Data analytics are also an intensive supplement to gut feeling at a traditional company like Röhlig Logistics GmbH & Co. KG, data analytics is the perfect complement to gut feeling. With more than 2300 employees in over 30 countries, the family-run company can look back on a long history of success since 1852.
As part of an ERP project, we were able to make a small excursion into the preparation of sales information with Power Bi - more as a side issue. The trigger was the question of whether there was potential for simpler, digitally mappable services in the target groups managed by Röhlig.
Analytics beats gut feeling
The logistics experts at Röhlig also provide us with excellent logistics services, particularly for complex freight forwarding and logistics tasks. According to the experts' gut feeling, there was neither a need nor a market for online quoting for simple logistics services.
However, as part of the data analytics we created in a very short time using Microsoft Power BI, we were able to identify significant but untapped potential in the existing data that contradicted the experts' gut feeling so impressively that an online quoting solution was subsequently designed and implemented for precisely these use cases.
The experts' misjudgement does not, of course, speak against their expertise, but merely shows that we are all within the scope of our experience and use of data.
Data analytics and machine learning are therefore ideal helpers for illuminating the "blind spots" of our perception with fact-based findings.
What is the purpose of data analytics in general?
Companies use data analytics for a variety of reasons, including
- Decision making: Data analytics helps companies make better decisions by collecting and analyzing data from various sources to identify trends, patterns and correlations.
- Improving efficiency: By analyzing data, companies can optimize processes and workflows to improve efficiency and reduce costs.
- Identification of business opportunities: By analyzing data, companies can identify new business opportunities and take advantage of chances to expand their business model.
- Customer satisfaction: Companies can better understand the needs and preferences of their customers by analyzing data on customer behavior and feedback.
- Risk management: Data analysis helps companies to identify and assess risks in order to solve potential problems before they become major challenges.
Overall, data analysis helps companies to optimize their business processes and decisions, which can lead to greater profitability and competitiveness.
