Credit management neemt toe in belang. Oorzaken hiervan zijn onder meer de toenemende concurrentiedruk, nieuwe regelgeving zoals Basel II en de grotere focus op werkkapitaal in het kader van sturen op aandeelhouderswaarde. Publicaties over credit management richten zich overwegend op proces- en procedurebeschrijvingen, kredietwaardigheidsbeoordeling en auditingchecklists. Deze onderwerpen zijn ontegenzeggelijk relevant voor de analyse van credit management, maar vormen geen antwoord op de vraag hoe de control van credit management moet worden ingericht. In dit artikel wordt een praktische controlaanpak geïntroduceerd die gebaseerd is op het management control framework van Merchant en de verschillende typen kredietbeleid zoals omschreven door Wallis. Het resultaat is een aanpak die eenvoudig en praktisch toepasbaar is, geschikt is voor verschillende typen kredietbeleid en rekening houdt met verschillende typen controls.
We are currently in a transition moving from a linear economy grounded on economic value maximization based on material transformation to a circular economy. Core of this transition is organising value preservation from various yet interlinked perspectives. The underlying fundamental shift is to move away from mere financial value maximization towards multiple value creation (WCED, 1987; Jonker, 2014; Raworth, 2017). This implies moving from mere economic value creation, to simultaneously and in a balanced way creating ecological and social value. A parallel development supporting this transition can be observed in accounting & control. Elkington (1994) introduced the triple bottom line (TBL) concept, referring to the economic, ecological and social impact of companies. The TBL should be seen more as a conceptual way of thinking, rather than a practical innovative accounting tool to monitor and control sustainable value (Rambaud & Richard, 2015). However, it has inspired accounting & control practitioners to develop accounting tools that not only aim at economic value (‘single capital’ accounting) but also at multiple forms of capital (‘multi capital’ accounting or integrated reporting). This has led to a variety of integrated reporting platforms such as Global Reporting Initiative (GRI), International Integrated Reporting Framework (IIRC), Dow Jones Sustainable Indexes (DJSI), True Costing, Reporting 3.0, etc. These integrated reporting platforms and corresponding accounting concepts, can be seen as a fundament for management control systems focussing on multiple value creation. This leads to the following research question: How are management control systems designed in practice to drive multiple value creation?
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This paper investigates how management accounting and control systems (operationalized by using Simons’ (1995a) levers of control framework) can be used as devices to support public value creation and as such it contributes to the literature on public value accounting. Using a mixed methods case study approach, including documentary analysis and semi-structured interviews, we found diverging uses of control systems in the Dutch university of applied sciences we investigated. While belief and interactive control systems are used intensively for strategy change and implementation, diagnostic controls were used mainly at the decentral level and seen as devices to make sure that operational and financial boundaries were not crossed. Therefore, belief and interactive control systems lay the foundation for the implementation of a new strategy, in which concepts of public value play a large role, using diagnostic controls to constrain actions at the operational level. We also found that whereas the institution wanted to have interaction with the external stakeholders, in daily practice this takes place only at the phase of strategy formulation, but not in the phase of intermediate strategy evaluation.
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Inleiding en praktijkvraag De groeiende wereldbevolking gecombineerd met de klimaatverandering zorgt voor een de noodzaak tot een duurzame voedselvoorziening (KIA missie Landbouw, voedsel & water). Een significante reductie van gewasbestrijdingsmiddelen is daarbinnen een belangrijke doelstelling. Robotica maakt als technologie motor van de precisielandbouw plant specifieke precisie-bestrijding mogelijk. Het projectconsortium onderzoekt een semiautonoom samenwerkend grond-luchtrobot platform voor de precisielandbouw. Projectdoelstelling De doelstelling van het project AGRobot Platform is dan ook: “Onderzoek de mogelijkheden van een semi-autonoom samenwerkend grond-lucht robotplatform voor de precisielandbouw”. De hoofddoelstelling wordt binnen dit project beantwoordt door de deliverables uit de volgende subdoelstellingen: 1. Case studie onderzoek naar de mogelijke voordelen van het grond-luchtrobotplatform 2. Onderzoek naar de benodigde technologieën voor een grond-luchtrobotplatform 3. Ontwikkelen van een eerste (mogelijk case-specifieke) demonstrator 4. Ontwikkelen van (nieuwe) samenwerkingsvormen. Vraagsturing & Netwerkvorming Riwo Engineering is een industriële automatiseeerder die met zijn grondrobots en control-besturingssytemen actief is in de veeteelt. DRONEXpert gebruikt hyperspectrale camera’s onder drones voor het bemeten van gewassen. Saxion mechatronica onderzoekt met de onderzoekslijn unmanned robotic systems hoe de nieuwste robotica technologieën systemen mogelijk maakt voor ongestructureerde omgevingen. De partners bezitten gezamenlijk een enorm netwerk (TValley, Space53, euRobotics) en klanten om via de case studies de kansen te achterhalen en te realiseren. Innovatie Nergens ter wereld is een samenwerkend grond-luchtrobot platform actief in de precisielandbouw. Voor OostNederland, met naast veel robotica kennis ook veel Agro-kennis, zal het project letterlijk de KIEM zijn voor nieuwe projecten waaruit de valorisatie kansen richting heel Europa gaan. Activiteitenplan & Projectorganisatie Het project wordt geleid door de lector Dr. Ir. D.A.Bekke en uitgevoerd door Abeje Mersha en Mark Reiling samen met het deelnemend MKB. Het project bestaat uit 4 werkpakketten die achtereenvolgens antwoordt geven op de gestelde subdoelstellingen. Aan elk werkpakket zijn deliverables gekoppeld.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
