Internet of Things (IoT) for Dynamic Change Management in Mass Customization Chin Yin Leong Ichiro Koshijima Nagoya Institute of Technology Nagoya Institute of Technology Aichi-ken, 466-8555 Aichi-ken, 466-8555 Nagoya, Japan Nagoya, Japan cyleong1984@yahoo.com koshijima.ichiro@nitech.ac.jp ABSTRACT 1 INTRODUCTION Mass customization manufacturers always find it challenging to Mass customization refers to the process to deliver wide-market produce high quality products at the lowest possible cost with min- goods and services, which are tailored to satisfy the specific needs imal lead-time. The challenge is even more severe for these man- of the customers. The implementation of this concept, initially in- ufacturers when it comes to their survival in today’s dynamically troduced by Davis [2], has been supported fundamentally through changing market where customers drive the process by searching theoretical and empirical studies [3–9] . Although many companies for the information they need in order to create their own products have operated based on this business model, only few managed and services [1]. As customer-centric mass customization manu- to achieve success [4]. This is because mass customization manu- facturers, organizations should increase their change-adaptability facturers face difficulties to effectively execute change process to to maintain their competitive edge in the ever-growing and ever- optimize their market and to meet the diverse product demands changing market. A successful mass customization strategy should by their customers. The change process for mass customization involve developing production lines that are highly agile to recon- should be very sophisticated to be implemented mainly due to the figuration, leading towards reduced setup time in order to cope complexity of equipment and labor used along with production with the expected or unexpected changes to the production process. lines [10], limiting the potential for mass customization. There is Besides this, the importance of ‘zero mistakes’ in all activities along much literature published related to mass customization. However, the value-creation process should also be prioritized. Therefore, the literature related to change management for mass customiza- the research aims to investigate the feasibility of IoT application tion is scarce. On top of this, Construction Industry Institute (CII) towards effective change implementation for mass customization research team also found out that there are no formal processes in a dynamic manufacturing environment. This paper presents an to assure that change in a mass customization setup can be prop- architecture for dynamic change management that could provide a erly implemented [11]. Thus, the potential of mass customization new competitive strategy for mass customization manufacturers. A implementation cannot be fulfilled. set of IoT devices is used as illustrative examples for the dynamic In this modern dynamic changing market, customer orders can change management implementation in mass customization manu- often vary in any moment of time even after the components/parts facturing. The architecture of such dynamic change management have already been delivered to the production line. Therefore, mass is to turn operation data in dynamic form and link all together, customization manufacturers must always be able to bring essential permitting them to integrate rapidly in the most optimized com- change ability of manufacturing processes to a higher level to assure bination or sequence required to perform change instantly. This effective mass customization environment. They should be able to architecture allows for well-informed real-time decision-making provide quick response and to swiftly adapt to product/process and more importantly, provides the manufacturers with the ability change to create a competitive edge over their competitors. Not to predict problems before they occur. capable of doing so would result in them drowning in the ever- growing changes of their market. KEYWORDS Change requirements from customers are forcing mass cus- Mass Customization, Internet of Things, Change Management tomization manufacturers to redesign and to modify product fre- quently. Typically, such change data is expected to be sufficient in supporting certain personnel in handling various mass customized products/process. However, traditional change process procedures commonly used in production lines interfere with the factory’s dynamic environment, as the information associated with the mass customization process is enormous and complex. Therefore, an Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed effective operating system in factory floor is required to ease the for profit or commercial advantage and that copies bear this notice and the full citation implementation of the efficient change process in a mass customiza- on the first page. Copyrights for third-party components of this work must be honored. tion environment. An adequate management system should be For all other uses, contact the owner/author(s). © 2017 Copyright held by the author/owners. prepared for the relationships and the interactions among tasks, SEMANTiCS 2017 workshops proceedings: LIDARI, September 11-14, 2017, functions, departments, and organizations, which promotes the flow Amsterdam, Netherlands of information, ideas and integration of dynamic change process. LIDARI2017, September 11, 2017, Amsterdam, Holland Chin Yin Leong and Ichiro Koshijima 1.1 Conventional Change Process in a Mass plans and facility allocations to avoid significant impact on the Customization Environment productivity time line. The overview of the conventional change process in mass cus- 1.2 Enabler for Efficient Change Process in tomization organization is showed in Figure 1. This shows that conventional change process practice is associated with certain Mass Customization difficulties and constraints. The formal use of conventional change Changes will not only affect production planning but will also influ- process is a centralized structure with extra layers in the hierarchy. ence cost and scheduling, directly or indirectly. When the impact of When a change is triggered by customer, the actual change process change can be predicted, then only time, cost and resource can be in production floor will only be started when the approval docu- allocated to affect the change. Well-managed change is important ments have been released. The impact of change will be severe if to avoid unwanted problems. Therefore, mass customization manu- it occurs when the production has already started. In order to get facturers must have the ability to respond to change effectively to the documents released from the engineering team, local operating minimize any form of negative impact on the production. Imple- team could have missed the golden hours to prepare for the required menting effective change process is a challenge in mass customiza- change, possibly causing late delivery of the product. Another issue tion manufacturing mainly due to the complexity of equipment and that arises in the change process is the lack of operational data that labor used along the production line [10]. is connected to enterprise applications. Neither the R&D team nor With the unveiling of the fourth industrial revolution or Industry the operation team has the actual manufacturing data when the 4.0 in recent years, Internet of Things (IoT), as one of the key ele- change is taking place. ments for industry 4.0, has become a hot topic among industrialist. IoT refers to physical devices that are inter-networked with elec- tronics, software, sensors, actuators and network connectivity. The internetwork created enables these physical devices to receive and exchange essential information in real-time. Internet technologies could be the key enabler for efficient dynamic change management in mass customization manufacturing. Internet technologies could prove to be essential in setting up a dynamic network that is more than capable of handling high-intensity information for efficient manufacturing planning and control system in real-time for mass customization manufacturers. This will provide visibility and con- trol of the factory floor by connecting human operators, sensors and operations data across multiple machines and lines, allowing Figure 1: Centralized change process structure for cus- for the ability to monitor performance and to identify inefficien- tomized product cies along the production line. The potential of IoT application in Industry 4.0 has lightened up once again the feasibility of mass customization. Therefore, this research aims to investigate the fea- Besides this, human operators, who are disconnected from rele- sibility of IoT application towards effective change implementation vant and essential production related electronic data (product data for mass customization in a dynamic manufacturing environment. management), will be confused by the delivery of new components This paper introduces the primary/fundamental concepts and before any paper-based operating manuals are provided to them. technologies of IoT that could benefit dynamic change management The human operators are constraint from interacting with avail- in mass customization manufacturing. This paper also proposes an able documents that is pre-formatted. Besides, the process to get architecture of dynamic change management, showing all operating the documents released is often associated with long lead times, modules that are connected for well-informed real-time decision- which is caused by extensive document management. This is time making to provide the ability to predict problems before they occur consuming to check-out the old documents and to prepare new in factory floor. The development of IoT for industrial application documents to send for approval. Centralized structure for change can be extended to a higher skill base for information technology process can be efficient if the manufacturing environment is very and computer-integrated manufacturing in order to implement stable and the parts changes very little. The bureaucracies of this dynamic change process in mass customization factory floor. This kind of change process are static and unresponsive to changes in will help to achieve sustainable competitive advantage for mass the environment that have limited the flexibility and speed of local customization manufacturers in the ever-growing changes market. decision-making. On top of that, human operators, in a mass customization sys- tem, tend not to question the basic design of the product that they are assigned to assemble. They would assume that it is what the customers want. This will lead to a high tendency of errors in as- sembly of the changed components, which might result in costly reworks and delays. As a consequences, OEM manufacturers could face difficulties in order to identify possible disturbances or changes in the need for the changed component and to readjust production Internet of Things (IoT) for Dynamic Change Management LIDARI2017, September 11, 2017, Amsterdam, Holland 2 PARADIGMS FOR DYNAMIC CHANGE MANAGEMENT A dynamic change management approach is adopted alongside with the integration of internet technologies in order to implement an effective mass customization environment. A dynamic change management architecture composes of three components, namely: i Dynamic linkage in operating field ii Real-time environment iii Monitoring and early error detection 2.1 Dynamic linkage in operating field One of the issues limiting the success of mass customization in (a) change processes is the lack of an integrated network to avoid man- ufacturing data loss along the production line. There is some useful computer-aided engineering software for engineering change man- agement. However, the electronic data is often disconnected from the human operators, who are working on the front line of pro- duction. On the other hand, automated systems cannot effectively and efficiently distribute planning and control tasks to human op- erators on the factory floor, who have the first-hand experience of the production process and could influence the process by their actions [6]. Hence, a participation of human operator in dynamic change management cannot be neglected. To complete the change activities, the human operator needs information or collection of data and requirements. However, communication methods between equipment and human operators are still cumbersome. This is be- (b) cause human operators’ hands will most probably be occupied with product assembling task. Internet of Things (IoT) presents an interesting approach to effec- tively integrate numerous connected devices that rely on sensory, communication, networking, and information processing technolo- gies with interface processors to form a global dynamic network infrastructure [20]. Myo armband has been used here to study human-computer interaction in the factory floor. Myo armband is a wearable technology that reads electrical activity of user’s mus- cles to control a robot with gestures and motion under hands-free environment. The interaction of controlling Parrot Bebop 2 by the gestures from Myo armband is presented in Figure 2. Gestural inter- action devices like Myo armband could be beneficial for the factory floor as it can be used without an external static sensor. Thus, the (c) human operator will have the freedom to still move around while performing their routine work properly. This kind of gestural in- teraction device provides an advantage where the human operator is not required to carry any mechanical devices to remote control equipment from a certain distance. Gestures are instinctive, human beings are skilled, and little thought is needed [21], and will allow for the human operator to focus on the production task itself. Such gestural interaction device will enable human operators to remotely control computers and machines in the factory plant for information support during occurrences of an unexpected change task. Application of gestural interactions via Myo armband will sig- nificantly improve the efficiency of dynamic change management implementation. With the assistance of such advanced wearable technologies, human operators can now fulfill their potential by (d) taking on the role as strategic decision-makers and flexible problem- solvers on the factory floor [22]. Figure 2: Remote control of drone using gestural commands based on Myo armband LIDARI2017, September 11, 2017, Amsterdam, Holland Chin Yin Leong and Ichiro Koshijima Another challenge as discussed above with relation to dynamic a multitude of data sources that need to be monitored and con- change management for mass customization manufacturers in- trolled in the manufacturing system. Connecting human operators, volves handling of higher-intensity information of the production machines and smart devices in the factory will generate big data. processes. The higher-intensity information refers to the raw data The big data needs smart infrastructure to capture, to manage and input, originating from a multitude of data sources that need to to process them within an acceptable time frame [20]. The emerg- be monitored and controlled in the manufacturing system. With ing and developing technology of cloud computing is considered growing number of smart devices used in the factory floor, it is as a promising computing paradigm for this big data. essential to establish high efficiency ways to tie into already ex- Mass customization can utilize numerous cloud platforms for big isting manufacturing information technologies through the use data management such as ThingWorx, Google Cloud, and Bosch IoT of standardized, platform-independent interfaces such as OPC-UA suite. Capitalizing on IoT for dynamic change management requires [22]. In addition, most of the smart devices, such as drones, Myo network infrastructure as IoT will generate an unprecedented vol- and Leap Motion, do not have sufficient computational power to ume and variety of data in the factory floor. Internet-based comput- process the sensor signal carrying the raw input data. Thus, an ing is required to provides shared computer processing resources interface processing device, such as laptop, tablet or raspberry PI, and data to multiple computers and other devices on demand. Cloud will have to be employed in the production floor to process the computing is not enough for real-time data processing due to its raw input from the aforementioned smart devices. The processed inherent problems, such as unreliable latency, lack of mobility sup- raw input data will then only be possible to be integrated into the port and location-awareness [? ]. Fog computing is a new kind of manufacturing system. network infrastructure that provides resources for services at the edge of the network. The fog computing extends the cloud com- puting to be closer to IoT devices through provisioning, trimming 2.2 Real-time environment and pre-processing the data before sending to the cloud. With the In a modern dynamic changing market, customer orders can often right tools, mass customization manufacturers will be capable of vary in any moment of time even after the components parts have managing the manufacturing data for greater agility in the change already been delivered to the production line. Therefore, mass cus- process. tomization manufacturers have to be highly agile in responding to the requested changes in order to avoid any potential slowdowns or bottlenecks across the production line. Implementation of a dy- 2.3 Monitoring and early error detection namic network linking equipments and human operators provides the means to take appropriate actions during a dynamic change Mass customization capability of a firm is determined by its ability process. Forewarned is forearmed, and the critical factor that deter- to produce customized products with cost effectiveness, volume mines the effectiveness of project change control is how fast the effectiveness, and responsiveness [8]. Errors in producing custom- right people is aware of the change and takes necessary actions made products will be extremely costly as compared to errors in pro- accordingly. ducing mass products. This is because the custom-made product is Changes in manufacturing conditions could be executed quickly unlikely to be sold to others aside from the customer who requested by relying on latest production related information. Thus, the adop- for it. Apart from cost, mistakes and errors during production could tion of real-time capability will provide manufacturers with real- cause late delivery of product to customers. Not to mention, these time information to make key manufacturing decisions. To execute could cause the customers to lose confidence towards the mass a successful change strategy, production line needs to be proac- customization manufacturers. Thus, a closed monitoring and early tive in reconfiguring and reducing setup time needed to cope with error detection method is critical to ensure the customized product changes in production. Greater planning capability is one of the is correctly built and delivered on time. Deployment of a system important criterion for mass customization manufacturers to foster that stresses the importance of ‘zero-mistakes’ in production is dynamic changes. Real-time information platforms provide greater indispensable for a dynamic change in mass customization manu- planning capability by allowing manufacturers to view up-to-the- facturing [15]. Therefore, the last component for effective dynamic minute production progress in factory-floor. change management requires monitoring and early error detection Planners and managers can use the production progress data along the production line of a mass customization manufacturer. to create better production plans whenever unpredicted changes In recent years, researchers used RFID technology to identify, are triggered. They can also identify disturbances or changes in trace and monitor objects locally or globally [12, 24]. However, the need for parts and readjust production plans or facility alloca- there is a drawback to RFID systems where they require human op- tions before these changes significantly impact productivity. Real- erators to tag and to read the tag manually. These will pose certain time operation data executed in the factory can then be imme- difficulties to implement this technology in large scale involving diately recorded in electronic documents for reporting, eliminat- complex products, such as vehicle or heavy-duty equipment produc- ing/reducing errors associated with human factors. This will save tion [24]. On top of this, the cost of RFID tags is another challenge the time to digitize the data collected in paper forms [12]. for manufacturers who produce large-scale products that make up Another challenge with relation to dynamic change manage- thousands of parts. The work in progress visibility and traceability ment for mass customization manufacturers involves handling of could be further improved through integration of more advanced high-intensity information of the production processes. The high- IoT technologies to form an autonomous surveillance system for intensity information refers to the raw data input, originating from early warning. Internet of Things (IoT) for Dynamic Change Management LIDARI2017, September 11, 2017, Amsterdam, Holland Using modern smart surveillance systems that can be integrated Each component is associated with a set of unified requirements. into computer vision and artificial intelligence community, these For dynamic linkage in operating field, the dynamic operating data will allow for real-time monitoring and detection, which is essential plays an important role as information support for the change pro- for dynamic change management in mass customization manufac- cess. The efficiency of information support to human operator is turing. With computer vision technology, video captured through improved by the use of human-computer interaction devices that en- the factory’s surveillance system can be transformed into digitized able human operator to remote control electronic devices or robots data for object detection and recognition [25]. The method will under hand free environment. This has to be supported by data allow for object detection and will capture manufacturing data for interface process devices to complete the data integration activities. seamless real-time synchronization with material and associated Communication technology, fog computing and cloud computing information flow on the factory floor. Using object recognition tech- are fundamental elements to provide real-time platform in dynamic nology, it will improve work in progress visibility and traceability change management. Efficient change implementation need mon- by enabling real-time adaptive decision mode to optimize opera- itoring, thus autonomous surveillance and visual computing are tional logistics [12]. The data generated from this visual computing must have elements for this paradigm. system can be integrated into existing manufacturing system based The approach for IoT deployment in dynamic change manage- on the provided data interface platform. ment for a mass customization manufacturer is illustrated in figure For automated cameras to be effective in error detection during 4. The combined platform to gather and consolidate changes of data manufacturing processes, the viewing range of the camera has to be across lines and locations are summarized in the given diagram. able to cover the whole factory floor. Implementing closed-circuit television (CCTV), which has limited camera viewing, could be costly. This is because during product assembly, the camera views could be impeded by different product orientations. Therefore, mass customization manufacturer could adopt unmanned aerial vehi- cle (UAV) equipped with a camera be used to provide substantial flexibility as compared to CCTV. The video stream from UAV is connected to the computer in real-time. UAV does not require a mounting platform and can fly to any location, thereby able to take photographs or real-time videos from a range of areas inside the factory. The UAV can also fly autonomously without colliding with obstacles and has faster speed as compared to ground robots [26]. This is helped by the technology advancements where latest UAV is also equipped with indoor navigation systems for autonomous navigation in an indoor environment. The UAV can also be pro- Figure 4: IoT deployment in dynamic change management grammed to perform scheduled flight for inspection of product for mass customization manufacturer assembly progress from time to time. Such autonomous surveil- lance system will be capable of providing real-time monitoring and error detection. This will reduce the risk of the wrong custom-made 4 CHALLENGES OF IOT INTEGRATION product being manufactured. Integration of IoT technology in dynamic change management for mass customization manufacturers are promising for higher 3 ARCHITECTURE FOR DYNAMIC CHANGE achievement. Despite the positive prospect, there are unsolved MANAGEMENT issues that could arise from both technological and usage point of view, such as 1) reliability and availability, 2) interoperability and From the three most important topics discussed above, it is summa- 3) security of IoT devices/applications. rized that a dynamic change management architecture composes of three essential components, namely: 4.1 Reliability and Availability i Dynamic network linking in operating system The availability of IoT must be accomplished at hardware and soft- ii Real-time environment ware levels in order to effectively implement the dynamic project iii Monitoring and early error detection change management as discussed in this paper. Availability of hard- ware refers to the existence of devices that are compatible with the functionalities and safety in the factory floor. Software availability refers to ability of the IoT applications that are compatible with existence manufacturing systems. Failure of IoT devices in field might put the human operator in danger and possibly affect the system operation. This could lead to further financial loss to the organization. Aside from the issue of availability, the reliability of the IoT systems should also be seriously considered. In order to have an ef- Figure 3: Dynamic change management architecture ficient dynamic change management in the factory floor, reliability LIDARI2017, September 11, 2017, Amsterdam, Holland Chin Yin Leong and Ichiro Koshijima check must be implemented in software and hardware throughout A framework is proposed to improve efficiencies of change man- all the IoT layers. Unreliable data gathering, processing and trans- agement in mass customization manufacturing processes through ferring could lead to disasters in the operating network, internally implementation of internet technologies. The challenges in having and externally. Reliability of the system should take into considera- successful dynamic change management in mass customization tion more critical requirements related to the emergency response manufacturing processes involve the need to create a dynamic applications [20]. As an example, let’s take the reliability and avail- network linking manufacturing equipment with human operators ability of the Myo armband. In the application of Myo armband to and also to have sufficient computational power to process so- remote control a drone, unreliable Myo armband detection of hand phisticated manufacturing data during the production phase. The gestures has been experienced, which will affect the outcome of the dynamic change management discussed in this paper requires ef- drone control, and lead to the crash of the drone. Such unreliability fective real-time monitoring and early detection of manufacturing of the system should be ironed out before full implementation of errors. the dynamic change management. IoT technologies are essential in the effective implementation of mass customization. The new concept of dynamic change manage- 4.2 Interoperability ment described in this paper, together with fast growing trends of the smart factory concept, will have major positive implications to Most of the IoT devices cannot directly connect with each other improve the competitive edge of mass customization manufacturers. because it requires a data interface process to manage the devices The importance of smart and dynamic change management to fully and get data out of one ’language’ and into another. For example, reveal the competitive strategy for mass customization manufactur- the drone itself cannot directly read the EMG raw data from Myo ing aligns well with the demands of the marketplace of tomorrow armband. Thus, a data interface processor, like raspberry PI, is em- has been presented in this paper. ployed to process the EMG raw data input from the Myo armband into programmable logic (PLC) data that can be sent to the drone to execute relevant movement commands. This would require a ACKNOWLEDGMENTS lot of custom code to account for all the different protocols and Author would like to acknowledge all of the community of Parrot brands of IoT devices used. The tedious setup to get all IoT devices Bebop, Myo Armband and Python users who published the infor- to effectively talk to each other and connect to the network of mation that author assembled, edited and merged to use in own existing manufacturing system has been holding back many com- tests. panies in getting IoT up and running in their factory. End-to-end interoperability of IoT devices/applications is still an open issue. REFERENCES Therefore, the need to handle a large number of heterogeneous [1] Jerry Wind and Arvind Rangaswamy. Customerization: The next revolution in raw data that belongs to different platforms remains a challenge in mass customization. Journal of interactive marketing, 15(1):13–32, 2001. designing and building effective IoT services in mass customization [2] Stanley M. Davis. From “future perfect”: Mass customizing. Planning Review, 17(2):16–21, 1989. manufacturing plants. [3] Frank Piller, Michael Koch, Kathrin Moeslein, and Petra Schubert. Managing high variety: how to overcome the mass confusion phenomenon of customer co-design. In Proceedings of the Proc. 3rd Annual Conf. of the European Academy 4.3 Security of Management (EURAM 2003), Milan, Italy, 2003. [4] Frank T Piller. Mass customization: reflections on the state of the concept. Many IoT technologies are limited to public use and are not suitable International journal of flexible manufacturing systems, 16(4):313–334, 2004. for industrial applications, which have strict requirements in terms [5] Giovani Da Silveira, Denis Borenstein, and Flavio S Fogliatto. Mass customization: of safety and security. IoT is vulnerable to cyber attacks as most of Literature review and research directions. International journal of production economics, 72(1):1–13, 2001. the communications are wireless. Besides wireless communication, [6] Jessica Bruch, Johan Karltun, and Kerstin Dencker. Assembly work settings many IoT devices has low capabilities in computing resources es- enabling proactivity-information requirements. Manufacturing Systems and Technologies for the New Frontier, pages 203–208, 2008. pecially passive components. Thus, they cannot support complex [7] Jianxin Jiao, Qinhai Ma, and Mitchell M Tseng. Towards high value-added security schemes. For example, the security of UAV is questioned products and services: mass customization and beyond. Technovation, 23(10):809– when a security researcher announced to public that he can hijack 821, 2003. [8] Pär Åhlström and Roy Westbrook. Implications of mass customization for opera- control other flying UAVs through a modified Parrot AR Drone 2 tions management: an exploratory survey. International Journal of Operations & with his custom software called SkyJack. This shows that security is Production Management, 19(3):262–275, 1999. still a significant open issue for IoT adoption in mass customization [9] Qiang Tu, Mark A Vonderembse, and TS Ragu-Nathan. The impact of time-based manufacturing practices on mass customization and value to customer. Journal manufacturing plants. Therefore, there is a need to have standard of Operations management, 19(2):201–217, 2001. and architecture for the IoT security in order to have a widespread [10] Jianxin Jiao, Lianfeng Zhang, and Shaligram Pokharel. Process platform plan- ning for variety coordination from design to production in mass customization adoption of IoT technologies in industrial. manufacturing. IEEE Transactions on Engineering Management, 54(1):112–129, 2007. [11] C William Ibbs, Clarence K Wong, and Young Hoon Kwak. Project change 5 CONCLUSIONS management system. Journal of Management in Engineering, 17(3):159–165, 2001. [12] Ray Y Zhong, QY Dai, T Qu, GJ Hu, and George Q Huang. Rfid-enabled real-time This paper investigates the feasibility of the feasibility of IoT ap- manufacturing execution system for mass-customization production. Robotics plication towards effective change implementation for mass cus- and Computer-Integrated Manufacturing, 29(2):283–292, 2013. tomization in a dynamic manufacturing environment. A set of IoT [13] Frost & Sullivan. From concept to production: a 5-step approach towards suc- cessful industry 4.0 projects, (accessed 26 June 2017). devices is used to demonstrate IoT application for dynamic change [14] B Joseph Pine, Bart Victor, and Andrew C Boynton. Making mass customization management implementation in mass customization manufacturing. work. Harvard business review, 71(5):108–11, 1993. Internet of Things (IoT) for Dynamic Change Management LIDARI2017, September 11, 2017, Amsterdam, Holland [15] Suresh Kotha. From mass production to mass customization: the case of the national industrial bicycle company of japan. European Management Journal, 14(5):442–450, 1996. [16] Peter Pikosz and Johan Malmqvist. A comparative study of engineering change management in three swedish engineering companies. In Proceedings of the DETC98 ASME design engineering technical conference, pages 78–85, 1998. [17] TAW Jarratt, Claudia M Eckert, NHM Caldwell, and P John Clarkson. Engineering change: an overview and perspective on the literature. Research in engineering design, 22(2):103–124, 2011. [18] BG Dale. The management of engineering change procedure. Engineering management international, 1(3):201–208, 1982. [19] C Elliott, L Paterson, G Clarke, B Whitby, and W Bardo. Autonomous systems: social, legal and ethical issues. The Royal Academy of Engineering, 2009. [20] Ala Al-Fuqaha, Mohsen Guizani, Mehdi Mohammadi, Mohammed Aledhari, and Moussa Ayyash. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4):2347–2376, 2015. [21] Yale Song, David Demirdjian, and Randall Davis. Continuous body and hand gesture recognition for natural human-computer interaction. ACM Transactions on Interactive Intelligent Systems (TiiS), 2(1):5, 2012. [22] Dominic Gorecky, Mathias Schmitt, Matthias Loskyll, and Detlef Zühlke. Human- machine-interaction in the industry 4.0 era. In Industrial Informatics (INDIN), 2014 12th IEEE International Conference on, pages 289–294. IEEE, 2014. [23] FERNANDO COSENTINO. Pyoconnect. http://www.fernandocosentino.net/ pyoconnect/, 2015. [24] Xiaolin Jia, Quanyuan Feng, Taihua Fan, and Quanshui Lei. Rfid technology and its applications in internet of things (iot). In Consumer Electronics, Commu- nications and Networks (CECNet), 2012 2nd International Conference on, pages 1282–1285. IEEE, 2012. [25] Richard Szeliski. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. [26] David Shim, Hoam Chung, H Jin Kim, and Shankar Sastry. Autonomous explo- ration in unknown urban environments for unmanned aerial vehicles. In Proc. AIAA GN&C Conference, 2005.