Academic literature on the topic 'Logistic Control Tower'

Abstract Background The cognitive reserve (CR) refers to the brain’s capacity to cope with pathology in order to minimize the symptoms. In the field of first episode psychosis (FEP), the CR was able to predict functional and neurocognitive performance. Nevertheless, CR has been estimated using heterogeneous methods, which, in term, difficult to compare studies. Therefore, there is a need to create a specific scale for the assessment of this relevant construct. The Cognitive Reserve Assessment Scale in Health (CRASH) is the first measure developed specifically for patients with severe mental illness with optimal psychometric properties, facilitating reliable and valid measurement of CR. The study of the internal structure of the CRASH determined a four-factor structure (Education, Occupation, Leisure activities and Sociability) that can be analyzed separately to know what kind of relationship they might have with other variables. The aim of this study was to analyze the effects of CR measured with CRASH scale on functioning and neurocognitive performance and to explore the relationship of each factor with the outcome in an adult sample of subjects with FEP. Methods The sample of this study came from a multicentre, naturalistic and longitudinal research project financed by a catalan grant (“Pla Estratègic de Recerca i Innovació en Salut” - PERIS 2016–2018). Expedient Nº: SLT006/17/00345; entitled “Identificación y caracterización del valor predictivo de la reserva cognitiva en el curso evolutivo y respuesta en terapéutica en personas con un primer episodio psicótico”. 23 FEP patients and 72 healthy control (HC) were enrolled. The premorbid IQ was estimated with the Wechsler Adult Intelligence Scale (WAIS-IV) vocabulary subtest. To assess processing speed, Trail Making Test-part A was used. Sustained attention was tested with the Continuous Performance Test–II. The working memory was assessed with the Letters and Numbers Subtest of the WAIS-IV. Finally, the executive functions tested set shifting, planning and cognitive flexibility using the Tower of London task and the Trail Making Test (TMT) part B. Results Significant differences between the total CRASH score of patients and HC groups have been found. The patient group obtained lower scores compared to the HC group (36.66±16.01 vs 49.83±11.08, p<0.001). After performing a logistic regression to assess the predictive power of CRASH for each group, the model correctly classified 83.2% of the cases (B=0.091; p<0.001; Exp(B)=1.095). In FEP patients, the CRASH score was associated with premorbid IQ (p<0.001), processing speed (p=0.005), executive function (TMT-B, p=0.005; London Tower task, p=0.039) and attention (CPT Hit SE ISI change, p=0.004). Specifically, the Education factor was associated with premorbid IQ, processing speed, working memory and executive function. The Occupation was only associated with executive function. Leisure activities factor was correlated with premorbid IQ and functioning. Finally, Sociability was correlated with psychosocial functioning and duration of untreated psychosis. In HC, CRASH was associated with premorbid IQ (p<0.001) and attention (p=0.015). Education and Occupation factors were associated with premorbid IQ and attention; Leisure activities with processing speed; and sociability with attention. Discussion FEP patients were shown to have lower CR than HC, and CRASH correctly classified 83.2% of the sample. Each CRASH factor was associated with different outcome, which is why it can be interesting to analyze the total CRASH score and each factor separately. Patients with higher CR showed a better cognitive performance. Therefore, enhancing each factor involved in cognitive reserve may improve outcomes in FEP.

The Tower of London (TOL) is used for evaluating planning skills, which is a component of the executive functions. Different versions and scoring criteria were developed for this task, and some of them present with different psychometrical properties. This study aimed to evaluate two specific scoring methods of the TOL in diagnosing Mild Cognitive Impairment and probable Alzheimer's disease. The TOL total scores from 60 patients of each diagnosis were compared with the performance of 60 healthy-aged controls using receiver operating characteristics analysis and multinomial logistic regression. Krikorian method better diagnosed Alzheimer's disease, while Portellas's was better at discriminating healthy controls from Mild Cognitive Impairment, but were not efficient at comparing this last group with Alzheimer's patients. Regression analysis indicates that in addition to screening tests, TOL improves the classification of the three groups. The results suggest the two scoring methods used for this task may be useful for different diagnostic purposes.

Colleges and universities are becoming more involved in providing training to companies in their regional service areas. Faculty who are proficient in a traditional classroom, however, do not always excel as corporate trainers. Instructional strategies that are inherently effective in academe seldom yield comparable results in an industrial setting. Employee training programmes generally demand a more applications-oriented approach and tend to focus succinctly on immediate outcomes. Furthermore, training initiatives often encounter considerable resistance from session participants. This can precipitate serious difficulties for professors who are accustomed to maintaining absolute control via the grading process. Corporate trainees want to know precisely how the information presented will assist them in the performance of their current job responsibilities or how it will help prepare them for future assignments. This paper explains how faculty members can adapt their academic teaching styles to meet the unique needs of company employees. Included in the discussion are explicit recommendations for preparing for a specific training assignment, developing a customized training plan, dealing with critical logistical considerations, and ultimately facilitating a successful training experience. Several problems that can occur are also described along with suggestions for resolving them as efficiently as possible. Finally, the importance of following-up after the training has been completed is addressed.

For improving the generality, expandability and accuracy, the general embedded intelligent monitoring system of tower crane is developed. The system can be applied to different kinds of tower cranes running at any lifting ratio, can be initialized using U disk with the information of tower crane, and fit the lifting torque curve automatically. In dangerous state, the system can sent out alarm signals with sounds and lights, and cut off power by sending signals to PLC through communication interface RS485. When electricity goes off suddenly, the system can record the real-time operating information automatically, and store them in a black box, which can be taken as the basis for confirming the accident responsibility.In recent years, tower cranes play a more and more important role in the construction of tall buildings, in other construction fields are also more widely used. For the safety of tower cranes, various monitors have been developed for monitoring the running information of crane tower [1-8]. These monitors can’t eliminate the errors caused by temperature variations automatically. The specific tower crane’s parameters such as geometric parameters, alarming parameters, lifting ratio, lifting torque should be embedded into the core program, so a monitor can only be applied to a specific type of tower crane, lack of generality and expansibility.For improving the defects of the existing monitors, a general intelligent monitoring modular system of tower crane with high precision is developed, which can initialize the system automatically, eliminate the temperature drift and creep effect of sensor, and store power-off data, which is the function of black box.Hardware design of the monitoring systemThe system uses modularized design mode. These modules include embedded motherboard module, sensor module, signal processing module, data acquisition module, power module, output control module, display and touch screen module. The hardware structure is shown in figure 1. Figure 1 Hardware structure of the monitoring systemEmbedded motherboard module is the core of the system. The motherboard uses the embedded microprocessor ARM 9 as MCU, onboard SDRAM and NAND Flash. Memory size can be chosen according to users’ needs. SDRAM is used for running procedure and cache data. NAND Flash is used to store embedded Linux operating system, applications and operating data of tower crane. Onboard clock with rechargeable batteries provides the information of year, month, day, hour, minute and second. This module provides time tag for real-time operating data. Most interfaces are taken out by the plugs on the embedded motherboard. They include I/O interface, RS232 interface, RS485 interface, USB interface, LCD interface, Audio interface, Touch Screen interface. Pull and plug structure is used between all interfaces and peripheral equipments, which not only makes the system to be aseismatic, but also makes its configuration flexible. Watch-dog circuit is designed on the embedded motherboard, which makes the system reset to normal state automatically after its crash because of interference, program fleet, or getting stuck in an infinite loop, so the system stability is improved greatly. In order to store operating data when power is down suddenly, the power-down protection circuit is designed. The saved data will be helpful to repeat the accident process later, confirm the accident responsibility, and provide the basis for structure optimization of tower crane.Sensor module is confirmed by the main parameters related to tower crane’s security, such as lifting weight, lifting torque, trolley luffing, lifting height, rotary angle and wind speed. Axle pin shear load cell is chosen to acquire lifting weight signals. Potentiometer accompanied with multi-stopper or incremental encoder is chosen to acquire trolley luffing and lifting height signals. Potentiometer accompanied with multi-stopper or absolute photoelectric encoder is chosen to acquire rotary angle signals. Photoelectric sensor is chosen to acquire wind speed signals. The output signals of these sensors can be 0~5V or 4~20mA analog signals, or digital signal from RS485 bus. The system can choose corresponding signal processing method according to the type of sensor signal, which increases the flexibility on the selection of sensors, and is helpful for the users to expand monitoring objects. If the acquired signal is analog signal, it will be processed with filtering, isolation, anti-interference processing by signal isolate module, and sent to A/D module for converting into digital signals, then transformed into RS485 signal by the communication protocol conversion device according to Modbus protocol. If the acquired signal is digital signal with RS485 interface, it can be linked to RS485 bus directly. All the acquired signals are sent to embedded motherboard for data processing through RS485 bus.The data acquisition module is linked to the data acquisition control module on embedded motherboard through RS485 interface. Under the control of program, the system inquires the sensors at regular intervals, and acquires the operating data of crane tower. Median filter technology is used to eliminate interferences from singularity signals. After analysis and processing, the data are stored in the database on ARM platform.Switch signal can be output to relay module or PLC from output control module through RS485 bus, then each actuator will be power on or power off according to demand, so the motion of tower crane will be under control.Video module is connected with motherboard through TFT interface. After being processed, real-time operating parameters are displayed on LCD. The working time, work cycle times, alarm, overweight and ultar-torque information will be stored into database automatically. For meeting the needs of different users, the video module is compatible with 5.7, 8.4 or 10.4 inches of color display.Touch screen is connected with embedded motherboard by touch screen interface, so human machine interaction is realized. Initialization, data download, alarm information inquire, parameter modification can be finished through touch screen.Speaker is linked with audio interface, thus alarm signals is human voice signal, not harsh buzz.USB interface can be linked to conventional U disk directly. Using U disk, users can upload basic parameters of tower crane, initialize system, download operating data, which provides the basis for the structural optimization and accident analysis. Software design of the monitoring systemAccording to the modular design principle, the system software is divided into grading encryption module, system update module, parameter settings module, calibration module, data acquisition and processor module, lifting parameters monitoring module, alarm query module, work statistics module.Alarm thresholds are guarantee for safety operation of the tower crane. Operating data of tower crane are the basis of service life prediction, structural optimization, accident analysis, accident responsibility confirmation. According to key field, the database is divided into different security levels for security requirements. Key fields are grade encryption with symmetrical encryption algorithm, and data keys are protected with elliptic curve encryption algorithm. The association is realized between the users’ permission and security grade of key fields, which will ensure authorized users with different grades to access the equivalent encrypted key fields. The user who meets the grade can access equivalent encrypted database and encrypted key field in the database, also can access low-grade encrypted key fields. This ensures the confidentiality and integrity of key data, and makes the system a real black box.The system is divided into operating mode and management mode in order to make the system toggle between the two states conveniently. The default state is operating mode. As long as the power is on, the monitoring system will be started by the system guide program, and monitor the operating state of the tower crane. The real-time operating data will be displayed on the display screen. At the dangerous state, warning signal will be sent to the driver through voice alarm and light alarm, and corresponding control signal will be output to execution unit to cut off relevant power for tower crane’s safety.By clicking at the mode switch button on the initial interface, the toggle can be finished between the management mode and the operating mode. Under the management mode, there are 4 grades encrypted modes, namely the system update, alarm query, parameter setting and data query. The driver only can browse relevant information. Ordinary administrator can download the alarm information for further analysis. Senior administrator can modify the alarm threshold. The highest administrator can reinitialize system to make it adapt to different types of tower crane. Only browse and download function are available in the key fields of alarm inquiry, anyone can't modify the data. The overload fields in alarm database are encrypted, only senior administrator can browse. The sensitive fields are prevented from being tampered to the great extent, which will provide the reliable basis for the structural optimization and accident analysis. The system can be initialized through the USB interface. Before initialization, type, structural parameters, alarm thresholds, control thresholds, lifting torque characteristics of tower crane should be made as Excel files and then converted to XML files by format conversion files developed specially, then the XML files are downloaded to U disk. The U disk is inserted into USB interface, then the highest administrator can initialize the system according to hints from system. After initialization, senior administrator can modify structural parameters, alarm thresholds, control thresholds by clicking on parameters setting menu. So long as users can make the corresponding excel form, the system initialization can be finished easily according to above steps and used for monitoring. This is very convenient for user.Tower crane belongs to mobile construction machinery. Over time, sensor signals may have some drift, so it is necessary to calibrate the system regularly for guaranteeing the monitoring accuracy. Considering the tower is a linear elastic structure, sensors are linear sensors,in calibration linear equation is used:y=kx+b (1)where x is sample value of sensor, y is actual value. k, b are calibration coefficients, and are calculated out by two-points method. At running mode, the relationship between x and y is:y=[(y1-y0)/(x1-x0)](x-x0)+y0 (2)After calibration, temperature drift and creep can be eliminated, so the monitoring accuracy is improved greatly.Lifting torque is the most important parameter of condition monitoring of tower crane. Comparing the real-time torque M(L) with rated torque Me(L), the movement of tower crane can be controlled under a safe status.M (L)= Q (L)×L (3)Where, Q(L)is actual lifting weight, L is trolley luffing. Me(L) = Qe(L)×L (4)Where, Q e(L) is rated lifting weight. The design values of rated lifting weight are discrete, while trolley luffing is continuous. Therefore there is a rated lifting weight in any position. According to the mechanical characteristics of tower crane, the rated lifting weight is calculated out at any point by 3 spline interpolation according to the rated lifting weight at design points.When lifting weight or lifting torque is beyond rated value, alarm signal and control signal will be sent out. The hoist motor with high, medium and low speed is controlled by the ratio of lifting weight Q and maximum lifting weight Qmax,so the hoisting speed can be controlled automatically by the lifting weight. The luffing motor with high and low speed is controlled by the ratio of lifting torque M and rated lifting torque Me. Thus the luffing speed can be controlled by the lifting torque automatically. The flow chart is shown in figure 2. Fig. 2 real-time control of lifting weight and lifting torqueWhen accidents take place, power will be off suddenly. It is vital for identifying accident liability to record the operating data at the time of power-off. If measures are not taken to save the operating data, the relevant departments is likely to shirk responsibility. In order to solve the problem, the power-off protection module is designed. The module can save the operating data within 120 seconds automatically before power is off suddenly. In this 120 seconds, data is recorded every 0.1 seconds, and stores in a 2D array with 6 rows 1200 columns in queue method. The elements of the first line are the recent time (year-month-day-hour-minute-second), the elements of the second line to sixth line are lifting weight, lifting torque, trolley luffing, lifting height and wind speed in turn. The initial values are zero, when a set of data are obtained, the elements in the first column are eliminated, the elements in the backward columns move frontwards, new elements are filled into the last column of the array, so the array always saves the operating data at the recent 120 seconds. In order to improve the real-time property of the response, and to extend the service life of the nonvolatile memory chip EEPROM-93C46, the array is cached in volatile flip SDRAM usually. So long as power-off signal produces, the array will be shift to EEPROM, at once.In order to achieve the task, the external interruption thread and the power-off monitoring thread of program is set up, the power-off monitoring thread of program is the highest priority. These two threads is idle during normal operation. When power is off, the power-off monitoring thread of program can be executed immediately. When power-off is monitored by power-off control circuit, the external interruption pins produces interrupt signal. The ARM microprocessor responds to external interrupt request, and wakes up the processing thread of external interruption, then sets synchronized events as informing state. After receiving the synchronized events, the data cached in SDRAM will be written to EEPROM in time.ConclusionThe general intelligence embedded monitoring system of tower crane, which can be applicable to various types of tower crane operating under any lifting rates, uses U disk with the information of the tower crane to finish the system initialization and fits the lifting torque curve automatically. In dangerous state, the system will give out the voice and light alarm, link with the relay or PLC by the RS485 communication interface, and cut off the power. When power is down suddenly, the instantaneous operating data can be recorded automatically, and stored in a black box, which can be taken as the proof for identifying accident responsibility. The system has been used to monitor the "JiangLu" series of tower cranes successfully, and achieved good social and economic benefits.AcknowledgementsThe authors wish to thank China Natural Science Foundation(50975289), China Postdoctoral Science Foundation(20100471229), Hunan science & technology plan, Jianglu Machinery & Electronics Co. Ltd for funding this work.Reference Leonard Bernold. Intelligent Technology for Crane Accident Prevention. Journal of Construction Engineering and Management. 1997, 9: 122~124.Gu Lichen,Lei Peng,Jia Yongfeng. Tower crane' monitor and control based on multi-sensor. Journal of Vibration, Measurement and Diagnosis. 2006, 26(SUPPL.): 174-178.Wang Ming,Zhang Guiqing,Yan Qiao,et, al. Development of a novel black box for tower crane based on an ARM-based embedded system. Proceedings of the IEEE International Conference on Automation and Logistics. 2007: 82-87.Wang Renqun, Yin Chenbo, Zhang Song, et, al. Tower Crane Safety Monitoring and Control System Based on CAN Bus. Instrument Techniques and Sensor. 2010(4): 48-51.Zheng Conghai,Li Yanming,Yang Shanhu,et, al. Intelligent Monitoring System for Tower Crane Based on BUS Architecture and Cut IEEE1451 Standard. Computer Measurement & Control. 2010, 18, (9): 1992-1995.Yang Yu,Zhenlian Zhao,Liang Chen. Research and Design of Tower Crane Condition Monitoring and Fault Diagnosis System. 2010 Proceedings of International Conference on Artificial Intelligence and Computational Intelligence. 2010: 405-408.Yu Yang, Chen Liang, Zhao Zhenlian. Research and design of tower crane condition monitoring and fault diagnosis system. International Conference on Artificial Intelligence and Computational Intelligence, 2010, 3: 405-408.Chen Baojiang, Zeng Xiaoyuan. Research on structural frame of the embedded monitoring and control system for tower crane. 2010 International Conference on Mechanic Automation and Control Engineering. 2010: 5374-5377.

This paper presents a set covering model based on route representation to solve the green ship routing and scheduling problem (GSRSP) with berth time-window constraints for multiple bulk ports. A bi-objective set covering model is constructed with features based on the minimization of the total CO2 equivalent emissions and the total travel time subject to a limited number of berths in each port, berthing time windows, and the time window for each job. The solutions are obtained using the ε-constraint method, after which a Pareto frontier is plotted. This problem is motivated by the operations of feeder barges and terminals, where the logistics control tower is used to coordinate the routing and berthing time of its barges. We show that the proposed method outperforms the weighted sum method in terms of the number of Pareto solutions and the value of the hypervolume indicator.

<p><strong>Abstract.</strong> With the drastic development of low-altitude UAV (Unmanned Aerial Vehicle) technology, UAV will be used for long-distance logistics in the near future. Many countries begin to develop UTM (UAV Traffic Management) system, and one of the objectives for the system is preparation of UAV-logistics era. In that era, hundreds of drone will simultaneously fly at one area. To prevent UAV collision in the air, UAV air road should be designed. The Korean government have supported research projects related with UAV air roads. This paper deals with development of UAV air roads by using 3D grid system. First, detail 3D spatial information for UAV air roads is constructed. In many cases, 3D digital map does not include transmission towers, utility poles, power lines, or trees, since the interests of 3D digital map are focussed on digital elevation model and digital surface model with buildings. The transmission towers, utility poles, and power lines could be obstacles when UAV perform its logistics mission. Therefore, detail 3D information should be constructed for UAV air roads. We constructed such detail 3D information by using MMS (Mobile Mapping System) and aerial survey with Lidar and digital photograph. Next, 3D grid system is proposed to present such detail 3D information. Usual object based 3D information is huge size and hard to control. To provide 3D information to a flying UAV, data should be light. Therefore, light-weight 3D grid system is effective to provide air road information to UAV. Proposed 3D grid based air roads can be used for UAV flight plan, traffic management etc.</p>

The process of wind farm operation requires proper logistics services, consisting of the supply of all resources necessary in this process. A critical moment in the operation of wind farms is the implementation of the repowering process, in the form of replacement of the basic elements of wind farms: blades, hub rotors, nacelles or even towers. The replacement of these basic elements of the wind farm relates to the necessity to provide logistics services for heavy and oversized deliveries. Therefore, this article presents a unique analysis of logistics processes related to wind farms. Therefore, the aim of the article is to identify the most energy-saving variant of logistics service of the wind farm repowering process. However, the criterion of selecting the optimal variant is based on the original methodology of energy consumed during logistics services, as opposed to the traditionally used cost-effectiveness criterion. The SolidWorks software with other methods and tools were used for this purpose. As a result of the conducted research, it was found that the differences in individual variants of logistics service implementation may range from 4.7% to even 19.4% in terms of energy savings. Due to the increasingly common need to implement the process of repowering wind farms, the presented original methodology for the analysis and selection of the delivery variant with lowest energy consumption fills the literature gap and formulates a model of business practice, thus constituting both theoretical and practical value.

This research paper in its entirety tries to articulate the various types of challenges faced by the Indian Transportation Sector in implementing new age technologies for better control and visibility throughout the entire Supply Chain. It mostly focuses on the road and rail mode of transportation, though briefly it also touch-bases upon the water and air modes. Indian railways and roads position themselves as the third and second largest network respectively worldwide. In addition to a set of questionnaires circulated among individuals working in the logistics domain to capture their point of view, a thorough literature review of the previous works in this field was done to identify the causes – both external and internal to an organization, which affects implementation of digital solutions to present transportation problems. In this paper, we discuss along with others, two of the greatest challenges posed to the digital transformation – firstly, adamant attitude of traditional players operating in this space, who have for long, functioned through traditional processes and legacy systems and secondly, the complexity of the road and rail network running through the length and breadth of the country. Though the average time taken by enterprises to digitize their transportation business has reduced tremendously due to need of remote operations in these unprecedented times during COVID-19, but still there exist some of the challenges which need to be addressed. Some of the major drivers of identifying these challenges are the need to - reduce lead times by setting up a platform/control tower which can automate the management of transportation with minimized touch points between organizations and their customers, need to reduce risk of manipulated data by minimizing human intervention and need to increase customer satisfaction by incorporating functionalities like real-time tracking, automated exception handling and other. The challenges which were identified in this study can be further analysed to improve on it and conceptualize a framework for transforming Indian logistics (majorly the transportation space). This paper is written from the point of view of current scenario of Indian transportation industry.

In the global trucking industry, vertical collaboration between shippers and carriers is attained by intermediaries, called brokers. Brokers organize carriers for a shipper in accordance with its quality and price requirements, and support carriers to collaborate horizontally by sharing a large distribution order from a shipper. Brokers also act as trustees, preventing the passing of private information of any party to the others. Despite these benefits, intermediaries in the trucking industry are involved in several sustainability problems, including high costs, high levels of carbon emissions, high percentages of empty miles, low-capacity utilizations, and driver shortages. Several studies have acknowledged the importance of improving collaboration to address these problems. Obviously, the major concern of brokers is not collaboration, but rather to optimize their own gains. This paper investigates the potential of blockchain technology to improve collaboration in the trucking industry, by eliminating brokers while preserving their responsibilities as organizers and trustees. This paper extends the transportation control tower concept from the logistics literature, and presents a system architecture for its implementation through smart contracts on a blockchain network. In the proposed system, the scalability and privacy of trucking operations are ensured through integration with privacy-preserving off-chain computation and storage solutions (running outside of the blockchain). The potential of this design artifact for fostering collaboration in the trucking industry was evaluated by both blockchain technology experts and trucking industry professionals.

The demand for shorter lead-times and increased delivery reliability puts pressure on companies to provide more efficient logistic services. One strategy to deal with this demand is by outsourcing logistics to a third part. The development of logistics information systems enables increased control over business supply chains. This is why Green Cargo aims to create a Logistics Control Tower (LCT) solution to provide it’s customers with better information with higher availability. One such system is the Green Cargo control tower solution, which aims to improve information quality and availability alike. This is also the origin of this thesis at Green Cargo Division Logistics. The aim of this project was to identify the information needs in the logistic processes connected to Green Cargo Division Logistics, and to present a requirement specification for a LCT. The LCT will be designed to work as an information and decision support system. The main source of information was collected through interviews with customers of Green Cargo, as well as personnel at Green Cargo.

The research findings suggest that there is potential for improvement within the information flow between Green Cargo and the other parties involved in the logistic process. A need for improved information flows and communication became apparent through the investigation. We therefore suggest that Green Cargo develops a LCT containing functions such as cost follow-up, statistics, stock level information, order information, proof of delivery, track&trace, notification, environment information, and invoice control. Whilst some of these functions are yet to be introduced, others are basic functions already provided for by Green Cargo, but could nonetheless be improved. Most of the suggested functions are generated through the customers needs while invoice control and automated notification is a demand from Green Cargo personnel. A web interface with the possibility to adapt the content to different customers could serve as a solution to these needs. Input to the LCT is collected from different information systems such as vendor management systems and databases at logistic service providers.

Syftet med den här undersökningen är att beskriva och analysera vilka fördelar och nackdelar det finns med att utkontraktera en transportplaneringsprocess till en extern part. Undersökningen genomfördes på uppdrag av BT-Europe i Mjölby på avdelningen som ansvarar för distribution av produkter och reservdelar. BT är i grunden ett svenskt företag som startade sin verksamhet 1946 och är idag världsledande tillverkare av eldrivna lagertruckar. År 2000 förvärvades BT av Toyota och detta skapade en världsledande konstellation inom truckbranschen.

Bakgrunden till undersökningens syfte är att arbetet med att effektivisera och förbättra transportplaneringsprocessen inom BT har blivit eftersatt på grund av den kraftiga produktionsökningen de senaste 5 åren. Detta har lett till stigande kostnader för distribution av färdiga produkter och reservdelar.

I dagsläget finns inget centralt kontrollorgan över transportplaneringsprocessen på BT utan all transportplanering av produkter och reservdelar till truckar, sker lokalt på varje marknadsbolag i Västeuropa. I en framtid har BT som förhoppning att bedriva transportplaneringsprocessen på central nivå, antingen inom koncernen eller genom att utkontraktera transportplaneringsprocessen till en extern part, det vill säga till ett fjärdepartslogistikföretag.

Vid beslut om transportplaneringsprocessen ska genomföras centralt internt eller utföras externt med hjälp av en fjärdepartslogistiker finns en rad viktiga faktorer att ta i beaktning. De faktorer som valts att studera i denna undersökning är kärnverksamhet, processens mognad, flexibilitet, kontroll, beroende och transaktionskostnader. Dessa faktorer studeras genom att undersöka inom vilka områden BT anser sig att besitta kärnkompetens idag och i en framtid, samt hur BT ser på faktorer som flexibilitet, beroende och kontroll vid en eventuell outsourcing. Genom att ställa detta mot vad fjärdepartlogistikföretag erbjuder för lösningar till sina kunder och hur de hantera faktorer som flexibilitet, kontroll och beroende, kan för- och nackdelar med en utkontraktering av transportplaneringsprocessen definieras.

En fördel med att utkontraktera transportplaneringsprocessen uppnås genom att ingå ett samarbete med en fjärdepartslogistiker och därigenom kan BT få tillgång till spetskompetens och tillgång till större distributionsnätverk.

En annan fördel med ett nära samarbete med en leverantör av fjärdepartslogistiska tjänster är att det kan skapa ökad flexibilitet för BT. Detta genom att BT i ett samarbete kan tillgå skräddarsydda lösningar för företagsunika behov samtidigt som möjligheten att byta transportleverantörer ökar, då fjärdepartsaktörer vanligen arbeter med korttidskontrakt emot transportföretagen

Nackdelar som kan uppkomma med utkontraktering av transportplaneringsprocessen är att denna process kan komma att ses som betydande för framtida verksamhet. Det kan då vara förenat med risk att tappa kompetens inom området.

En nackdel är även att outsourcing av transportplaneringsprocessen kan innebär en beroendeställning för BT gentemot en tilltänkt fjärdepartsleverantör. Kontrollen av processen som koordinerar försörjningen av färdiga produkter och reservdelar från BT till företagets kunder eller marknadsbolag överlämnas därmed till en extern part.

Competing supply chain networks all around the globe are under scrutiny due to ever-growing demand for service improvement and cost reduction. A major field of action in this respect is the realization of real-time monitoring means for supply chain processes including a constant comparison of the respective progress status with the planning guidelines and the best possible management of deviations and exceptions. Control towers have been named as the future tool of supply chain monitoring for quite a while. They are defined as decision-support systems merging different data streams from various subordinate levels and displaying the consolidated information at a higher level for the purpose of monitoring and control of processes while pursuing the goal of optimal process operation. Contrary to the technological constraints of the past which prevented a continuous and fully transparent real-time monitoring of supply chain processes, innovative evolving so-called Future Internet technologies enable genuine transparency and the handling of exceptions in a timely and cost-efficient manner nowadays. With the help of such technologies, newly designed and built control towers are supposed to assist actors on the planning and execution levels of their respective supply chain networks in their decision-making in case of relevant deviations or exceptions. This again raises the market acceptance of such control towers. This paper presents a novel approach to the functional principle of Future-Internet-based control tower solutions and describes the different components therein. Especially, the incorporation of manifold information sources from the Future Internet technologies for the purpose of real-time monitoring and control of supply chain processes is highlighted in the paper.

Due to the lack of integration of existing IT solutions between the planning level and the execution level, many enterprises in the logistics sector are not able to achieve optimal process executions, particularly in cases of deviations and exceptions. Despite a greater supply of sensor devices over the past years, a wider spread of sensor devices has not yet taken place because the required information are either unavailable, unreliable or badly remunerative. Thereby, process control and deviation management often ends in mere troubleshooting rather than in searching for the optima in an organized manner. By adopting control towers at the interface between the two concerned levels and using low-cost cameras and image processing algorithms for the detection of objects, such lack of trust in the yielded information can be overcome. Such a vision-based monitoring tool is capable of supporting functions which are useful for the decision-making process on executional level. In the present paper, the different information which are provided by a vision-based monitoring tool are presented, followed by three application examples which show the different functionalities provided and the potential disclosed by the tool.

Abstract Due to the ever higher demands from the energy market, the quantity, dimension and power capacity of newly installed offshore wind turbines are continuously increasing. In terms of logistical management, economic feasibility and engineering difficulty, the traditional installation methods, predominantly represented by using Jack-up vessel and offshore cranes, will hit their limitations soon in the future. Offshore turbines have a relatively fixed geometric profile and physical characteristics: a slender cylindrical tower with huge blades attached on the top end. In this work, we exploited these features and designed a low-height lifting system for deploying wind turbine onto a floating spar platform. The low-height lifting system lifts the wind turbine with wires attached to the bottom of the tower, and keeps the balance of the tower with extra tug lines on the mid-section. The wires and tug lines are controlled by an active 6DOF compensation system. The low-height lifting system removes the necessity of a huge offshore crane onboard and can scale well to even larger wind turbines. The design is virtual prototyped in the simulator of Offshore Simulator Centre using FATHOM simulation software. Different design configurations are discussed in terms of the general arrangement, system dimensions and control methods.