FAILURE MODES AND EFFECTS ANALYSIS
(FMEA)
The Failure modes and effects analysis (FMEA) is a systematic step-by-step approach for evaluating a process and identifying all possible failures in the design or assembly process to determine areas where corrections are needed. "Failure modes" refers to the ways something might fail, e.g. due to having errors or defects. The "effects analysis" means studying the consequences of those failures. FMEA aims at identifying and assessing the causes of failure already in the concept phase to be able to take adequate actions in order to avoid or eliminate errors and save costs. Ideally, FMEA is used during the earliest conceptual stages and continues throughout the life of the product or service.
The objective of our project is to evaluate the communication strategy of the Hermann-Gmeiner-Akademie. Therefore, we will compare the old communication methods used during the last years with the new ones that are under development. As the new version is still in the conceptual phase, we can use the FMEA approach to identify potential causes for failures. Possible failures could be that the content of the marketing material such as flyers or portfolios is difficult to understand, that the website is incomprehensible, or that the communication channels such as social media or mailing lists are not used efficiently. To ensure an objective evaluation, we will question independent people such as students or potential clients of the Hermann-Gmeiner-Akademie with the help of a survey. This shall help to assess the new marketing approach according to different, previously defined quality criteria, and draw the attention to potential failures. As a next step of the FMEA method, we will then gather all the findings to provide critical feedback on the product so far developed. We will also give recommendations to help prevent failures and further improve the communication strategy of the Hermann-Gmeiner-Akademie.
PREDICTIVE ANALYTICS
Predictive analytics uses historical data to predict future events. Generally, historical data is used to build a mathematical model that captures key trends. This predictive model is then applied to current data to predict what will happen next, or to suggest actions that will achieve optimal results. The term "Predictive Analytics" describes the application of a statistical or machine learning technique to produce a quantitative forecast of the future. Monitoring machine learning techniques are often used to predict a future value or estimate a probability.
Predictive analytics starts with a business objective: to use data to reduce waste, save time or cut costs. The process incorporates heterogeneous, often very large amounts of data into models that can produce clear, actionable results to help achieve this goal. Results can include less waste, reduced inventory and a manufactured product that meets specifications. Predictive analytics can yield a substantial ROI. It can help to optimize existing processes, better understand customer behaviour, identify unexpected opportunities, and anticipate problems before they happen.
KNOWLEDGE CLUSTERS
Cooperations between different companies and institutions can be powerful drivers of innovation. As a rather small non-profit-organization, the Hermann-Gmeiner-Akademie cannot afford to invest a lot of money in research, innovation and organizational development. However, there might be other NGOs or enterprises facing similar difficulties in fields like communication, donor satisfaction or effectiveness of in-house trainings. Therefore, forming knowledge clusters with other organisations could help to get ahead. Cooperations can help fostering knowledge sharing and the exchange of best-practice-examples.
The knowledge cluster can and should include universities, in order to base decisions on empirical and scientific evidence. This enables scientists and students to apply their knowledge in real-life problems and get access to relevant information for case studies or projects. On the other hand, the Hermann-Gmeiner-Akademie and other organisations that are part of the cooperations benefit from tailored research in their field of interest and empiric data to support their cause. For the Hermann-Gmeiner-Akademie this is also a rather efficient way to drive innovation, because they do not have to cover all these competencies within their work force and can make use of the knowledge cluster whenever convenient or necessary.
Cooperations with other institutions and organisations can also enhance valuable synergy effects and might even bring up ideas for new products or services to offer. As the Hermann-Gmeiner-Akademie wants to offer their trainings also to customers outside of the SOS children’s villages federation, this could be a suitable setting to identify potential customers and test their concepts with new partners. This is a first example of how a variety of stakeholders could benefit from forming research and knowledge cooperations.