A model of cause-related marketing for profit-driven and non-profit organizations

Purpose – The purpose of this paper is to describe a model of cause-related marketing (CRM) for both profit-driven (PD) and non-profit (NP) organizations.
Design/methodology/approach – The model consists of two parallel internal and external organizational processes – one representing the process of a NP organization and the other a PD organization. They are interlinked as the outcome of a CRM-partnership is dependent upon their mutual efforts.
Findings – The authors argue that it is essential to remember that a CRM-partnership is a challenge and risk for both the PD and NP organizations that may harm their reputation and position in the marketplace and/or society. CRM has benefits as well as downsides that should not be underestimated nor neglected.
Research limitations/implications – Will the involvement of the PD or NP organizations in the resultant partnership be perceived as commercialism, altruism or a combination of both, in the marketplace and society? A focus on both processes opens up opportunities for further research.
Practical implications – A contribution is that the CRM-model may be used as a guide for both PD and NP organizations in order to reveal whether a CRM-partnership is appropriate for them with a potential partner or not. It may also indicate whether the motives are based upon commercial reasons or altruistic reasons or a combination of both.
Originality/value – The model enables these organizations to think through the process prior to engaging in CRM.

A family of planar parallel manipulators

A family of planar parallel manipulators is investigated and some novel members are proposed. The common feature of the studied manipulators is that the rotation axes of the actuated arms coincide. This feature makes it possible to rotate the whole arm system an infinite number of revolutions around the center of the manipulator. The result is a large workspace in relation to the footprint. Both 2- and 3-DOF variants are presented and the suitability of this family of manipulators for kinematic analysis is demonstrated. Thus, different methods to find optimal manipulability with respect to platform positioning and rotation have been analyzed.

Parallel manipulators with a rotation-symmetric arm system

Parallel manipulators with a rotation-symmetric arm system possess all the typical advantages of parallel robots, such as high acceleration and high-accuracy positioning. Contrary to the majority of proposed parallel manipulators, the rotation-symmetric arm system leads to a large workspace in relation to the footprint of the manipulator. This paper focuses on a subclass of these manipulators with additional favorable qualities, including low inertia and high eigenfrequencies. These qualities are achieved using only 5-DOF lower arm links and by mounting all actuators on the nonmoving base column of the manipulator. The common feature of all previously proposed manipulators in this subclass is identified and several novel 3-DOF and 4-DOF members are introduced.

Kinematics analysis of 6-DOF parallel micro-manipulators with offset U-Joints : a case study

This paper analyses the kinematics of a special 6-DOF parallel micro-manipulator with offset RR-joint configuration. Kinematics equations are derived and numerical methodologies to solve the inverse and forward kinematics are presented. The inverse and forward kinematics of such robots compared with those of 6-UCU parallel robots are more complicated due to the existence of offsets between joints of RR-pairs. The characteristics of RR-pairs used in this manipulator are investigated and kinematics constraints of these offset U-joints are mathematically explained in order to find the best initial guesses for the numerical solution. Both inverse and forward kinematics of the case study 6-DOF parallel micro-manipulator are modelled and computational analyses are performed to numerically verify accuracy and effectiveness of the proposed methodologies.

Kinematics analysis of 6-DOF parallel micro-manipulators with offset u-joints : a case study

This paper analyses the kinematics of a special 6-DOF parallel micro-manipulator with offset RR-joint configuration. Kinematics equations are derived and numerical methodologies to solve the inverse and forward kinematics are presented. The inverse and forward kinematics of such robots compared with those of 6-UCU parallel robots are more complicated due to the existence of offsets between joints of RR-pairs. The characteristics of RR-pairs used in this manipulator are investigated and kinematics constraints of these offset U-joints are mathematically explained in order to find the best initial guesses for the numerical solution. Both inverse and forward kinematics of the case study 6-DOF parallel micro-manipulator are modelled and computational analyses are performed to numerically verify accuracy and effectiveness of the proposed methodologies.

Automation of a complex transfer operation using a polar manipulator

Purpose – The objective of this paper included developing a polar robot (SPBot) for rotating and transferring car engine block (CEB) around and along two different axes in a confined workspace envelope.

Design/methodology/approach – The complex transfer operations of the CEB requires sweeping complete surface of the half sphere, and thus a polar robot is best suited to such a task in a confined space. Considering the limited space for this operation, a specially designed manipulator is built comprising 2 degrees of freedom driven by electrical servo motors. Also due to the special form of CEB, an especially designed pneumatic gripper is developed. Kinematics models, static and dynamic equations, together with trajectory planning for such a manipulator are described.

Findings – The high-speed complex transfer in a limited environment is successfully implemented.

Originality/value – The developed polar robot provides for complex transfer operations that significantly increases the speed of the product line and thus reducing the cycle time from 60?s using manpower to just 20?s using the robot.

A MapReduce-based nearest neighbor approach for big-data-driven traffic flow prediction

In big-data-driven traffic flow prediction systems, the robustness of prediction performance depends on accuracy and timeliness. This paper presents a new MapReduce-based nearest neighbor (NN) approach for traffic flow prediction using correlation analysis (TFPC) on a Hadoop platform. In particular, we develop a real-time prediction system including two key modules, i.e., offline distributed training (ODT) and online parallel prediction (OPP). Moreover, we build a parallel k-nearest neighbor optimization classifier, which incorporates correlation information among traffic flows into the classification process. Finally, we propose a novel prediction calculation method, combining the current data observed in OPP and the classification results obtained from large-scale historical data in ODT, to generate traffic flow prediction in real time. The empirical study on real-world traffic flow big data using the leave-one-out cross validation method shows that TFPC significantly outperforms four state-of-the-art prediction approaches, i.e., autoregressive integrated moving average, Naïve Bayes, multilayer perceptron neural networks, and NN regression, in terms of accuracy, which can be improved 90.07% in the best case, with an average mean absolute percent error of 5.53%. In addition, it displays excellent speedup, scaleup, and sizeup.