Drawing on club theory, this study examines the challenges and opportunities facing a sustainability certification program, the Green Key scheme, in terms of its recruitment and retention of members within the Dutch tourism and hospitality industry. Extant literature on sustainability certification in this industry tends to focus narrowly on motivations and retention problems at the firm level, or else on drivers of or barriers to the adoption of sustainability certification schemes. The links between scheme design characteristics and scheme effectiveness and their implications for recruitment and retention thus have remained relatively unexamined. To address this gap, this study proposes a theoretical framework that highlights how different design features of sustainability certification schemes might inform the recruitment and retention challenges that scheme managers often face.
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Smallholders, who cultivate ±30% of the global palm oil land, are critical to the realization of a sustainable palm oil sector. However, particularly independent smallholders, untied to mills, lag behind in yields and experience challenges to market their produce. Sustainability certification, such as by the Roundtable on Sustainable Palm Oil (RSPO), is proposed as a way to improve smallholder livelihoods, while protecting the environment. However, independent smallholders experience barriers to obtain certification. Through interviews with 18 RSPOcertified independent smallholder groups in Indonesia and 9 certification facilitators, this study examines how pre-certification conditions regarding smallholders’ socio-economic backgrounds, legality, group organization, planation management practices, and local supply chain conditions impact prospects for RSPO certification, and how groups who successfully achieved certification have dealt with challenges during the certification process. We found that the majority (77%) of RSPO certified independent smallholders in Indonesia consists of ‘former scheme’ smallholders. These smallholders often have clear land legality and are organized in groups prior to certification, which increases their eligibility for RSPO certification. However, due to upfront and recurrent costs for certification, as well as complexities in meeting RSPO standards, access to certification is strongly dependent on external facilitators. To up-scale certification for independent oil palm smallholders, and include more nonscheme smallholders, certification projects should involve more local actors including local governments and certified smallholder groups. In addition, certification should focus on core social and environmental concerns for smallholders, while being flexible with regards to the forms of proof needed to fulfil legality requirements.
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Research has shown that it is difficult to motivate professionals to contribute to certification. Little research has been done on the reasons why. The paper provides more insight into the difficulties that organizations face to commit their professionals to become involved in certification and turns these into requirements to be fulfilled to achieve commitment. These are relevant for organisations, which need the support of their professional employees to achieve management system certification.
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
The automobile industry is presently going through a rapid transformation towards autonomous driving. Nearly all vehicle manufacturers (such as Mercedes Benz, Tesla, BMW) have commercial products, promising some level of vehicle automation. Even though the safe and reliable introduction of technology depends on the quality standards and certification process, but the focus is primarily on the introduction of (uncertified) technology and not on developing knowledge for certification. Both industry and governments see the lack of knowledge about certification, which can ensure the safety of autonomous technology and thus will guarantee the safety of the driver, passenger, and environment. HAN-AR recognized the lack of knowledge and the need for novel certification methodology for emerging vehicle technology and initiated the PRAUTOCOL project together with its SME partners. The PRAUTOCOL project investigated certification methodology for two use-cases: certification for automated highway overtaking pilot; and certification for automatic valet parking. The PRAUTOCOL research is conducted in two parallel streams: certification of the driver by human factors experts and certification of vehicle by technology experts. The results from both streams are published and presented in respective but limited target groups. Also, an overview of the PRAUTOCOL certification methodology is missing, which can enable its translation to different use-cases of automated technology (other than the used ones). Therefore, to realize a better pass-through of PRAUTOCOL's results to a broader audience, the top-up is required. Firstly, to write a (peer-reviewed) Open Access article, focusing on the application and translation of PRAUTOCOL's methodology to other automated technology use-cases. Secondly, to write a journal article, focusing on the validation of automatic highway overtaking system using naturalistic driving data. Thirdly, to organize a workshop to present PRAUTOCOL's results (valorization) to industrial, research, and government representatives and to discuss a follow-up initiative.
Our country contains a very dense and challenging transport and mobility system. National research agendas and roadmaps of multiple sectors such as HTSM, Logistics and Agri&food, promote vehicle automation as a means to increase transport safety and efficiency. SMEs applying vehicle automation require compliance to application/sector specific standards and legislation. A key aspect is the safety of the automated vehicle within its design domain, to be proven by manufacturers and assessed by authorities. The various standards and procedures show many similarities but also lead to significant differences in application experience and available safety related solutions. For example: Industrial AGVs (Automated Guided Vehicles) have been around for many years, while autonomous road vehicles are only found in limited testing environments and pilots. Companies are confronted with an increasing need to cover multiple application environments, such restricted areas and public roads, leading to complex technical choices and parallel certification/homologation procedures. SafeCLAI addresses this challenge by developing a framework for a generic safety layer in the control of autonomous vehicles that can be re-used in different applications across sectors. This is done by extensive consolidation and application of cross-sectoral knowledge and experience – including analysis of related standards and procedures. The framework promises shorter development times and enables more efficient assessment procedures. SafeCLAI will focus on low-speed applications since they are most wanted and technically best feasible. Nevertheless, higher speed aspects will be considered to allow for future extension. SafeCLAI will practically validate (parts) of the foreseen safety layer and publish the foreseen framework as a baseline for future R&D, allowing coverage of broader design domains. SafeCLAI will disseminate the results in the Dutch arena of autonomous vehicle development and application, and also integrate the project learnings into educational modules.
