Why we developed tablano
The history of tablano is a very particular one. The need for a new mobile solution for collecting data emerged out of three quite different areas.
One of our customers needed to document the inner workings of its cable distribution cabinets. The customer had already recorded several cable distribution cabinets on a conventional basis – with paper. As a next step he wanted to digitalize this documentation and was inquiring about the costs of this procedure. The effort was calculated at approximately 20-30 minutes per sheet. The area included 4,500 cable distribution cabinets. Thus the total expense would amount to ca. 80,000 €. Upon asking why the customer doesn’t carry out his documentation digitally, we received the answer that there is currently no mobile solution for data collecting which would be suitable for his employees. This encounter gave us a first idea to develop a new mobile solution like tablano.
Unsettling data quality
20 years ago our company started to digitize utility networks. The initially simple CAD drawings were gradually expanded with data banks and transferred to intelligent information systems. The goal was and still is to create a digital and intelligent network. However, in the last decade, we detected that the previously recorded data got “worse”. The immediacy of the data was no longer given. As a consequence, the energy suppliers were unable to offer precise information. What exactly caused “bad” data? We managed to find out that important information regarding construction sites, installations of lines, cables and pipelines as well as house connections were mostly documented and drawn on paper. These sketches either were put away into dubious shelves or were scanned and saved as PDF files digitally. Point being – they were not transferred to information systems provided for this purpose. There was no way to capture this missing information digitally, on the spot. This “documentation gap” is now closed by tablano.
The goal of Industry 4.0 in the area of maintenance and repair for utility companies is to carry out the work only when it is absolutely necessary. This “predictive maintenance” saves both human and financial resources. However, in order to make reliable predictions about when something needs to be repaired or replaced, it is necessary to be able evaluate as much data as possible. This digital data must be collected during every inspection and afterwards stored in a safedatabase. This creates a history (big data) for each object, which can be used for exact evaluation and reliable predictions.