Closing the loop of products and materials in Product Service Systems (PSS) can be approached by designers in several ways. One promising strategy is to invoke a greater sense of ownership of the products and materials that are used within a PSS. To develop and evaluate a design tool in the context of PSS, our case study focused on a bicycle sharing service. The central question was whether and how designers can be supported with a design tool, based on psychological ownership, to involve users in closing the loop activities. We developed a PSS design tool based on psychological ownership literature and implemented it in a range of design iterations. This resulted in ten design proposals and two implemented design interventions. To evaluate the design tool, 42 project members were interviewed about their design process. The design interventions were evaluated through site visits, an interview with the bicycle repairer responsible, and nine users of the bicycle service. We conclude that a psychological ownership-based design tool shows potential to contribute to closing the resource loop by allowing end users and service provider of PSS to collaborate on repair and maintenance activities. Our evaluation resulted in suggestions for revising the psychological ownership design tool, including adding ‘Giving Feedback’ to the list of affordances, prioritizing ‘Enabling’ and ‘Simplification’ over others and recognize a reciprocal relationship between service provider and service user when closing the loop activities.
Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx BlockRAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution (1080 × 1920) @103 fcps.
Human Resource Management (HRM) is widely believed to have a positive effect on the performance of company. However, empirical proof of this is hard to come by. In this study, we try to establish a linkage between HRM and financial output of two case studies in the profit sector. To do this, we have developed a performance measurement system that is tailored to the specific needs of measuring HRM-performance in for-profit of company. Although we do not try to generalize the outcome of this study, it looks promising in the way that more case studies should be conducted using this specific performance measurement system. If nothing else, management and controllers could use the system to evaluate the performance of their HRM-tools.
Globalization has opened new markets to Small and Medium Enterprise (SMEs) and given them access to better suppliers. However, the resulting lengthening of supply chains has increased their vulnerability to disruptions. SMEs now recognize the importance of reliable and resilient supply chains to meet customer requirements and gain competitive advantage. Data analytics play a crucial role in developing the insights needed to identify and deal with disruptions. At the company level, this entails the development of data analytic capability, a complex socio-technical process consisting of people, technology, and processes. At the supply chain level, the complexity is compounded by the fact that multiple actors are involved, each with their own resources and capabilities. Each company’s data analytic capability, in combination with how they work together to share information and thus create visibility in the supply chain will affect the reliability and resilience of the supply chain. The proposed study therefore examines how SMEs can leverage data analytics in a way that fits with their available resources and capabilities to improve the reliability and resilience of their supply chain. The consortium for this project consists of Breda University of Applied Sciences (BUas), Logistics Community Brabant (LCB), Transport en Logistiek Nederland (TLN), Logistiek Digitaal, Kennis Transport, Smink and Devoteam. Together, the partners will develop a tool to benchmark SMEs’ progress towards developing data analytic capability that enhances the reliability of their supply chain. Interviews will be conducted with various actors of the supply chain to identify the enablers and inhibitors of using data analytics across the supply chain. Finally, the findings will be used to conduct action research with the two SMEs partners, Kennis and Smink to identify which technological tools and processes companies need to adopt to develop the use of data analytics to enhance their resilience in case of disruptions.
Globalization has opened new markets to Small and Medium Enterprise (SMEs) and given them access to better suppliers. However, the resulting lengthening of supply chains has increased their vulnerability to disruptions. SMEs now recognize the importance of reliable and resilient supply chains to meet customer requirements and gain competitive advantage. Data analytics play a crucial role in developing the insights needed to identify and deal with disruptions. At the company level, this entails the development of data analytic capability, a complex socio-technical process consisting of people, technology, and processes.At the supply chain level, the complexity is compounded by the fact that multiple actors are involved, each with their own resources and capabilities. Each company’s data analytic capability, in combination with how they work together to share information and thus create visibility in the supply chain will affect the reliability and resilience of the supply chain. The proposed study therefore examines how SMEs can leverage data analytics in a way that fits with their available resources and capabilities to improve the reliability and resilience of their supply chain.Collaborative partners:Logistics Community Brabant, Transport & Logistiek Nederlands (TLN), SMINK, Kennis Transport, Logistiek Digitaal, Devoteam.
