The Centre of Research in Knowledge Organisations and Knowledge Management of Zuyd University has developed a knowledge management scan. The scan initiates from two models. The first model is based on the Value Based Knowledge Management approach (Tissen, Andriessen & Lekanne Deprez, 1998) and includes 6 basic abilities of a knowledge-intensive organisation that will enable the organisation to operate successfully in a knowledge based economy (.The second model, developed by Wierdsma and Swieringa (2002), categorises organisations according to their level of learning that is to say, how it develops a specific learning ability. Both models are briefly reviewed within this paper. This knowledge management scan is a tool that enables an organisation to assess the development of its six basic abilities. Once the organisation has a clear insight into its own abilities, it will be able to strengthen its overall learning ability and improve the organisations’ competitive position. Additionally we take a close look at our research approach for developing and implementing the knowledge management scan. The scan encompasses 15 statements per ability (90 statements in total). The complete scan will be assessed on a five-point scale by a representative group of selected employees and managers of an organization, supervised by a researcher/consultant. During the analysis of the results and the presentation of recommendations, specific attention is paid to those statements that achieve high and low scores respectively (invitation to implement improvement actions) and statements that have a relatively high spread across a broad range (differences of opinion or the statement is open to different interpretations). In particular we have examined how the knowledge management scan was put into practice in one of the departments of Zuyd University. After a short summary of the organisation's initial situation, we discuss subsequent steps taken during the assessment, analysis and the advisory process. This paragraph is followed by a concise summary of the results generated by the scan. Finally we offer the recommendations and subsequent steps to be taken to implement these advices in the near future.
Legal management is een nieuw vakgebied, dat de praktijkvan juridische professionals vanuit een organisatiekundigperspectief benadert. Binnen organisaties heeft legalmanagement het doel de kwaliteit van de juridische functiete optimaliseren. In deze unieke uitgave schetsen experts deontwikkeling van dit nieuwe vakgebied en gaan zij in op destand van zaken van het legal management in verschillendesectoren van de Nederlandse rechtspraktijk: de centrale endecentrale overheid, het bedrijfsleven en de advocatuur.Voor iedereen die geïnteresseerd is in de organisatie vanjuridische dienstverlening en juridische processen.
Digital transformation has been recognized for its potential to contribute to sustainability goals. It requires companies to develop their Data Analytic Capability (DAC), defined as their ability to collect, manage and analyze data effectively. Despite the governmental efforts to promote digitalization, there seems to be a knowledge gap on how to proceed, with 37% of Dutch SMEs reporting a lack of knowledge, and 33% reporting a lack of support in developing DAC. Participants in the interviews that we organized preparing this proposal indicated a need for guidance on how to develop DAC within their organization given their unique context (e.g. age and experience of the workforce, presence of legacy systems, high daily workload, lack of knowledge of digitalization). While a lot of attention has been given to the technological aspects of DAC, the people, process, and organizational culture aspects are as important, requiring a comprehensive approach and thus a bundling of knowledge from different expertise. Therefore, the objective of this KIEM proposal is to identify organizational enablers and inhibitors of DAC through a series of interviews and case studies, and use these to formulate a preliminary roadmap to DAC. From a structure perspective, the objective of the KIEM proposal will be to explore and solidify the partnership between Breda University of Applied Sciences (BUas), Avans University of Applied Sciences (Avans), Logistics Community Brabant (LCB), van Berkel Logistics BV, Smink Group BV, and iValueImprovement BV. This partnership will be used to develop the preliminary roadmap and pre-test it using action methodology. The action research protocol and preliminary roadmap thereby developed in this KIEM project will form the basis for a subsequent RAAK proposal.
The SPRONG-collaboration “Collective process development for an innovative chemical industry” (CONNECT) aims to accelerate the chemical industry’s climate/sustainability transition by process development of innovative chemical processes. The CONNECT SPRONG-group integrates the expertise of the research groups “Material Sciences” (Zuyd Hogeschool), “Making Industry Sustainable” (Hogeschool Rotterdam), “Innovative Testing in Life Sciences & Chemistry” and “Circular Water” (both Hogeschool Utrecht) and affiliated knowledge centres (Centres of Expertise CHILL [affiliated to Zuyd] and HRTech, and Utrecht Science Park InnovationLab). The combined CONNECT-expertise generates critical mass to facilitate process development of necessary energy-/material-efficient processes for the 2050 goals of the Knowledge and Innovation Agenda (KIA) Climate and Energy (mission C) using Chemical Key Technologies. CONNECT focuses on process development/chemical engineering. We will collaborate with SPRONG-groups centred on chemistry and other non-SPRONG initiatives. The CONNECT-consortium will generate a Learning Community of the core group (universities of applied science and knowledge centres), companies (high-tech equipment, engineering and chemical end-users), secondary vocational training, universities, sustainability institutes and regional network organizations that will facilitate research, demand articulation and professionalization of students and professionals. In the CONNECT-trajectory, four field labs will be integrated and strengthened with necessary coordination, organisation, expertise and equipment to facilitate chemical innovations to bridge the innovation valley-of-death between feasibility studies and high technology-readiness-level pilot plant infrastructure. The CONNECT-field labs will combine experimental and theoretical approaches to generate high-quality data that can be used for modelling and predict the impact of flow chemical technologies. The CONNECT-trajectory will optimize research quality systems (e.g. PDCA, data management, impact). At the end of the CONNECT-trajectory, the SPRONG-group will have become the process development/chemical engineering SPRONG-group in the Netherlands. We can then meaningfully contribute to further integrate the (inter)national research ecosystem to valorise innovative chemical processes for the KIA Climate and Energy.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
