In 2017, I introduced a new theoretical framework in Archival Science, that of the ‘Archive–as–Is’. This framework proposes a theoretical foundation for Enterprise Information Management (EIM) in World 2.0, the virtual, interactive, and hyper connected platform that is developing around us. This framework should allow EIM to end the existing ‘information chaos’, to computerize information management, to improve the organizational ability to reach business objectives, and to define business strategies. The concepts of records and archives are crucial for those endeavours. The framework of the ‘Archive–as–Is’ is an organization–oriented archival theory, consisting of five components, namely: [1] four dimensions of information, [2] two archival principles, [3] five requirements of information accessibility, [4] the information value chain; and [5] organizational behaviour. In this paper, the subject of research is component 5 of the framework: organizational behaviour. Behaviour of employees (including archivists) is one of the most complicated aspects within organizations when creating, processing, managing, and preserving information, records, and archives. There is an almost universal ‘sound of silence’ in scholarly literature from archival and information studies although this subject and its effects on information management are studied extensively in many other disciplines, like psychology, sociology, anthropology, and organization science. In this paper, I want to study how and why employees behave as they do when they are working with records and archives and how EIM is influenced by this behaviour.
DOCUMENT
More than 80 % of all information in an organization is unstructured, created by knowledge workers engaged in peer-to-peer networks of expertise to share knowledge across organizational boundaries. Enterprise Information Systems (EIS) do not integrate unstructured information. At best, they integrate links to unstructured information connected with structured information in their databases. The amount of unstructured information is rising quickly. Ensuring the quality of this unstructured information is difficult. It is often inaccessible, unavailable, incomplete, irrelevant, untimely, inaccurate, and/or incomprehensible. It becomes problematic to reconstruct what has happened in organizations. When used for organizational policies, decisions, products, actions and transactions, structured and unstructured information are called records. They are an entity of information, consisting out of an information object (structured or unstructured) and its metadata. They are important for organizational accountability and business process performance, for without them reconstruction of past happenings and meaningful production become an impossibility. Organization-wide management of records is not a common functionality for EIS, resulting in [1] a fragmentation in the management of records, where structured and unstructured information objects are stored in a variety of systems, unconnected with their metadata; [2] a fragmentation in metadata management, leading to a loss of contextuality because metadata are separated from their information objects; and [3] a declining quality or records, because their provenance, integrity, and preservation are in peril. Organizational accountability is based on records and their context to reconstruct the past. Because records are not controlled by EIS, they can only marginally be used for accountability. The challenge for organizational accountability is to generate trusted records, fixed and contextual information objects inseparately linked with metadata that capture context to regain evidential value and to allow for the reconstruction of the past. The research question of this paper is how to capture records and their context within EIS to regain the evidential value of records to allow for a more robust organizational accountability. To find an answer, we need to pay attention to the concept of context, on how to capture context in metadata, and how to embed and manage records in EIS.
DOCUMENT
The main question in this PhD thesis is: How can Business Rules Management be configured and valued in organizations? A BRM problem space framework is proposed, existing of service systems, as a solution to the BRM problems. In total 94 vendor documents and approximately 32 hours of semi-structured interviews were analyzed. This analysis revealed nine individual service systems, in casu elicitation, design, verification, validation, deployment, execution, monitor, audit, and version. In the second part of this dissertation, BRM is positioned in relation to BPM (Business Process Management) by means of a literature study. An extension study was conducted: a qualitative study on a list of business rules formulated by a consulting organization based on the Committee of Sponsoring Organizations of the Treadway Commission risk framework. (from the summary of the Thesis p. 165)
DOCUMENT
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.
