In a business world characterized by a flood of data and time pressure, management is often faced with the challenge of filtering out the most important topics from a multitude of topics. It is important to find the signal in all the noise, i.e. to identify the signals from the constant stream of information that are really relevant for decisions and processes. This ability to separate what is important from what is unimportant is crucial to being able to act effectively and purposefully. In this context, both structured information processing for people and advanced algorithms from a technological perspective play a role. They make it possible to set priorities clearly and ensure that management focuses its attention on the issues that have the greatest impact on company performance. Below we show how oee.ai helps to identify the most important information and optimize management decision-making through the use of visualizations and algorithms.
Pyramid principle: Structuring for clarity and efficiency through cause-and-effect relationships
The Pyramid Principle, developed by Barbara Minto during her time at McKinsey, is a method for structuring information that aims to reduce complexity and create clarity in communication. The core of the principle is to arrange information hierarchically, with the most important and overarching points presented first. This approach follows a pyramid structure: At the top is the core statement or main message, followed by supporting arguments or data with increasing levels of detail. This type of information preparation is particularly effective in management because it allows managers to quickly grasp the essence of a topic without getting lost in details. By applying the pyramid principle, complex information can be put into a clear, logical and easy-to-understand form, making decision-making much easier and faster.
Algorithms: Directly to the goal based on data
The opposite approach is called a scientific funnel, where all the details are communicated and presented until you get to the core message. What may be appropriate for scientific work but it’s not practical for humans in the daily practice of operational management – looking at all the details before making a decision. However, you can largely rely on algorithms to help you prioritize activities. These start with the analysis at the lowest data level and gradually consolidate the insights gained upwards. This type of analytics can be completely automated with oee.ai. The algorithm can make the consolidation steps transparent, but it doesn’t have to.
Prioritization methods for managing production
The pyramid principle can be a handy “1:3:10” approach. The decision-making is structured in time: In one second the process status is recorded, in three seconds trends are identified and with ten seconds of attention, causes are visualized in order to then start analyzing the problem and identifying activities. This approach was presented as an example on LinkedIn. In oee.ai, the corresponding widgets are configured so that the information is presented in the necessary order.
Alternatively, algorithmic prioritization can be used in oee.ai. Using specialized algorithms, oee.ai identifies the most significant losses or anomalies from the extensive data stream of the systems and actively indicates the need for action via notification. The prioritization, which is carried out in the “1:3:10” approach with visual guidance by humans, is in this case carried out by an algorithm and can be communicated independently of the shopfloor management routine. The algorithm can also make the decision-making process transparent, but it does not have to.
Focusing on prioritized topics in shopfloor management
The prioritized topics discussed as part of shopfloor management are crucial to the operational efficiency of a company. By prioritizing topics, whether through visual dashboard configuration or algorithmic analysis, shopfloor management can focus on the most important and urgent matters.
In practice, this means that shopfloor meetings are more focused and goal-oriented. Instead of spending time reviewing data, participants can focus on the most critical points that have the greatest impact on production performance and quality. This enables management and teams to react quickly to changes or challenges, develop effective solution strategies and thus continuously improve operational performance.
Using prioritized information in shopfloor management leads to improved decision making, optimized processes and ultimately an increase in the overall efficiency of the company.
Conclusion
In a data-driven business world, the ability to efficiently filter and prioritize relevant information is critical. Two prioritization methods – visual dashboard configuration and algorithmic analysis – optimize information preparation, prioritization and ultimately decision making. Applying this prioritized information to shopfloor management improves operational efficiency, facilitates rapid response to operational challenges, and therefore promotes overall company performance.
Both methods seek to find the signal in the data noise and allow management to focus on what matters.
Have we piqued your interest? Then please contact us at info@oee.ai.
Author: Linus Steinbeck