650 Management
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In the current age of innovative business financing opportunities available from fintech apps, social media crowdfunding sites such as Kickstarter, Indiegogo, and RocketHub, et.al., and friends and family private equity investors, start-up firms can strategically source their venture capital funds from many globally disperse organizations and individuals. As the firm in this case learned, the benefit of alternative investing sources comes with a critical hidden risk for corporate governance. After a financial restructuring, a typical Silicon Valley software start-up found itself with close to 300 external individual shareholders, some of whom had not been documented as accredited investors. The regulatory agency could decide that the prior actions of the founders and the decisions of the board had been prejudicial to the interests of the minority investors. The management of this small private company faced an atypical investor relations dilemma, before its initial public offering (IPO).
Context: Agile practices as well as UX methods are nowadays well-known and often adopted to develop complex software and products more efficiently and effectively. However, in the so called VUCA environment, which many companies are confronted with, the sole use of UX research is not sufficient to find the best solutions for customers. The implementation of Design Thinking can support this process. But many companies and their product owners don’t know how much resources they should spend for conducting Design Thinking.
Objective: This paper aims at suggesting a supportive tool, the “Discovery Effort Worthiness (DEW) Index”, for product owners and agile teams to determine a suitable amount of effort that should be spent for Design Thinking activities.
Method: A case study was conducted for the development of the DEW index. Design Thinking was introduced into the regular development cycle of an industry Scrum team. With the support of UX and Design Thinking experts, a formula was developed to determine the appropriate effort for Design Thinking.
Results: The developed “Discovery Effort Worthiness Index” provides an easy-to-use tool for companies and their product owners to determine how much effort they should spend on Design Thinking methods to discover and validate requirements. A company can map the corresponding Design Thinking methods to the results of the DEW Index calculation, and product owners can select the appropriate measures from this mapping. Therefore, they can optimize the effort spent for discovery and validation.
Problem: Die Covid-19 Pandemie verschärft nicht nur die wirtschaftlichen, sondern auch die öko-sozialen Rahmenbedingungen vieler Unternehmen. Nachhaltiges Handeln ist daher wichtiger denn je. Unternehmen wählen unterschiedliche Wege, um Nachhaltigkeit in das Managementsystem der oberen Führungsebene zu integrieren. Dadurch besteht die Chance, Nachhaltigkeit nicht nur in Form von Einzelmaßnahmen zu sehen, sondern als Element der Strategie- und Organisationsentwicklung zu verstehen. Für die gesamthafte Betrachtung kommen u. a. die Gemeinwohlbilanz (GWB) und die Nachhaltigkeits-Balanced Scorecard (N-BSC) in Betracht, wie die Beispiele von Vaude und der Sparda Bank München, die die GWB nutzen (siehe https://web.ecogood.org/de/die-bewegung/pionier-unternehmen/), sowie Alpha und Axel Springer, die Nachhaltigkeit in ihre BSC integrieren (Hansen/Schaltegger, 2016, S. 207), zeigen.
Ziel: Diskussion der GWB und der N-BSC als Möglichkeiten zur Integration öko-sozialer Aspekte in das Managementsystem.
Methode: Aufzeigen wesentlicher Grundzüge der GWB und N-BSC
Trotz Niedrigzinsphase bleibt das Working Capital Management ein wichtiger Treiber für Wertgrößen in Unternehmen und wichtiges Managementinstrument. Unsere Ergebnisse über 115 Unternehmen aus den wichtigsten deutschen Indizes in den Jahren 2011 bis 2017 zeigen, dass effektives Working Capital Management einen positiven Einfluss auf die Rentabilität und den Unternehmenswert haben kann. Gleichzeitig zeigen unsere Ergebnisse aber auch, dass dem Working Capital Management jüngst weniger Aufmerksamkeit zuteilgeworden ist und digitale Innovationen vermutlich noch nicht in dem Umfang zur Effizienzsteigerung eingesetzt werden, wie dies möglich erscheint. Selbst vor dem Hintergrund andauernd niedriger Kapitalmarktzinsen ist dies kritisch zu sehen.
Forschungsfrage: Was sind Motivationen, Bedenken und Karriereerwartungen von Hochschulabsolventen, die sich für eine selbstinitiierte Auslandsentsendung direkt nach dem Studienabschluss entscheiden?
Methodik: Quantitative Befragung unter Absolventen deutscher Hochschulen
Praktische Implikationen: Aus den Ergebnissen lassen sich Maßnahmen zur Gewinnung und Bindung von Hochschulabsolventen als selbstinitiierte Entsandte und damit zur Nutzung des Potenzials der selbstinitiierten Entsendung ableiten.
Coopetitive endeavors offer valuable strategic options for firms. Yet, many of them are failure-prone as partners must balance collective and private interest. While interpartner trust is considered central for alliance success, paradoxically, the role and dynamics of trust is still not understood. We synthesize a computational model, capturing relational dynamics of an alliance, encompassing coevolution of trust, partner contributions, and (relative) alliance interactions. Analyzing alliance dynamics using simulation we find and explore a tipping boundary, separating a regime of alliance failure and success. We identify implications for collaborative (aspirations) and private strategies (openness). Our analyses reveal that strategies informed by a static mental model of partner trust, contributions, and openness tend to yield subpar alliance results and hidden failure-risk. We discuss implications for management theory.
Today's logistics systems are characterized by uncertainty and constantly changing requirements. Rising demand for customized products, short product life cycles and a large number of variants increases the complexity of these systems enormously. In particular, intralogistics material flow systems must be able to adapt to changing conditions at short notice, with little effort and at low cost. To fulfil these requirements, the material flow system needs to be flexible in three important parameters, namely layout, throughput and product. While the scope of the flexibility parameters is described in literature, the respective effects on an intralogistics material flow system and the influencing factors are mostly unknown. This paper describes how flexibility parameters of an intralogistics system can be determined using a multi-method simulation. The study was conducted in the learning factory “Werk150” on the campus of Reutlingen University with its different means of transport and processes and validated in terms of practical experiments.
The production environment experiences copious challenges, but likewise discovers many new potential opportunities. To meet the new requirements, caused by the developments towards mass-customization, human-robot-cooperation (HRC) was identified as a key piece of technology and is becoming more and more important. HRC combines the strengths of robots, such as reliability, endurance and repeatability, with the strengths of humans, for instance flexibility and decision-making skills. Notwithstanding the high potential of HRC applications, the technology has not achieved a breakthrough in production so far. Studies have shown that one of the biggest obstacles for implementing HRC is the allocation of tasks. Another key technology that offers various opportunities to improve the production environment is Artificial Intelligence (AI). Therefore, this paper describes an AI supported method to improve the work organization in HRC in regards to the task-allocation. The aim of this method is to build a dynamic, semi-autonomous group work environment which keeps not just employee motivation at a high level, but also the product quality due to a decreased failure rate. The AI helps to detect the perfect condition in which the employee delivers the best performance and also supports at identifying the time when the worker leaves this optimal state. As soon as the employee reaches this trigger event, the allocation of the tasks adapts based on the identified stress. This adaptation aims to return the employee to the state of the optimal performance. In order to realize such a dynamic allocation, this method describes the creation of a pool with various interaction scenarios, as well as the AI supported recognition of the defined trigger event.
Manufacturing companies are confronted with external (e.g. short-term change of product configuration by the customer) and internal (e.g. production process deviations) turbulences which are affecting the performance of production. Predefined, centrally controlled logistics processes are limiting the possibilities of production to initiate countermeasures to react in an optimized way to these turbulences. The autonomous control of intralogistics offers a great potential to cope with these turbulences by using the respective flexibility corridors of production systems and applying intelligent logistic objects with decentralized decision and process execution capabilities to maintain a target-optimized production. A method for AI-based storage-location- and material-handling-optimization to achieve performance-optimized intralogistics system through continuous monitoring of performance-relevant parameters and influencing factors by using AI (e.g. for pattern recognition) has been developed. To provide the basis to investigate and demonstrate the potentials of autonomously controlled intralogistics in connection with turbulences of production and in combination with AI, an intelligent warehouse involving an indoor localization system, smart bins, manual, semi-automated/collaborative and autonomous transport systems has been developed and implemented at Werk150, the factory on campus of ESB Business School (Reutlingen University). This scenario, which has been integrated into graduate training modules, allows the analysis and demonstration of different measures of intralogistics to cope with turbulences in production involving amongst others storage and material provision processes. The target fulfilment of the applied intralogistics measures to master arising turbulences is assessed based on the overall performance of production considering lead times and adherence to delivery dates. By applying artificial intelligence (AI) algorithms the intelligent logistical objects (smart bin, transport systems, etc.) as well as the entire logistics system should be enabled to improve their decision and process execution capabilities to master short-term turbulences in the production system autonomously.