investments as the main hindrance for automation implementation while some address the lack of competence of the operators as the main problem.
In the previous paragraph the advantages and disadvantages of automation implementation were highlighted, hereafter an examination of the factors influencing it will be analyzed following the study of Achraya et al. (Achraya et al., 2017). After a first presentation of the factors and sub-factors taken into account, they are prioritized according to the AHP (analytic hierarchy process) method. Thanks to this analysis, companies can understand on which aspects direct their first actions managing the low-priority factors at a later stage.
- Environmental factors are the first macro group of factors and can be defined as the composition of all those identifiable elements in the physical, demographic, cultural, economic, regulatory, political or technological environment that can influence the operational, survival and growth of an
organization. Environmental factors can be furtherly divided into government policies (tax incentives, interest loans, technology assistance, export/import rules, personnel training, ..), labor union, vocational education and unemployment (because it is well known that automation, though it creates new types of job, reduces employment at a global level). - Driving forces factors influence the decisions and policies that an
organization makes and adopt to stay competitive. The sub-factors included in this area are: management support, that depends on the firm size, the organizational objectives and the organization structure, effective strategic planning and deployment of plans, where planning takes into account all the budgeting, scheduling and resource activities, competitive priorities of the organization, that depends on the market development, on the share and on the integration of production, database management and CAD. - Technical factors impact how an organization acts towards the equipment used within the organization’s environment. The three main sub-factors comprehended in this group are advanced manufacturing technologies (AMT), production procedures and man-machine interactive approaches, also known as ergonomic models.
The output of the AHP analysis shows that the technical factors are the most important ones, followed by driven forces and environmental at the end. Actually, no automation implementation can occur without the presence of the right machines and equipment and, with new machines inside a company, procedures and standards to well use them are needed. The basis of automation is the new way of interacting between humans and machines, so also ergonomic models are a crucial factor.
Driven forces factors mainly refer to the behavior of automation towards the implementation of automation. Since competitive factors have the highest priority, this means that managers give particular importance to the opportunities of the market and on how to exploit them thanks to automation.
Also planning is an important factor within this group, it is beneficial for manufacturing industries to provide a proper plan for automation implementation.
Environmental factors should be considered especially as of government policies: government policies, positive or negative, influence a lot the decision of implementation. Another very influent factor for a good insertion of automation inside a manufacturing company is vocational education: new technologies imply a huge effort in workers’ education both to teach them how to use them and to accept them.
Considering only the sub-factors, coherently with the results found when considering them within the macro groups, priority has to be given to advanced manufacturing technology, process organization and restructuring, competitive priorities, ergonomic models, management support, government policies and planning. The remaining factors all have similarly low priorities.
As pointed out at the beginning of the paragraph, all the factors are important, but the factors with high priority are the ones that have to be immediately considered when implementing automation. As the priority decreases, the urgency decreases as well: the low priority factors can be addressed at later stages.
