Authors: Herdís Hanna Yngvadóttir, Lawrence Jongi, Sandeep Raju
Abstract
This paper covers an experimental research methodology that is both qualitative and quantitative in investigating practicality of RTLS in manufacturing, lagging, path and position precision and accuracy. The research questions sought to answer the practicality of application and connectivity in line with sustainability in supply chain and internal and external logistics. The results showcased that the technologies tested were either accurate or precise and GEPS sensor could be applicable in production logistics environments. The implementation of RTLS is still in its research phase but the findings show promising results. However, there are still some challenges relating to sustainable implications of its usage.
Keywords — RTLS, interconnectivity, sustainability, production logistics
I. INTRODUCTION
Logistics and supply chain network visibility and collaboration through information sharing, both internal and external to the organization, have been the drivers to give an organization and its network the following benefits and competitive advantages: risk management, decision making support, safety, quality, customer-service, agility, lead time management, demand management, access to information and inventory management [1]. This has been seen as the development of visibility enabling technologies from SKU (Stock Keeping Unit), Bar Code, and QR Codes which needs a line of sight without sensors, to Cyber Physical Systems (CPS) that collects data using sensor technologies, wireless technology communication systems and transmit data to Industrial Internet of Things (IIoT) with no line of sight such technologies include Radio Frequency Identification (RFID tags), Smart Label (Real Time Locating System/RLTS), Geographic Information System (GIS), Global Positioning System (GPS) [2]. However, these technologies present the following challenges: budget constraints, risk of losing business, reluctant to provide data, conflict of interest, disparate sources of information, lack of standardization, lack of skills and knowledge, and supply chain complexity [1]. The purpose of this research is to focus on the RLTS technology and expose its behavior in automotive industrial setup. It is very important to understand how RTLS technologies work, their implication to the environment and society throughout their life cycle and the applicability of this technology in logistics. To that end the research has the following research questions:
Is RTLS technologies compatible with other technologies in production logistics and supply chain management?
What challenges, enablers and opportunities are presented by RTLS technologies in production logistics and supply chain management?
Is RTLS technologies sustainable in regard to production logistics and supply chain management?
The paper will start with an abstract followed by an introduction, followed by methodology, then a systematic literature review (RLTS technology, application of RTLS, interconnectivity and sustainability), then followed by the experimental setup, followed by results, discussion and analysis and lastly conclusion.
II. METHODOLOGY
The research method is experimental research design, which is quantitative in nature, however, some qualitative research will be done through a systematic literature review on the topic, comparing with results of experiments conducted on two different RTLS technologies provided by Scania. The theoretical perspective will showcase the possibilities of RTLS in production logistics while the analysis of experiment will indicate whether the sensors are applicable in the industry. The comparison will result in discussion of relevant applicability and future direction new reports could take. The authors will collect continuous data about the performance on the sensors to investigate accuracy and precision on path, position and lagging, and make an investigation on the technologies, there applicability with respect to sustainability. The authors will collect the data from an automotive industrial site (controlled and uncontrolled – with less electro-magnetic waves). These technologies have a human-machine interface which gives their location in real-time, which the authors record.
III. SYSTEMATIC LITERATURE REVIEW
A. Design of review
The need for this literature review stems from the gap that the authors stood in front of when first being introduced to the concepts of RTLS. The experiment was supposed to be designed according to current knowledge of the technology. Because of that, questions that we looked answers for were related to how the technology worked, when is it applicable and can it increase information sharing and sustainability efforts in production logistics. The review protocol was then designed around these three topics: technology, application, and interconnectivity & sustainability.
From that, the stage of conducting the review was begun. The three topics were split between the authors and the search for research papers was not limited to one web source. Each topic had a different focus area as can be seen on figure 1. Example of search terms for the application part was: (RTLS or “real time locating system”) and (application or “decision making”). Papers discussing the use of RTLS outside of logistics environment were excluded Abstracts were read and papers were chosen that fit each cut-off criteria. The quality of papers’ results was also a factor if it was used or not. In the end, the number of papers used for the literature review was 15.
B. Report of review
1) RTLS Technology, advantages, and disadvantages
The demands on the automatic identification, localization and the condition monitoring of logistics objects, as sources of data for a secure supply chain’s documentation and control, are increasing. By using such technologies, Smart Logistics is a key approach to more efficient organization of physical and information logistics in cross-company and international transportation chains and networks. Such networks are exposed to environments characterized by increased volatility, uncertainty and unpredictability. Therefore, the need for robustness, flexibility, agility and resilience must become the focus of attention for future logistics system designs [3]. These RTLS commonly use radio technology such as Ultra-Wideband (UWB), which allows for an accuracy of down to a decimeter but needs line of sight. Bluetooth Low Energy (BLE) where accuracy and cost are dependent upon the number of receivers and tags used and it does not require a line of sight. Radio Frequency Identification (RFID) which allows for the localization of an object in a specific zone, rather than actual real-time tracking of the object and Wi- Fi that is very cost-efficient because the already existing infrastructure can be used, however, with the advent of 5G which comes with its many benefits, but its maturity for Industrial Internet of Things applications is currently being tested [4].
In the paper Smart Factory by Magnus W, smart sensors are amongst the eight core technologies for smart manufacturing to achieve autonomy, predictivity and proactive operations based on real-time data from a shop floor (considering productivity, quality and logistics), however, the main challenge is integrating ‘smart-factory-capabilities’ with corporate production systems, applications, value adding processes as well as enabling technologies [5]. Using smart sensors, IoT and big data analytics, a Digital Twin model which can evaluate and predict errors in productivity, logistics and quality is developed and implemented as shown in Figure 2. The data are gathered from furnaces, machines and MES (Manufacturing Execution System) in real time. In 2017, quality problem decreased by 26% and net profits increased by 14%, in an automotive company understudy [5].
Digital Twin can be visualized as augmented reality, or numerical performance of the system on computer interfaces. This promotes cloud computing and big data analytics, however, it exposes the firm to cyber attacks and volatility of connectivity.
The technological requirements for the identification, localization and condition monitoring systems have to be met throughout all stages of supply chains since their weak point is inevitably located wherever information is not acquired with absolute certainty. One of the key challenges for seamless smart logistics is the creation of comparable technical levels at all points in supply chains. This defines the requirements for Smart Standardized Logistics Zones subsuming the different requirements. The creation of Smart Standardized Logistics Zones facilitates the integration of all information relevant to logistics throughout the transportation chain and thus pursues the vision of a secure supply chain as shown in table 1 [3].
Significant synergies for safety and security tasks can be obtained by combining the use of radio and image identification and positioning technologies to automatically determine the status of logistics processes by motion and state analyses according to M. Kirch. He further elaborated that, a key challenge of information logistics is the necessity for compatible and interfaced execution of the numerous subsystems, from sensor sources to performance measurement functions, which exist on the individual levels of a logistics zones in parallel and partly in competition [3]. IEEE 1451 defines a Transducer Interface Module (TIM) and Network Capable Application Processor (NCAP). The TIM can be a wired or wireless IEEE 1451.X module consisting of up to 255 transducers, signal conversion and processing electronics, and Transducer Electronic Data Sheets (TEDS). The TEDS provide transducer ID (identification), measurement range, location, calibration and user information, and more. The NCAP can access the TIM and pass the transducer information to the network. There are three possible ways to access the sensors and actuators from a network, IEEE 1451.1, IEEE 1451.0 Hyper Text Transfer Protocol (HTTP), and a proposed Smart Transducer Web Services (STWS), respectively [6].
2) Application
The potential of RTLS is great and the ways to apply it throughout the value chain are numerous. S. Thiede et al split the applications into four categories: people (staff and visitors), logistic elements (AGVs and other moving equipment), material & finished products, and machines & tools [7]. These categories are a way to see what type of entities RTLS can track when used. Continuous improvement is considered a central element of an implementation project of RTLS and Rácz-Szabó et al. showcased how the information received by RTLS can assist with improvement objectives in the manufacturing environment. They also found out that RTLS technology was applicable in four levels of identification: pallet unit, transport unit, package, and item. It was not considered suitable to use RTLS in outdoor transportation, whether it would be in a truck, airplane, or a sea container [8].
This reveals that RTLS technology can be used across the value chain to better control all objects, whether it is people, equipment or material. When it comes to the effects the technology can have on these operations, many things have been mentioned. It can help with process visibility and control and more efficiency in logistics processes [9]. Data collected by RTLS can be merged with a semi-automated Value Stream Mapping for better real-time analysis of operations and decision making [10]. The shop floor and warehouse are filled with big machinery that can cause accidents, by having a continuous overview of objects’ location, it can decrease the risk of collisions or resources being moved to the wrong places. This leads to better safety of people and quality of operations [8].
Lastly, using RTLS in a production logistics environment increases interaction between different manufacturing elements such as the four categories previously mentioned. This can affect the ability of information sharing across the value chain since the clear overview of elements has been established as well having better predictability of operation [7]. Quality and safety management, control over logistics and production, collaborate with Industry 4.0 solutions and monitoring efficiency are all categories that can be heavily affected by introducing RTLS as can be seen on figure 3 [8].
Even though the literature describes many benefits of RTLS, not a lot of case studies are available to show the actual results of the implementation of the technology. The reason for it could be the high cost related to the technology if everything is to be documented with the sensors. Another factor is the complexity of production and logistics systems that can affect the performance of RTLS and it might be impractical to use or breach privacy for workers [10]. So there seems to be missing any clear guidelines for in what context RTLS is applicable and to which degree.
3) Interconnectivity and sustainability
3.1 RTLS in Lean
RTLS has proven to improve Lean Production systems, like JIT, and waste reduction using wireless technologies. Realtime is also a pillar in many XPS’ like TPS and Scania production systems. There is also evidence that companies employing RTLS would see a return on investment within 21 months conducted by Eric Lutters [7]. Figure 4 stated the main findings and conclusions of a few selected articles that investigated the positive effects of wireless technologies on JIT.
Many authors point out that the most vital concern is to take the purpose, usage, and environment of the RTLS into consideration [11].
3.2 Sustainability and triple bottom line (TBL) RTLS reduces hazards as employees can avoid obstacles and restricted areas that are predefined in the facility; it reduces energy consumption when employees are moving around the facility using vehicles and from an economic perspective, it increases efficiency indicators in the facility. On the other hand, battery life is the weakness of the sensors [12]. To control battery life some communication protocols use the sleep mode, especially the ZigBee module. This is achieved by defining the periodic sleep and wake cycles, for instance, during the sleep cycles the coordinator station holds the API data packets from the sleeping sensor station. When the sensor station wakes up from sleep, it transmits a poll request to the coordinator station and at this stage the coordinator can request data packets from the sensor station [13].
The development in Artificial Intelligence is demanding consideration of the human factor system through humancentric design because autonomy of production should not compromise the creativity, ethics, participation and value of human beings in an enterprise. This calls for curricula review and design in sync with industrial development so that the human resource is equally equipped and competent for the task [14]. The few studies that have compared the environmental impacts of wireless technologies to applications have concluded that wireless technologies impose a lower environmental burden [15]. While telecommunication technologies are still seen as clean technologies, they can have some harmful impacts on the environment. The whole humanity is now exposed to various levels of so-called Effects of Electromagnetic Fields (EMF) and surely, the level of these fields will continue to grow with the advancement of science and development of technology. The waves classified as microwave, in which the mobile systems operate, are the electromagnetic radiation with wavelength ranging from 10 cm to 1 m and are called nonionizing radiation and its effects are the object of study worldwide, and scientist are skeptic to give a conclusion to that effect [16].
The effect of these technologies on human health is having a mixed view till date, hence further research is demanded. However, the effect of these rare materials, for example Indium Gallium Phosphide (InGaP), on land degradation during extraction and the footprint it leaves during processing can never be over-emphasized. 5G and connectivity are critical to solutions representing potential reductions of 20% of the EU´s total emissions. That’s the equivalent of the annual emissions of Spain and Italy combined. Solutions such as the development of renewable energy generators rely on connectivity and could reduce EU CO2 emissions by 550 million tons –15 percent of the EU’s total annual emissions in 2017 [17]. However, all SCOPE 1, 2 and 3 of the entire value chain of the use of wireless technology needs a multilense approach analysis and clearly expose its benefits and disadvantages in terms of social, economic and environmental sustainability.
3.3 Interconnectivity
RTLS can be connected with IoT systems to gain full advantage of real-time, accurate reporting. RTLS also connects the digital twin to the physical world as illustrated in figure 5.RTLS can be connected with IoT systems to gain full advantage of real-time, accurate reporting and visualizations as seen on Figure 6. RTLS also connects the digital twin to the physical world.
Using service-oriented architecture (SOA) [19], as shown in figure 6, the software components like the application programming interface (API) of each device (services) can be made to communicate with each other across platforms and languages and create business applications. Tools like Marvelmind Robotics, Cisco, ARISTA Flow solution, Link Labs solution, Siemens and Air Finder have data gathering and visualization of gathered data [20], [21]. Data from the RTLS may be retrieved by systems like Manufacturing execution systems (MES), Warehouse management system (WMS), and Enterprise resource planning (ERP), enabling intelligent decision-making based on facts in real time. A conventional ERP cannot take unforeseen occurrences into account. The ERP may obtain real-time data with RTLS data and adapt operations like planning and scheduling to actual events [7].
3.4 Challenges and risks
The main challenges can be classified into (1) system related, (2) business-related, and (3) sustainability [23] which are graphically represented from various sources in figure 7.
When considering the sustainability aspects, companies often seem to be struggling with the practical applications of RTLS. The technology issues consist of poor storage capacity of the tags, slow response when several tags are used simultaneously, and poor processing time when a lot of data was transmitted. RTLS systems should be strongly evaluated for EM wave pollution in the environment. If using multiple makes of tags, there are no common zero-point coordinates [12], [24], [25].
IV. EXPERIMENTAL SET-UP
The experiment covers the performance of the available RTLS technology provided by Scania. It was decided to limit the scope to a simple experimental layout to see the results more clearer. That means that variables were not moving over time with the help of AGVs and were only moved between places by us. What could be measured were coordinates given by the systems, that are better described in section A, as well as the lag of reaction from the system. Those coordinates could be used to analyze the performance in a quantitative way.
A. Specifications of technologies
1) Cargo beacon
Ela technology can be used to locate number of operators and tools present on the site, knowing in which zone or on which floor they are, will reduce amounts of accidents in the event of incidents (fire, collapse) each operator will receive an audible and visual alert telling them to evacuate. Improving the safety of a section, through restricting people with certain badges, means improving its overall performance by avoiding potential delays caused by lost time injury. The infrastructure is made of both fixed (anchors) and mobile network beacon which create a network mesh. The mobile beacons (TIM) does not use GPS technology but Blue Tooth (IEEE1451,5) to transmit data to the nearest NCAP (fixed beacon) and then to the industrial gateway (IEEE802,X). Placed at a few strategic locations, these gateways will centralize the raw data and transmit it to the client’s server (Hyper Text Transfer Protocol) over internet or local area network. The information will be transformed into GPS data by the Wirepas Positioning Engine. Once the data is transformed, the end user will be able to visualize it on the business application. Time to locate inventory is less when using this technology [26].
2) GEPS
H&D Wireless' proprietary solution GEPS for Industry (Griffin Enterprise Positioning Services) enables a higher degree of visualization and automation for the logistics flow of manufacturing companies. The system digitizes and visualizes physical processes and identifies, among other things, the handling of materials, bottlenecks in production, utilization rate of resources and unexpected machine interruptions through radio positioning and artificial intelligence (AI). The ambition is to reduce manufacturing costs, reduce lead times and tied-up capital. GEPS for Industry currently has five sub-services for Asset management, Safety, Fleet management, Production Logistics and External Logistics [27]. The Wi-Fi solutions from GEPS Wireless are supported on leading Microcontroller platforms including 8-bit, 32-bit AVR, ARM, Corte [28]. The architecture and communication protocol standards are the same as Cargo Beacon. However, the only difference is that GEPS sensors work with wireless. Table 2 shows the main difference between the two technologies.
B. Design of experiments and set-up
Using the steps described by V. Astakhov in 2012 the design of experiments (DOE) was established [29]. The problem faced was the lack of understanding how the sensors worked and what performance they could result in. This understanding was needed to decide if the technology could be recommended or not. Overall, the objective of the experiment was to find out if these two technologies were applicable in production logistics; both when it came to accuracy of the sensed location of the tags as well as in the responsiveness of the system used. The solution would give more clarity if RTLS could truly be used as was stated in the literature.
The variables that could be measured were the coordinates from the system as well as the time of lag in it. The controllable factors that could affect the outcome were the three different sensors available and where they were located in Scania’s Smart Factory Lab, either as coordinates or if they were in a specific controlled environment or not. Uncontrollable variables were people walking around, AGVs moving near the experiment, electric cables which produced EM signals, Bluetooth because of other devices, and Wifi volatility in the lab. The coordinates the systems measured had an accuracy of 15 decimal points in the Cargo Beacon but GEPS used a different reference point, so the coordinates were all integers.
The experiment itself was conducted on the floor of Scania’s Smart Factory Lab. It could be split into two general parts; all iterations would be done both in a more controlled area and on top of a magnetic strip to see if that affects the performance. The controlled area was still affected by other environmental disturbances in the room, just not the magnetic strip. Within each part, two separate factors were measured, position and path, and the response of lag was also measured.
The set-up in the lab was as follows; the magnetic strip was measured with the length of 4210mm, so a tape of same length was put on the floor on the other side of the room. One part of the strip and tape was marked A and the other B for documentation purposes. Description of each part of the experiment are as following:
1) The position was measured by moving each sensor ten times between A and B, each time collecting data about the coordinates. This would show if the sensors were giving out the same coordinates for the same positions or not. Standards IEEE 802.15.1 WPAN;Blue Tooth (Cargo Beacon) IEEE802.11 WLAN Wifi (GEPS) Range 100m 5km Data Rate 1-3kbps 1-45Mbps Frequencies (Bandwidth) 2.4GHz 2.4, 3.7 & 5GHz Network Topology Star Star, Tree, P2P Applications Wireless sensors (Monitoring and Control) PC-based Data Acquisition, Mobile Internet 6
2) In the path part, the strip and tape were split into ten intervals with eleven positions and sensors moved stepwise from A to B and coordinates documented. The path was repeated two times for each sensor, so 22 measurements in total per sensor. By documenting the path, it was possible to see deviations of coordinates from the actual location on the line.
3) The lag of the systems was measured by doing the same movement of sensors as in the position part, but here the time from when the sensor was moved until the movement was shown in the system, would be measured. Lag was measured ten times for each sensor. The amount of lagging will show how responsive the system was and assist with the realization of where the technology would be applicable.
V. RESULTS
The experiment was conducted on three separate occasions, and the same set-up was used each time. The same sensors were used and had the following tag ID.
Cargo Beacon, circle: 9168862 and 5357987
Cargo Beacon, rectangle: 11830416
GEPS: AGV1
After the introduction of the equipment, we realized that the two software programs gave different data, Cargo Beacon (CB) only gave the last known coordinates while GEPS could track the movement. Also, the coordination system for both was different so it was not possible to compare results directly. Lastly, the output given by the GEPS website was just the location itself in the environment but not the coordinates, so a Scania employee had to design an API that resulted in coordinates. That caused us to only being able to test the GEPS on the last two occasions. Examples of the data collected for the sensors can be seen on table 3.
During the last session at the lab, the Cargo Beacon sensors did not work so the team was unable to take all measurements that were planned. It was also realized during the last session that different orientations of GEPS sensor on A/B positions gave different results. These findings will be disregarded in the analysis and discussion since this was not considered for most measurements.
A. Analysis of data
For preparing these reports, the assumptions taken are as below
For experiments in position, the expected positions of points A and B are the median values of the 10 individual points respectively.
For experiments in Path, the expected values are the interpolation between the median values of position A and B from the Position data.
For calculating the mean errors and RMSE, the distance between the measured and the interpolated values are considered. As the distance in coordinate geometry is always positive, measures like RMSE make more sense compared to other calculations.
For the three Cargo Beacon sensors position experiment results, as shown in the figure 8 below, are normally distributed with kurtosis of 0.5 to 2.9 (moderately to peak skewness), skewness of 0.48 to 1.50 and are fairly symmetrical, meaning the data near the mean were more frequent in occurrence than data far from the mean, this clearly shows how precise these sensors are.
While as for GEPS sensors clearly show that there are homogenous types of groups because of positive skewness (0.6 to 1.25 and kurtosis of -0.82 to 2.28) distribution, most values on the graph are on the left side, and the curve is longer towards the right trail, meaning it has more outliers, meaning it is less precise in measurement. The magnetic strip (figure 9) shows more outliers evidenced by high skewness and kurtosis mainly because of magnetic noise in the strip.
The graphs below show the levels of accuracy of the CB and the GEPS sensors. Although the CB is precise it is inaccurate as shown by figure 10. However, GEPS sensors (figure 11), although less precise are more accurate in terms of position and distancing.
Figure 12 shows the high variations of path behavior of CB sensors with very high RMSE error for controlled path of 4.02 and that of uncontrolled path was 4.61. This could be mainly because of noise from the near-by AGV and the magnetic strip which provided signal interference. The CB sensors sometimes worked and sometimes they did not. The authors assume that it goes into sleep mode for battery saving mode and then submits the requested data when back online.
The RMSE error for controlled experiment for GEPS sensor shown in figure 13 gave an error of 0.11 and that for uncontrolled gave an error of 0.13, it clearly shows that magnetic noise has a bearing on the accuracy of the path.
As shown in figure 14, the lag of CB sensors has a flat peakness, kurtosis of –1.59 which shows less outliers, the certain gap of data is experienced when the sensors fail to give real time data, when it activates sleep mode, the average lag is 43 seconds, the skewness of 0.11 shows fair symmetricity of the data.
The lag of GEPS sensors, figure 15, are flat peaked with kurtosis of 0.16 and skewness of 0.12 which is a clear indication of normal distribution. However, there is a tendency to be negatively skewed, with an average lag of 70 seconds. The level of accuracy of this sensor is so high that it causes the lag to be high because of high resolution.
The major challenge throughout the experiment was the uncertainty of the sensors working since the Cargo Beacon ones had a habit of stopping suddenly. It was either fixed by picking the sensor up and vigorously moving it around to activate it or having a Scania employee contact the sensor supplier to fix it. This could have resulted in inaccuracies if the coordinates given by the system were collected when we were moving it around or when it was sitting in its position, it is impossible to know. The two circle Cargo Beacon sensors were used as one to shorten the experimental time by concurrently collecting measurements.
VI. DISCUSSION
A. Technology
Currently the RTLS under study used Bluetooth and Wi-Fi technologies, which displayed destructive interference with electro-magnetic waves within the work set-up. Supply chain visibility relies heavily on timely availability of accurate data, which is accessible to all stakeholders, of which the Cargo Beacon were found wanting in the sense that they constantly switched to sleep mode, although it saves battery life, it will defeat the purpose, which is to handle promptly disturbances and enhance vigilance and resilience of the supply chain network. RTLS can only provide location of assets within the firm premises, therefore, it needs other technologies to create a digital supply chain network organization that is connected within itself and outside itself. Such technologies includes, Stock Keeping Unit, Bar Codes or QR Codes which will provide more information about the product, material or assets, which is critical for Order Sequencing, Order Management System, Purchase-to-Pay processing, Customer Management System, Line Side Feeding, Kitting, Block Chains, and Consolidation Warehouse. All these techniques will enhance decision-making support for management, planning and control for inbound, outbound and internal logistics with multi-lance approach towards environmental, social and economic sustainability.
RTLS can be coupled with visual technologies such as scanning cameras, which means in the supply chain network can see each actors’ capabilities and performances in terms of ability to fulfill orders, social and ethical issues of drivers (long hours of freight drivers and salary of drivers). The RTLS coupled with other technologies gives the fill rate of the packaging system which has a bearing on environmental and economical sustainability. However, these technologies syphon a lot of electric power, environmental and social considerations and it comes with it huge costs. Technology is moving at a fast pace but negating the human factor subsystem, which will usually receive negative support. Therefore, there is need to acknowledge the human in the sustainability of supply chain visibility.
B. Application
Because of its short range and inaccuracy, CB is used in scenarios of less accuracy, for example, tracking personnel in highly hazardous work areas, which are less agile while GEPS can be used in highly accurate applications. Supply chain visibility can be grouped into three elements: customer related, internal related, and supplier related. Customerrelated visibility is required in the following: deliveries, demand management, order management, disturbance management, forecasting, product or material information, and planning. For internal related visibility it is required in the following: inventory level management, capacity management, lead time, production and operation aggregate planning (kitting, line side feeding and sequencing), and route planning. For the supplier related visibility is needed in the following: procurement management (Procure-to-Pay process), supplier capabilities, supplier location, track and trace. However, there are still some constraints that come with these two technologies that were tested.
First, they only give results through a coordinate system on a screen or in x and y coordinates. It is therefore missing the factor of z-axis, i.e. how high the object is located withing a facility, and also trace the movement history of an object as a visual is missing from these technologies, which is critical. If this were to be added, it could help with inventory management since the height of the correct rack would be known. If track and trace of the historical movement of the assets is incorporated, it will enhance competencies in transport engineering within the firm. However, adding these functions will come at a cost, in system architecture and high electric power consumption.
Secondly, the promising use of RTLS in safety management is demanding when a lag is in place. Because of that, managers cannot see in actual real time the movement of objects in a production logistics environment, especially if the sensors decide to go to sleep mode without notice. This brings light to the operator’s problem of deciding what they want to have tracked and what should not be tracked. If the target is to know the real time location of all assets to enable simultaneous control over all fields from figure 3, it would result in an abundance of data that is difficult to have a good overview of. To make this technology bring value to production logistics, companies need to define what assets should be tracked and what is suitable at each instance according to the constraints of the technology. This can be realized by an enabling technology like blockchain to manage both internal and external connectivity, which will present the following benefits: 1. Distributed and decentralized operations within the supply chain network, 2. Transparency to all stakeholders, including the consumers, 3. Blockchains use high level encryption, this means more secure, 3. Reduces transaction cost (time and monetary when exchanging information) due to decentralization which enhances growth and efficiency.
Lastly, even though these sensors have some problems, the GEPS type is still showing good accuracy with a simple interface that enables control and overview of tracked assets. According to the results of the experiment, it could be applicable in production logistics where complete precision is not mandatory but rather the general understanding of where objects are located. However, using the GEPS type in applications that need high precision in real time, there is need to trade-off between time of lagging and the level of accuracy needed, because the higher the accuracy the higher the lag.
C. Interconnectivity and sustainability
To avoid use of so many gateways in the workplace for the two technologies they can utilize Long Range Low Power Wide Area Network (LoRaWAN) gateways. It is an upper layer protocol that defines the network’s communication architecture, which means the firm does not pay for data usage since it has its own private network and present IIoT cyber security challenges. LoRaWAN goes to sleep if not in use which mean it saves battery life of the system, it works with a frequency of 1GHz, which is not impressive for complex networks, hence, demanding a frequency of 5GHz which presents its challenges as this technology is still emerging. Because of its low frequency, the operators can send a limited number of messages per day, which will limit the traffic of critical sites, activities in the plant or within the supply chain network. Interconnectivity provides intelligence within the supply chain to have responsible and sustainable procurement, since all the stakeholders can witness the consequences of unresponsible production and consumption. Connectivity reduces inventory because all actors only produce when need is activated by the consumer. However, lack of standardization in the technologies of connectivity poses a threat on costs and environmental sustainability. To enhance interconnectivity with the firms’ operations and outside the firms’ supply chain this technology needs to be integrated with blockchains through blockchain platforms.
It is important for corporate social responsibility to be part of the supply chain network, knowing that safety risks are also financial risks, improving job satisfaction often also increases productivity and investing in environmentally friendly 9 technologies or operations will bring new business opportunities. This demands the supply chain logistics to be as environmentally friendly as possible through digital communication, that is, electronic invoicing, recycling and reuse of packaging, training on staff on environmental issues, tracking the environmental effect of investments, ensuring that the movement of material is low through optimizing the fill rate, milk-run and inventory consolidation.
VII. CONCLUSION
RTLS is one of the building blocks for logistic connectivity and it comes with many benefits such as visibility, performance measurement, sustainability, data analytics, accountability, and employee safety. However, it also comes with the following challenge: lack of standards to integrate the sensor output with other legacies like excel spread sheet which results in too much data handling and magnify errors at source. The GEPS sensor which is very accurate displayed volatility because of high resolution (high processing time), which takes too much time to acquire a steady data point. This means that for application of this technology, there must be a tradeoff between the level of accuracy and sensor resolution fit for the application especially in production logistics. CB technology performed excellent on high precision, which means it is suitable for asset management where repeatability is demanded with less accuracy and quick data output such applications as the general location of personnel near a hazardous area. Since the results on both sensors showed outliers, meaning to a certain extent these technologies will behave funny, it suffices to say there is need to integrate them with other technologies, e.g. visual sensors, for comprehensive information in real time.
For flow of information within an organization and outside an organization and for supply chain logistics connectivity, there are certain conditions that need to be primary. It is evident that the Wi-Fi and the Bluetooth differ in performance, but one thing which is common is that current bandwidth and bitrate will be challenged when intensity increases. Their application in external logistics might present a challenge of installation of at least four gateways on every truck which is costly to implement.
A multi-lance approach is demanded to analyze the effect of digitalization to the economy, environment and society throughout all stakeholders through the supply chain network, because all players are responsible for their environmental, economic, and social footprint, especially when the effects of electromagnetic waves effect on the biota eco-system are not known and research on the subject matter is pending. It becomes a more risk to dive into the space without proper due diligence. Although, the advantages are very attractive, the technology is still facing many challenges, e.g. long signal processing time within the data acquisition of the communication systems, connectivity issues, and resistance to change of the human sub-system as it threatens peoples’ livelihoods. It is paramount to mention that as RTLS technology increases in use, environmental and social footprint will suffer upstream as well as the increase of power consumption due to the increase of Internet of Things.
Our experiment clearly shows that the RLTS technology is considerably affected by electromagnetic waves, which will increase as digitalization and electrification of society and industry intensify, which presents a dilemma. Which means further research is needed to understand the level of interference that affects RTLS performance for data accuracy and precision. Having exposed all these issues, it is critical that due diligence in research and development be invested before high capital investment for the technology to be operational without glitch.
VIII.REFERENCES
[1] R. Kalaiarasan, J. Olhager, T. K. Agrawal, and M. Wiktorsson, “The ABCDE of supply chain visibility: A systematic literature review and framework,” Int J Prod Econ, vol. 248, Jun. 2022, doi: 10.1016/j.ijpe.2022.108464.
[2] D. Mueller and F. Vogelsang, “Towards smart manufacturing logistics: A case study of potentials of smart label data in electronics manufacturing,” in Procedia CIRP, 2021, vol. 104, pp. 1741–1746. doi: 10.1016/j.procir.2021.11.293.
[3] M. Kirch, O. Poenicke, and K. Richter, “RFID in Logistics and Production –Applications, Research and Visions for Smart Logistics Zones,” Procedia Eng, vol. 178, pp. 526–533, Jan. 2017, doi: 10.1016/J.PROENG.2017.01.101.
[4] C. Küpper, J. Rösch, and H. Winkler, “Empirical findings for the usage of 5G as a basis for real time locating systems (RTLS) in the automotive industry,” Procedia CIRP, vol. 107, pp. 1287– 1292, Jan. 2022, doi: 10.1016/J.PROCIR.2022.05.146.
[5] M. Wiktorsson, S. do Noh, M. Bellgran, and L. Hanson, “Smart Factories: South Korean and Swedish examples on manufacturing settings,” Procedia Manuf, vol. 25, pp. 471–478, Jan. 2018, doi: 10.1016/J.PROMFG.2018.06.128.
[6] L. Kang and S. Eugene, “Wireless sensor network based on IEEE 1451.0 and IEEE 1451.5-802.11,” 2007 8th International Conference on Electronic Measurement and Instruments, ICEMI, pp. 47–411, 2007, doi: 10.1109/ICEMI.2007.4351239.
[7] S. Thiede, B. Sullivan, R. Damgrave, and E. Lutters, “Real-time locating systems (RTLS) in future factories: Technology review, morphology and application potentials,” in Procedia CIRP, 2021, vol. 104, pp. 671–676. doi: 10.1016/j.procir.2021.11.113.
[8] A. Rácz-Szabó, T. Ruppert, L. Bántay, A. Löcklin, L. Jakab, and J. Abonyi, “Real-time locating system in production management,” Sensors (Switzerland), vol. 20, no. 23, pp. 1–22, Nov. 2020, doi: 10.3390/s20236766.
[9] F. Thiesse and E. Fleisch, “On the value of location information to lot scheduling in complex manufacturing processes,” Int J Prod Econ, vol. 112, no. 2, pp. 532–547, Apr. 2008, doi: 10.1016/j.ijpe.2007.05.006.
[10] B. P. Sullivan, P. G. Yazdi, A. Suresh, and S. Thiede, “Digital Value Stream Mapping: Application of UWB Real Time Location Systems,” in Procedia CIRP, 2022, vol. 107, pp. 1186– 1191. doi: 10.1016/j.procir.2022.05.129.
[11] A. Budak and A. Ustundag, “Selection Criteria: Fuzzy decision making model for selection of real time location systems,” Appl Soft Comput, vol. 36, pp. 177–184, Nov. 2015, doi: 10.1016/j.asoc.2015.05.057.
[12] V. Sidiropoulos, D. Bechtsis, and D. Vlachos, “An Augmented Reality Symbiosis Software Tool for Sustainable Logistics Activities,” Sustainability, vol. 13, no. 19, p. 10929, Sep. 2021, doi: 10.3390/su131910929.
[13] S. C. Mukhopadhyay, “Intelligent Sensing, Instrumentation and Measurements,” vol. 5, 2013, doi: 10.1007/978-3-642-37027-4.
[14] S. Waschull and C. Emmanouilidis, “Development and application of a human-centric co-creation design method for AIenabled systems in manufacturing,” IFAC-PapersOnLine, vol. 55, no. 2, pp. 516–521, Jan. 2022, doi: 10.1016/J.IFACOL.2022.04.246.
[15] M. W. Toffel and A. Horvath, “Environmental implications of wireless technologies: News delivery and business meetings,” Environ Sci Technol, vol. 38, no. 11, pp. 2961–2970, Jun. 2004, doi: 10.1021/ES035035O/ASSET/IMAGES/LARGE/ES035035OF00 002.JPEG. [16] J. F. Morais and G. L. Siqueira, “Wireless technologies environmental impacts,” in International Microwave and Optoelectronics Conference Proceedings, 2009, pp. 523–527. doi: 10.1109/IMOC.2009.5427529.
[17] “Connectivity and climate change,” Ericsson. https://www.ericsson.com/4ab228/assets/local/aboutericsson/sustainability-and-corporateresponsibility/environment/accelerate-5g-report-27102021.pdf (accessed Nov. 29, 2022).
[18] E. Flores-García, Y. Jeong, S. Liu, M. Wiktorsson, and L. Wang, “Enabling industrial internet of things-based digital servitization in smart production logistics,” Int J Prod Res, pp. 1–26, Jun. 2022, doi: 10.1080/00207543.2022.2081099.
[19] M. Endrei et al., IBM: Patterns: service-oriented architecture and web services. 2004. [20] Aneta Ciurkot, “IoT vs RTLS in Healthcare - Knowing the Difference,” https://kontakt.io/blog/iot-vs-rtls-in-healthcareknowing-the-difference/, 2021.
[21] S. H. Gustav Nacke, “Evaluating implementation areas of RealTime Location System (RTLS) in the production at Scania CV AB Oskarshamn,” Lund University, Lund, 2021.
[22] V. Alcácer and V. Cruz-Machado, “Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems,” Engineering Science and Technology, an International Journal vol. 22, no. 3, pp. 899–919, Jun. 2019, doi: 10.1016/j.jestch.2019.01.006.
[23] Peter Liss, “RTLS Challenges,” Nov. 04, 2021.
[24] Q. Dai, R. Zhong, G. Q. Huang, T. Qu, T. Zhang, and T. Y. Luo, “Sustainability: Radio frequency identification-enabled real-time manufacturing execution system: a case study in an automotive part manufacturer,” Int J Comput Integr Manuf, vol. 25, no. 1, pp. 51–65, Jan. 2012, doi: 10.1080/0951192X.2011.562546.
[25] F. Wolters, “Real Time Location System -Industrial Implementation and Future Potential,” KTH, SWEDEN, 2018.
[26] “Lone worker monitoring on construction sites ,” Ela Innovation. https://elainnovation.com/en/lone-workers-monitoring-onconstruction-sites/ (accessed Nov. 26, 2022).
[27] “H&D Wireless får genombrottsorder på GEPS från AstraZeneca AB ,” H&D Wireless, 2022. https://www.hdwireless.com/blog/2022/09/22/genombrottsorder-pa-geps-franastrazeneca-ab/ (accessed Nov. 26, 2022).
[28] “WiFi supported Microcontroller platforms,” H&D Wireless. http://ww.hdwireless.se/index.php/products/wifi-supportedmicrocontroller-platforms (accessed Nov. 26, 2022).
[29] V. P. Astakhov, “Design of experiment methods in manufacturing: Basics and practical applications,” in Statistical and Computational Techniques in Manufacturing, vol. 9783642258596, Springer-Verlag Berlin Heidelberg, 2012, pp. 1–54. doi: 10.1007/978-3-642-25859-6_1.
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